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ASSOCIATION MAPPING OF ROOT TRAITS FOR DROUGHT
TOLERANCE IN BREAD WHEAT
ISRAR AHMAD
DEPARTMENT OF GENETICS HAZARA UNIVERSITY MANSEHRA
2015
HAZARA UNIVERSITY MANSEHRA
Department of Genetics
ASSOCIATION MAPPING OF ROOT TRAITS FOR DROUGHT
TOLERANCE IN BREAD WHEAT
By
Israr Ahmad
This research study has been conducted and reported as partial fulfillment of the
requirements of PhD degree in Genetics awarded by Hazara University
Mansehra, Pakistan
Mansehra
The Monday 02, February 2015
Approval Sheet
ASSOCIATION MAPPING OF ROOT TRAITS FOR
DROUGHT TOLERANCE IN BREAD WHEAT
SUBMITTED BY ISRAR AHMAD
PhD Scholar
RESEARCH SUPERVISOR PROF. DR. HABIB AHMAD (TI)
Dean Faculty of Science
Hazara University, Mansehra
CO -SUPERVISOR DR. INAMULLAH
Assistant Professor
Department of Genetics
Hazara University, Mansehra
DEPARTMENT OF GENETICS
HAZARA UNIVERSITY MANSEHRA
2015
DEDICATION
This humble work is dedicated to my honorable parents and my cute son
Sonain Khan
I
AKNOWLEDGMENTS
I bow my head before Almighty Allah, The omnipotent, The omnipresent, The
merciful, The most gracious, The compassionate, The beneficent, who is the entire
and only source of every knowledge and wisdom endowed to mankind and who
blessed me with the ability to complete this work. It is the blessing of Almighty Allah
and His Prophet Hazrat Muhammad Sallallaho Alaihe Wasallam, which enabled me
to achieve this goal.
I would like to thank my supervisor prof. Dr Habib Ahmad (TI) and Co-supervisor
Dr. Inamullah for their extraordinary guidance and mentoring throughout my study
at Hazara University Mansehra Pakistan. Dr. Shahid Masood at Comsat Institute
Abbottabad also deserves a credit for his advice on statistical data analyses.
I wish to extend my thanks to JS (Pat) Heslop-Harrison, Dr Trude Schwarzacher and
Dr John Bailey, for their support in Molecular Cytogenetics laboratory at University of
Leicester UK.
I am grateful to Higher Education Commission of Pakistan (HEC) for financial
support under Indigenous Scholarship Program and six months UK visit under IRSIP
program to complete this research.
These lines provided me an opportunity to rightly acknowledge the unmatched
personalities of my affectionate parents and family members especially my brothers
for their inspiration, encouragement, huge sacrifice, moral support, cooperation,
patience, tolerance and prayers for my health and prayers for my success and
prosperities in all walks of life. Their confidence in me served as great motivation
throughout my research. May Allah Almighty bless all, and be with them
everywhere.
I am very thankful to Prof. Mehbob ur Rehman, chairman Department of Botany,
Govt. Degree College Matta Swat and Mr. Ajmal Iqbal, Lecturer in Botany, Govt.
Degree College Matta Swat for their kind cooperation during the research work. I am
II
also thankful to all the administrative and laboratory staff of the Department of
Genetics, Hazara University Mansehra for their kind support. Especial thanks to Mr.
Muhammad Jawad for providing pleasant environment during thesis write up.
I am also highly indebted to my best friends and research fellows Mr. Ikram
Muhammad, Mr. Inam Ullah, Mr. Sadiq Ullah, Mr. Muhammad Ali, Muhammad
Jawad (Superintendent) and my Juniors Farid Ullah, Ayaz Ahmad, my students
Ziaullah, Najeeb Ullah, Ayaz Ahmad, Dawood khan and Murad Khan for their
assistance, good company, marvelous behavior and friendly attitude.
Israr Ahmad
III
CONTENTS
AKNOWLEDGMENTS ................................................................................................... I
LIST OF TABLES ............................................................................................................ IX
LIST OF FIGURES ........................................................................................................... X
LIST ABBREVIATIONS ................................................................................................ XI
ABSTRACT .................................................................................................................... XII
Chapter-1 INTRODUCTION .......................................................................................... 1
2.1 Origin of wheat ......................................................................................................... 1
2.2 Evolution of hexaploid wheat................................................................................. 1
2.3 Taxonomy of wheat .................................................................................................. 2
2.4 Cytogentics of wheat ................................................................................................ 2
2.5 Nutritional value of wheat ...................................................................................... 4
2.6 Introduction of alien material into wheat ............................................................. 4
2.7 Genetic resources for wheat improvement ........................................................... 5
2.8 Wheat roots architecture ......................................................................................... 6
2.9 Importance of wheat ................................................................................................ 7
2.10 Global wheat production ....................................................................................... 8
2.11 Wheat production in Pakistan .............................................................................. 9
2.12 Commercial uses of wheat .................................................................................. 10
2.13 Wheat pests ........................................................................................................... 11
2.14 Diseases of wheat ................................................................................................. 12
2.14.1 Black Stem rust of wheat ............................................................................... 13
2.14.2 Orange or leaf rust of wheat ......................................................................... 13
2.14.3 Yellow or stripe rust of wheat ...................................................................... 14
2.14.4 Loose smut of wheat ...................................................................................... 14
2.14.5 Powdery mildew disease of wheat.............................................................. 15
2.15 QTL (Quantitative trait loci) mapping .............................................................. 15
2.16 Association mapping ........................................................................................... 16
2.16.1 Genome Wide Association Mapping .......................................................... 16
2.16.2 Candidate Gene Association Mapping ....................................................... 17
2.17 Drought resistant genes ....................................................................................... 17
2.18 The use of molecular markers for drought related traits ................................ 18
IV
2.18.1 Microsatellite Markers .................................................................................. 19
2.19 Flourescent in situ hybridization ....................................................................... 20
2.20 Major abiotic stresses ........................................................................................... 21
2.20.1 Temperature (Heat) Stress ............................................................................ 21
2.20.2 Salinity Stress .................................................................................................. 22
2.20.3 Frost or cold stress ......................................................................................... 23
2.20.4 Drought stress ................................................................................................ 24
2.20.5.1 Drought stress in Pakistan ..................................................................... 25
2.20.5.2 Drought stress in Khyber Pakhtunkhwa ............................................. 25
Chapter-2 MATERIALS AND METHODS ................................................................ 27
2.1 MORPHOLOGICAL CHARACTERIZATION OF WHEAT GERMPLASM . 27
2.1.1 Plant height ....................................................................................................... 27
2.1.2 Leaf area ............................................................................................................ 28
2.1.3 Number of days to 50% heading ................................................................... 28
2.1.4 Number of days to maturity........................................................................... 28
2.1.5 Number of tillers per plant ............................................................................. 28
2.1.6 Peduncle length ................................................................................................ 29
2.1.7 Spike length ...................................................................................................... 29
2.1.8 Awn length ....................................................................................................... 29
2.1.9 Number of spikelets per spike ....................................................................... 29
2.1.10 Number of grains per spike .......................................................................... 29
2.1.11 Yield per plant ................................................................................................ 30
2.1.12 Harvest index ................................................................................................. 30
2.1.13 1000-grain weight .......................................................................................... 30
2.1.14 Spike density................................................................................................... 30
2.1.15 Total weight per plant ................................................................................... 31
2.2 PHYSIOLOGICAL CHARACTERIZATION .................................................. 31
2.2.1 Relative leaf water content ............................................................................ 31
2.2.2 Water loss rate .................................................................................................. 32
2.2.3 Water-use efficiency ........................................................................................ 33
2.3 ROOT TRAIT ANALYSIS ..................................................................................... 35
2.3.1 Root fresh weight ............................................................................................ 35
V
2.3.2 Root dry weight ............................................................................................... 35
2.3.3 Shoot fresh weight .......................................................................................... 36
2.3.4 Shoot dry weight ............................................................................................. 36
2.3.5 Root shoot ratio ............................................................................................... 36
2.3.6 Root diameter .................................................................................................. 36
2.3.7 Number of Nodal roots .................................................................................. 36
2.3.8 Number of seminal roots ............................................................................... 36
2.3.9 Root angle ........................................................................................................ 36
2.3.10 Total roots length .......................................................................................... 37
2.3.11 Root density ................................................................................................... 37
2.3.12 Maximum root length .................................................................................. 37
2.4 FLUORESCENT IN SITU HYBRIDIZATION ................................................... 37
2.4.1 Seeds germination and digestion .................................................................. 37
2.4.2 Slide preparation .............................................................................................. 38
2.4.3 Pretreatments .................................................................................................... 38
2.4.3.1 Post-fixation of air dried slides ............................................................... 38
2.4.3.2 RNase treatment ........................................................................................ 39
2.4.3.3 Paraformaldehyde fixation ...................................................................... 39
2.4.3.4 Dehydration ............................................................................................... 39
2.4.4 Hybridization ................................................................................................... 39
2.4.5 Post hybridization ............................................................................................ 39
2.4.5.1 Stringent washes........................................................................................ 40
2.4.5.2 Detection ..................................................................................................... 40
2.4.5.3 DAPI staining and mounting .................................................................. 40
2.5 MOLECULAR CHARACTERIZATIONS OF WHEAT VARIETIES ............... 41
2.5.1 DNA Isolation .................................................................................................. 41
2.5.2 Nanodrop measurement ................................................................................. 41
2.5.3 Polymerase Chain Reaction ............................................................................ 42
2.5.4 Metaphore agarose gel .................................................................................... 42
2.5.5 Reagents used during DNA isolation and gel electrophoresis ................. 43
2.6 STATISTICAL ANALYSES ................................................................................... 49
VI
2.6.1 Structure ............................................................................................................ 49
2.6.2 Structure harvester .......................................................................................... 49
2.6.3 Tassel ................................................................................................................. 49
2.6.3.1 General linear model ............................................................................... 50
2.6.3.2 Mixed linear model .................................................................................. 50
Chapter -3 RESULTS ...................................................................................................... 51
3.1 COMPARATIVE PERFORMANCE OF THE MORPHOLOGICAL TRAITS 51
3.1.1 Plant height ....................................................................................................... 51
3.1.2 Flag leaf area .................................................................................................... 52
3.1.3 Peduncle length ............................................................................................... 52
3.1.4 Days to 50% heading ...................................................................................... 53
3.1.5 Days to 50% maturity ..................................................................................... 54
3.1.6 Awn length ...................................................................................................... 54
3.1.7 Number of tillers per plant ............................................................................ 55
3.1.8 Spike length ..................................................................................................... 55
3.1.9 Spikelets per spike .......................................................................................... 56
3.1.10 Spike density .................................................................................................. 56
3.1.11 Number of grains per spike ......................................................................... 57
3.1.12 1000 grain weight .......................................................................................... 58
3.1.13 Yield per plant ............................................................................................... 58
3.1.14 Harvest index ................................................................................................ 59
3.1.15 Total weight per plant .................................................................................. 59
3.2 COMPARATIVE PERFORMANCE OF PHYSIOLOGICAL TRAITS ............. 69
3.2.1 Relative water content .................................................................................... 69
3.2.2 Water loss rate .................................................................................................. 71
3.2.3 Water use efficiency ......................................................................................... 72
3.3 ROOT TRAIT ANALYSIS ..................................................................................... 73
3.3.1 Root fresh weight ............................................................................................ 73
3.3.2 Root dry weight ............................................................................................... 74
3.3.4 Shoot dry weight .............................................................................................. 75
3.3.5 Root shoot ratio ............................................................................................... 75
3.3.6 Root diameter .................................................................................................. 75
VII
3.3.7 Number of nodal roots .................................................................................... 76
3.3.8 Number of seminal roots ................................................................................ 76
3.3.9 Root angle ........................................................................................................ 77
3.3.10 Total roots length .......................................................................................... 77
3.3.11 Root density ................................................................................................... 77
3.3.12 Maximum roots length ................................................................................. 78
3.4 FLOURESCENT IN SITU HYBRIDIZATION ................................................... 83
3.5 MOLECULAR ANALYSES ................................................................................... 84
3.5.1 Molecular markers polymorphism ................................................................... 86
3.5.2 Population structure and linkage disequilibrium .......................................... 86
3.5.3.1 Total root length MTA ................................................................................. 92
3.5.3.2 Root fresh weight MTA ............................................................................... 92
3.5.3.3 Root dry weight MTA ................................................................................. 93
3.5.3.4 Maximum root length MTA ........................................................................ 93
3.5.3.5 Number of nodal roots MTA....................................................................... 93
3.5.3.6 Root angle MTA ............................................................................................ 94
3.5.3.7 Root density MTA ......................................................................................... 96
3.5.3.8 Root Diameter MTA ..................................................................................... 96
Chapter-4 DISCUSSION ............................................................................................... 98
4.1 EVALUATION OF YIELD AND YIELD ASSOCIATED TRAITS ................... 99
4.1.1 Number of tillers per plant ............................................................................. 99
4.1.2 Plant height ....................................................................................................... 99
4.1.3 Spike length .................................................................................................... 100
4.1.4 Spikelets per spike ......................................................................................... 100
4.1.5 Spike density................................................................................................... 101
4.1.6 Grains per spike ............................................................................................. 101
4.1.7 1000 grain weight ........................................................................................... 102
4.1.8 Harvest index ................................................................................................. 103
4.1.9 Days to 50% heading ..................................................................................... 103
4.1.10 Days to 50% maturity .................................................................................. 104
4.1.11 Yield per plant .............................................................................................. 105
4.2 EVALUATION OF PHYSIOLOGICAL TESTS ................................................ 107
VIII
4.2.1 Relative water content ................................................................................... 107
4.2.2 Water loss rate ................................................................................................ 109
4.2.3 Water use efficiency ....................................................................................... 109
4.3 EVALUATION OF ROOT TRAITS .................................................................... 110
4.3.1 Total root length ............................................................................................. 111
4.3.2 Root diameter ................................................................................................. 111
4.3.3 Root density .................................................................................................... 112
4.3.4 Maximum root length ................................................................................... 113
4.3.5 Number of seminal roots .............................................................................. 113
4.3.6 Root dry weight.............................................................................................. 114
4.3.7 Root fresh weight ........................................................................................... 114
4.3.8 Root shoot ratio .............................................................................................. 115
4.3.9 Number of nodal roots .................................................................................. 116
4.3.10 Number of seminal roots ............................................................................ 116
4.4 ALIEN MATERIALS DETECTION USING FISH TECHINQUE .................. 118
4.5 MARKER TRAIT ASSOCIATION ..................................................................... 118
4.5.1 Total root length MTAs ................................................................................. 119
4.5.2 Root fresh weight MTAs ............................................................................... 119
4.5.3 Root dry weight MTAs ................................................................................. 120
4.5.4 Maximum root length MTAs ....................................................................... 120
4.5.5 Number of nodal roots MTAs ...................................................................... 120
4.5.6 Root angle MTAs ........................................................................................... 120
4.5.7 Root density MTAs ........................................................................................ 121
4.5.8 Root diameter MTAs ..................................................................................... 121
CONCLUSION .............................................................................................................. 122
RECOMMENDATIONS .............................................................................................. 124
REFERENCES ................................................................................................................ 127
ANNEXURES ................................................................................................................. 165
Annexure 1 .................................................................................................................. 165
Annexure 2 .................................................................................................................. 176
Annexure 3 .................................................................................................................. 178
Annexure 4 .................................................................................................................. 180
IX
LIST OF TABLES
Table 1 Nanodrop measurement of genomic DNA extracted from
hundred landraces of wheat (Triticum aestivum)
45
Table 2 Analysis of Variance for morphological traits of wheat
genotypes
57
Table 3 (a) Sorted table of top ten superior wheat (Triticum aestivum)
genotypes on the base of yield and yield related traits
61
Table 3 (b) Sorted table of top ten superior wheat (Triticum aestivum)
genotypes on the base of yield and yield related traits
62
Table 3 (c) Sorted table of top ten superior wheat (Triticum aestivum) genotypes on the base of yield and yield related traits
63
Table 4 Correlation analysis of morphological traits of wheat (Triticum
aestivum) 64
Table 5 Morphological traits showing maximum number of
repetition (presence (+) and absence (-))
65
Table 6 Analysis of Variance for physiological traits of wheat
genotypes
70
Table 7 Sorted table of top ten superior wheat genotypes on the base of physiological trait RWCN and RWCS
70
Table 8 Sorted table of top ten superior wheat genotypes on the
base of physiological trait WLRN, WLRS and WUE
73
Table 9 Analysis of Variance for root traits associated with drought
tolerance
79
Table 10 Correlation analysis of root traits with physiological tests and yield per plant
80
Table 11 (a) Top ten superior genotypes on the base of root traits 81
Table 11 (b) Top ten superior genotypes on the base of root traits 82
Table 12 SSR markers, their chromosome position (ch pos), Major Allele frequency (MAF), allele No, genetic diversity (H) and polymorphic information content (PIC) used for profiling of hundred wheat genotypes
88
Table 13 Significant SSR markers for each QTLs associated with root
traits
97
X
LIST OF FIGURES
Figure 1 Test tubes showing leaves of wheat after rehydration 32
Figure 2 (a) Leaves harvested for fresh weight 33
Figure (b) Oven dried leaves for dry weight 33
Figure 3 Pots covered with plastic sheet with small pores in the center 34
Figure 3 (b) Total weight of pot after no more plant extractible water left 35
Figure 3 (c) Shoot harvested for fresh weight 35
Figure 4 (a) Showing root arctechiture of Triticum aestivum 37
Figure 4 (b) The longest root (37cm) recorded in Triticum aestivum 37
Figure 5 pTa 794 (Kiran) 83
Figure 6 pTa 794 (Pirsabak-85) 83
Figure 6 pSc 119.2 (Wadanak-85) repititive probes (green) 84
Figure 7 (a) pSc 119.2 (Wadanak-85) (green) repititive probes 84
Figure 7 (b) FISH pattern of the wheat chromosomes pTa 794 (pink) and
pSc 119.2 (Pari-73)
84
Figure 8 Representative gel pictures of (A) Xbarc 264, (B) Xwmc 606, (C) VRN AF, (D) Xcfd 18 and (E) Xgwm 443, L: 100 bp ladder
85
Figure 9 UPGMA tree constructed using molecular markers showing
diversity across hundred wheat genotypes
90
Figure 10
(a,b,c)
Population structure analysis of wheat genotypes based on SSR markers (a) Line graph. The X-axis shows LnP (D) value and Y-axis shows k. (b) Graphical bar plot at k=2 presenting two subgroup (G1 & G2). (c) Graphical bar plot at k=13 presenting thirteen subgroup (G1- G13). The X-axis shows accessions numbers and Y-axis shows sub group membership
91
Figure 11(a) QTL identified for TRL on the basis of LOD in GLM 94
Figure 11(b) QTL identified for RFW on the basis of LOD in GLM 94
Figure 11(c) QTL identified for RDW on the basis of LOD in GLM 96
Figure 11(d) QTL identified for MRL on the basis of LOD in GLM 94
Figure 11(e) QTL identified for MRL on the basis of LOD in MLM 94
Figure 11(f) QTL identified for NNR on the basis of LOD in GLM 95
Figure 11(g) QTL identified for RA on the basis of LOD in GLM 95
Figure 11(h) QTL identified for RDT on the basis of LOD in GLM 95
Figure 11(i) QTL identified for RDT on the basis of LOD in MLM 95
Figure 11(j) QTL identified for RD on the basis of LOD in GLM 95
Figure 11(k) QTL identified for RD on the basis of LOD in MLM 95
XI
LIST ABBREVIATIONS
AL Awn length
AM Association mapping
ANOVA Analysis of variance
CIMMYT International maize and wheat improvement centre
DH Days to 50% heading
DM Days to 50% maturity
FISH Flourscent In Situ hybridization
FLA Flag leaf area
GISH Genomic In Situ hybridization
GLM General linear model
HI Harvest index
MCMC Monte Carlo Markov Chain
MLM Mixed linear model
MRL Maximum root length
MTA Marker trait association
NGS Number of grains per spike
NNR Number of Nodal roots
NSR Number of Seminal roots
NTP Number of tillers per plant
PCR Polymerase chain reaction
PH Plant height
QTL Quatative trait analysis
RD Root diameter
RDT Root density
RDW Root dry weight
RFW Root fresh weight
RWC Relative water content
SD Spike density
SDW Shoot dry weight
SFW Shoot fresh weight
SL Spike length
SPS Spikelets per spike
SSR Simple sequence repeats
TRL Total root length
TWP Total weight per plant
WLR Water loss rate
WUE Water use efficiency
YPP Yield per plant
XII
ABSTRACT
Bread wheat (Triticum aestivum; of 2n=6x=42) having hexaploid genome
(AABBDD) of 17 Gb is the major staple food of Pakistan. The wheat production in
Pakistan shows a long standing instability due to drought stress in wheat growing
season. The introduction of drought tolerant commercial varieties is therefore the
cry of the day, which needs marker assisted selection evolving promising lines.
This dissertation communicates the results of a research endeavor based upon
evaluation of 100 wheat accessions for drought stress under lab and field
conditions. The data was obtained on morphological, physiological and marker
associated assays for genome wide association mapping of the major alleles
against drought. Reults of the morphological analysis showed that genotype
Bahawalpur-79 ranked first on the basis of days to maturity, Barani-70 showed
highest number of tillers, Marwat-01 has highest spike length, Margalla-99 has
greatest spikelets per spike, Zarghoon-79 has highest 1000 grain weight and C-273
have highest harvest index and Uqab-2000 showed optimum plant height. These
genotypes could be used for further breeding programs to improve wheat
production under drought stress conditions of Pakistan. Analysis of Variance of
the physiological data provided highly significant differences among the
genotypes both in normal and drought stress. Margalla-99 recorded the highest
relative water content in normal while NIAB-83 recorded the highest relative
water content in drought stress conditions. Faisalabad-83 and Iqbal-2000 was
ranked first on the basis of water loss rate in normal and water loss rate in stress
conditions while NIAB-83 was ranked first in water use efficiency test. These
XIII
genotypes may be recommended for commercial cultivation in irrigated and
rainfed areas of Pakistan.
The correlation analysis revealed that root dry weight, maximum root length,
total root length, root shoot ratio, root diameter and number of seminal roots were
positively correlated with water loss rate stress and relative water content stress
and considered to be best root traits for drought tolerance. Pirsabak-85, AS-2002,
Abdaghar-97, Marwat-01 and Soghat-90 were ranked first on the basis of root
traits and considered to be best for drought stress areas of Pakistan. All the
genotypes were screened with 102 SSR markers in which most of the markers
were showed high level of polymorphism. Sum of 271 polymorphic alleles
generated. The alleles per locus ranged from 1-3 with an average of 2.63 per locus.
Polymorphic Information Content (PIC) values of the markers were calculated in
the range of 0.03–0.59. The association analysis through linkage disequilibrium of
100 accessions clustered into thirteen distinct groups. Our analyses identified
significant association between Xgdm5 and total root length, Xwmc235 and root
fresh weight, Ppd-D1 and root dry weight, Xwmc149 and maximum root length,
Xwmc175 and number of nodal roots, Xgwm302 and root angle, Xwmc175 and root
density and Xwmc233 and root diameter. All the marker/trait associations were
located on seven chromosomes (2D, 5B, 2A, 2B, 7B, 6D and 5D. The marker/trait
association for maximum root length was not reported previously. The genetic
information obtained might be used in marker-assisted selection to improve
drought tolerance of wheat.
1
Chapter-1 INTRODUCTION
2.1 Origin of wheat
The exact origin of wheat is not known till now. However, biogeographical
studies show that wheat originated in areas having the wild grasses somewhere in
the East (Ozkan, 2002). In ancient times, the naked (free threshing) wheat
cultivation was very common until the late fifth and early fourth millennium B.C
in the Fertile Crescent (A region with rich soil in the upper stretches of the ―Tigris-
Euphrates drainage basin‖) and the Nile Delta which includes South Eastern parts
of Syria, Turkey, Levant, Egypt and Israel (Briggle and Curtis, 1987). The wild
diploid and polyploid wheat are very common in this area even now, and in grow
separate polymorphic as well as mixed populations (Eckardt, 2010, Feldman and
Kislev, 2007). It is also assumed that wild relatives of common wheat first grew in
the Middle East while in the new world (USA and Canada) hexaploid wheat was
first grown during 16th century (McFadden and Sears, 1946; Briggle and Curtis,
1987). It is thought to have originated and expanded both the present tetraploid
(AABB; T.turgidum ssp. Durum Desf., 2𝑛=4𝑥=28) Durum wheat and hexaploid
(AABBDD (T. aestivum L.), 2𝑛=6𝑥=42) bread wheat from the tetraploid wild
emmer (T. turgidum ssp. dicoccoides) having genome AABB Near East Fertile
Crescent (Budak et al., 2013).
2.2 Evolution of hexaploid wheat
Triticum aestivum (Bread wheat 2n=6x=42) belongs to family Poaceae having
hexaploid genome (AABBDD) of 17 Gb (Blake et al., 1999; Huang et al., 2002). Due
to a problem in distinguishing between threshing free wheats (tetrapolid Durum
2
and hexaploid bread wheat) in early times, the evolutionary history of these crops
are still uncertain (Oliveira, 2012). The hexaploid bread wheat of genome
AABBDD has been evolved from two different polyploidization events. The first
event was completed approx. 0.5 million years ago (MYA) when the diploid
donor A genome (derived from T. urartu) hybridized to another species having B
genome (derived from Aegilops speltoides) resulting in tetraploid Triticum turgidum
(Feldman and Levy, 2005). The second allopolyploidization event occurred
(approx. 10,000 years ago) between the tetraploid T. turgidum spp. dicoccum and
the diploid (D genome donor) Aegilops tauschii (Dubcovsky and Dvorak, 2007;
Salse et al., 2008).
2.3 Taxonomy of wheat
Wheat is a major cereal crop in many parts of the world (Zahid et al., 2003; Tunio,
2006). Common wheat (Triticum aestivum L.) belongs to phylum Streptophyta,
class Liliopsida, tribe Triticeae and family Poaceae (Ijaz and Khan, 2009; Soreng,
2009). Poaceae or Grass family is the fourth largest family among angiosperms
having 700 genera and 10,000 species (Gaut, 2002). The genus Triticum contains 10
species, in which six are cultivated and four are wild. The economically
important species, Triticum aestivum, has five subspecies. Some of the important
species of Triticum genus are einkorn (Triticum monococcum), emmer (Triticum
dicoccum), and spelt (Triticum spelta) (Soreng, 2009).
2.4 Cytogentics of wheat
3
The cytological work of Sakamura, Sax, Sears and Kihara publicized that species
of tribe Triticeae has three ploidy levels. They also called that the basic set of
chromosomes in most of the species are seven (n=7). These species have large
chromosomes and show frequent polyploidy (Feldman and Levy, 2005; Heslop-
Harrison and Schwarzacher, 2011a). Diploid wheat has the basic set of
chromosome number is x=7 and contain two haploid sets of seven chromosomes.
Similarly tetraploid wheat has four haploid sets of chromosomes, hexaploid has
six and so on. The chromosomes of hexaploid wheats are designated as A, B and
D genomes (Sears, 1966). The chromosomes of A, B and D genomes may be
genetically similar (homologous) or genetically related (homeologous) (Hao et al.,
2011). The bread wheat genome is structured into 21 pairs of chromosomes
designated as A, B and D and has size of 17 billion bp (Heslop-Harrison and
Schwarzacher, 2011a). Various techniques have been employed to identify these
chromosomes including Molecular karyotyping, C-banding, Genomic in situ
hybridization (GISH) and Fluorescent in situ hybridization (FISH) (Heslop-
Harrison, 2000; Schwarzacher, 2003). GISH and FISH are widely used for
chromosomal mapping and genomic analysis. The cytogenetic research in wheat
is greatly modernized using the somatic chromosomes identification from root
tips cells (meristem) (Gill et al., 2011; Schwarzacher et al., 2011; Harper et al., 2011).
Different cytological markers are also used to identify specific wheat
chromosomes (Castillo and Heslop-Harrison, 1995).
4
2.5 Nutritional value of wheat
Wheat is classified into two groups based on its sowing period, the winter wheat
and spring wheat. There is no doubt that billions of people dependent on wheat
(winter and spring) and consider significant part of their diet Worldwide. Mostly
in under developed countries where bread, pasta, noodles and other wheat stuffs
may provide a large percentage of the diet calories. Wheat provides nearly 55% of
carbohydrate and 20% of the food calories. On average 100 grams of wheat
contains 78.10% carbohydrate, 14.70% protein, 2.10% fat, 2.10% minerals (zinc,
iron, selenium and magnesium) and substantial amount of vitamins (thiamine
and vitamin-B)(Adam et al., 2002; Shewry et al., 2006; USDA, 2006; Topping, 2007;
Imtiaz et al., 2010). Wheat grain is technically called caryopsis comprises of the
pericarp and the true seed. In the seed endosperm, approximately 72% of the
protein is deposited, which forms 8-15% of total protein per grain weight. Beside
this, Wheat also contains pantothenic acid, riboflavin, and sugars. The pericarp
and aleurone layer is also used as a nutritional source for fiber and some minerals
(potassium, phosphorus, magnesium and calcium) (Kumar et al., 2011).
2.6 Introduction of alien material into wheat
The introduction of alien genetic materials into wheat for useful traits is a good
and well established practice for wheat improvement (Gale & Miller, 1987).
Successful transfers of alien material can be achieved having good knowledge of
cytogenetics (chromosome pairing), recombination, interaction, genetic balance
and chromosome engineering for identification of alien material in the progenies
(Miller et al., 1996; Song et al., 2013). Various techniques like C-banding technique,
5
Genomic In Situ Hybridization (GISH), Fluorescent In Situ Hybridization (FISH)
and number of molecular markers (RFLP) are presently used for identification of
alien materials (genes) in wheat (Schlegel & Weryszko, 1979; Hutchinson et al.,
1983; Jia et al., 2002; Ping et al., 2003). The wild relatives of wheat are the best gene
pool for different agronomic traits and resistance against various abiotic (drought,
salinity, cold) and biotic (pathogens) stresses (Jauhar, 2006). The incorporation of
alien genes into wheat background is necessary to increase its yield for the
growing human population (Faris et al., 2008; Luan et al., 2010).
The introduction of alien gene Lr19 (Leaf rust resistance gene) from Lophopyrum
ponticum chromosome 7E (Zhang et al., 2011), the 3Ns chromosome in
Psathyrostachys huashanica carries stripe rust resistance genes (Kang et al., 2011),
Pm21 (Powdery mildew resistance) is located on the chromosome 6VS of
Haynaldia villosa (Cao et al., 2011), The gene(s) for high number of florets and
kernels per spike are present on chromosome 6P of Agropyron cristatum (Wu et al.,
2006) to wheat has significantly improve the cultivars as well as production.
2.7 Genetic resources for wheat improvement
The ability to meet the food demand for growing population around the world is
a challenge for crop breeders. The current genotypes in the modern agriculture
are usually vulnerable and susceptible to abiotic and biotic stresses. The wild
ancestors and their genetic resources offer best source for current crop
improvement due to their ability to resist environmental fluctuations (Feldman
and Sears 1981; Nevo, 2004). Wild emmer (T. dicoccoides) is a good wild progenitor
for a number of genetic resources including agronomic traits (biomass, earliness
6
and yield) (Nevo, 2001), high grain protein (Qi et al., 2006; Li et al., 2007), abiotic
stress including drought and salinity (Peleg et al., 2005) and biotic stress including
powdery mildew, leaf rust, stem rust, stripe rust and Fusarium head blight (Nevo
et al., 1985; Oliver et al., 2007; Anikster et al., 2005). Some of the genes controlling
protein content and disease resistance have been identified and mapped in wild
emmer (Peng et al., 2003). The introgression of stripe rust resistance gene Yr15
from wild emmer into tetrapolid as well as hexaploid wheat was the first
achievement (Grama and Gerechter-Amitai, 1974). Powdery mildew resistance
gene Pm16 has been transferred to several Chinese wheat successfully (Zhou et al.,
2005). Yellow rust resistance gene Yr15 (McIntosh et al., 1995), Yr35 and the linked
leaf rust resistance gene Lr53 (Marais et al., 2005) and Gpc-B1 (Khan et al., 2000)
gene for protein content have been transferred successfully to common wheat.
The introgression of these genes (genetic resources) from wild emmer into
common wheat has contributed to wheat nutrition and yield to a greater extent
(Xie and Nevo, 2008).
2.8 Wheat roots architecture
Food crops are facing immense pressure of water accessibility, reduction in
cultivable land and change in climatic conditions (Tardieu, 2011). Variability in
climatic conditions would increase the risk of high temperature in the next 30
years and the yield of food crops would decrease to greater extent (Brisson et al.,
2010). Therefore, food crops particularly wheat breeders need to develop varieties
with improved stability and appreciable yield for water deficit areas (Christopher
et al., 2013). Cultivation of wheat in droughty (water deficit) conditions depend on
7
root architecture that increase uptake of soil moisture in drought conditions
(Kirkegaard et al., 2007). The greater root length trait increases the chance of
uptake from deeper layer during grain filling duration (Manschadi et al., 2006). In
wheat two types of roots, the seminal roots (arise directly from the embryo) and
nodal roots (arise from tiller nodes) are found. The seminal roots reach to the
deeper layer of soil before the nodal roots therefore, are considered more
important in the uptake of soil moisture (Christopher et al., 2013). Beside root
length root angle also greatly affect water uptake (Manschadi et al., 2010).
Root traits are controlled by many genes and several QTLs have been studied for
root system e.g. root length (Price et al., 2002), root number (Courtois et al., 2009),
seminal root angle and number (Christopher et al., 2013).
2.9 Importance of wheat
Wheat (Triticum spp.) is one of the most important and widely cultivated crops
with the annual 694 million metric tons. More than 40 countries and over 35% of
the world population use Wheat as the staple food (Curtis et al., 2002; Peng et al.,
2004; Matsuoka, 2011). Wheat is cultivated on larger area than other cereals and
modified to different climatic conditions (Gustafson et al., 2009). Bread wheat
(2n=6x=42) and durum wheat (4x=28) are the two common cultivated species.
Bread wheat supply about 95% wheat globally, while durum and other wheats
(emmer (4x=28), einkorn (2x=14) and spelt (6x=42)) provide only 5% of the world
wheat (Curtis et al., 2002; Dubcovsky and Dvorak, 2007). Human population is
increasing rapidly and is estimated to reach 9.4 billion by 2050. Therefore, food
8
production will require a greater yield from the present cropland without
horizontal expansion (Foulkes et al., 2011). Population growth, environmental
pollution and utilization of crop lands for other purposes will reduce the world
crop land by 10- 20% (Nellemann et al., 2009). The world cereal production will
reduce more upto 25% if climatic changes continue and melt the Himalayan
glaciers, change the monsoon or flooding patterns or drought regimes in Asia
(Chakraborty and Newton, 2011). To meet the growing demand of global food
shortage of 2050, total food production must increase by 50% at least to meet out
demands of 2050. Among the crop plants wheat is an economical and rich source
of energy, proteins and supplies one fifth of all human calories for the world
population (Kumar and Sharma, 2011). Plant breeders are always trying to find
wheat germplasm having desirable traits such as tolerance to diseases and other
abiotic stresses (Foulkes et al., 2011; Mujeeb-Kazi and Hettel, 1995).
2.10 Global wheat production
Wheat is one of the most important cereal and staple food crop around the world
(Reynolds et al., 2011). It ranks first due to its area and production and contributes
more calories to the world‘s human diet than any other cereals. On the other hand
Wheat also maintains its first rank among major cereals due to its higher protein
and gluten content (Jagshoran et al., 2004).
In 1986-87 the wheat production across the world was 521 million metric tons,
was increased to approximately 572 million metric tons in 2005-06 from an area of
220 million hectares (Anonymous, 2006) and 694 million metric tons in 2011-2012.
In 2011, European Union (137 million tons) become on top of ranking in wheat
9
production countries followed by China (118 million tons) and the United States
of America (54 million tons). Further Canada, Australia, India, Pakistan and
Argentina contributes about 79% of the total wheat production. The world trade
market was very feasible for wheat in 2011 and 129 million tons of wheat was
traded in the world market (Taylor and Koo, 2012).
Source: www.fao.org
2.11 Wheat production in Pakistan
Efficient agricultural system plays an important role in the overall development of
any country. Similarly, the crops sub sector plays a very crucial role sharing about
60% of the value added. Wheat crop contributes 13.7% of the value added to the
agriculture sector and 2.9% to GDP in Pakistan (Muhammad et al., 2005;
Anonymous, 2009). Pakistan is occupies 9th position in terms of area, 5th position
in terms of yield per hectare and 8th in terms of wheat production (Manzoor et al.,
2010). Wheat is the staple food of Pakistan and covers 37% of the cultivable land
10
and contributes 80% of the grains for human consumption while shares 70%
grains for food production. Wheat shares 50% of the total calories and 10% of the
proteins to Pakistani people in both urban and rural areas (Ahmad et al., 2007).
Pakistan is one of the world largest wheat producing country; the area under
cultivation is 9.062 million hectare, producing about 23.42 million tons and grain
yield 2585 kg/hectare (Anonymous, 2009). The wheat production in Pakistan
show a great instability due to lack of proper forecast knowledge as in 2010 the
production was verified as 23.9 million tons which has been improved in 2011
upto 25 million tons. Subsequently the production of the crop reduced to 23.3
million tons in 2012 (FOASTAT, 2012). Some of the existing wheat varieties
(Inqilab-91 and Bhakkar-2002) are susceptible to different races of rust pathogens
(Anonymous, 2005). Therefore, for improved wheat production a better
understanding of wheat knowledge as well as the release new rust resistant wheat
varieties is required (Khan, 1987).
2.12 Commercial uses of wheat
Bread wheat is the most common food crop across the world than any other crop
(Reynolds et al., 2011). The global wheat production was 533.92 million tonnes in
2003-04 (FAS, 2005). The wheat consumption in developed countries was long
standing in ten years period while in developing countries it increased by 73%
(Briggle and Curtis, 1987). In 1996-97, the utilization of wheat crop in the
developing countries for food and other uses was 330 million tonnes while the
developed countries utilized only 248 million tonnes (FAO,1998). The per capita
consumption of bread wheat per year ranged from 40 Kg to 300 Kg. Besides bread
11
making, bread wheat is also used in making of biscuits, confectionary products,
Pizza and wheat gluten or seitan in vegetarian cooking (Pomeranz, 1987). Wheat
is also used as a feed for animals and hay used as a fodder for livestock (Rowland
and Perry, 2000). The wheat grain and the leftovers from flour milling (middlings)
are also used to feed poultry and fish industry in USA. Ethanol is produced from
wheat by the conversion of wheat starch into glucose or sucrose. These sugars are
then fermented to ethanol and carbon dioxide. There is huge interest in the
ethanol production and is currently used as an alternate source of fuel in
Australia (Thyer, 2005). In Europe, France is the largest producer of biofuels
where 13885 hectare of wheat is consumed in ethanol and ethyl tertio butyl ester
production (Kotati and Henard, 2001).
2.13 Wheat pests
Plant diseases have been the most important limiting factors for food production
and it is a great challenge for scientists to make sure food security for future
(Baker et al., 1997). Plant diseases may reduce crop yields by more than 50 %
(Oerke, 2006). Climatic changes play an important role to modify the flora and
fauna (Li and Yap, 2011; Manole and Bazga, 2011). In the last few decades, the
global climate changed greatly due to human activities. The global temperature
has been raised to 0.74°C and atmospheric CO2 level reach to 368 ppm from 280
ppm in the last two centuries (Chakraborty and Newton, 2011). These changes
have influenced plant growth as well as the interaction between plant and
pathogen (Heslop-Harrison and Schwarzacher 2011b).
12
The growing population will need significant increase of crop yield from the
existing cultivable land. Therefore, Shielding food crop from pathogens is the
most important aspect for increase of crop yield. A number of fungicides are
successfully applied to control the fungal diseases but they are not affordable for
local farmers as well as biologically hazardous (Liu et al., 2011). The plant
breeders have been trying to find the germplasm with desirable traits (Mujeeb-
Kazi and Hettel, 1995; Foulkes et al., 2011). Increase of genetic diversity in host
retard the rate of pathogen virulence. Therefore, introduction of Ug99 group
(stem rust races) for deployment of resistance genes in wheat is a good mean
safeguard to wheat against this deadly pathogen (Singh et al., 2008b; Gill et al.,
2011).
2.14 Diseases of wheat
More than 200 different types of wheat diseases have been reported. Majority
being pathogenic and infectious and are transmitted from plant to plant. About
10-16% of the world yield is lost due to plant diseases (equivalent to 220 billion
US dollars) excluding the postharvest loss of 8-12% of the under develop
countries (Chakraborty and Newton, 2011).
In Pakistan more than 50 diseases of wheat have been reported. Among them, rust
is considered to be the more damaging and more common in wheat crop. (Bhatti
and Soomro, 1996). The history shows that black stem rust of wheat in 1906-1908
had badly affected the wheat crop in Mirpur (Sindh). Stripe rust (yellow) and leaf
rust (Orange) of wheat in 1978 had made great loss to wheat crop all over in
Pakistan over the recent years (Bashir, 1988).
13
2.14.1 Black Stem rust of wheat
Stem rust has been declared as a severe threat to wheat, barley, oat and rye, as
well some other important grasses. The stem rust fungus (Puccinia graminis) is an
obligate parasite belongs to family Basidiomycota of family Pucciniaceae (Kurt et al.,
2005; Kirk et al., 2001). From ancient times, stem rust is considered one of the
frightened diseases of wheat in the world. Indeed, several locations in the Bible
narrate that the increase of cereal rusts and smut is due to the cause of Israeli sins
as punishment for them (Chester, 1946). Symptoms include the appearance of
long and slender strips or spots on stem and leaf sheaths but on maturity they
spread to leaf blades and glumes as well. After infection the spots (pustules) have
brick red color and few millimeters in length. The spots rupture with pressure
and release brick red color urediniospores. On maturity, the spots produce black
color teliospores instead of urediniospores. (Agrios, 1970; Kurt et al., 2005).
2.14.2 Orange or leaf rust of wheat
Leaf rust of wheat is caused by fungus Puccinia recondita. Disease Symptoms are
the formation of small, red, oval shaped uredinia, scattered mainly on upper
surface of leaves. These uredinia produce orange red to dark red, round shaped
urediospores. Leaf rust appears before than black rust. Temperature range
between 18- 20°C is favorable for spread of the disease (Bashir, 1988).
The breeders have identified more than 60 leaf rust resistance genes and QTLs till
now. Some are race specific genes while other are used for development of new
cultivars. However, the continuous evolution of new races of pathogen Puccinia
triticina is being more virulent against these genes. Therefore for long term
14
protection against leaf rust disease require several ―slow rusting genes‖ to be
used along with race specific genes (McIntosh et al., 2012). A small group of slow
rusting genes include Lr 34 and Lr 46 (Martínez et al., 2001; Singh et al., 2003).
These genes are more dependent on environmental condition. Nowadays, to get
more resistant cultivars, wheat breeders also use slow rusting genes along with
race specific genes (McIntosh et al., 2012).
2.14.3 Yellow or stripe rust of wheat
Wheat yellow rust (stripe rust) is one of the most upsetting diseases globally.
Stripe rust is caused by the fungus Puccinia striiformis (Basidiomycetes) Westend. f.
sp. tritici Eriks. In favorable weather the Yellow rust disease spread like wild fire
in wheat susceptible varieties (Wan et al., 2004). An obligate parasite cause great
loss to worldwide wheat production (Chen, 2005; Hovmøller et al., 2010; Hale et
al., 2013). Among the three rusts yellow rust is first to appear on wheat. Disease
symptoms include appearance of spots (pustules) on leaves as well as ears in
stripes and other green parts. Initially spots are bright yellow and on maturity
change into black color. Cold weather (15 °C) along with moisture is good for
disease spread (Bhatti and Soomro, 1996).
2.14.4 Loose smut of wheat
Ustilago tritici is the causal agent for loose smut of wheat. The disease symptoms
include dusty black appearance of diseased heads. The infected heads emerge
earlier than those of healthy plants. All the chaff (glumes) and grain in a smutted
head are completely converted into black powder. This dusty head is composed of
15
millions of microscopic teliospores (smut spores). The spores dispersed by the
wind quickly leaving the bare rachis (Bhatti and Jiskani, 1996).
2.14.5 Powdery mildew disease of wheat
Powdery mildew is one of the most important diseases of wheat worldwide. The
causal agent for Powdery mildew is Erysiphe graminis f.sp. tritici (Xie et al., 2003).
Powdery mildew is very common in cold weather and the most damaging foliar
wheat diseases globally (Huang et al., 2000). The affected areas on underside of
the leaf become pale green to yellow in color. Conidia on upper surface of leaf are
hyaline, oval and single celled structures. The mature leaf has fruiting bodies
cleistothecia develop from modified mycelia. Early symptoms include appearance
of white to light grey colonies on upper surface of leaf blade (zillinsky, 1983).
2.15 QTL (Quantitative trait loci) mapping
The standard mapping population in crops can be obtained by crossing of two
parents having contrasting characters e.g. drought tolerant versus drought
susceptible. These bi-parental populations are used for identification of
Quantitative trait loci (QTL) across the genome. QTL mapping require lesser
number of markers to cover the whole genome (Sorrells and Yu, 2009), but in QTL
mapping only two alleles could be studied at a time, low mapping resolution and
require a longer time. In wheat, a number of QTLs have been identified for root
angle (Christopher et al., 2013), grain yield (Kuchel et al., 2007), Heat and drought
(Pinto et al., 2010), plant height (Cui et al., 2011), drought tolerance (Ibrahim et al.,
2012).
16
2.16 Association mapping
Association mapping (AM) is also called linkage disequilibrium (LD) or
association analysis (AA) is a common method of QTL mapping that doesn‘t need
bi-parental populations. Association mapping is advantage over QTL mapping
due to high resolution, more alleles coverage as well as cost effectiveness.
Association mapping require diverse populations rather than bi-parental crosses
to study maker trait association (MTAs). AM could be used for identification of
QTLs associated with a particular trait and even polymorphism with in a gene
(Gupta et al., 2005).
2.16.1 Genome Wide Association Mapping
Genome wide association mapping require huge set of molecular markers to
cover the whole genome for detection of marker traits associations (MTAs)
(Zhang et al., 2009). The resolution and effectiveness of association mapping
depends on the extent of linkage disequilibrium (LD) and LD depends on the
recombination frequency, population history, chromosomes history and mutation
across the whole genome (Ersoz et al., 2009; Zhang et al., 2009; Chao et al., 2010).
LD has been calculated by different ways in different crops like rice, barley and
wheat (Agrama et al., 2007; Comadran et al., 2009; Chao et al., 2007). AM is mainly
used to show the quantitative traits with high resolution mapping at gene level
(Ersoz et al., 2009). Therefore genes with uncertain effects could be mapped easily
with LD (Hirschorn and Daly, 2005).
17
2.16.2 Candidate Gene Association Mapping
Candidate gene association mapping is used for association of targeted gene with
a particular phenotypic trait (Gonzalez-Martinez et al., 2008). Candidate gene AM
is also depending on the availability of molecular markers as well as extent of LD.
Candidate gene AM has also been used in many crops for tracing of QTLs of a
candidate gene. Candidate gene AM is important for mapping of specific gene
with known function (Tabor et al., 2002).
2.17 Drought resistant genes
A lot of drought resistant genes have been screened for identification of their
functions (Shinozaki and Yamaguchi-Shinozaki, 2007). On the base of abscisic acid
(ABA) hormone, drought tolerant genes can be classified into two groups as ABA
independent and ABA dependent. DREB (Dehydration Responsive Element
Binding) and 1-FEH (Fructan 1-exohydrolase) are ABA independent drought
tolerant genes. DREB1 and DREB2 have been from different crops like wheat,
maize and rice (Wei et al., 2009). DREB1A gene in transgenic wheat revealed more
drought resistance, better spike length and branches as compared to non-
transgenic wheat (Pellegrineschi et al., 2004). Though, DREB1A gene did not show
any positive effect on grain yield as compared to control lines under water deficit
conditions (Saint Pierre et al., 2012). DREB2 gene recovered from wheat showed
good result against freezing and osmotic stress (Kobayashi et al., 2008).
The ABA dependent genes expression depend on soil water deficit (drought)
conditions (Shinozaki and Yamaguchi-Shinozaki, 2007). The enhanced response to
ABA (ERA1) gene has been cloned from Triticum eastivum is ABA dependent in
18
function. It has been confirmed that ERA1 help in increasing drought stress by
closing stomata in wheat (Ziegelhoffer et al., 2000).
2.18 The use of molecular markers for drought related traits
Molecular markers are the short DNA sequences that can be used to trace the
process of inheritance, variation and circulation of parental DNA in the next
generation (progeny) (Schlotterer 2004). Nowadays, molecular markers are mostly
used to detect genome regions and Quantitative trait loci (QTL) for various
disease linked traits, different stresses (cold, heat and drought) in cereal crops
(Gupta and Varshney, 2004). Molecular markers are very important for mapping
genes of choice, molecular breeding, cloning of genes, and introgression of genes,
germplasm diversity and detection of phylogenetic relationship (Hayashi et al.,
2004).
Nowadays molecular markers are abundantly used in association mapping as
well as in segregation to trace valuable alleles both in cultivated varieties and wild
relatives. Most of the statistics on drought resistance is recorded on the base of
segregation mapping and QTL mapping. Association mapping can analyze
recombination and selection of thousands of generations; therefore, in present era
association mapping is considered to be the more influential tool than ‗classical‘
genetic linkage mapping (Syva¨nen, 2005). Many monogenic (single genes) traits
like flowering time (Ppd), plant height (Rht) and ear type in wheat were already
mapped and play important roles in drought tolerance (Forster et al., 2004). In the
last two decades, QTL analysis had made a strong breakthrough in identification
of variation in chromosomal regions controlling the physiological, morphological
19
and developmental changes during plant growth in drought conditions.
Reasonable results of QTL analysis proved that large proportion of wheat genome
has responsible for physiological and agronomic traits for drought. Extensive
data about QTL is available on databases like GRAMENE
(http://www.gramene.org/) or GRAINGENES (http://wheat.pw.usda.gov/GG2/)
(Cattivelli et al., 2008).
The association and variation between quantitative trait loci could be diagnosed
using molecular markers. Molecular markers for QTL analysis is the best tool
instead of physiological trait measurements for drought in breeding program
(Lanceras et al., 2004).
2.18.1 Microsatellite Markers
Microsatellites are short motifs consisting of one to six base pairs present in
coding and non-coding regions. The pioneer of Microsatellite is Litt and Luty
(Litt and Luty, 1989). Microsatellite markers are also known simple sequence
repeats (SSR) markers. Microsatellite markers are widely used in crops for the
purpose of characterization and evaluation, screening diversity, breeding, tracing
genes of interest and broad spreading all over the genome (Tautz, 1989). The SSR
or micrsattelite markers are widely used for genetic diversity because of their high
level of polymorphism, co-dominant nature, mostly monolocus, easy to develop
and use, more informative and high rate of reproducibility but the only drawback
is they are highly cost effective and difficult than others (Steliana et al., 2010). The
SSR (Microsettalites) markers are also very important in fluorescent In situ
20
hybridization (FISH) technique. The utility for genetic mapping of Micrsettalites
(SSR) are species specific.
Microsattelites (SSR) markers may be grouped on the base of repeated units and
their position in the genome. Microsattelites may be mononucleotide (A)n,
Dinucleotide (CA)n, Trinucleotide (CGT)n, Tetranucleotide (CAGA)n,
Pentanucleotide (AAATT)n and Hexanucleotide (CTTTAA)n (Semagn, et al.,
2006). Microsattelites are perfect primers for genetic mapping studies (Jarne and
Lagoda, 1996).
2.19 Flourescent in situ hybridization (FISH)
In present era, different methods are used for tracing chromosomal translocation
as Feulgen method and acetoorcein staining (Juchimiuk et al., 2007). However, the
advance cytogenetic methods In situ hybridization (ISH) and flourescent in situ
hybridization (FISH) has provided the new tackles for finding of chromosomal
aberration (Juchimiuk et al., 2007). ISH is a prevailing and exclusive method that
link molecular information stored in DNA sequences with its physical location
along chromosomes and genomes (Schwarzacher, 2003). FISH is very useful
technique for identification of individual chromosome in the genome or any alien
fragment attached to a particular chromosome (Schwarzacher et al., 1992) or the
identification of repeated DNA sequences (Maluszynska et al., 2003; Miller et al.,
1996). However a complete chromosomal map of plant cells is not possible until
now due to lack of chromosomes specific probes. FISH has confirmed
translocation and cell ploidy level in Arabidopsis thaliana and Secale cereal
(Hasterok et al., 2002a).
21
The Ph1 gene sited on chromosome 5B of wheat preventing the chromosome
pairing of wheat and alien material during meiosis (Riley et al., 1959). Deletion of
Ph1 gene from chromosome 5B could allow pairing of homoelogous chromosomes
of wheat genomes (AABBDD) or between wheat and alien chromosomes
fragments (Miller et al., 1996). Genes located on homeologous group 3
chromosomes can also affect the pairing during meiosis, therefore knowledge of
such genes that could affect pairing between wheat genome and alien
chromosomes is of considerable importance (Mello-Sampayo & Canas, 1973;
Miller et al., 1996). In fluorescence In situ hybridization analysis various repetitive
sequences i.e pAs1, pSc119.2, pTa-535, pTa71, pTa 794, CCS1, and pAWRC1 have
been used as a probes (Tanq et al., 2014). In FISH technique two different probes
are applied with a two by two combinations for identification of different
chromosomes either forming pairs with alien chromosomes or unpaired
(Cuadrado et al., 1997). Highly repeated DNA probes are used in FISH technique
for identification and judgment of chromosomes of wheat (belonging to A, B or D
genomes) and chromosomes of any alien material (Mukai et al., 1993; Cuadrado et
al., 1995a). The highly repeated telomeric and centromeric DNA sequences may be
used as probes for detection and analysis of chromosomal aberration induced by
chemicals (Juchimiuk et al., 2007).
2.20 Major abiotic stresses
2.20.1 Temperature (Heat) Stress
Heat stress is main abiotic stress affecting wheat crop production more than any
other stresses like frost or drought. Data on wheat stress research revealed that 3-
22
5% (190Kg/ha) grain reduction can occur for every one degree increase of
temperature from the usual (Gibson and Paulsen, 1999; Kuchel et al., 2007; Bennett
et al., 2012). The high temperature affects both reproductive stage as well as
physiological processes of the crop like low photosynthetic rate, reduced grain fill
duration, reduced grain size and finally reduces grain yield (Stone and Nicolas,
1995). Therefore development of heat and temperature tolerant wheat varieties is
crucial for production of high yield under heat stress conditions (Balla, 2012).
2.20.2 Salinity Stress
The cereal crops are is often grown worldwide on saline soil that badly reduce
crop yield (Clark and Duncan, 1992; Ali et al., 2012). The germination of crop is
badly affected by saline soil. The early crop response to excess salts is reduction in
leaf area that result little photosynthesis (Munns and Termant 1986; Shalaby et al.,
1993; Yadv et al., 2011). The treatment of seeds with boric acid, calcium, water and
different hormones may enhance the germination of crops on saline soil (Babu
and Kumar, 1975; Huang and Redmann, 1995; Marambe and Ando, 1995).
In saline soil rice wilt up in early stages of growth, wheat is moderately tolerant
while barley is more tolerant (Richard, 1952; Munns, 2006). The low cost salinity
tolerant varieties have been made to overcome the salinity problem (Hollington
2000). The salt tolerant varieties include LU-26S and SARC-1 (in Pakistan), Sakha
8 (in Egypt) and KLR 1-4, KLR 19 (in India) but the farmers did not approved
these varieties due to some agronomic defects (Munns, 2006). Salt tolerance is
different for different species even different organs of the same plant show great
variation for salt tolerance (Flowers and Hajibagheri 2001; Ismail, 2003). Through
23
genetic breeding different modification for reliable agronomic as well as
physiological traits has been testified for salinity tolerance in wheat at various
growth stages with in different species (El-Hendawy et al., 2005; Ali, 2007).
2.20.3 Frost or cold stress
Millions of kilograms of possible grain yield of wheat drop every year due to
various stresses. Among these stresses frost or cold temperature or freezing
temperature is very important (Skinner, 2009). The cold temperature cause great
losses in susceptible varieties. Therefore, winter frost tolerance is an important
agronomic trait in crops. In autumn, duration of cold hardening is requiring for
induction of freezing tolerance under natural conditions (Thomashow, 1999). The
capability of wheat crops to grow under freezing depends upon the efficiency of
two processes as cold acclimation and freezing stress response. Cold acclimation
depends on the transcriptional response of at least 450 genes in wheat located on
all chromosomes (Fowler et al., 2005; Monroy et al., 2007). Winter frost (cold
acclimation) ensures plant existence. This process involves a number of changes in
the transcriptome, controlled by tandemly duplicated C- repeat Binding Factor
(CBF). Transcription factors are situated at the Frost Resistance-2 (Fr-2) locus. The
CBF family includes fifteen known genes, out of which eleven are located at Fr-2
loci on homeologous chromosomes 5 (Motomura et al., 2013). These CBF genes
control the regulatory pathways of freezing tolerance in wheat (Winfield et al.,
2010).
24
2.20.4 Drought stress
Drought is defined as water deficiency in the root zone of crops that result
decrease in yield during plant life cycle (Ji et al., 2010). The capability of a plant to
grow and reproduce in water limited area is known as drought tolerance (Fleury
et al., 2010). Drought stress is changeable in its intensity, length and effectiveness
(Kadam et al., 2012). Drought is the main environmental problem that causes high
negative effect on cereals crops particularly wheat. During drought conditions
plants shows a wide range of behaviors varying from great sensitivity to high
tolerance (Eslam, 2011). Seasonal cyclic drought has great involvement in
reduction of wheat, barley and other cereals yield (Izanloo et al., 2008). Drought
stress greatly affects plant growth, gene expression, distribution, yield and quality
of crop in arid and semi-arid areas around the world (Yang et al., 2004; Shi et al.,
2009). About 60% of crop production around the world is from arid and semi-arid
regions. The rate of rain fall is critically fluctuated in these areas. In developing
countries 37% of wheat is commonly grown in drought susceptible areas
(Nakashima et al., 2000). The major constraint to wheat production around the
world is inadequate supply of water. The plants reaction to drought stress
depends on plant growth (development), stress period and plant heredities
(Beltrano & Marta, 2008, Khan et al., 2011). Drought can also shake
morphophysiological features of plant as growth, anatomy, morphology,
physiology (stomatal closure, low photosynthesis and transpiration rate),
biochemistry and finally the yield of crop (Jones et al., 2003; Hafiz et al., 2004;
Demirevska, 2008). Yield is the basic criteria for cultivation of crop varieties under
25
drought conditions (Lonbani and Arzani, 2011). Therefore, it is a great challenge
for crop breeders to produce cultivars having good potential of survival in
stressed (drought, salinity, cool) environment (Sivamani et al., 2000; Inoue et al.,
2004; Araus et al., 2008). Drought tolerance breeding may be effective if the marker
assisted selection based molecular linkage maps for crop species are available
(Nguyen et al., 1997).
2.20.5.1 Drought stress in Pakistan
Diverse climatic and soil conditions are available for wheat growing in Pakistan.
About one third of the total land area comes under rainfed regions where rainfall
is unusual (Khanzada et al., 2001). However, drought and salinity are very
common around the world and are most serious problems to agriculture in
Pakistan (Altman, 2003). Arid and semi-arid regions of the world are badly
affected by water stress and as result crop production is reduced (Ranjana et al.,
2006). Irrigated areas are sometime face drought conditions due to inadequate
supply of water and canal shutting (Hafeez et al., 2003). Drought tolerant varieties
whose grain yield will not reduce significantly due to drought stress or drought
tolerant crops to be those as to take up maximum amount of water and minimum
loss of water during dry conditions (Laszlo, 2009).
2.20.5.2 Drought stress in Khyber Pakhtunkhwa
In Pakistan, wheat production in drought stress areas (20 %) is much lesser than
that of irrigated areas. In Khyber Pakhtunkhwa (KPK) province of Pakistan 60%
of wheat farming depends on rain water. Therefore, the grain yields in this
province show great variation as compare to other provinces (Khakwani et al.,
26
2011). To ensure high crop production in these areas, different aspects of
agriculture like holding precipitation, reducing evapotranspiration and sowing of
drought tolerant varieties are important (Anonymous, 2007). Wheat varieties
cultivated in rain fed areas are usually low yielding as well as pests and diseases
prone but are well adapted and flourish well in dry conditions. Still need to
increase yield for growing population to ensure food security. The use of
molecular markers for genetic improvement and other agronomic traits are highly
appreciated. Therefore, still need to work more on tracing of genomic regions
related to root traits and reproductive traits for drought tolerance (Khakwani et
al., 2011).
The goal of the present study is to screen out hundred wheat landraces cultivated
in Pakistan for drought tolerance as well as their yielding capability which are
better suited economically for drought conditions. The objectives of the present
research were to screen different wheat landraces for various morphological,
physiological and molecular traits as:
1. Different varieties of wheat and their association with drought on the
basis of morphological characterization.
2. To identify physiological screening test for evaluation of wheat
germplasm for their drought potential.
3. Association mapping for root traits associated with drought stress and
improving wheat tolerance to drought.
27
Chapter-2 MATERIALS AND METHODS
The plant materials consisted of hundred cultivars of common wheat collected
from different region of Pakistan and was examined for genetic diversity. The
cultivars were sown in the control environment as well as in the field of Hazara
University Mansehra, Pakistan for three years (2011-2013).
2.1 MORPHOLOGICAL CHARACTERIZATION OF WHEAT GERMPLASM
Plants were grown in field each year in rows having length of three meter and
row to row space was kept 6 inches. When plants became mature, data for
morphological parameters were collected from different genotypes at appropriate
stage of growth to examine variation in qualitative and quantitative traits
including Plant height, leaf length, leaf width (leaf area), peduncle length, days to
50% heading, Days to maturity, Awn length, number of tillers per plant, Spike
length, spikelets per spike, spike density, Number of grains per spike, 1000 grain
weight and yield per plant, harvest index and total weight per plant.
2.1.1 Plant height
At the maturity three randomly selected plants were measured from each row.
The plant height was measured in centimeters by meter rod in such a way that
one end of the rod touching ground and the other to the apex of the spike
excluding awn. Then the average was taken as the plant height for getting
excellent results.
28
2.1.2 Leaf area
Measurement of the leaf area was taken from randomly selected plants and was
measured to get maximum length and breadth when the plant was mature and
turgid. In order to get the correct measurement plastic scale was used. The leaf
area was calculated by using Muller (1991) formula.
Leaf area = maximum length x maximum width x 0.74
2.1.3 Number of days to 50% heading
The data for 50% heading was taken in such a way that when 50% heading was
observed in the whole row during heading days, the reading data was recorded
with respect to the whole row either they sprouted or not for this the field was
observed on daily basis and the correct day was recorded. The days were counted
from the sowing date till 50% heading for each variety.
2.1.4 Number of days to maturity
The data was conducted at the maturity of spike and change in its color from
green to pale yellow. For data recording fields were visited on daily basis and the
date recorded for each variety in three of the replication, the days to maturity was
obtained by subtracting the date of data recording from the date of sowing.
2.1.5 Number of tillers per plant
At maturity number of tillers per plant was counted from three randomly selected
plants of each germplasm throughout the row and the average no of tillers per
plant obtained for each variety in each row.
29
2.1.6 Peduncle length
The peduncle is the stalk from the last node till the starting of spike. Peduncle
length was measured in centimeters through the meter tape, the average of three
observations for each variety were taken in order to get the precise reading.
2.1.7 Spike length
The spike length of the mother shoot was measured by the plastic scale form the
base to the tip of the spike excluding awns. In each variety three randomly
selected spikes were measured and their average was taken as spike length.
2.1.8 Awn length
The awn is the needle like structure present on the spikelets. Awn length was
measured in centimeters using the plastic scale from the base to the tip of awn. In
order to get the accurate reading three randomly selected awns of three plants
throughout the replications were measured and their average was taken as awn
length.
2.1.9 Number of spikelets per spike
From each replication three spikes of each germplasm were randomly selected
and the number of spikelets in these spikes was counted. The average number of
spikelets per spike was taken in order to minimize the damage among the
spikelets in spikes.
2.1.10 Number of grains per spike
Number of grains per spike was counted by threshing mother shoot manually.
Then for the random selection the numbers of grains of whole plant was counted
30
and divide it by the number of spikes in a plant and so their average obtained
which was taken as number of grains per spike.
2.1.11 Yield per plant
From each replication three randomly selected plants of each germplasm were
taken and each plant was threshed manually and as a result the grains obtained
were then weighted through electric balance. The average of the three readings
was taken as yield per plant.
2.1.12 Harvest index
The randomly selected plants from each replication of each harvested were
harvested and the whole plant along with the leaves and straws were weighted
and its dry weight was recorded in grams which were taken as the biological
yield. Then the spikes were threshed and the grains weight was taken in grams
known as the grain yield. For Harvest index following formula was used.
Harvest index =grain yield / biological yield ×100
2.1.13 1000-grain weight
The grains obtained from every variety were counted to complete a digit of 1000,
and then these grains were weighted by electronic balance.
2.1.14 Spike density
Spike density was calculated by dividing number of spikelets per spike on spike
length.
Spike density= No of spikelets per spike/spike length
31
2.1.15 Total weight per plant
Yield per plant was calculated by dividing yield per plant on harvest index (HI)
multiplied by 100 as
Total weight per plant= yield per plant /HI × 100
2.2 PHYSIOLOGICAL CHARACTERIZATION
Data on physiological traits associated with drought and yield were recorded and
the following physiological tests were carried out for screening germplasms for
drought resistant.
2.2.1 Relative leaf water content (RWC)
Leaf relative water content (RWC) has been proposed as an important indicator of
water status than other water potential parameters under drought stress
conditions (Carter & Patterson, 1985). Relative water content is influenced by
osmotic adjustment and by water absorption and transpiration (Schonfeld et al.,
1988). Screening techniques for selecting plant drought resistance must be
accurate, rapid and inexpensive. Plant water status was estimated by measuring
the relative leaf water content (RWC). The RWC measured on the youngest
emerging leaf to ensure uniformity across all the plants (Smirnoff, 1993). Leaves
were harvested directly in test tubes and place on ice to prevent any further water
loss, and then weight to determine fresh weight (FW). One mL of distilled water
was added to the tubes and the leaves placed in a cold room overnight (for 24 h at
4°C) to allow rehydration as shown in figure 1. Following rehydration, the leaves
32
were re-weighted for turgid weight (TW). Then the leaves were dried at 65°C for
24 hours and weighted for dry weight (DW). RWC determined by the formula:
RWC% = ((FW-DW)/(TW-DW)) x 100
Figure 1: test tubes showing leaves of wheat after rehydration
2.2.2 Water loss rate
Water loss of excised leaves (WLR) was measured for each genotype. Leaves were
sampled from the upper half of the plants weighed (FW 1) and allowed to
desiccate at 25 °C in dark. After 24 h samples reweighed (fw2) and next oven
dried at 70 °C and weighed again (DW)(Stainislaw, et al., 2003) as shown in figure
2 (a, b). Water loss of excised leaf was calculated by the following formula.
WLR=FW1-FW2/DW
33
Figure 2(a): leaves harvested for fresh Figure 2(b): oven dried leaves for dry weight weight
2.2.3 Water-use efficiency
Plants were grown in a control environment in a growth chamber with 16/8 h
photoperiod under a control temperature and humidity. Germination of seeds
was carried out at room temperature in small pots of equal size. Pots covered with
plastic sheet with a small hole in the center of plastic sheet. Total weight of each
pot was recorded (figure 3a) and also set up three control pots with no plant for
estimating water loss via evaporation from the hole in the plastic sheet. Plants
were grown in a
34
Figure 3a: pots covered with plastic sheet with small pores in the center
control condition so that there is no more extractable water. Shoots were
harvested for recording the fresh weight as shown in (figure 3b). Shoots were dry
in oven at 60ºC for 4 days. Latter, the dry weights of shoots was recorded and
calculated the water use efficiency by the following formula:
Total plant water use = total weight of each pot after no more plant extractable
water left –total weight of each pot + harvest shoots and record the fresh shoot wt
– (water loss in control pots with no plant × 0.7*).
35
Figure 3b: total weight of pot after no Figure 3c: shoot harvested for fresh more plant extractible water left weight
2.3 ROOT TRAIT ANALYSIS
A well-organized root system is necessary for efficient water uptake. In crops,
fibrous root system consists of two types as seminal and nodal roots (Fitter, 2002).
Well develop root system could play positive role in water deficit (drought) areas.
Root morphological traits greatly affect water and nutrients uptake. Herbaceous
plants with fine roots, smaller diameter and greater root length are better adapted
to dry conditions (Henry et al., 2012).
2.3.1 Root fresh weight (RFW)
The plants were removed from soil along with roots. The roots were detached and
clean carefully to remove soil particles. Root fresh weight (RFW) was recorded in
mg.
2.3.2 Root dry weight (RDW)
The fresh roots were dried in incubator for three days at 70 ºC. The root dry
weight (RDW) was recorded in mg.
36
2.3.3 Shoot fresh weight (SFW)
The above ground plants were detached after 60 days and weighted for fresh
weight. The shoot fresh weight was recorded in mg.
2.3.4 Shoot dry weight (SDW)
The detached shoots were then dried in incubator at 70 ºC for taking dry weight.
The dry weight was recorded in mg.
2.3.5 Root shoot ratio (R: S)
Root shoot ratio is an important parameter used for relationship between
underground (root) and aboveground (shoot) parts.
2.3.6 Root diameter (RD)
Mean of root diameter (RD) was taken by measuring the RD randomly using the
digital vernier calliper.
2.3.7 Number of Nodal roots (NNR)
Nodal root are those which arise directly from lateral nodes of coleoptile. NNR
was counted manually. Mean of NNR was taken for statistical analysis.
2.3.8 Number of seminal roots (NSR)
Seminal roots arise directly from base of the germinating seeds. NSR was counted
manually.
2.3.9 Root angle (RA)
RA plays an important role in uptake of soil moisture. The RA was measured by
protector. Mean of RA of all three replications was taken for further analysis.
37
2.3.10 Total roots length (TRL)
Total roots length (TRL) was calculated by adding lengths of all the nodal and
seminal roots. Root length was measured in mm.
2.3.11 Root density (RDT)
Root density (RDT) was calculated by dividing number of lateral roots of longest
root on maximum root length as.
RDT= number of lateral roots of longest root/ MRL
2.3.12 Maximum root length (MRL)
Maximum root length was taken by measuring the longest root. The root length
was measured in mm.
Figure 4(a): Showing root arctechiture of Figure 4(b): The longest root (37cm) Triticum aestivum recorded in Triticum aestivum 2.4 FLUORESCENT IN SITU HYBRIDIZATION (FISH)
2.4.1 Seeds germination and digestion
Out of hundred landraces, seeds of fifteen (Kiran, Janbaz, Sindh-81, Lasani-08,
Pirsabak-85, Zamindar-80, Barani-83, Pak-81, Potohar-70, AUP-5008, Saleem-2000,
Sonalika, Manther, Margalla-99 and Raskoh) were germinated in petri plates for
38
48 hrs at room temperature. The root tips were cut off and put them in distilled
water. Put the bottles in ice racks in cold room overnight. The distilled water was
discarded completely and ethanol (100%) and acetic acid 100% (3:1) were added to
the tubes. The tubes were left at room temperature for 7-8 hours, and then
transferred to cold room for 24 hours. Wash with distilled water twice (5 min for
drying). Few root tips were taken in small petri plates, add Enzyme solution and
put in incubator (37 ºC) for 45 min. Removed from enzyme solution and added
enzyme buffer (1X) for 10 minutes. Enzyme solution can be used 5 times again
and again.
2.4.2 Slide preparation
Put one or more tips on slide. Remove all the buffer solution using blotting paper.
Add one drop of acetic acid (45%) and crush the root tips in solution. Put cover
slide and dry the slide by blotting paper (if air bubbles trap in the slide, match
glow can be used). Check the slide in microscope (low power 16X, 26X and 40X).
If the chromosomes appear so put the slide in dry ice quickly for one hour.
Remove the cover slip and mark the border on diamond pencil and then dry the
slide at room temperature for 24 hours before use. The air dried slides stored for
longer time in silica gel at -20 ºC.
2.4.3 Pretreatments
2.4.3.1 Post-fixation of air dried slides
In post- fixation steps, ethanol and acetic acid (3:1) added to the air dried slides
for 15-30 minutes. Then washed twice with 100% ethanol for 5 minutes and air
dried.
39
2.4.3.2 RNase treatment
200µl RNase solution was applied to each slide, cover with a plastic cover slip.
Incubation was carried out for 1hour at 37°C in humid chamber, and then washed
twice in 2xSSC for 2 minutes and 10 minutes.
2.4.3.3 Paraformaldehyde fixation
Incubation of slides was done with paraformaldehyde in fume hood at RT for 10
minutes. Wash twice in 2xSSC for 2 minutes and then 10 minutes.
2.4.3.4 Dehydration
During dehydration step the slides were washed with 70% ethanol for 2 minutes.
Again wash in 85% ethanol for 2 minutes and third time washed in 100% ethanol
for 2 minutes and then air-dried.
2.4.4 Hybridization
Probe mixes of 30μl was applied on marked areas of pretreated slides. Put plastic cover
slip on marked areas before putting in thermal cycler. Denaturation was done at a
temperature between 60 oC -90 oC for 10 minutes and was cooled at room temperature for
20 hours.
2.4.5 Post hybridization
Stringent washes are require for Post hybridization are usually done in shaking
water bath and then transfer to flat-bed shaker in fume hood. Formamide solution
use in high stringency washes is highly toxic. Therefore it should be dispose with
great care.
40
2.4.5.1 Stringent washes
After hybridization, the 2xSSC solution was put on the slides at 35-40°C to float
off the plastic cover slips. Then wash in 2xSSC solution for 2 minutes at 43°C.
Apply low stringent wash for 5 minutes at 45°C. Again wash in high stringent
solution for 5 minutes at equal temperature. Wash in 0.1xSSC at 42°C for 2
minutes. Again Wash in new 0.1xSSC at 42°C for 10 minutes and then again wash
in 2xSSC for 5 min. Allow to cool at room temperature.
2.4.5.2 Detection
Transfer slides to detection buffer for 5 minutes. Add 200µl of blocking solution to
each slide and cover with a plastic cover slip. Incubate at 37°C for 5-30 minutes.
Remove coverslip, dry the slides and apply 40-50µl of antibody solution to each
slide, replace the coverslip. Incubate slides again at 37° for one hour. Wash in
detection buffer at 40°C for 2 minutes. Wash again in detection buffer at 40°C for 8
min.
2.4.5.3 DAPI staining and mounting
Incubate slides with 100µl DAPI solution (4µg/ml in McIlvaine‘s buffer) under a
plastic cover slip at RT for 10-30 min in the dark. Rinse slides in detection buffer
and add two drops of anti-fade. Put a large cover slip (24x40 mm) on each slides
and squash gently but firmly. Observe slides under U.V microscope best to leave
them in the dark overnight in the fridge before looking.
41
2.5 MOLECULAR CHARACTERIZATIONS OF WHEAT VARIETIES
2.5.1 DNA Isolation
Small scale DNA isolation protocol (Weining and Langridge, 1992) was used to
isolate DNA from leaves of the plants. 10cm long fresh leaves were collected and
placed in an eppendorf tube in Liquid Nitrogen. In the laboratory, leaf material
was crushed with a knitting needle and 500µL DNA extraction buffer was added
and mixed well. 500µL of Phenol: Chlorofom: Isoamyalcohol (in ratio of 25:24:1)
was then added and vortex until homogenous mixture was obtained. The tubes
were then centrifuged at 13000 rpm for 5 minutes. Aqueous phase was
transferred in a fresh tube and 50µL 3M Sodium acetate (PH=4.8) and 500µL
Isopropanol was added and mixed gently. Tubes were centrifuged at 13000 rpm
for 5 minutes to make the DNA pellet. Supernatant was discarded and DNA
pellet was dissolved in 40µL TE. DNA was then treated with RNAse A to remove
RNA and was analyzed on 1% agarose gel to check quality and quantity of DNA.
2.5.2 Nanodrop measurement
The DNA concentration was measured through Nano Drop Analyzer (model ND-
1000 Spectrophotometer NanoDrop Technologies, Inc. Wilmington, USA.) at
University of Leicester UK (Table 1). The concentrated DNA was diluted further
to the required (50 ng/ul) quantity by the following dilution formula:
42
Genomic DNA in ng/ul x X = 50 ng/ul x 100
So X= 50 ng/ul x 100/ Genomic DNA in ng/ul
2.5.3 Polymerase Chain Reaction
Polymerase Chain Reaction (PCR) was carried out using protocol described by
Roder et al., 1998. Each PCR was carried out in a 25μL reaction volume, containing
11.3μL double-distilled deionized H2O, 2.5μL 10X buffer, 2μL MgCl2, 2μL dNTPs,
0.2μL Taq polymerase, 1μL of each primer, and 5μL DNA.
Thermocycling conditions were required as:
(i) Denaturation of double standard genomic DNA template at 94ºC.
(ii) Annealing of specific primer with the template DNA at specific
temperature.
(iii) Extension of Primer at 72ºC and formation of new DNA strand.
2.5.4 Metaphore agarose gel
Metaphor agarose (BioWhittaker Molecular Applications, Vallensbaek Strand,
Denmark) has the high resolving property of PCR product and can differentiate
DNA fragments of very small size. As recommended by the company that 2%
metaphor agarose in 1X TBE can easily resolve DNA fragments of size from 50-
250 bp.
After PCR, Electrophoresis was completed in 2% Electrophoratic gel and the PCR
bands of required bp obtained were scored for association mapping (AM) analysis
of root traits for drought resistance varieties.
43
2.5.5 Reagents used during DNA isolation and gel electrophoresis
During the present study the following chemicals/reagents were used.
Tris Solution (I M) :
Tris powder 12.11 gm Distilled water 100 ml
DNA extraction Buffer:
Tris pure 12.1 gm NaCl 5.2 gm EDTA 3.2 gm SDS 10 gm Distilled water 1 L
NaoH was added for adjusting pH to 8.5
Phenol solution :
Phenol 50 ml Chloroform 48 ml Isoamyl alcohol 02 ml
3M sodium acetate :
Sodium acetate 40 gm Distilled water 1 Liter pH 4.8
EDTA (0.5 M) :
EDTA powder 18.7 gm Distilled Water 100 ml
NaOH pellet was added for maintaining pH at 8.5
1X TBE Buffer :
Tris pure 4.87 gm Boric acid 2.43 gm EDTA 0.5 M solution 1.8 ml
44
Distilled water 450 ml Total volume 500 ml
Ethedium bromide:
Ethedium bromide 10 mg Distilled water 100 ml
DNA loading dye:
Bromophenol blue 0.6 g Glycerol 15 g 5 x TBE 20 ml Distilled water 65 ml
45
Table 1: Nanodrop measurement of genomic DNA extracted from hundred landraces of wheat (Triticum aestivum)
sample ID User ID Date Time ng/ul A260 A280 260/280 260/230 Constant Cursor Pos.
Cursor abs.
340 raw
Sonalika Default 7/11/2013 11:24 2371.76 47.435 24.007 1.98 1.75 50 230 27.158 5.092
Merco-2007 Default 7/11/2013 11:25 2418.97 48.379 25.519 1.9 1.5 50 230 32.262 9.097
Manther Default 7/11/2013 11:31 1959.56 39.191 18.551 2.11 1.46 50 230 26.856 18.48
Lr-230 Default 7/11/2013 11:34 2967.32 59.346 29.978 1.98 2.18 50 230 27.205 0.753
Ksk Default 7/11/2013 11:36 1911.09 38.222 19.455 1.96 2.01 50 230 18.969 0.684
Maxipak Default 7/11/2013 11:37 4216.92 84.338 47.699 1.77 1.92 50 230 43.861 1.652
Indus-79 Default 7/11/2013 11:38 3150.92 63.018 32.19 1.96 2.16 50 230 29.212 0.584
Bakhtawar 94 Default 1/1/1900 11:39 3691.94 73.839 38.007 1.94 1.98 50 230 37.378 1.758
Wadanak-85 Default 7/11/2013 11:40 3045.48 60.91 29.405 2.07 2 50 230 30.427 1.517
Abdaghar-97 Default 7/11/2013 11:42 1709.8 34.196 17.401 1.97 1.74 50 230 19.606 2.764
Margalla-99 Default 7/11/2013 11:45 1311.49 26.23 12.245 2.14 1.18 50 230 22.242 24.888
Uqab-2000 Default 7/11/2013 11:46 3215.94 64.319 32.383 1.99 2.01 50 230 31.954 1.669
Raskoh Default 7/11/2013 11:47 2587.56 51.751 25.937 2 1.67 50 230 30.989 3.483
Haider-2002 Default 7/11/2013 11:49 3327.54 66.551 33.114 2.01 2.03 50 230 32.854 6.613
Local white Default 7/11/2013 11:50 4154.25 83.085 47.13 1.76 2.01 50 230 41.382 1.254
MH-97 Default 7/11/2013 11:51 2547.28 50.946 25.209 2.02 2.02 50 230 25.193 2.398
Zarlashta-90 Default 7/11/2013 11:54 550.23 11.005 7.093 1.55 1.05 50 230 10.481 7.622
Punjab-76 Default 7/11/2013 11:56 4663.42 93.268 62.355 1.5 1.55 50 230 60.031 3.392
Faisalabad-85 Default 7/11/2013 11:57 1343 26.86 14.884 1.8 1.17 50 230 22.933 8.293
Barani-70 Default 7/11/2013 11:59 4476.29 89.526 53.997 1.66 1.77 50 230 50.439 2.1
Rawal-87 Default 7/11/2013 12:03 56.53 0.011 0.018 0.58 1.01 50 230 0.011 0.003
NIAB-83 Default 7/11/2013 12:05 227.56 4.551 2.986 1.52 0.84 50 230 5.391 4.283
GA-2002 Default 7/11/2013 12:06 3861.75 77.235 40.736 1.9 2.08 50 230 37.198 1.54
Chenab-79 Default 7/11/2013 12:07 1957.56 39.151 19.762 1.98 1.74 50 230 22.458 3.445
Saleem-2000 Default 7/11/2013 12:08 2180.44 43.609 21.78 2 1.64 50 230 26.616 8.916
Shalimar-88 Default 7/11/2013 12:10 883.1 17.662 12.321 1.43 0.6 50 230 29.367 17.074
46
Khyber-83 Default 7/11/2013 12:12 1248.03 24.961 13.906 1.79 1.27 50 230 19.651 4.494
Chenab-70 Default 7/11/2013 12:13 3846.91 76.938 45.822 1.68 1.51 50 230 50.791 6.384
Soghat-90 Default 7/11/2013 12:14 1671.1 33.422 18.936 1.77 1.23 50 230 27.086 52.667
Pari-73 Default 7/11/2013 12:17 250.02 -0.5 16.396 -0.03 0.07 50 230 -7.087 21.56
Chakwal-86 Default 7/11/2013 12:19 4081.06 81.621 45.445 1.8 1.99 50 230 41.085 2.508
Wadanak-98 Default 7/11/2013 12:19 3153.36 63.067 32.043 1.97 2.08 50 230 30.357 1.381
Nori-70 Default 7/11/2013 12:21 3893.93 77.879 41.803 1.86 1.74 50 230 44.848 8.252
ZA-77 Default 7/11/2013 12:22 42.52 0.45 14.973 0.03 NaN 50 230 NaN 14.1
Kaghan-93 Default 7/11/2013 12:23 3782.27 75.645 40.181 1.88 1.89 50 230 40.036 2.902
Dawar-96 Default 7/11/2013 12:24 4681.24 93.625 64.429 1.45 1.5 50 230 62.454 5.011
Suliman-96 Default 7/11/2013 12:25 964.99 19.3 9.088 2.12 1.46 50 230 13.183 9.462
AS-2002 Default 7/11/2013 12:27 4622.64 92.453 67.65 1.37 1.38 50 230 66.824 6.567
LYP-73 Default 7/11/2013 12:27 4667.43 93.349 57.48 1.62 1.75 50 230 53.271 2.458
Noshera-96 Default 7/11/2013 12:29 432.56 8.651 5.467 1.58 1.09 50 230 7.946 12.34
Sindh-81 Default 7/11/2013 12:30 4608.4 92.168 62.71 1.47 1.59 50 230 57.988 4.835
Fakhri-sarhad Default 7/11/2013 12:30 4101.56 82.031 43.56 1.88 1.8 50 230 45.645 4.156
10737 Default 7/11/2013 12:31 3538.12 70.762 34.128 2.07 1.97 50 230 35.942 4.773
10776 Default 7/11/2013 12:32 3743.25 74.865 36.915 2.03 2.05 50 230 36.57 1.543
10748 Default 7/11/2013 12:33 4203.7 84.074 45.171 1.86 1.75 50 230 48.15 3.242
10724 Default 7/11/2013 12:34 4573.88 91.478 55.318 1.65 1.69 50 230 54.121 1.942
10792 Default 7/11/2013 12:35 3053.18 61.064 29.219 2.09 2.23 50 230 27.436 1.19
Pirsabak-2008 Default 7/11/2013 12:36 2868.03 57.361 27.217 2.11 2.11 50 230 27.223 1.93
Punjab-96 Default 7/12/2013 10:05 4391.64 87.833 49.311 1.78 1.84 50 230 47.64 1.888
Mumal-2002 Default 7/12/2013 10:06 4623.93 92.479 56.166 1.65 1.67 50 230 55.282 2.422
Zamindar-80 Default 7/12/2013 10:08 3219.62 64.392 35.268 1.83 1.42 50 230 45.267 10.809
Iqbal-2000 Default 7/12/2013 10:09 3103.64 62.073 29.719 2.09 1.93 50 230 32.219 3.81
SH-2003 Default 7/12/2013 10:10 4285.81 85.716 45.135 1.9 1.93 50 230 44.509 1.964
Anmol-91 Default 7/12/2013 10:11 4041.37 80.827 41.035 1.97 1.92 50 230 42.143 3.452
LU-26 Default 7/12/2013 10:12 3863.31 77.266 38.733 1.99 1.8 50 230 43.014 3.999
Chenab-96 Default 7/12/2013 10:13 1187.58 23.752 11.19 2.12 2.22 50 230 10.682 0.352
47
Faisalabad-83 Default 7/12/2013 10:15 1620.18 32.404 16.572 1.96 1.3 50 230 24.883 2.777
Zarghoon-79 Default 7/12/2013 10:16 3016.82 60.336 30.69 1.97 2.06 50 230 29.258 3.706
C-228 Default 7/12/2013 10:17 1587.29 31.746 14.888 2.13 1.98 50 230 16.046 3.583
Shahkar- 95 Default 7/12/2013 10:18 3392.26 67.845 32.14 2.11 2.2 50 230 30.77 1.413
Punjab-88 Default 7/12/2013 10:21 1752.69 35.054 16.608 2.11 2.29 50 230 15.32 0.591
10793 Default 7/12/2013 10:22 4654.21 93.084 58.786 1.58 1.6 50 230 58.1 2.827
Punjab-81 Default 7/12/2013 10:23 3089.97 61.799 29.119 2.12 2.1 50 230 29.368 4.119
C-591 Default 7/12/2013 10:24 3204.99 64.1 30.028 2.13 2.12 50 230 30.17 1.534
Sutlag-86 Default 7/12/2013 10:25 3489.02 69.78 33.91 2.06 2.17 50 230 32.128 1.109
C-250 Default 7/12/2013 10:26 128.01 2.56 1.708 1.5 0.6 50 230 4.263 1.513
Blue silver Default 7/12/2013 10:27 3987.72 79.754 40.638 1.96 2.03 50 230 39.223 2.152
RWP-94 Default 7/12/2013 10:29 1744.4 34.888 16.595 2.1 1.75 50 230 19.888 3.172
Sariab-92 Default 7/12/2013 10:30 4172.48 83.45 44.358 1.88 1.78 50 230 46.968 5.685
Wafaq-2008 Default 7/12/2013 10:31 3895.68 77.914 39.605 1.97 2.02 50 230 38.653 1.955
10742 Default 7/12/2013 10:33 2135.75 42.715 19.035 2.24 1.65 50 230 25.877 4.09
010724 Default 7/12/2013 10:34 2975.06 59.501 26.778 2.22 1.95 50 230 30.584 4.652
AUP-5000 Default 7/12/2013 10:35 2730.76 54.615 26.031 2.1 2 50 230 27.26 0.765
WL-711 Default 7/12/2013 10:36 2748.84 54.977 26.808 2.05 2.08 50 230 26.392 1.776
SA-75 Default 7/12/2013 10:37 2545.18 50.904 24.001 2.12 2.25 50 230 22.673 0.813
SA-42 Default 7/12/2013 10:38 2731.91 54.638 27.472 1.99 1.81 50 230 30.229 2.721
Marwat-01 Default 7/12/2013 10:39 2056.12 41.122 19.27 2.13 2.03 50 230 20.299 1.791
Barani-83 Default 7/12/2013 10:40 4320.12 86.402 46.048 1.88 1.92 50 230 45.118 2.801
Potohar-93 Default 7/12/2013 10:41 3784.56 75.691 38.023 1.99 2.06 50 230 36.697 1.895
Kohinoor-83 Default 7/12/2013 10:43 4805.25 96.105 70.16 1.37 1.38 50 230 69.826 3.263
Potohar-70 Default 7/12/2013 10:45 2119.8 42.396 19.234 2.2 1.91 50 230 22.14 4.439
Pak-81 Default 7/12/2013 10:46 4151.33 83.027 44.219 1.88 1.83 50 230 45.401 6.137
Pirsabak-85 Default 7/12/2013 10:47 3369 67.38 33.603 2.01 2.07 50 230 32.6 2.071
C-273 Default 7/12/2013 10:48 4530.27 90.605 53.809 1.68 1.78 50 230 50.848 2.02
Tandojam-83 Default 7/12/2013 10:49 2751.58 55.032 25.917 2.12 1.84 50 230 29.948 3.579
Dirk Default 7/12/2013 10:50 3812.73 76.255 36.55 2.09 2.13 50 230 35.81 1.686
48
Bahalwapur-79 Default 7/12/2013 10:51 1563.23 31.265 14.764 2.12 2.21 50 230 14.164 1.251
Lasani-08 Default 7/12/2013 10:53 3316.57 66.331 33.217 2 2.08 50 230 31.947 1.131
Sussi Default 7/12/2013 10:54 4575.04 91.501 58.8 1.56 1.62 50 230 56.359 3.417
Khyber-79 Default 7/12/2013 10:55 3837.48 76.75 38.222 2.01 2 50 230 38.421 2.254
FPD-08 Default 7/12/2013 10:57 4193.4 83.868 43.72 1.92 1.93 50 230 43.451 1.787
Sandal Default 7/12/2013 10:58 4379.96 87.599 48.911 1.79 1.87 50 230 46.942 1.482
Kiran Default 7/12/2013 10:59 4625.65 92.513 56.76 1.63 1.63 50 230 56.739 3.599
Wardak-85 Default 7/12/2013 11:00 4658.83 93.177 58.801 1.58 1.6 50 230 58.198 4.945
Meraj-08 Default 7/12/2013 11:01 4419.38 88.388 55.027 1.61 1.56 50 230 56.592 6.21
C-518 Default 7/12/2013 11:02 4507.98 90.16 52.013 1.73 1.77 50 230 50.96 3.194
Potohar-90 Default 7/12/2013 11:03 2608.67 52.173 25.714 2.03 2.11 50 230 24.783 2.027
Mehran-89 Default 7/12/2013 11:04 3953.77 79.075 39.465 2 1.91 50 230 41.364 4.3
Janbaz Default 7/12/2013 11:06 3793.74 75.875 37.529 2.02 2.1 50 230 36.124 2.893
AUP-4008 Default 7/12/2013 11:07 2361.13 47.223 22.32 2.12 2.24 50 230 21.039 0.69
49
2.6 STATISTICAL ANALYSES
Different compute softwares and methods were applied in the current study.
Morphological, physiological and root traits data were analyzed using SPSS version 22.
The molecular data was analyzed using different softwares as power marker version
3.2, Mega 6, structure version 2.3.4 structure harvester and Tassel version 2.1.
2.6.1 Structure
Structure software commonly used for determination of population structure of diverse
populations (Pritchard et al., 2000). Structure commonly used to overcome spurious
association between markers and traits. A burn-in of 20000 runs and MCMC 20000
iterations were used to test the K value in the range of 2-20.
2.6.2 Structure harvester
The online structure harvester program was used for estimation of number of clusters
(K) using logarithmic likelihood LnP(D) (Yu et al., 2006).
2.6.3 Tassel
Trait analysis by association, evolution and linkage (Tassel) software is mostly used to
find association between kinship and population structure in individuals belongs to
different populations (Bradbury et al., 2007). The calculation and graphical
representation of linkage disequilibrium (LD) and population structure was done on the
base of Q matrix and GD obtained from structure software. The Tassel version 2.1 used
for only SSR markers while the tassel version 3.0.146 deals with both SNPs and SSR.
Tassel software could be used for two approaches as.
50
2.6.3.1 General linear model (GLM)
GLM is used for association between molecular markers and phenotypic traits. GLM
does not need kinship information for identification of phenotype and genotype
correlation.
2.6.3.2 Mixed linear model (MLM)
MLM require both population structure and kinship for association analysis. In MLM
both Q matrix and K clusters (Q+K) were used. The MLM model is better as compare to
Q model or K model alone. The MLM approach was applied in wheat (Breseghello and
Sorrells, 2006b).
51
Chapter -3
RESULTS
3.1 COMPARATIVE PERFORMANCE OF THE MORPHOLOGICAL TRAITS
The morphological traits (qualitative and quantitative) of wheat in the present research
included Plant height (PH), Flag Leaf area (FLA), peduncle length (PL), days to 50%
heading (DH), Days to maturity (DM), Awn length (AL), number of tillers per plant
(NTP), Spike length (SL), spikelets per spike (SPS), spike density (SD), Number of
grains per spike (NGP), 1000 grain weight (1000GW), yield per plant (YPP), harvest
index (HI) and total weight per plant (TWP) (Annexure 1). The Analysis of variance
confirmed that all the morphological traits were significant at (P≤0.01) level except
number of tillers per plant as shown in table 2.
3.1.1 Plant height (PH)
Analysis of variance showed that the hundred wheat genotypes were highly significant
(P≤0.01). Among all genotypes the highest plant height was observed in C-518, Local-
white, Lasani-08, Bahawalpur-79, Saleem-2000, Rawal-87, WL-711, Margalla-99,
Chakwal-86, Haider-2002 which was 119 cm, 117.766 cm, 114.33 cm, 113 cm, 112.76 cm,
112.66 cm, 112.33 cm, 110.56 cm, 110.48 cm and 109 cm, while the lowest plant height
was noted in Bakkar-2008 i.e 68.61 cm as shown in table 3a.
52
Statistical analysis of Correlation concluded that plant height was positively correlated
to Flag leaf area, Days to 50% heading, days to 50% maturity, number of tillers per
plant, number of grains per plant, yield per plant, harvest index and total weight per
plant while negatively correlated with spike length, peduncle length, awn length and
nmber of spikelets per spike as shown in table 4.
3.1.2 Flag leaf area (FLA)
Flag leaf area was found to be highly significant (P≤0.01) among all the genotypes (table
2). The largest flag leaf area was noted in top ten superior genotypes as Pari-73, Chenab-
79, Rawal-87, LYP-73, Dawar-96, Nori-70, Margalla-99, Wadanak-98, Chakwal-86 and
Soghat-90 as 92.48 cm, 68.7 cm, 67.11 cm, 66.22 cm, 64.61 cm, 62.27 cm, 58.45 cm, 57.78
cm, 57.64 cm and 57.55 cm while lowest flag leaf area was observed in sutleg-86 as 14.71
cm (table 3a).
The correlation analysis confirmed that Flag leaf area was positively correlated with
number of tillers, days to maturity, days to heading, awn length, spikelets per spike,
spike density, grains per spike while negatively correlated to 1000 grain weight, yield
per plant, peduncle length and total weight per plant (Table 4).
3.1.3 Peduncle length (PL)
The Analysis of Variance showed that peduncle length was highly significant (P≤0.01)
in all the hundred wheat genotypes (Table 2). Maximum peduncle length was observed
53
in C-591, Dirk, C-228, Barani-83, Bahawalpur-79, Sutleg-86, SA-75, RWP-94, Sandal and
Punjab-76 as 48 cm, 45 cm, 43.66 cm, 43 cm, 43 cm, 42.66 cm, 42.66 cm, 41.66 cm, 41.66
cm, 41.57 cm and the minimum peduncle length was observed in Pirsabak-85 as 22. 33
cm (table 3a).
Correlation analysis further confirmed that peduncle length was positively correlated to
number of tillers per plant, days to 50% maturity, days to 50% heading, awn length,
spikelets per spike, spike density, number of grains per spike, yield per plant, harvest
index and total weight per plant while negatively correlated to plant height and 1000
grain weight respectively.
3.1.4 Days to 50% heading (DH)
Days to 50% heading is yield associated trait and the ANOVA result confirmed that
50% heading was highly significant (P≤0.01) among all genotypes (Table 2). 010776,
010737, Abdaghar-97, NIAB-83, 010748, Bakhtawar-94, uqab-2000, Kaghan-93, Raskoh
and Indus-79 were took more days to heading as 147, 145.33, 144, 143.66, 143.66, 142.66,
142.66, 142.66, 141.66 and 141.33 while Mehran-89 took lesser number days to 50%
heading i.e 122.66 (table 3b).
From Statistical analysis it is concluded that days to 50 % heading was positively
correlated with flag leaf area, peduncle length, plant height, spike length, days to
maturity, awn length, spikelets per spike, spike density, number of grains per spike and
total grain weight while negatively correlated to number of tillers per plant, 1000 grain
weight, yield per plant and HI (Table 4).
54
3.1.5 Days to 50% maturity (DM)
Analysis of variance (ANOVA) concluded that days to 50% maturity was highly
significant (P≤0.01) and the genotypes Bahawalpur-79 (182.66), Meraj-08 (182.33),
Potohar-93 (181), 010742 (180.66), C-518 (180), C-591(179.33), AUP-5000 (178.66), C-228
(178.33), Sutleg-86 (178.33) and 010724 (178.33) showed maximum number of days to
50% maturity while Raskoh (159) showed minimum number of days to maturity (table
3a).
Days to 50% maturity was positively correlated to flag leaf area, peduncle length, plant
height, number of tillers per plant, days to 50% heading, awn length, spikelets per
spike, spike density, number of grains per spike, yield per plant, HI and total weight
per plant respectively. The negative correlation was found with spike length and 1000
grain weight (Table 4).
3.1.6 Awn length (AL)
ANOVA showed that awn length was highly significant (P≤0.01) among hundred
wheat genotypes as shown in table 2. Lr-230 (7.6 cm) showed maximum awn length
followed by Uqab-2000 (7.5 cm), Local white (7.24 cm), Faisalabad-85 (7.13 cm),
010776(7.13 cm), Pari-73 (7.03 cm), ZA-77 (6.93 cm), Margalla-99 (6.9 cm), 010792 (6.9
cm) and Chenab-70 (6.8 cm) while in C-518 (3.45 cm) minimum awn length was
recorded (table 3b).
The correlation analysis confirmed that Awn length was positively correlated to flag
leaf area, peduncle length, spike length, number of tillers per plant, days to 50%
heading, days to 50% maturity, spikelets per spike, spike density and grains per spike
55
while negatively correlated to 1000 grain weight, yield per plant, HI and total weight
per plant (Table 4).
3.1.7 Number of tillers per plant (NTP)
ANOVA showed that number of tillers per plant was found non-significant (P≤0.01)
among all the morphological traits (Table 2). The maximum number of tillers was
observed in Barani-70 (6.33) followed by Rawal-87 (6.33), Pak-81 (6.33), Chenab-79 (6),
Soghat-90 (6), 010737 (6), 010724 (6), Pirsabak-2008 (6), Nori-70 (6) and RWP-94 (6) while
minimum number of tillers was found in Mumal-2002 (3.33) as shown in table 3b.
The results of statistical analysis showed that number of tillers were positively
correlated to flag leaf area, peduncle length, plant height, days to 50% maturity, awn
length, spikelets per spike, spike density, number of grains per spike, yield per plant
and total weight per plant while negatively correlated to spike length, 1000 grain
weight, HI and days to 50% heading (table 4).
3.1.8 Spike length (SL)
Spike length was observed highly significant (P≤0.01) among all the genotypes. The
highest spike length was noted in Marwat-01, Sussi, Barani-83, Shalimar-88, Faisalabad-
83, Nowshera-96, Potohar-70, Pak-81, 010737 and Wadanak-85 as 16.66 cm, 16.66 cm, 15
cm, 14.8 cm, 14.8 cm, 14.7 cm, 14.4 cm, 14.33 cm, 14.3 cm and 14.16 cm respectively and
the lowest spike length was observed in Sandal as 8.13 cm (Table 3a)
Spike length was positively correlated to flag leaf area, days to 50% heading, awn
length, spikelets per spike, no of grains per spike, yield per plant and total weight per
plant. In correlation analysis the spike length was negatively correlated to peduncle
56
length, plant height, number of tillers per plant, days to 50% maturity, spike density,
100 grain weight and HI (Table 4).
3.1.9 Spikelets per spike (SPS)
Analysis of Variance concluded that spikelets per spike are highly significant (P≤0.01) in
all the genotypes as shown in table 2. The highest number of spikelets per spike was
studied in Margalla-99 (24) followed by Barani-70 (24), Zarlashta-90 (23.33), Manther
(22.66), Wadanak-85 (22.66), Rawal-87 (22.66), 010748 (22.66), Maxipak (22.33), ZA-77
(22.33) and Uqab-2000 (22) while the lowest number of spikelets per spike was studied
in Saleem-2000 (15.33)(table 3b).
The analysis of correlation showed that spikelets per spike was positively correlated to
flag leaf area, spike length, peduncle length, number of tillers per plant, days to 50%
heading, days to 50% maturity, awn length, spike density, number of grains per spike,
yield per plant and total weight per plant respectively and found negatively correlated
to plant height, 1000 grain weight and HI (Table 4).
3.1.10 Spike density (SD)
ANOVA showed that spike density was highly significant (P≤0.01) as shown in Table 2.
Highest spike density was found in ten genotypes as Sandal, Margalla-99, 010792,
010724, AUP-2004, Sindh-81, Rawal-87, Local white, Potohar-90 and Wadanak-98 as
2.17, 2.10, 2.05, 1.99, 1.97, 1.95, 1.92, 1.92, 1.90 and 1.89 while lowest spike density was
found in Marwat-01 as 1.04 as shown in Table 3b.
The statistical analysis of correlation observed that spike density was positively
correlated to flag leaf area, peduncle length, plant height, number of tillers per plant,
57
days to 50% heading, days to 50% maturity, awn length, spikelets per spike, grains per
spike, yield per plant and total weight per plant while spike density was negatively
correlated to spike length, 1000 grain weight and HI (table 4).
Table 2: Analysis of Variance for morphological traits of wheat genotypes
Sum of Squares Df Mean Square F Sig.
replication 0.000 99 0.000 0.000 1.000
FLA 65722.564 99 663.864 90.017 .000
SL 532.306 99 5.377 2.933 .000
PL 6630.545 99 66.975 6.099 .000
PH 30513.892 99 308.221 3.703 .000
NTP 69.333 99 .700 1.313 .054
DM 7069.667 99 71.411 2.250 .000
DH 8421.237 99 85.063 3.610 .000
AL 279.874 99 2.827 2.383 .000
SPS 1081.370 99 10.923 5.904 .000
SD 16.102 99 .163 3.351 .000
NGS 116090.707 99 1172.633 15.613 .000
1000 GW 6989.370 99 70.600 10.150 .000
YPP 692.795 99 6.998 5.046 .000
HI 13795.047 99 139.344 7.340 .000
TWP 6923.414 99 69.933 4.366 .000
3.1.11 Number of grains per spike (NGS)
Grains per spike were highly significant (P≤0.01). Maximum number of grains per spike
was counted in Chenab-79 (99.33) followed by Indus-79 (94.73), 010748 (93.66), 010724
(91.52), Saleem-2000 (91.33), Chenab-70 (90.39), Zarlashta-90 (89.14), Soghat-90 (88.68),
Wadanak-85 (86.72) and Lr-230 (83.47) while the minimum number of grains per spike
was counted in Punjab-96 (16.66) (Table 3b).
Number of grains per spike was positively correlated to flag leaf area, spike length,
peduncle length, days to 50% heading, days to 50% maturity, plant height, number of
58
tillers per plant, awn length, spikelets per spike, spike density, yield per plant, HI and
total weight per plant respectively. Number of grains per spike was found negatively
correlated to 1000 grain weight (Table 4).
3.1.12 1000 grain weight (1000GW)
On the base of ANOVA given as table 2 it was concluded that 1000 grain weight is
highly significant (P≤0.01). Among hundred wheat genotypes top ten genotypes
showed highest grain weight i.e Zarghoon-79 was on top (48.67) followed by
Faisalabad-85 (48.61), Mumal-2002 (48.57), Sutlag-86 (48.43), C-591 (47.41), Punjab-81
(47.40), Potohar-70 (46.72), Punjab-96 (46.63), Zamindar-80 (46.39) and Lu-26 (44.78) and
the lowest grain weight was recorded in AS-2002 (30.10) respectively (Table 3b).
The statistical analysis of correlation showed that 1000 grain weight was positively
correlated to plant height, yield per plant, HI and total weight per plant while was
found negatively correlated to flag leaf area, peduncle length, spike length, days to 50%
heading, days to 50% maturity, awn length, Number of tillers per plant, spikelets per
spike, spike density and number of grains per spike (Table 4).
3.1.13 Yield per plant (YPP)
Yield per plant is the crucial trait and was found highly significant (P≤0.01) as shown in
table 2. The maximum yield per plant was observed in Uqab-2000 i.e 11.16 gm followed
by Haider-2002 (9.06 gm), Sutlag-86 (8.91 gm), Rawal-87 (8.7 gm), Wadanak-85 (8.6 gm),
Barani-70 (8.6 gm), C-273 (8.6 gm), Margalla-99 (8.4 gm), Potohar-70 (8.2 gm) and Indus-
79 (7.8 gm) and the lowest yield per plant was observed in AS-2002 (2.6 gm) as shown
in table 3c.
59
Correlation analysis results confirmed that yield per plant was positively correlated to
plant height, spike length, peduncle length, number of tillers per plant, days to 50%
maturity, spikelets per spike, spike density, grains per spike, 1000 grain weight, HI and
total weight per plant respectively while yield per plant was found negatively
correlated to flag leaf area, awn length and days to 50% heading (table 4).
3.1.14 Harvest index (HI)
ANOVA showed that Harvest Index (HI) was highly significant at (P≤0.01) as shown in
table 2. Among all the hundred genotypes harvest index of ten superior genotypes is
shown in table 3c. C-273 showed the highest HI (49.68) followed by C-518 (47.23),
Sutlag-86 (45.88), Wardak-85 (45.01), Dirk (43.65), Faisalabad-83 (43.61), Chenab-96
(43.48), Punjab-88 (43.01). Potohar-90 (43.01) and Iqbal-2000 (42.23) respectively and
010792 showed the lowest HI as 16.20.
The results of correlation analysis revealed that harvest index (HI) was positively
correlated to flag leaf area, peduncle length, plant height, days to 50% maturity, grains
per spike, spike density, 1000 grain weight, yield per plant and total weight per plant
while negatively correlated to spike length, number of tillers per plant, days to 50%
heading, awn length and spikelets per spike (Table 4).
3.1.15 Total weight per plant (TWP)
ANOVA results showed that Total weight per plant for all hundred genotypes were
highly significant at (P≤0.01) as shown in table 2. The top ten superior genotypes on the
base of total weight per plant were observed as Uqab-2000, AUP-5000, Barani-70,
Rawal-87, Wadanak-85, Margalla-99, Potohar-70, NIAB-83, Indus-79 and C-228. The
60
total weight of these superior genotypes is 32.74, 31.35, 27.52, 25.59, 25.06, 24.05, 23.67,
23.59, 23.58 and 23.23. The lowest total weight was calculated in Manther as 7.01 as
shown in table 3c.
Total weight per plant was found positively correlated to spike length, peduncle length,
number of tillers per plant, plant height, days to 50% heading, days to 50% maturity,
spikelets per spike, spike density, grains per spike, 1000 grain weight and yield per
plant and was negatively correlated to flag leaf area, awn length and HI (Table 4).
61
Table 3(a): Sorted table of top ten desirable wheat genotypes on the base of yield and yield related traits
S.No Variety FLA Variety SL Variety PL Variety PH Variety NTP Variety DM
1 Pari-73 14.7 Marwat-01 16.6 C-591 48 C-518 83.4 Barani-70 6.3 Bahawalpur-
79 159
2 Chenab-79 15.2 Sussi 16.6 Dirk 45 Local white 83.4 Rawal-87 6.3 Meraj-08 160
3 Rawal-87 16 Barani-83 15 C-228 43.6 Lasani-08 84 Pak-81 6.3 Potohar-93 160
4 LYP-73 16.7 Shalimar-88 14.8 Barani-83 43 Bahawalpur-79 85.3 Chenab-79 6 010742 160.7
5 Dawar-96 17.5 Faisalabad-
83 14.8 Bahawalpur-79 43 Saleem-2000 85.7 Soghat-90 6 C-518 160.7
6 Nori-70 19.2 Nowshera-
96 14.7 Sutlag-86 42.6 Rawal-87 86.2 010737 6 C-591 161.7
7 Margalla-
99 19.3 Potohar-70 14.4 SA-75 42.6 WL-711 86.7 010724 6 AUP-5000 161.7
8 Wadanak-
98 19.4 Pak-81 14.3 RWP-94 41.6 Margalla-99 87.3
Pirsabak- 2008
6 C-228 162.7
9 Chakwal-
86 20.1 010737 14.3 Sandal 41.6 Chakwal-86 88.6 010793 6 Sutlag-86 162.7
10 Soghat-90 20.4 Wadanak-85 14.1 Punjab-76 41.5 Haider-2002 89.4 RWP-94 6 010724 162.7
62
Table 3(b): Sorted table of top ten desirable wheat genotypes on the base of yield and yield related traits
S No Genotypes DH genotypes AL genotype SPS genotype SD genotype NGS genotype 1000GW
1 010776 121.7 Lr-230 7.5 Margalla-99
24 Sandal 2.1 Chenab-79
99.3 Zarghoon-79
48.6
2 010737 122.3 Uqab-2000 7.5 Barani-70 24 Margalla-99
2.1 Indus-79 94.7 Faisalabad-85
48.6
3 Abdaghar-97 123 Local white
7.2 Zarlashta-90
23.3 010792 2.0 010748 93.6 Mumal-2002
48.5
4 NIAB-83 126.3 Faisalabad- 85
7.1 Manther 22.6 010724 1.9 010724 91.5 Sutlag-86 48.4
5 010748 126.7 010776 7.1 Wadanak-85
22.6 AUP-4008 1.9 Saleem-2000
91.3 C-591 47.4
6 Bakhtawar- 94
127 Pari-73 7 Rawal-87 22.6 Sindh-81 1.9 Chenab-70
90.3 Punjab-81 47.4
7 Uqab-2000 127.3 ZA-77 6.9 010748 22.6 Rawal-87 1.9 Zarlashta-90
89.1 Potohar-70 46.7
8 Kaghan-93 142.6 Margalla- 99
6.9 Maxipak 22.3 Local white
1.9 Soghat-90 88.6 Punjab-96 46.6
9 Raskoh 128 010792 6.9 ZA-77 22.3 potohar-90 1.9 Wadanak-85
86.7 Zamindar-80
46.3
10 Indus-79 128.3 Chenab-70 6.8 Uqab-2000 22 Wadanak-98
1.8 Lr-230 83.4 LU-26 44.7
63
Table3(C): Sorted table of top ten desirable wheat genotypes on the base of yield and yield related traits.
S.No Genotypes YP Genotypes HI Genotypes TWP
1 Uqab-2000 11.1 C-273 49.6 Uqab-2000 32.7
2 Haider-2002 9 C-518 47.2 AUP 5000 31.3
3 Sutlag-86 8.9 Sutlag-86 45.8 Barani-70 27.5
4 Rawal-87 8.7 Wardak-85 45 Rawal-87 25.5
5 Wadanak-85 8.6 Dirk 43.6 Wadanak-85 25
6 Barani-70 8.6 Faisalabad-83 43.6 Margalla-99 24
7 C-273 8.6 Chenab-96 43.4 Potohar-70 23.6
8 Margalla-99 8.4 Punjab-88 43 NIAB-83 23.5
9 Potohar-70 8.2 Potohar-90 43 Indus-79 23.5
10 Indus-79 7.8 Iqbal-2000 42.2 C-228 23.2
64
Table 4: Correlation analysis of morphological traits of Triticum aestivum
FLA SL PL PH NTP DM DH AL SPS SD NGS 1000GW YPP HI TWP
FLA 1
SL .017 1
PL -.001 -.171** 1
PH .082 -.102 -.022 1
NTP .153** -.019 .069 .051 1
DM .289** -.106 .093 .089 .169** 1
DH .331** .064 .048 .073 -.031 .023 1
AL .432** .036 .032 -.057 .047 .092 .343** 1
SPS .471** .000 .106 -.014 .179** .211** .250** .275** 1
SD .259** -.780** .189** .074 .133* .197** .084 .129* .596** 1
NGS .630** .212** .077 .000 .094 .229** .324** .329** .387** .058 1
1000GW -.501** -.016 -.006 .027 -.021 -.240** -.196** -.220** -.376** -.217** -.363** 1
YPP -.101 .053 .063 .074 .110 .112 -.031 .080 .129* .030 .036 .146* 1
HI .029 -.058 .040 .059 -.025 .104 -.135* -.058 -.061 .013 .042 .013 .063 1
TWP -.089 .074 .072 .042 .120* .059 .055 -.032 .138* .015 .040 .166** .807** -.355** 1
65
Table No 5: Frequency table of morphological traits denoted by (+) for presence and (-) for absence of a trait
Variety Frequency FLA SL PL PH NTP DM DH AL SPS SD NGS 1000GW YP HI TWP
Sonalika 0 - - - - - - - - - - - - - - -
Merco-2007 0 - - - - - - - - - - - - - - -
Manther 1 - - - - - - - - + - - - - - -
Lr-230 2 - - - - - - - + - - + - - - -
KSK 0 - - - - - - - - - - - - - - -
Maxipak 1 - - - - - - - - + - - - - - -
Indus-79 3 - - - - - - + - - - + - + - -
Bakhtawar-94 1 - - - - - - + - - - - - - - -
Wadanak-85 2 - - - - - - - - + - - - + - -
Abdaghar-97 1 - - - - - - + - - - - - - - -
Margalla-99 7 + - - + - - - + + + - - + - +
Uqab-2000 5 - - - - - - + + + - - - + - +
Raskoh 1 - - - - - - + - - - - - - - -
Haider-2002 2 - - - + - - - - - - - - + - -
Local white 3 - - - + - - - + - + - - - - -
MH-97 0 - - - - - - - - - - - - - - -
Zarlashta-90 2 - - - - - - - - + - + - - - -
Punjab-76 1 - - + - - - - - - - - - - - -
Faisalabad-85 2 - - - - - - - + - - - + - - -
Barani-70 3 - - - - + - - - + - - - + - -
Rawal-87 7 + - - + + - - - + + - - + - +
NIAB-83 1 - - - - - - + - - - - - - - -
GA-2002 0 - - - - - - - - - - - - - - -
Chenab-79 4 + - - - + - - - - - + - - - +
Saleem-2000 2 - - - + - - - - - - + - - - -
Shalimar-88 1 - + - - - - - - - - - - - - -
Khyber-83 0 - - - - - - - - - - - - - - -
66
Chenab-70 2 - - - - - - - + - - + - - - -
Soghat-90 4 + - - - + - - - - - + - - - +
Pari-73 3 + - - - - - - + - - - - - - +
Chakwal-86 2 + - - + - - - - - - - - - - -
Wadanak-98 2 + - - - - - - - - + - - - - -
Nori-70 2 + - - - - - - - - - - - - - +
ZA-77 2 - - - - - - - + - + - - - - -
Kaghan-93 1 - - - - - - + - - - - - - - -
Dawar-96 0 + - - - - - - - - - - - - - +
Suliman-96 1 - - - - - - - - - - - - - - +
AS-2002 0 - - - - - - - - - - - - - - -
LYP-73 2 + - - - - - - - - - - - - - +
Nowshera-96 1 - + - - - - - - - - - - - - -
Sindh-81 1 - - - - - - - - - + - - - - -
Fakhre-sarhad 0 - - - - - - - - - - - - - - -
010737 3 - + - - - - + - - + - - - - -
010776 2 - - - - - - + + - - - - - - -
010748 3 - - - - - - + - + - + - - - -
010724 2 - - - - + - - - - - + - - - -
010792 2 - - - - - - - + - + - - - - -
Pirsabak-2008 1 - - - - + - - - - - - - - - -
Punjab-96 1 - - - - - - - - - - - + - - -
Mumal-2002 1 - - - - - - - - - - - + - - -
Zamindar-80 1 - - - - - - - - - - - + - - -
Iqbal-2000 1 - - - - - - - - - - - - - + -
SH-2003 0 - - - - - - - - - - - - - - -
Anmol-91 0 - - - - - - - - - - - - - - -
LU-26 1 - - - - - - - - - - - + - - -
Chenab-96 1 - - - - - - - - - - - - - + -
Faisalabad-83 2 - + - - - - - - - - - - - + -
67
Zarghoon-79 1 - - - - - - - - - - - + - - -
C-228 2 - - + - - + - - - - - - - - -
Shahkar-95 0 - - - - - - - - - - - - - - -
Punjab-88 1 - - - - - - - - - - - - - + -
010793 0 - - - - - - - - - - - - - - -
Punjab-81 1 - - - - - - - - - - - + - - -
C-591 3 - - + - - + - - - - - + - - -
Sutlag-86 5 - - + - - + - - - - - + + + -
C-250 0 - - - - - - - - - - - - - - -
Blue silver 0 - - - - - - - - - - - - - - -
RWP-94 2 - - + - + - - - - - - - - - -
Sariab-92 0 - - - - - - - - - - - - - - -
Wafaq-2008 0 - - - - - - - - - - - - - - -
010742 1 - - - - - + - - - - - - - - -
010737 0 - - - - - - - - - - - - - - -
AUP-5000 2 - - - - - + - - - + - - - - -
WL-711 1 - - - + - - - - - - - - - - -
SA-75 1 - - + - - - - - - - - - - - -
SA-42 0 - - - - - - - - - - - - - - -
Marwat-01 1 - + - - - - - - - - - - - - -
Barani-83 2 - + + - - - - - - - - - - - -
Potohar-93 1 - - - - - + - - - - - - - - -
Kohinoor-83 0 - - - - - - - - - - - - - - -
Potohar-70 3 - + - - - - - - - - - + + - -
Pak-81 0 - - - - - - - - - - - - - - -
Pirsabak-85 2 - + - - + - - - - - - - - - -
C-273 2 - - - - - - - - - - - - + + -
Tandojam-83 0 - - - - - - - - - - - - - - -
Dirk 2 - - + - - - - - - - - - - + -
Bahawalpur-79 3 - - + + - + - - - - - - - - -
68
Lasani-08 1 - - - + - - - - - - - - - - -
Sussi 1 - + - - - - - - - - - - - - -
Khyber-79 0 - - - - - - - - - - - - - - -
FPD-08 0 - - - - - - - - - - - - - - -
Sandal 2 - - + - - - - - - + - - - - -
Kiran 0 - - - - - - - - - - - - - - -
Wardak-85 0 - - - - - - - - - - - - - + -
Meraj-08 1 - - - - - - - - - - - - - - -
C-518 3 - - - + - + - - - - - - - + -
Potohar-90 2 - - - - - - - - - + - - - + -
Mehran-89 0 - - - - - - - - - - - - - - -
Janbaz 0 - - - - - - - - - - - - - - -
AUP-4008 1 - - - - - - - - - + - - - - -
69
3.2 COMPARATIVE PERFORMANCE OF PHYSIOLOGICAL TRAITS
There are a number of physiological traits that have been recognized as screening
criteria for drought tolerance in crop plants particularly in wheat. The common
physiological traits are relative water content (RWC), Water loss rate (WLR) and Water
use efficiency (WUE).
3.2.1 Relative water content (RWC)
The physiological trait RWC is under the control of dominant genes. Species that are
better adopted in dry environments have the ability of retaining more water at given
water potential. The ANOVA results confirmed that relative water content normal
(RWCN) and relative water content stress (RWCS) were found highly significant at
(P≤ 0.01) level as shown in table 6. The highest RWC in normal (RWCN) environment
was noted in Morgalla-99 (99%) followed by Wafaq-2008 (98%), Anmol-91 (98%),
Mumal-2002 (97%), C-518 (97%), Uqab-2000 (97%), Meraj-08 (97%), Nori-70 (96%),
Lasani-08 (96%) and Punjab-81 (96%) as in table 7 while the lowest RWC was found in
Zarghoon-79 (34%) (Annexure 2). Similarly the highest RWC in stress (RWCS)
environment was noted in NIAB-83 showed greater resistant to drought as (93%)
followed by Tandojam-83 (91%), Local white (91%), Rawal-87 (90%), Soghat-90 (89%),
Potohar-93 (89%), Indus-79 (88%), Punjab-81 (88%), Potohar-90 (88%) and Sindh-81
(86%) while the lowest RWC was observed in Chakwal-86 (7%) as shown in table 7.
(Annexure 3).
70
Relative water content was found to be positively correlated with root diameter,
number of nodal roots, number of seminal roots, root angle, total root length, water loss
rate normal, water loss rate stress and yield per plant while negatively correlated with
root fresh weight, root dry weight, root shoot ratio and root density.
Table 6: Analysis of Variance for physiological traits of wheat genotypes
ANOVA
Sum of Squares Df Mean Square F Sig.
RWCS Between Groups 108101.459 99 1091.934 1091.934 .000
RWCN Between Groups 92308.567 99 932.410 932.410 .000
WLRS Between Groups 316.096 99 3.193 3.193 .000
WLRN Between Groups 277.304 99 2.801 2.801 .000
Table 7: Sorted table of top ten superior wheat genotypes on the base of physiological trait RWCN and RWCS
Genotypes RWCN % Genotypes RWCS%
Margalla-99 99 NIAB-83 93
Wafaq-2008 98 Tandojam-83 91
Anmol-91 98 Local white 91
Mumal-2002 97 Rawal-87 90
C-518 97 Soghat-90 89
Uqab-2000 97 Potohar-93 89
Meraj-08 97 Indus-79 88
Nori-70 96 Punjab-81 88
Lasani-08 96 potohar-90 88
Punjab-81 96 Sindh-81 86
71
3.2.2 Water loss rate
It has been observed that better criteria for survival in drought stress conditions are low
water loss rate (water retention) from leaves. Cuticular transpiration rate could be used
for screening of wheat germplasms in water stress environment. Water retention rate
could increase the yield of wheat to considerable level. The ANOVA result confirmed
that water loss rate stress and normal was found highly significant at (P≤ 0.01) level
(table 6). The lowest water loss rate (Normal) was noted in Iqbal-2000 (0.4) followed by
010797 (0.9), 010748 (0.9), ZA-77 (1), Chenab-79 (1), 010724 (1.2), Chakwal-86 (1.3),
Nowshera-96 (1.4), Shahkar-95 (1.4) and Sonalika (1.5) while the highest WLR was
found in Manther (6.3). Similarly the lowest WLRS in stress condition was noted in
Faisalabad-83 (0.2) followed by Chenab-79 (0.2), Pirsabak-2008 (0.3), Barani-83 (0.3),
NIAB-83 (0.3), 010792 (0.6), Nowshera-96 (0.7), Mumal-2002 (0.8), Manther (0.8) and
Iqbal-2000 (0.8) while the highest WLR was found in Pirsabak-85 (5.4) as shown in table
8 (Annexure 4).
The correlation analysis confirmed that water loss rate normal was positively correlated
to root diameter, number of seminal roots, root angle, total root length, relative water
content normal and yield per plant while negatively correlated to root fresh weight,
root dry weight, root shoot ratio, number of nodal roots, root density, maximum root
length and water use efficiency (table 10).
72
3.2.3 Water use efficiency
The water use (WU) is the water consumed and water use efficiency (WUE) is the
efficiency of consumed water to produce biomass or grain yield are serious parameters
in water deficient regions. It is suggested that improved water use efficiency (WUE)
could improve yield of wheat in drought stress conditions. The Analysis of Variance
(ANOVA) observed that WUE was found highly significant at (P≤ 0.01) level (Table 6).
Among the one hundred wheat germplasm, ten superior germplasms were screened
out for WUE. The highest WUE was noted in NIAB-83 (1.68) followed by C-273 (1.61),
010742 (1.53), Kiran (1.50), ZA-77 (1.49), Punjab-76 (1.49), AS-2002 (1.48), Potohar-93
(1.47), Zamindar-80 (1.47) and Bakhtawar-94 (1.46) while the lowest WUE was noted in
Sonalika (0.37) as shown in table 8.
Water use efficiency was found positively correlated to root dry weight, number of
nodal roots, root density, relative water content and yield per plant while negatively
correlated to root fresh weight, root shoot ratio, root diameter, number of seminal roots,
root angle, total root length, maximum root length and water loss rate (table 10).
73
Table 8: Sorted table of top ten superior wheat genotypes on the base of physiological trait WLRN, WLRS and WUE
Genotypes WLRN Genotypes WLRS Genotypes WUE
Sonalika 1.5 Iqbal-2000 0.8 NIAB-83 1.6
Shahkar-95 1.4 Manther 0.8 C-273 1.6
Noshehra-96 1.4 Mumal-2002 0.8 010742 1.5
Chakwal-86 1.3 Noshehra-96 0.7 Kiran 1.5
010724 1.2 010792 0.6 ZA-77 1.4
Chenab-79 1 NIAB-83 0.3 Punjab-76 1.4
ZA-77 1 Barani-83 0.3 AS-2002 1.4
010748 0.9 Pirsabak-2008 0.3 Potohar-93 1.4
010792 0.9 Chenab-79 0.2 Zamindar-80 1.4
Iqbal-2000 0.4 Faisalabad-83 0.2 Bakhtawar-94 1.4
3.3 ROOT TRAIT ANALYSIS
To understand the performance of wheat crop under drought conditions, it is necessary
to have a sound knowledge about root traits. Root traits vary from species to species on
the base of water availability, growth, physiology and architecture. Root surface area
and root length in wheat crop play an important role in water uptake. Wheat crop has
two types of root systems i.e seminal root system and Nodal root system. Seminal root
arise directly from the tip of main stem after seed germination while the nodal roots
arise from the sides (lateral) of the main stem. All these constitute primary root system.
The number of roots depends on the number of tillers in wheat and grows till anthesis
(Annexure 5).
3.3.1 Root fresh weight (RFW)
The ANOVA at (P≥0.01) level showed that root fresh weight (RFW) was observed
highly significant as in table 9. The genotype AUP-5000 was showed the highest weight
of 0.36 mg followed by Soghat-90 (0.15 mg), NIAB-83 (0.12 mg), Faisalabad-85 (0.09 mg),
74
Rawal-87 (0.08 mg), Blue silver (0.08 mg), C-273 (0.08 mg), Lasani-08 (0.08 mg), AUP-
4008 (0.08 mg) and sutlag-86 (0.07 mg) while the lowest root fresh weight was noted in
Pak-81 (0.008 mg) as shown in Table 11A. The correlation analysis revealed that RFW is
positively correlated with RDW, SFW, SDW, R:S, NSR, RA, TRL, RDT and MRL while
negatively correlated with RD and NNR (table 10).
3.3.2 Root dry weight (RDW)
The ANOVA results confirmed that root dry weight (RDW) was highly significant at
(P≥0.01) level (Table 9). The genotypes Soghat-90, NIAB-83, Sutlag-86, C-273, Pirsabak-
85, Blue silver, AUP-4008, Zamindar-80, Sandal and Bahawalpur-79 showed highest
root dry weight in descending order as 0.10 mg, 0.07 mg, 0.06 mg, 0.06 mg, 0.05 mg, 0.05
mg, 0.05 mg, 0.05 mg, 0.05 mg and 0.04mg respectively while the genotype Barani-70
showed the lowest root dry weight as 0.006 mg (Table 11A). RDW also showed positive
correlation to RFW, SFW, SDW, R:S, TRL, NSR, RDT, MRL and showed negative
correlation to RD, NNR and RA (table 10).
3.3.3 Shoot fresh weight (SFW)
ANOVA at (P≥0.01) level observed that shoot fresh weight (SFW) was highly significant
(Table 9). Saleem-2000 showed highest shoot fresh weight (0.93 mg) followed by
Zarlashta-90 (0.83 mg), NIAB-83 (0.76 mg), Lr-230 (0.75 mg), Indus-79 (0.75 mg), Raskoh
(0.72 mg), 010742 (0.71 mg), Manther (0.69 mg), Bakhtawar-94 (0.69 mg) and Sindh-81
(0.68 mg) while the lowest SFW was noted in Mehran-89 as 0.10 mg as in Table 11A. The
correlation analysis of SFW revealed that positive correlation is present with RFW,
75
RDW, SDW, RD, TRL, NNR, NSR and RA while negative correlation is found with R:S,
RDT and MRL (table 10).
3.3.4 Shoot dry weight (SDW)
The Shoot Dry weight (SDW) was found highly significant at (P≥0.01) level (Table 9).
The top ten superior genotypes was calculated on the base of SDW as Saleem-2000 (0.63
mg), NIAB-83 (0.58 mg), Zarlashta-90 (0.46 mg), Faisalabad-85 (0.38 mg), Chenab-79
(0.36 mg), 010724 (0.35 mg), Abdaghar-97 (0.33 mg), Khyber-83 (0.31 mg), GA-002 (0.28
mg) and SA-42 (0.27 mg) while the lowest SDW was observed in Iqbal-2000 (0.05 mg) as
shown in Table 11A. The statistical correlation showed that SDW is positively correlated
with RFW, RDW, SFW, NNR, NSR, RA, TRL and MRL while negatively correlated with
R:S, RD and RDT (table 10).
3.3.5 Root shoot ratio (R:S)
ANOVA of root shoot ratio (R: S) was found highly significant (Table 9). The maximum
root shoot ratio was found in Pirsabak-2008 as (2.54) followed by AUP-5000 (0.86),
Janbaz (0.75), Soghat-90 (0.50), FPD-08 (0.46), Lasani-08 (0.42), Bahawalpur-79 (0.40),
potohar-90 (0.37), Wardak-85 (0.35) and Mehran-89 (0.32) respectively while the lowest
root shoot ratio was noted in Haider-2002 (0.02) as in Table 11A. R:S was found
positively correlated with RFW, RDW, NSR, RA, TRL, RDT and MRL while showed
negative correlation with SFW, SDW, RD and NNR (Table 10).
3.3.6 Root diameter (RD)
The root diameter (RD) play an important role in crops for water uptake as larger
diameter of roots increases the uptake of water and salts from the soil (Atta et al., 2013).
76
The root diameter (RD) of all the 100 wheat genotypes was found highly significant at
P≥0.01 level (Table 9). The highest root diameter was found in the genotype AS-2002
(0.53) followed by Manther (0.40), Haider-2002 (0.4), KSK (0.35), Margalla-99 (0.33),
Local white (0.3), Kohinoor-83 (0.29), Rawal-87 (0.29) and Merco-2007 (0.28) while the
lowest root diameter was observed in Sulaiman-96 (0.06) as in Table 11A. The RD was
found positively correlated with SFW, NNR, NSR and RA and showed negative
correlation with RFW, RDW, SDW, R:S, TRL, RDT and MRL (table 10).
3.3.7 Number of nodal roots (NNR)
Number of nodal roots (NNR) was found highly significant at P≥0.01 level in all
genotypes (table 9). The highest number of NNR was recorded in Meraj-08 (4) followed
by Iqbal-2000 (3), 010742 (2.66), Lasani-08 (2.66), Sariab-92 (2.66), pirsabak-2008 (2.66),
Faisalabad-83 (2.66), GA-2002 (2.66), Barani-70 (2.33) and LYP-73 (2.33) while lowest
NNR was recorded in AUP-5000 (0) (table 11). The correlation analysis revealed that
NNR was positively correlated to SFW, SDW, RD, RA, TRL, YPP and RDT while
negatively correlated with RFW, RDW, R:S, NSR and MRL (table 10).
3.3.8 Number of seminal roots (NSR)
ANOVA result showed that number of seminal roots (NSR) was also found highly
significant in hundred genotypes at P≥0.01 level (table 9) and Marwat-01 showed
maximum NSR as 6.666, AS-2002, Chenab-96, 010724, AUP-5000 recorded 6.333 each.
MH-97, Kaghan-93, Nowhera-96, 010792, C-273 showed 6 NSR each while lowest NSR
(2) was calculated in C-518 (table 11b). NSR was positively correlated with RFW, SFW,
77
SDW, R:S, RD, RA, TRL and RDT while negatively correlated with RDW, NNR and
MRL (table 10).
3.3.9 Root angle (RA)
Root angle (RA) is an important trait found highly significant at P≥0.01 level of ANOVA
test (table 9). The top ten superior genotypes were recorded on the base of highest RA
viz. MH-97 (113), Potohar-70 (110), Manther (106), Lr-230 (103), C-518 (100), 010724 (96),
Pirsabak-85 (96), Lasani-08 (96), Sonalika (93) and Maxipak (93) while the lowest RA
was calculated in Wafaq-2008 (33.33) (table 11b). The correlation analysis showed
positive correlation of RA with RFW, SFW, SDW, R:S, RD, NNR, NSR and TRL while
negative correlation showed with RDW, RDT and MRL (table 10).
3.3.10 Total roots length (TRL)
The statistical analysis of ANOVA confirmed that total roots length (TRL) was highly
significant at P≥0.01 level (table 9). The highest TRL was recorded in Pirsabak-2008
(56.3mm) followed by Chenab-79 (53 mm), 010776 (42.6 mm), Bakhtawar-94 (42 mm),
NIAB-83 (42 mm), Faisalabad-85 (41 mm), Blue silver (40.6 mm), Sutleg-86 (39.3 mm),
C-273 (39 mm) and Kohinoor-83 (38.3 mm) while the lowest TRL was recorded in
Chenab-70 (11 mm) (table 11b). The correlation analysis confirmed that TRL is
positively correlated with RFW, RDW, SFW, SDW, R:S, NNR, NSR, RA, RDT and MRL
while negatively correlated to RD (table 10).
3.3.11 Root density (RDT)
ANOVA confirmed that root density (RDT) was found highly significant at P≥0.01 level
(table 9). The highest RDT was showed by the genotypes Soghat-90 (11.6), 010748 (9),
78
010724 (8.6), Lasani-08 (8.6), LYP-73 (8.3), Marwat-01 (7.6), Barani-83 (7.6), Barani-70
(7.3), C-591 (7.3) and Kohinoor-83 (7.3) while lowest root density was recorded in
Khyber-83 (2.3) (table 11b). The correlation analysis revealed that RDT is positively
correlated with RFW, RDW, R:S, NSR, TRL, MRL and WUE and RWCS while
negatively correlated with SFW, SDW, NNR, RD, RA, WLRS, WLRN and RWCN (table
10).
3.3.12 Maximum roots length (MRL)
The ANOVA results confirmed that maximum roots length (MRL) is highly significant
at P≥0.01 level (table 9). In genotypes Abdaghar-97 (30.33 mm) and Punjab-96 (30.33
mm) highest MRL was recorded followed by Fakhre-Sarhad (29.3 mm), Chenab-79
(26.33 mm), Sonalika (23.66 mm), C-228 (23 mm), Punjab-76 (20.66 mm), Faisalabad-85
(20.66 mm), Anmol-91 (20.66 mm) and NIAB-83 (20.33 mm) while the lowest MRL was
showed by SA-75 (4.33 mm) (table 11b). The correlation analysis revealed that MRL is
positively correlated with RFW, RDW, SDW, R:S, TRL, NSR, RDT, RWCS, RWCN and
WLRS while negatively correlated with SFW, RD, NNR, RA, WLRN and WUE (table
10).
79
Table 9: Analysis of Variance for root traits associated with drought tolerance
Sum of Squares Df Mean Square F Sig.
RFW .499 99 .005 3.349 .000
RDW .079 99 .001 3.339 .000
SFW 9.660 99 .098 4.229 .000
SDW 3.050 99 .031 6.701 .000
R:S 22.726 99 .230 4.160 .000
RD 1.866 99 .019 12.457 .000
NNR 187.530 99 1.894 2.388 .000
NSR 268.013 99 2.707 2.454 .000
RA 103348.000 99 1043.919 3.173 .000
TRL 20506.000 99 207.131 3.345 .000
RDT 697.347 99 7.044 2.844 .000
MRL 5850.413 99 59.095 2.646 .000
80
Table 10: Correlation analysis of root traits with physiological tests and yield per plant RFW RDW SFW SDW R:S RD NNR NSR RA TRL RDT MRL WLRN WLRS WUE RWCN RWCS YPP
RFW 1
RDW .567** 1
SFW .015 -.082 1
SDW .034 .001 .683** 1
R:S .415** .425** -.100 -.085 1
RD -.020 -.039 .114 -.131 -.065 1
NNR -.119 -.045 .316** .159 -.152 .132 1
NSR .192 .035 .029 .145 .108 .060 -.422** 1
RA .082 -.038 .258** .126 .095 .246* .227* -.056 1
TRL .266** .493** .021 .133 .410** -.038 .032 .195 -.034 1
RDT .260** .282** -.256* -.176 .161 -.007 -.025 .085 -.215* .066 1
MRL .176 .398** -.098 .001 .152 -.029 -.009 .006 -.056 .673** .070 1
WLRN -.104 -.019 .120 .063 -.059 .131 -.046 .048 .043 .076 -.066 -.055 1
WLRS .210* .098 .075 -.089 -.071 .036 .084 -.113 .069 .008 -.045 .090 .286** 1
WUE -.049 .031 .043 .016 -.084 -.060 .048 -.036 -.210* -.137 .131 -.088 -.055 -.016 1
RWCN -.151 -.049 .121 .072 -.076 .235* .071 .025 .144 .042 -.053 .047 .753** .259** .050 1
RWCS .032 .102 .015 -.094 -.141 .060 .057 -.127 .104 .030 -.033 .115 .317** .900** .046 .290** 1
YPP .157 .125 .031 .014 .071 -.027 .114 .018 .074 .098 -.025 .083 .110 .184 .116 .128 .141 1
81
Table 11 (A): Top ten superior genotypes on the base of root traits Genotype RFW Genotype SFW Genotype RDW Genotype SDW Genotype R:S Genotype RD
AUP 5000 0.36 Saleem-2000 0.93 Soghat-90 0.10 Saleem-2000
0.63 Pirsabak-2008
2.54 AS -2002 0.53
Soghat-90 0.15 Zarlashta-90 0.83 NIAB-83 0.07 NIAB-83 0.58 AUP-5000 0.86 Maxipak 0.41
NIAB-83 0.12 NIAB-83 0.76 Sutlag-86 0.06 Zarlashta-90
0.46 Janbaz 0.75 Manther 0.40
Faisalabad-85
0.09 Lr-230 0.75 C-273 0.06 Faisalabad-85
0.38 Soghat-90 0.50 Haider-2002 0.4
Rawal-87 0.08 Indus-79 0.75 Pirsabak-85 0.05 Chenab-79 0.36 FPD-08 0.46 KSK 0.35
Blue silver 0.08 Raskoh 0.72 Blue silver 0.05 010724 0.35 Lasani-08 0.42 Margalla-99 0.33
C-273 0.08 010742 0.71 AUP-4008 0.05 Abdaghar- 97
0.33 Bahawalpur-79
0.40 Local white 0.3
Lasani-08 0.08 Manther 0.69 Zamindar-80
0.05 Khyber-83 0.31 Potohar-90 0.37 Kohinoor-83 0.29
AUP-4008 0.08 Bakhtawar-94
0.69 Sandal 0.05 GA-2002 0.28 Wardak-85 0.35 Rawal-87 0.29
Sutlag-86 0.07 Sindh-81 0.68 Bahawalpur-79
0.04 SA-42 0.27 Mehran-89 0.32 Merco-2007 0.28
82
Table 11 (B): Top ten superior genotypes on the base of root traits
Genotype NNR genotype NSR genotype RA Genotype TRL genotype RDT genotype MRL
Meraj-08 4 Marwat-01 6.66 MH-97 113.3 Pirsabak-2008
56.33 Soghat-90 11.66 Abdaghar-97 30.33
Iqbal-2000 3 AS-2002 6.33 Potohar-70
110 Chenab-79 53 010748 9 Punjab-96 30.33
010742 2.66 Chenab-96 6.33 Manther 106.6 010776 42.66 010737 8.66 Fakhre-sarhad
29.33
Lasani-08 2.66 010724 6.33 Lr-230 103.3 Bakhtawar-94
42 Lasani-08 8.66 Chenab-79 26.33
Sariab-92 2.66 AUP-5000 6.33 C-518 100 NIAB-83 42 LYP-73 8.33 Sonalika 23.66
Pirsabak- 2008
2.66 MH-97 6 010724 96.66 Faisalabad-85
41 Marwat-01 7.66 C-228 23
Faisalabad-83
2.66 Kaghan-93 6 Pirsabak-85
96.66 Blue silver 40.66 Barani-83 7.66 Punjab-76 20.66
GA-2002 2.66 Noshehra- 96
6 Lasani-08 96.66 Sutlag-86 39.33 Barani-70 7.33 Faisalabad-85 20.66
Barani-70 2.33 Bakhar-2008
6 Sonalika 93.33 C-273 39 C-591 7.33 Anmol-91 20.66
LYP-73 2.33 C-273 6 Maxipak 93.33 Kohinoor-83 38.33 Kohinoor-83 7.33 NIAB-83 20.33
83
3.4 FLOURESCENT IN SITU HYBRIDIZATION (FISH)
FISH was done using labelled repetitive probes in combination of two as pTa 71 and
pTa 794, pSc 119.2 and pTa 794. The probes are hybridized on mitotic chromosomes of
metaphase cells of fifteen wheat germplasm i.e Kiran, Janbaz, Sindh-81, Lasani-08,
Pirsabak-85, Zamindar-80, Barani-83, Pak-81, Potohar-70, AUP-5008, Saleem-2000,
Sonalika, Manther, Wadanak-85 and Pari-73. In Kiran and Pirsabak-85 pTa 794
successfully hybridized while pTa 71 did not show any positive signal (Fig.5a and b).
The probe pSc 119.2 was hybridized successfully in Wadanak-85 (Fig. 6) while in Pari-
73 both pTa 794 and pSc119.2 hybridized simultaneously (Fig. 7).
Fig 5: pTa 794 (Kiran) Fig 6: pTa 794 (Pirsabak-85)
84
Fig 7: pSc 119.2 (Wadanak-85) Fig 7: FISH pattern of the wheat (green) repititive probes chromosomes pTa 794 (pink) and pSc 119.2
(Pari-73) 3.5 MOLECULAR ANALYSES
One hundred wheat germplasm profiled at 102 SSR markers. SSR markers were selected
from online grain gene 2 data base (http://wheat.pw.usda.gov). SSR markers included
in the present study are as BARC (Xbarc), CFD (Xcfd), WMC (Xwmc), WMS (Xgwm),
VRN and Ppd. The PCR product of primers having small fragment size was run in
metaphor agarose gel (2X) due to high resolving power. Out of 150 SSR markers only
102 markers produced scorable bands. The primers produced faint bands were not
included in the scorable spread sheet. The sharp and visible bands were scored by
various symbols as a/a, a/b, b/b, a/c, b/c, c/c and a/b/c while the absent bands were
denoted by n/n.
85
Figure 8: representative gel pictures of (A) Xbarc 264, (B) Xwmc 606, (C) VRN AF, (D) Xcfd 18 and (E) Xgwm 443, L: 100 bp ladder
A
B
C
D
E
L
L
L
L
L
86
3.5.1 Molecular markers polymorphism
A total of 102 molecular markers included in the present study. These markers
produced a total of 271 alleles across the hundred wheat genotypes. The number of
alleles per locus ranged from 1-3 with an average of 2.63 per locus. All the markers
showed relatively high polymorphism (Figure 9). Most of the primers have displayed a
maximum of 3 and minimum of 1 allele. The highest marker diversity (66%) was
showed by Xwmc 798, Xbarc 147, Xgwm 60, Xgwm 469, Xgwm 471 followed by Xbarc 154
(65%), Xgwm 372 (65%), Xwmc 52 (65%), VRN AF (65%) Xbarc 172 (64%), Xgwm 261
(64%) while the lowest diversity (9%) was found in Xbarc 137. The overall mean
diversity among all the markers was recorded as 47%. Polymorphic information content
(PIC) values of the markers was also calculated in the range of 0.03 – 0.59 . The highest
PIC value was confirmed in Xgwm 471(0.59), Xbarc 147 (0.59) and the lowest (0.03) was
recorded in Xwmc606. The overall average of PIC values was found as 0.40. The alleles
of high frequency per locus (major allele‘s frequency) ranged from 0.38 to 1 with mean
of 0.62 (table 12). Our results of molecular markers polymorphism matching with
results of Liu et al (2010b) for association mapping of wheat for agronomic traits and
Maccaferri et al (2011) for association mapping of durum wheat.
3.5.2 Population structure and linkage disequilibrium
Genotypic data of 102 SSR markers was applied across the whole genome of wheat for
analysis of population structure. An admixture model with correlated allele frequencies
for determination of population structure was used (Falush et al., 2003). The analysis of
population structure was accomplished using structure software (Pritchard et al., 2000).
87
Burn-in of 20,000 iterations followed by 20,000 MCMC (Monte Carlo Markov Chain)
replicates was used to test K values (number of subpopulations) in the range of 2-20
while performed 10 runs for K values. The suitable cluster numbers (K) was calculated
using online structure harvester software (Yu et al., 2006) by applying logarithmic
likelihood LnP(D) (natural log of probability data) method (figure 10a). Two major
peaks have been detected at K=2 and K=13 (Evanno et al., 2005).
The hundred wheat genotypes at K=2 were separated into two subgroups, G1 and G2.
Group G1 comprised of local land races while G2 contain CIMMYT lines (010724,
010737, 010748, 010776 and 010792) as well as local land races. Bar plot shows all the
hundred genotypes are admixed due to complex and long history of evolution (figure
10b). All the hundred genotypes showed 100% admix with no purity.
All the genotypes were divided into 13 sub-groups at K=13 as G1, G6, G11, G 12 and
G13 comprised of 44 (44%) genotypes consisting of both local and CIMMYT lines
88
Table 12: SSR markers, their chromosome position (ch pos), Major Allele frequency (MAF), allele No, genetic diversity (H) and polymorphic information content (PIC) used for profiling of hundred wheat genotypes.
Marker Ch pos MAF Allele No H PIC Marker Chr pos MAF Allele No H PIC
Cfd 15 1AS,1D 0.94 3 0.11 0.11 Xbarc 154 7A 0.39 3 0.65 0.58
Cfd 18 5D 0.92 2 0.15 0.14 Xbarc 158 1AL 0.49 3 0.61 0.53
Xwmc 24 1AS 0.61 2 0.48 0.36 Xbarc 159 2BL 0.63 3 0.53 0.46
Xwmc 25 2B 0.54 3 0.60 0.53 Xbarc 163 4BS 0.77 2 0.35 0.29
Xwmc 27 2B,5B 0.69 3 0.47 0.42 Xbarc 164 3BL 0.67 3 0.50 0.45
Xwmc 43 3B,3D 0.59 2 0.48 0.37 Xbarc 165 5AL 0.48 3 0.62 0.54
Xwmc 51 7B 0.58 2 0.49 0.37 Xbarc 167 2BS 0.44 3 0.63 0.56
Xwmc 52 1B,4D 0.43 3 0.65 0.58 Xbarc 172 7DL 0.46 3 0.64 0.57
Xwmc 94 7D 0.59 2 0.48 0.37 Xbarc 173 6DS 0.45 3 0.64 0.56
Xwmc 97 5D 0.51 3 0.62 0.55 Xbarc 175 6DL 0.47 3 0.64 0.57
Xwmc 104 1A,6B 0.69 3 0.47 0.42 Xbarc 264 7AL 1.00 1 0.00 0.00
Xwmc 147 1D,3A 0.77 3 0.38 0.34 Xgwm 4 4AS 0.85 2 0.26 0.22
Xwmc 149 5B 0.66 3 0.51 0.45 Xgwm 10 2AS 0.51 3 0.62 0.55
Xwmc 153 1D,3A 0.79 3 0.35 0.32 Xgwm 33 1DS 1.00 1 0.00 0.00
Xwmc 154 2B 0.70 2 0.42 0.33 Xgwm 37 7DL 0.49 3 0.61 0.53
Xwmc 157 7D 0.79 2 0.33 0.28 Xgwm 55 2BL 0.51 3 0.58 0.49
Xwmc 161 4A 0.53 2 0.50 0.37 Xgwm 60 7AS 0.38 3 0.66 0.59
Xwmc 163 6A 0.53 2 0.50 0.37 Xgwm 71 3DS 0.59 3 0.57 0.50
Xwmc 166 7B 0.66 3 0.48 0.40 Xgwm 99 1AL 0.52 2 0.50 0.37
Xwmc 167 2D 0.66 3 0.47 0.38 Xgwm 111 7DS 0.52 2 0.50 0.37
Xwmc 168 7A 0.47 3 0.63 0.56 Xgwm 136 1A 0.69 3 0.48 0.43
Xwmc 169 3A 0.66 3 0.48 0.41 Xgwm 194 4DL 0.48 3 0.56 0.46
Xwmc 175 2B 0.66 3 0.50 0.45 Xgwm 261 2DS 0.45 3 0.64 0.57
Xwmc 177 2A 0.52 2 0.50 0.37 Xgwm 293 5AS 0.49 3 0.54 0.43
Xwmc 181 2D 0.89 2 0.20 0.18 Xgwm 299 3BL 0.53 3 0.60 0.53
Xwmc 182 7B 1.00 1 0.00 0.00 Xgwm 302 7BL 0.48 3 0.62 0.55
Xwmc 216 1D 0.57 2 0.49 0.37 Xgwm 325 6DS 0.88 2 0.21 0.19
89
Xwmc 219 4A 0.55 3 0.52 0.41 Xgwm 359 2AS 0.72 3 0.43 0.37
Xwmc 232 4A 0.91 2 0.16 0.15 Xgwm 372 2AL 0.39 3 0.65 0.58
Xwmc 233 5D 0.62 3 0.48 0.37 Xgwm 389 3BS 0.50 3 0.63 0.55
Xwmc 235 5BL 0.48 3 0.57 0.47 Xgwm 443 5BS 0.56 3 0.59 0.52
Xwmc 398 6BC 0.50 3 0.60 0.52 Xgwm 471 7AS 0.37 3 0.66 0.59
Xwmc 420 4AS 0.53 3 0.60 0.53 Xgwm 469 6DS 0.38 3 0.66 0.59
Xwmc 606 7BS 0.98 2 0.04 0.04 Xgwm 484 2DS 0.45 3 0.62 0.53
Xwmc 718 4AL 0.56 3 0.59 0.52 Xgwm 544 5BS 1.00 1 0.00 0.00
Xwmc 749 6DC 0.50 3 0.62 0.55 Xgwm 608 4DC 0.50 3 0.61 0.53
Xwmc 798 1BS 0.38 3 0.66 0.59 Xgwm 609 4DL 0.62 3 0.54 0.48
Xbarc 42 3DS 0.85 2 0.26 0.22 Xgwm 642 1DL 0.93 2 0.13 0.12
Xbarc 45 3AS 0.63 3 0.53 0.47 xgwm 908 2DS 1.00 1 0.00 0.00
Xbarc 76 6BS 0.56 3 0.59 0.52 Xgdm 3 5DS 0.84 2 0.27 0.23
Xbarc 101 2BL 0.60 3 0.56 0.50 Xgdm 5 2DS 0.82 3 0.31 0.29
Xbarc 127 6B 0.49 3 0.56 0.46 Xgdm 6 2DL 0.66 2 0.45 0.35
Xbarc 128 2BL 0.59 3 0.55 0.48 Xgdm 19 1DL 0.76 2 0.36 0.30
Xbarc 134 6BL 0.52 3 0.60 0.53 Xgdm 28 1BS 0.68 3 0.49 0.44
Xbarc 137 1BL 0.95 2 0.10 0.09 Xgdm 33 1DS 0.70 3 0.46 0.41
Xbarc 140 5BL 0.81 3 0.33 0.30 Xgdm 46 7DL 0.63 3 0.53 0.46
Xbarc 141 5AL 0.68 3 0.48 0.43 Xgdm 114 2BS 0.48 3 0.61 0.53
Xbarc 144 5DL 0.47 3 0.61 0.53 VRN AF 5A 0.38 3 0.66 0.58
Xbarc 147 3BS 0.36 3 0.67 0.59 VRN B1 R3 5B 0.55 2 0.50 0.37
Xbarc 148 1AS 0.62 3 0.54 0.48 PpD1 R1 2A 0.68 3 0.47 0.40
Xbarc 149 1DS 0.68 2 0.44 0.34 PpD 1 R2 4D 0.55 3 0.60 0.53
Mean 0.62 3 0.47 0.41
90
Figure 9: UPGMA tree constructed using molecular markers showing diversity across hundred wheat genotypes
Soghat9
0
ZA
-77
Khyber-
79
Kiran
C-5
18
Mera
j-08
C-2
73
Pirsa
bak-
85
FP
D-0
8W
arda
k-85
San
dal
DirkTa
ndoj
am-8
3
Mum
al-2
002
Nori-
70
Faisala
bad-83
Wafa
q-2008
10742
C-250
SH-2003
10792
Pirsabak2008
Sindh81
Sutlag-86
Bluesilver
Punjab-88
Shahkar-95
010724-YRWL-711AUP5000Sariab-92Iqbal-2000
LU-26Punjab-9610748Anmol-91SussiZarlashta90
Haider2002
Chakw
al86
Ksk
Pari-73
Kaghan93
Wadanak98
AS
-2002
Daw
ar9
6
Khyb
er8
3
Shalim
ar8
8
Loca
lwhite
Sulim
an96
Faisa
labad85
Pun
jab
-76
MH
-97
Rasko
h
10737
10776
Maxip
ak
Abdaghar9
7W
adanak8
5U
qab2000
Marg
alla
99
Bakhtaw
ar94Indus79
RW
P-9
4
1079
3
Che
nab7
9
Zamin
dar-8
0
SA-42C-5
91SA-7510724Chenab-96Pak-81Bahalwapur-79Lasani-08Kohinoor-83Potohar-93
Barani-83Marwat-01
GA2002
Fakhrisarhad
Lr-230
Barani70
LYP-73
sonalika
Punjab-81
C-228
Zarghoon-79
NIAB83
Rawal87
Noshera96
Saleem2000
Manther
Merco2007
Chenab70
Potohar-70
Mehran-89
poto
har-9
0A
UP
-4008
Janbaz
0.05
91
G1
G2
G1 G2 G3 G4 G5 G6 G7
G8 G9 G10 G11 G12 G13
Figure 10(a,b,c): Population structure analysis of wheat genotypes based on SSR markers (a) Line graph. The X-axis shows LnP (D) value and Y-axis shows k. (b) Graphical bar plot at k=2 presenting two subgroup (G1 & G2). (c) Graphical bar plot at k=13 presenting thirteen subgroup (G1- G13). The X-axis shows accessions numbers and Y-axis shows sub group membership.
(a)
(b)
(c)
92
(figure 10c). Group G2, G3, G4, G5, G7, G8, G9 and G10 include 56 (56%) admix
genotypes (all were local genotypes).
3.5.3 Association mapping between root traits and SSR markers
In the present study association mapping was applied for identification of association
between root traits and SSR markers. Marker-trait association (MTA) based on
polymorphism found in SSR markers applied on diverse wheat genotypes. Two
different models were used for identification of QTLs associated with root traits as,
GLM (general linear model) and MLM (mixed linear model).
In GLM require no kinship and only Q matrix was used to determine association
between markers and mean of phenotypic traits. The level of significance of P value was
measured at p≤0.01 in both GLM and MLM models. The QTLs having LOD values
above 2.5 were considered for both GLM and MLM.
3.5.3.1 Total root length (TRL) MTA
In GLM model the SSR marker Xgdm 5 on chromosome 2 was significantly associated
with total root length but no association of marker with TRL was found in MLM. The
phenotypic variance (r2) was 0.10. The p value was recorded as 0.0016 and LOD is 2.78
as shown in (table 13) and figure 11 (a).
3.5.3.2 Root fresh weight (RFW) MTA
Xwmc 235 showed significant association with RFW in GLM model. The QTL identified
on chromosome 5 at position of 47 cM. The p value was recoded as 0.000271,
93
phenotypic variance (r2) was found as 0.10 and LOD was 3.56. The MLM model did not
show any marker association for RFW (table 13) and figure 11 (b).
3.5.3.3 Root dry weight (RDW) MTA
PpD1 marker revealed marker trait association (MTA) for RDW in GLM model only.
The QTL identified on chromosome 2 at 38.1 cM. The p value and LOD for the above
MTA was recorded as 0.001711 and 2.76 respectively. The (r2) was found 0.41 (figure
11(c)).
3.5.3.4 Maximum root length (MRL) MTA
Two SSR markers were found to be associated with MTA for MRL. The SSR marker
Xwmc 149 showed MTA with MRL in GLM model. The QTL found on chromosome 5 at
158.5 cM. The p value, LOD and (r2) were recorded as 0.00136, 2.86 and 0.84 (figure 11
(d,e)).
SSR marker Xgwm 10 identified QTL for MRL in MLM. The marker associated with
chromosome 2 at 82 cM. The p value (0.00208), LOD (2.68) and phenotypic variance
(0.26) were calculated for same marker.
3.5.3.5 Number of nodal roots (NNR) MTA
The SSR marker Xwmc 175 identified QTL for NNR on chromosome 2 at 158.5 cM. The
QTL identified only in GLM model having p value 0.00306 and LOD 2.5 while the (r2)
0.17 (figure 11(f)).
94
3.5.3.6 Root angle (RA) MTA
Two MTA (QTLs) was found associated with RA in GLM model while MLM did not
show any MTA. The SSR marker Xgwm 302 located on chromosome 7 at 86 cM and
Xwmc 749 on chromosome 6 at 27 cM. The p value ranged from 0.00155 - 0.00280. The
LOD was 2.80 and 2.55 respectively while the (r2) 0.15 and 0.12 (figure 11(g)).
Fig 11(a): QTL identified for TRL on the basis of Fig 11(b): QTL identified for RFW on the basis of LOD in GLM LOD in GLM
Fig 11(d) : QTL identified for MRL on the basis of Fig 11(e) : QTL identified for MRL on the basis of LOD in GLM LOD in MLM
95
Fig 11(f): QTL identified for NNR on the basis of Fig 11(g) : QTL identified for RA on the basis of LOD in GLM LOD in GLM
Fig 11(h): QTL identified for RDT on the basis of Fig 11(i) : QTL identified for RDT on the basis of LOD in GLM LOD in MLM
Fig 11(j) : QTL identified for RD on the basis of Fig 11(k) : QTL identified for RDT on the basis of LOD in GLM LOD in MLM
96
Fig 11(c): QTL identified for RDW on the basis of LOD in GLM
3.5.3.7 Root density (RDT) MTA
The SSR markers identified two QTLs for root density, one each in GLM and MLM. The
marker Xwmc 175 associated with chromosome 2 at 158.5 cM while Xwmc 235 with
chromosome 5 at 47 cM. The p value 0.00143 and LOD 3.28 was recorded in GLM while
p (0.00286) and LOD 2.5 in MLM respectively (figure 11 (h,i)).
3.5.3.8 Root Diameter (RD) MTA
An SSR marker Xwmc 233 identified two QTLs for RD, one each in GLM and MLM. The
marker associated with chromosome 5 at 4.5 cM. The p value, LOD and (r2) in GLM
model was recoded as 0.00342, 4.10 and 0.36 respectively while in MLM p value
0.000047, LOD 4.32 and (r2) 0.34 was recorded (figure 11 (j,k)).
97
Table 13: Significant SSR markers for each QTLs associated with root traits GLM MLM
Trait Marker Ch cM p value LOD (r2) p value LOD (r2)
TRL Xgdm5 2 3.4 0.001642 2.78 0.10 - - -
RFW Xwmc235 5 47 0.000271 3.56 0.13 - - -
RDW PpD1R1 2 38.1 0.001711 2.76 0.41 - - -
MRL Xwmc149 5 158.5 0.001362 2.86 0.84 - - -
NNR Xwmc175 2 158.5 0.003064 2.51 0.17 - - -
RA Xgwm302 7 86 0.001556 2.80 0.15 - - -
RA Xwmc749 6 27 0.0028 2.55 0.12 - - -
RDT Xwmc175 2 158.5 0.001436 3.28 0.22 - - -
RD Xwmc233 5 4.5 0.003422 3.10 0.36 0.000047 3.326121 0.34
RDT Xwmc 235 5 47 - - - 0.002867 2.542644 0.10
MRL Xgwm10 2 82 - - - 0.002086 2.680788 0.26
98
Chapter-4
DISCUSSION
Economy of an agricultural country mostly depends on crops. Pakistani societies use
wheat as food crop. The existing population of Pakistan is 180 million and by 2030 the
projected population would be 300 million. Therefore, it is needed to increase the
existing production by introducing the new abiotic stress resistant cultivars to overcome
the demand for growing population. The climate of Pakistan is better for agricultural
crops but still the indigenous production does not fulfill the demand of growing
population due to non-availability of biotic and abiotic resistant seeds, late sowing,
improper irrigation, low crop yield, inappropriate cropping and lack of knowledge
(Ejaz-ul-hasan, 2008).
Wheat breeders always busy to produce new wheat varieties having high yield as well
as resistant to abiotic stresses i.e. drought, heat, cold and salinity. Grain yield could be
increased by improving yield components such as spike length, spikelets per spike,
grains per spike and grain filling duration (Ashfaq et al., 2003). Crop breeders
recommend those varieties for cultivation having drought tolerant genes as well as
improved morphological traits such as leaf area, plant height, number of tillers per
plant, peduncle length, number of spikelet per spike, spike length, number of grains per
spike, spike density, harvest index, yield per plant, grain yield and 1000 grains weight
(Ashfaq et al., 2003; Saleem et al., 2006). Wheat breeders are trying to produce cultivars
99
having genetically incorporated short grain filling duration and short life cycle to reach
maturity before water deficit in rain fed areas (Khan et al., 2014).
4.1 EVALUATION OF YIELD AND YIELD ASSOCIATED TRAITS
In the present study one hundred wheat genotypes were evaluated for different yield
and yield associated traits in the experimental field at Department of Genetics Hazara
University Mansehra Pakistan.
4.1.1 Number of tillers per plant
The analysis of variance (ANOVA) was performed for fifteen morphological parameters
and was found that all the parameters are highly significant at p≤0.01 level except
number of tillers per plant. Rabnawaz et al., (2013) reported that genotypes showed
significant differences for flag leaf area, plant height, peduncle length, number of nodes
per plant, Spike length, number of spikelets per spike, awn length, number of grains per
spike, yield per plant and harvest index while non-significant for number of tillers per
plant. Therefore, our results were in accordance with earlier study.
4.1.2 Plant height
The result of mean value confirmed that genotype C-518 showed the highest plant
height. Richards (1990) reported, maximum grain yield could be achieved in plants
having height between 70-100 cm. Iftikhar et al., (2012) reported that taller genotypes are
more susceptible to lodging and drought and hence low in grain yield. Our results
confirmed highest grain yield in Uqab-2000 having plant height 83cm. Therefore, it is
concluded that Uqab-2000 is suitable for grain yield under rainfed areas.
100
4.1.3 Spike length
The correlation analysis showed that spike length is positively correlated with yield per
plant. The result of mean value revealed that genotype Marwat-01 has highest spike
length (16.6 cm) followed by Sussi (16.6 cm), Barani-83 (15 cm), Shalimar-88 (14.8 cm)
and Faisalabad-83 (14.8 cm). The present study confirmed that increase in spike length
would increase the 1000 grain weight and yield per plant. Iftikhar et al., (2012) reported
that spike length, peduncle length, grains per spike and 1000 grains weight showed
positive correlation with yield per plant. Fida et al., (2011) reported that spike length is
less influenced by environmental factors due to high heritability. Therefore, spike
length could be good criteria to improve yield.
4.1.4 Spikelets per spike
ANOVA concluded that spikelets per spike are highly significant at (P≤0.01) in all the
genotypes as shown in table 2. The highest number of spikelets per spike was studied in
Margalla-99 followed by Barani-70, Zarlashta-90, Manther, Wadanak-85, Rawal-87,
010748, Maxipak, ZA-77 and Uqab-2000 while the lowest number of spikelets per spike
was studied in Saleem-2000. Our findings are in contradiction to Khattak et al., (2001),
who reported non-significant differences in wheat. The correlation analysis showed that
spiklets per spike is positively correlated with spike length, number of grains per spike,
number of tillers per plant and yield per plant while negatively correlated to plant
height. Our results confirmed that lesser the plant height would result greater spike
length due to more nourishment supply that finally would increase yield per plant.
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4.1.5 Spike density
ANOVA showed that spike density was highly significant (P≤0.01) as shown in table 2.
Highest spike density was found in genotypes Sandal, Margalla-99, 010792, 010724,
AUP-2004, Sindh-81, Rawal-87, Local white, Potohar-90 and Wadanak-98 while lowest
spike density was found in Marwat-01. The statistical analysis of correlation observed
that spike density was positively correlated to flag leaf area, peduncle length, plant
height, number of tillers per plant, days to 50% heading, days to 50% maturity, awn
length, spikelets per spike, grains per spike, yield per plant and total weight per plant
while spike density was negatively correlated to spike length, 1000 grain weight and
harvest index. Kalimullah et al., (2012) reported that spike density is positively
correlated with grains per spike, number of tillers and grain yield while negatively
correlated with 1000 grain weight and flag leaf area.
4.1.6 Grains per spike
Analysis of variance showed that grains per spike were highly significant (P≤0.01).
Maximum number of grains per spike was counted in Chenab-79 followed by Indus-79,
010748, 010724, Saleem-2000, Chenab-70, Zarlashta-90, Soghat-90, Wadanak-85 and Lr-
230 while the minimum number of grains per spike was counted in Punjab-96 (Table
3b).
Correlation analysis revealed that number of grains per spike was positively correlated
to flag leaf area, spike length, peduncle length, days to 50% heading, days to 50%
maturity, plant height, number of tillers per plant, awn length, spikelets per spike, spike
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density, yield per plant, HI and total weight per plant respectively. Number of grains
per spike was found negatively correlated to 1000 grain weight. Kalimullah et al., (2012)
reported that grains per spike were positively correlated to number of tillers per plant,
flag leaf area, spike density and yield per plant while negatively correlated with 1000
grain weight. Iftikhar et al., (2012) also reported that grains per spike is positively
correlated with days to 50% heading, peduncle length, number of tillers per plant, spike
length and yield per plant while negatively correlated with negatively correlated with
plant height.
4.1.7 1000 grain weight
The ANOVA revealed that 1000 grain weight is highly significant (P≤0.01). Among one
hundred wheat genotypes top ten genotypes showed highest grain weight i.e
Zarghoon-79 was on top followed by Faisalabad-85, Mumal-2002, Sutlag-86, C-591,
Punjab-81, Potohar-70, Punjab-96, Zamindar-80 and Lu-26 and the lowest grain weight
was recorded in AS-2002 respectively (Table 3b). The statistical analysis of correlation
showed that 1000 grain weight was found positively correlated to plant height, yield
per plant, HI and total weight per plant while negatively correlated to flag leaf area,
peduncle length, spike length, days to 50% heading, days to 50% maturity, awn length,
number of tillers per plant, spikelets per spike, spike density and number of grains per
spike. The positive correlation between 1000 grain weight and yield per plant is a good
selection for developing high yielding genotypes in wheat. These results matching with
previous reports of Iftikhar et al., (2012), who reported that positive relation of 1000
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grain weight with yield per plant indicating as suitable selection criteria for developing
high yielding wheat genotypes for rainfed areas.
4.1.8 Harvest index
ANOVA showed that Harvest Index (HI) is highly significant at (P≤0.01). The genotype
C-273 showed the highest HI followed by C-518, Sutlag-86, Wardak-85, Dirk,
Faisalabad-83, Chenab-96, Punjab-88. Potohar-90 and Iqbal-2000 respectively and
010792 showed the lowest HI.
The harvest index (HI) showed positive correlation to flag leaf area, peduncle length,
plant height, days to 50% maturity, grains per spike, spike density, 1000 grain weight,
yield per plant and total weight per plant while negatively correlated to spike length,
number of tillers per plant, days to 50% heading, awn length and spikelets per spike.
These results show great variation in yield associated traits. The results of present
study are in accordance with previous reports. Khazaei et al., (2009) also reported that
the traits under study show great variation among different varieties as well as in the
same variety at different geographical locations.
4.1.9 Days to 50% heading
Days to 50% heading is yield associated trait and the ANOVA result confirmed that
50% heading was highly significant (P≤0.01) among all genotypes. 010776, 010737,
Abdaghar-97, NIAB-83, 010748, Bakhtawar-94, Uqab-2000, Kaghan-93, Raskoh and
Indus-79 were took more days to heading while Mehran-89 took lesser number days to
50% heading. Days to 50 % heading was found positively correlated with flag leaf area,
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peduncle length, plant height, spike length, days to maturity, awn length, spikelets per
spike, spike density, number of grains per spike and total grain weight while negatively
correlated to number of tillers per plant, 1000 grain weight, yield per plant and HI. The
present study confirmed that proper timing of the life cycle components is critical for
high yielding potential in wheat. The main components are the duration between
sowing and emergence, the growth of the crops till floral initiation and the duration of
floral initiation to terminal spikelet. Snape et al., (2001) reported that wheat life cycle is
under the control of three set of genes as vernalization response (Vrn), photoperiod
response (Ppd) and developmental rate (Eps).
4.1.10 Days to 50% maturity
Analysis of variance concluded that Days to 50% maturity was highly significant
(P≤0.01) and the genotypes Bahawalpur-79, Meraj-08, Potohar-93, 010742, C-518, C-591,
AUP-5000, C-228, Sutleg-86 and 010724 showed maximum number of days to 50%
maturity while Raskoh showed minimum number of days to maturity.
Days to 50% maturity was found positively correlated to flag leaf area, peduncle length,
plant height, no of tillers per plant, days to 50% heading, awn length, spikelets per
spike, spike density, number of grains per spike, yield per plant, HI and total weight
per plant respectively. The negative correlation was found with spike length and 1000
grain weight (Table 4). Yield per plant and early maturity are important traits in
breeding programs. Iqbal et al., (2007) reported that yield per plant showed positive
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correlation with maturity, grain filling duration and harvest index. Khan et al., (2010)
reported that days to maturity revealed positive correlation to yield per plant.
4.1.11 Yield per plant
Yield per plant is the crucial trait and was found highly significant (P≤0.01). The
maximum yield per plant was observed in Uqab-2000 followed by Haider-2002, Sutlag-
86, Rawal-87, Wadanak-85, Barani-70, C-273, Margalla-99, Potohar-70 and Indus-79 and
the lowest yield per plant was observed in AS-2002.
Correlation analysis results confirmed that yield per plant was positively correlated to
plant height, spike length, peduncle length, number of tillers per plant, days to 50%
maturity, spikelets per spike, spike density, grains per spike, 1000 grain weight, harvest
index and total weight per plant respectively while yield per plant was found
negatively correlated to flag leaf area, awn length and days to 50% heading (table 4).
The results of the study are in accordance with previous reports. Kalimullah et al.,
(2012) reported that yield per plant is positively correlated with number of tillers per
plant, spike length, grains per spike, spike density and 1000 grain weight.
Over all morphological traits of the present study revealed that Bahawalpur-79 have
highest days to maturity, Barani-70 have highest no of tillers per plant, Marwat-01 have
highest spike length, C-591 have highest peduncle length, Margalla-99 have greatest
spikelets per spike, Zarghoon-79 have highest 1000 grain weight and C-273 have
highest harvest index. Khazaei et al., (2009), Mohammady et al., (2009) also reported that
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the varieties under study showed great variation on the base of morphological
characters.
Iftikhar et al., (2012) reported that peduncle length, spike length, grains per spike and
1000 grains weight showed positive correlation with yield per plant. Fida et al., (2011)
also reported that most of the morphological traits are positively correlated with each
other except harvest index. Khan et al., (2010) reported that Grain yield was positively
correlated with days to maturity, number of tillers per plant and number of grains per
spike while negatively correlated with plant height, spike length, peduncle length,
sheath length and 1000 grain weight. Rabnawaz et al., (2013) reported that number of
grains per plant was positively correlated with grains per spike and HI. The results of
the present study is matching with earlier reports that yield per plant is positively
correlated with spike length, peduncle length, No of tillers per plant, days to maturity,
spikelets per spike, spike density, grains per spike, 1000 grain weight, HI and biological
yield while negatively correlated with flag leaf area, days to 50% heading and awn
length.
The present research revealed that different morphological traits were repeated many
times in different genotypes. The trait repetition per genotype was ranged from 0 to 7.
Out of fifteen traits only one trait per genotype was recorded in Manther, Maxipak,
Bakhtawar-94, Abdaghar-97, Raskoh, Punjab-76, NIAB-83, Shalimar-88, Kaghan-93,
Suliman-96, Nowshera-96, Sindh-81, Pirsabak-2008, Punjab-96, Mumal-2002, Zamindar-
80, Iqbal-2000, LU-26, Chenab-96, Zarghoon-79, Punjab-88, Punjab-81, 010742, WL-711,
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SA-75, Marwat-01, Potohar-93, Lasani-08, Sussi, Meraj-08 and AUP-4008. Two traits
each per genotype was recorded in Lr-230, Wadanak-85, Haider-2002, Zarlashta-90,
Faisalabad-85, Saleem-2000, Chenab-70, Chakwal-86, Wadanak-98, Nori-70, ZA-77,
LYP-73, 010776, 010724, 010792, Faisalabad-83, C-228, RWP-94, AUP-5000, Barani-83,
Pirsabak-85, C-273, Dirk, Sandal and Potohar-90. Three morphological traits each per
genotype was found in Indus-79, Local white, Barani-70, pari-73, 010737, 010748, C-591,
potohar-70, Bahawalpur-79 and C-518. Four traits each per genotype was found in only
two genotypes which are Chenab-79 and Soghat-90. Five traits each per genotype was
noted in Uqab-2000 and Sutlag-86 while seven highest numbers of traits per genotype
was recorded in Margalla-99 and Rawal-87. These two genotypes are also best on the
base of yield per plant in top ten superior genotypes. Therefore, on the base of
morphological traits Margalla-99 and Rawal-87 could be cultivated for high grain yield
in irrigated areas of Pakistan.
4.2 EVALUATION OF PHYSIOLOGICAL TESTS
Drought stress could reduce the crop yield under rain fed areas. The crop response to
drought can be traced by various physiological tests. Therefore, physiological trait
selection is critical for yield improvement in wheat. Various physiological traits are
implementing in rain fed areas to increase yield in wheat.
4.2.1 Relative water content
Relative water content (RWC) is used as a good biochemical indicator for stress
intensity during drought conditions (Alizade, 2002). The rate of RWC is high in drought
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tolerant plants as compared to drought susceptible plant (Arjenaki et al., 2012). In some
plants the low rate of RWC under drought conditions is due to plant vigor (Liu et al.,
2002). Schonfeld et al. (1988) reported low rate of RWC in wheat growing under drought
stress conditions and high RWC rate in drought tolerant wheat.
In the present study, one hundred wheat genotypes were also evaluated for RWC in
both normal (RWCN) and stress (RWCS) conditions. The ANOVA result revealed
highly significant differences among all the genotypes at p≤0.001 level. The results
showed that out of top ten superior genotypes Margalla-99 recorded the highest RWC
(99%) in normal (RWCN) conditions followed by Wafaq-2008, Anmol-91, Mumal-2002,
C-518, Uqab-2000, Meraj-08, Nori-70, Lasani-08 and Punjab-81 while Zarghoon-79
revealed the lowest value (34%) in normal conditions. In stress conditions, NIAB-83
showed the highest value for RWC (93%) followed by Tandojam-83, Local white,
Rawal-87, Soghat-90, Potohar-93, Indus-79, Punjab-81, potohar-70 and Sindh-81 while
the lowest value was calculated in Chakwal-86 (7%). Therefore, our results are
consistent with that of (Arjenaki et al., 2012) who reported that plant having high yield
under drought stress should have high RWC. Among hundred genotypes, high yield
was recorded in Rawal-87 (8.7) having high RWCS (90%), Potohar-70 (8.2) having
RWCS (89%) and Indus-79 (7.8) having RWCS (88%). The genotypes Margalla-99 and
Uqab-2000 recorded highest yield per plant were showed highest RWC rates in normal
conditions.
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4.2.2 Water loss rate
Water loss rate (WLR) could be used as a best screening technique for drought stress in
breeding program (Teulat et al., 1997). The effect of WLR is significant and primarily
influenced by soil moisture (Lugojan and Ciulca, 2011). The present study confirmed
lowest WLRN in Iqbal-2000 (0.4) followed by 010792 (0.9), 010748 (0.9), ZA-77 (1),
Chenab-79 (1), 010724 (1.2), Chakwal-86 (1.3), Nowshera-96 (1.4), Shahkar-95 (1.4) and
Sonalika (1.5) while the highest WLRN was recorded in Manther (6.3). Similarly, the
lowest WLRS was found in Faisalabad-83 (0.2) followed by Chenab-79 (0.2), Pirsabak-
2008 (0.3), Barani-83 (0.3), NIAB-83 (0.3), 010792 (0.6), Nowshera-96 (0.7), Mumal-2002
(0.8), Manther (0.8) and Iqbal-2000 (0.8) while the highest value was recorded in
Pirsabak-85 (5.4). The results are accordingly with that of Lonbani and Arzani, (2011)
who observed low WLR in wheat genotypes under drought stress.
4.2.3 Water use efficiency
Drought stress mainly affects crops growth and production (Seghatoleslami et al., 2008).
The main goal of breeding programs is the selection of high yielding genotypes with
improved WUE (Richards et al., 2002). The higher WUE in drought conditions would
mean strong stomatal and mesophyll resistance in the given genotypes (Ahmad et al.,
2014) and cultivars having high WUE will tend to flower earlier (Zhang et al., 2009). In
the present study a highly significant differences found among the genotypes. The
maximum WUE was recorded in ten superior genotypes. The genotype NIAB-83
showed the highest WUE followed by C-273, 10742, Kiran, ZA-77, Punjab-76, AS-2002,
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Potohar-93, Zamindar-80 and Bakhtawar-94 while the lowest WUE was recorded in
Sonalika. The WUE will be high when the WLR is low and vice versa. Therefore, the
correlation analysis also confirmed the negative correlation between WUE and WLR.
The WUE was also found to be negatively correlated with TRL, RD, RFW and MRL.
Roots are very important for water and nutrients uptake both in drought stress and
irrigated conditions which ultimately affect WUE and grain yield (Atta et al., 2013).
Passioura, (1983) pointed out that drought resistance might be improved by decreasing
the size of the root system. Wheat WUE was negatively correlated with root system
growth in wheat evolution, and WUE decreased with the increase of root system
growth (Zhang et al., 2002). The present study is in accordance with the earlier reports
therefore, the strong root system will reduce the WUE and hence will reduce biomass
production. It is needed to improve the root system function rather than a strong root
growth for wheat survival in drought conditions.
4.3 EVALUATION OF ROOT TRAITS
To understand the performance of wheat crop under drought conditions, it is necessary
to have a sound knowledge about root traits. Root traits vary from species to species on
the base of water availability, growth, physiology and architecture (Corre-Hellou et al.,
2007). Root surface area and root length in wheat crop play an important role in water
uptake. Wheat crop has two types of root systems i.e seminal root system and Nodal
root system. Seminal root arise directly from the tip of main stem after seed germination
while the nodal roots arise from the sides (lateral) of the main stem. Root traits greatly
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influence the resource uptake and sustaining crop yield under drought stress conditions
(Sruthi et al., 2014). For maximum grain yield in wheat active and develop root system
is necessary (Munoz-Romero et al., 2010). In the present study destructive root sampling
method was applied.
4.3.1 Total root length
Total root length (TRL) is associated with drought tolerance in wheat because it marks
the spreading of roots in the soil and affects the resources uptake (Manschadi et al.,
2006). The genotype Pirsabak-85 ranked high on the base of TRL and R:S and
considered best for drought tolerance by extracting water stored in the deep soil layers.
The correlation analysis revealed that total root length was positively correlated to root
fresh weight, root dry weight, root shoot ratio, number of nodal roots, number of
seminal roots, water loss rate, relative water content and yield per plant while
negatively correlated to root diameter, root angle, root density, maximum root length
and water use efficiency. The present research showed that high the number of nodal
and seminal roots would result in high root fresh weight that would increase the water
absorption in rain fed areas and finally would enhance the yield per plant.
4.3.2 Root diameter
The high root diameter (RD) is associated with drought tolerance in wheat (Clark et al.,
2008). The genotype AS-2002 showed the highest RD and supported for drought stress
tolerance due to large xylem vessels with increased resource uptake and is well-
organized in searching deep soil layers to extract water. The correlation analysis
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showed that root diameter was positively correlated to number of nodal roots, number
of seminal roots, root angle, water loss rate and relative water content while negatively
correlated to root fresh weight, root dry weight, root shoot ratio, total root length, root
density, maximum root length, water use efficiency and yield per plant. Our results are
in accordance with the previous report of Louise et al., (2013), who reported that total
root length, maximum root length and root density increase or decrease extremely with
a small change in root diameter. Wasson et al., (2012) reported that decrease in root
diameter would increase crop yield under drought. Significant reduction in root
diameter (Munoz-Romero et al., 2010), total root length (Asseng et al., 1998) and root
density (Schweiger et al., 2009) under drought conditions were previously reported.
4.3.3 Root density
Root density (RDT) increases the efficiency of the root system, and is considered to be
the most important trait for uptake of phosphorus in wheat (Manske et al., 2000). The
genotype Soghat-90 ranked first on the base of RDT and is considered to be good for
phosphorus uptake. The correlation analysis confirmed that root density was positively
correlated to root fresh weight, root dry weight, root shoot ratio, number of seminal
roots, total root length, maximum root length water use efficiency while negatively
correlated to root diameter, number of nodal roots, root angle, water loss rate, relative
water content and yield per plant. Our results are in accordance with the previous
report (Atta et al., 2013), who reported that root density is positively correlated with
total root length, root diameter and water use efficiency.
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4.3.4 Maximum root length
The MRL evolved to capture deeper water from the soil under drought stress (Manske
and Vlek, 2002). The Abdaghar-97 genotype recorded the maximum root length (MRL)
to capture deep soil moisture in dry areas. The correlation analysis showed that
maximum root length was positively correlated to root fresh weight, root dry weight,
root shoot ratio, number of seminal roots, total root length, root density and water use
efficiency while negatively correlated to root diameter, number of nodal roots, root
angle, water loss rate, relative water content and yield per plant.
4.3.5 Number of seminal roots
The correlation analysis showed that number of seminal roots were positively
correlated to root fresh weight, root dry weight, root shoot ratio, root diameter, total
root length, water loss rate, relative water content and yield per plant while negatively
correlated to number of nodal roots, root angle and water use efficiency. Manschadi et
al. (2008) reported that number of seminal roots may result in better adaptation to
drought conditions in wheat. Ahmad et al., (2013) reported that number of seminal roots
was negatively correlated with water use efficiency. The strong root system will reduce
the WUE and hence will reduce biomass production. Therefore, it is needed to improve
the root system function rather than a strong root growth for wheat survival in drought
conditions. In the present study, the genotype Marwat-01 recorded the highest NSR and
is suggested to be good in more water uptake in rain fed areas.
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4.3.6 Root dry weight
The analysis of variance revealed that root dry weight was found to be highly
significant. The correlation analysis confirmed that root dry weight was positively
correlated to root fresh weight, shoot dry weight, root shoot ratio, number of seminal
roots, total root length, root density, maximum root length, water loss rate stress,
relative water content normal, water use efficiency and yield per plant while negatively
correlated to shoot fresh weight, root diameter, number of nodal roots, root angle, water
loss rate normal and relative water content stress. In the present study the RDW was
found to be positively correlated with SDW under drought conditions. The result is in
accordance with earlier reports that shoot dry weight might have contributed to the
increase of root dry weight (Serraj et al., 2004). Root dry weight (RDW) and root: shoot
ratio (R:S) were found positively correlated in drought tolerant rice (Champoux et al.,
1995). In the present study Pirsabak-85 ranked high for RDW and AS-2002 ranked first
on the basis of R:S. RDW is positively correlated with WLRN and RWCN while
negatively correlated with WLRS and RWCS. The decrease of WLR in stress conditions
might have resulted increased of RWC and WUE that contributed the increased RDW.
As a result, the surplus of photoassimilates increased the shoot growth and ultimately
the HI and yield.
4.3.7 Root fresh weight
The analysis of variance showed that root fresh weight was found to be highly
significant at (P≤0.01). The genotype AUP-5000 was showed the highest root fresh
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weight followed by Soghat-90, NIAB-83, Faisalabad-85, Rawal-87, Blue silver, C-273,
Lasani-08, AUP-4008 and sutlag-86 while the lowest root fresh weight was noted in Pak-
81. The correlation results revealed that root fresh weight was positively correlated to
root dry weight, shoot fresh weight, shoot dry weight, root shoot ratio, number of
seminal roots, root angle, total root length, root density and maximum root length while
negatively correlated with root diameter and number of nodal roots. Results of present
study supported the findings of earlier report. Khan et al., (2002) reported that root fresh
weight is positively correlated to shoot fresh weight, shoot dry weight, total root length
and root dry weight.
4.3.8 Root shoot ratio
ANOVA of root shoot ratio (R:S) was found highly significant. The maximum root
shoot ratio was found in Pirsabak-2008 as followed by AUP-5000, Janbaz, Soghat-90,
FPD-08, Lasani-08, Bahawalpur-79, Potohar-90, Wardak-85 and Mehran-89 respectively
while the lowest root shoot ratio was noted in Haider-2002. R:S was found positively
correlated to root fresh weight, root dry weight, number of seminal roots, root angle,
total root length, root density, maximum root length and yield per plant while showed
negative correlation with shoot fresh weight, shoot dry weight, root diameter and
number of nodal roots. Root shoot ratio depends on plant growth and development to
shift resources above and below ground (Louise et al., 2013). Change in root shoot ratio
would change plant size particularly in young plants (Muller et al., 2000). Our results
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confirmed that high would be the root shoot ratio high would be the resources
absorption in tissues, and finally higher would be the biomass production.
4.3.9 Number of nodal roots
Number of nodal roots (NNR) was found highly significant at P≥0.01 level in all
genotypes. The highest number of NNR was recorded in Meraj-08 followed by Iqbal-
2000, 010742, Lasani-08, Sariab-92, Pirsabak-2008, Faisalabad-83, GA-2002, Barani-70
and LYP-73 while lowest NNR was recorded in AUP-5000. The correlation analysis
revealed that NNR was positively correlated to shoot fresh weight, shoot dry weight,
root diameter, root angle, total root length, yield per plant and root density while
negatively correlated with root fresh weight, root dry weight, root shoot ratio, number
of seminal roots and maximum root length. Louise et al., (2013) reported that bulk mass
of roots would be increased with the increase of tillers. Herrera (2007) reported nitrogen
uptake is affected by length and number of nodal roots. Kuhlmann and Barraclough
(2007) reported that uptake of nutrients is 2-6 times more for nodal roots than seminal
roots. The results of the present study confirmed that Meraj-08 showed high number of
nodal roots and would be better for nitrogen and water uptake in rain fed areas.
4.3.10 Number of seminal roots
ANOVA result showed that number of seminal roots (NSR) was highly significant in
one hundred genotypes at P≥0.01 level. Marwat-01 showed maximum NSR followed by
AS-2002, Chenab-96, 010724, AUP-5000, MH-97, Kaghan-93, Nowhera-96, 010792 and C-
273 while lowest NSR was calculated in C-518. NSR was positively correlated with root
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fresh weight, shoot fresh weight, shoot dry weight, root shoot ratio, root diameter, root
angle, total root length and root density while negatively correlated with root dry
weight, number of nodal roots and maximum root length. Abdollahi et al., (2012) also
reported that number of seminal roots was negatively correlated with root fresh weight,
root dry weight and number of nodal roots.
Generally, the analysis of variance confirmed significant differences among the root
traits at P≥0.01 level. Genotypes NIAB-83 and Lasani-08 ranked high and Mehran-89
ranked low for most of the root traits. All the genotypes showed great variation on the
base of root traits. AUP-5000 showed the highest RFW, Soghat-90 the highest RDW,
Pirsabak-2008 the highest R:S, AS-2002 the highest RD, Meraj-08 the highest NNR,
Marwat-01 the highest NSR, MH-97 the maximum RA, Pirsabak-2008 the highest TRL,
Soghat-90 the maximum RDT and Abdaghar-97 the MRL.
The present research also concluded that geographical region has significant impact on
root traits. One hundred wheat genotypes were analyzed for different root traits,
collected from different geographical regions of Pakistan. All the root traits showed
great variation among all the genotypes. Our results are consistent with the earlier
report that the geographic regions from which wheat genotypes originated had
significant impacts on root morphology (Sruthi et al., 2014). Furthermore, all the
superior genotypes for various traits recorded in this study could be used further for
breeding programs to get the modern cultivars suitable for drought tolerant
geographical regions of Pakistan.
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4.4 ALIEN MATERIALS DETECTION USING FISH TECHINQUE
Physical localization of DNA sequences to chromosomal regions is extremely important
and may be applied to understand the evolutionary polymorphism within species.
Therefore FISH was carried out to see if the lines in this study contain wheat alien
recombinant chromosomes. We aimed to understand and compare the banding pattern
of the repetitive DNA probes in 15 different wheat land races. Though, successful
hybridization of the repetitive DNA probes was achieved in only four lines, still all
known sites were not hybridized. By and large the banding pattern could was
comparable to the standard karyotypes of hexaploid wheat (Mukai et al., 1993). The
reasons may include sub-optimum labelling of probes or its concentration as well as
few chromosomes was lost in the in situ washes (Figure 5b).
4.5 MARKER TRAIT ASSOCIATION
Sum of 102 molecular markers were used in the present study. Most of the markers
were showed high level of polymorphism. Total of 271 polymorphic alleles generated.
The alleles per locus was ranged from 1-3 and an average of 2.63 per locus. Polymorphic
information content (PIC) values of the markers was also calculated in the range of
0.03–0.59. Initially, in order to investigate the genetic diversity of the material, hundred
wheat genotypes were grouped into different clusters populations (Figure 8). However,
the association analysis also concluded that the hundred genotypes having different
genetic background were classified into thirteen distinct group‘s viz. G1, G2, G3, G4,
G5, G6, G7, G8, G9, G10, G11, G12 and G13. Population structure may lead to spurious
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association between marker and traits (Zhao et al., 2007). Therefore, a model based
approach was used for association mapping. Both the general linear model (GLM) and
mixed linear model (MLM) were applied.
A total of 12 QTLs (MTAs) were identified for eight root traits in both GLM and MLM.
All the MTAs were trait specific and located on seven chromosomes (2D, 5B, 2A, 2B, 7B,
6D and 5D).
4.5.1 Total root length MTAs
The present research revealed that GLM model confirmed MTA for TRL was found to
be located on chromosome 2D marked by Xgdm 5. The phenotypic variance detected
was 0.10 and LOD was 2.78. Ibrahim et al, (2012) reported that QTL for TRL was
confirmed on chromosome 2D. The results are accordance with Xiao-bo et al., (2008)
who reported that MTA for TRL was located on chromosome 2 at 3.4 cM.
4.5.2 Root fresh weight MTAs
The GLM model identified MTA associated with RFW, located on chromosome 5B. The
marker Xwmc 235 attributed to trace the QTL on specific chromosome for RFW. The
phenotypic variance (r2) was found as 0.10 and LOD was 3.56. In the previous report of
Ayman et al., (2013) confirmed that four QTLs are associated with RFW located on 2B,
5B, 6A, 6B chromosomes. Our results did not localized other QTLs due to lesser number
of markers have been used.
120
4.5.3 Root dry weight MTAs
PpD1 marker revealed marker trait association (MTA) for RDW in GLM model only.
The MTA was found to be located on chromosome 2A having r2 0.41 and LOD of 2.7.
These results were partly in agreement with results of Quarrie et al. (2006) who found
that 5 QTLs for RDW were grouped on chromosome 2A and 7A.
4.5.4 Maximum root length MTAs
Two MTAs were identified for MRL located on chromosome 2A and 5B. MTA of
chromosome 2A was marked by Xgwm 10 having LOD (2.68) and that of 5B was
attributed by Xwmc 149 having LOD of 2.86. Kadam et al. (2012) identified only one QTL
for maximum root length located on chromosome 4B and (Somers et al., 2004), reported,
that QTL identified for MRL located on chromosome 5 at 158.5 cM. Therefore, the MTA
identified on 2A chromosome in the present study was not reported before and
considered to be novel QTL for MRL.
4.5.5 Number of nodal roots MTAs
The MTA for NNR located on chromosome 2B. SSR marker Xwmc 175 recognized the
MTA for NNR on chromosome 2B. MTA for NNR was found at LOD 2.5, p value
0.00306 while the (r2) 0.17. Our results were accordance with result of Semagn et al.,
(2006) who reported the same QTL on chromosome 2B.
4.5.6 Root angle MTAs
Two MTA (QTLs) was found associated with RA in GLM model. The MTAs were found
to be located on chromosomes 7B and 6D. The MTA located on chromosome 7B
121
recognized by Xgwm 302 and that of 6D was identified by Xwmc 749. The results are
consistent with the results of (Roder et al., 1998) who reported that QTL for RA was
located on chromosome 7B at 86 cM and Christopher et al, (2013) reported four QTLs for
RA was located on chromosome 2A, 3D, 6A and 6D.
4.5.7 Root density MTAs
Two MTAs were identified for root density (RDT) in both GLM and MLM models
located on chromosomes 2B and 5B. The MTA for chromosome 2B attributed by Xwmc
175 and 5B by Xwmc 235 having LOD of 3.28 and 2.5. The results of the present study
are in accordance with the earlier reports. Semagn et al., (2006) reported that QTL for
RDT was located on chromosome 2B at 158.5 cM. Ramya et al., (2010) reported that QTL
for RDT was located on 5B at 47 cM.
4.5.8 Root diameter MTAs
Two MTAs were identified for RD, one each in GLM and MLM. Both MTAs was located
on chromosome 5B, attributed by Xwmc 233 having LOD 3.1 and 3.3. Our results were
consistent with earlier reports. Gupta et al., (2002) reported QTLs for RD located on
chromosome 5B at 4.5 cM.
122
CONCLUSION
The hundred bread wheat genotypes were evaluated for physiological tests,
phenological parameters, Fluorescent In situ hybridization (FISH) and molecular
analysis. Data of three years was recorded for morphological traits including FLA, SL,
PL, PH, NTP, DM, DH, AL, SPS, SD, NGS, 1000GW, YP, HI and TWP. The ANOVA test
showed significant differences among the genotypes.
Bahawalpur-79 has highest DM, Barani-70 has highest NTP, Marwat-01 has highest SL,
C-591 has highest PL, Margalla-99 has greatest SPS, Zarghoon-79 has highest 1000 GW
and C-273 have highest HI. So these genotypes could be used for further breeding
programs to improve wheat production under drought stress conditions of Pakistan.
The same genotypes were also evaluated for physiological tests including RWCN,
RWCS, WLRN, WLRS and WUE under both normal and drought stress conditions. The
ANOVA results concluded that highly significant differences were found among the
genotypes in both normal and drought stress. Out of top ten superior genotypes
Margalla-99 recorded the highest RWC in normal while NIAB-83 recorded the highest
RWC in drought stress conditions. Faisalabad-83 and Iqbal-2000 was ranked first on the
base of WLRN and WLRS while NIAB-83 was ranked first in WUE test. So these
genotypes may suggest for further cultivation in irrigated and rainfed areas of Pakistan.
In the present study thirteen root traits were evaluated for drought tolerance. All the
root traits showed significant differences among the genotypes. The analysis of
correlation confirmed that RDW, MRL, TRL, R:S, RD and NSR were positively
123
correlated with WLRS and RWCS and considered to be best root traits for drought
tolerance. Pirsabak-2008, AS-2002, Abdaghar-97, Marwat-01 and Soghat-90 were ranked
first on the base of best root traits and considered to be best for drought stress areas of
Pakistan.
All the genotypes were screened with 102 molecular SSR markers. The 32 markers were
belonging to A, 37 belong to B and 33 belong to D genomes. Most of the markers were
showed high level of polymorphism. Total of 271 polymorphic alleles generated. The
alleles per locus was ranged from 1-3 and an average of 2.63 per locus. Polymorphic
information content (PIC) values of the markers was also calculated in the range of
0.03–0.59. Initially, in order to investigate the genetic diversity of the material using
association mapping. The association analysis using STRUCTURE software concluded
that the hundred genotypes having different genetic background were classified into
thirteen distinct groups. Furthermore, the TASSAL software confirmed total 12
attributed MTAs in both GLM and MLM models. Out of 12 MTAs, nine MTAs were
identified in GLM and three were identified in MLM model. The genetic information
obtained in the present study in the form of MTAs/QTLs could be utilized for breeding
programs to improve drought stress tolerance.
Furthermore, the genome wide association mapping (GWAS) are strongly depend on
choice of material, population size and number of markers to be used. Large population
and large number of molecular markers are needed to investigate genetic diversity.
124
RECOMMENDATIONS
The genotype Bahawalpur-79 ranked first on the basis of days to maturity (DM), Barani-
70 showed highest NTP, Marwat-01 has highest SL, C-591 has highest PL, Margalla-99
has greatest SPS, Zarghoon-79 has highest 1000 GW and C-273 have highest HI and
Uqab-2000 showed optimum plant height. So these genotypes could be used for further
breeding programs to improve wheat production under drought stress conditions of
Pakistan.
NIAB-83 and Iqbal-2000 recorded as superior genotypes on the basis of physiological
tests and recommended for cultivation in rainfed areas of the country.
The correlation analysis of root traits with WLRS and RWCS showed that Pirsabak-
2008, AS-2002, Abdaghar-97, Marwat-01 and Soghat-90 were ranked first on the basis of
best root traits performance and considered to be best for drought stress areas of
Pakistan.
A total of 12 QTLs (MTAs) with one novel MTA were identified for eight root traits in
both GLM and MLM. All the MTAs were trait specific and located on seven
chromosomes (2D, 5B, 2A, 2B, 7B, 6D and 5D). Eleven QTLs/MTAs are already
reported but the QTL for MRL was not reported before and therefore, could be allow
the breeders to incorporate desirable alleles proficiently into wheat germplasm.
The agro-climatic conditions of specific regions might influence the evolution of root
traits in crop plants. Therefore, it is strongly recommended that those cultivars which
125
are suitable for specific agro-climatic conditions should be grown for better root system
and crop yield.
Crop breeding programs have largely ignored root traits, mainly because of the
difficulties associated with root recovery and evaluating root traits in situ. The root
traits greatly influence the crop yield and biomass. Therefore, more research would be
needed for root traits rather than shoot traits to improve crop yield for growing
population.
Limited information is available on genetic variability of root traits in wheat. Exploring
genetic variability of root traits could assist wheat improvement programs in
developing varieties with desired root traits for drought tolerance or target
environments
Landraces that were created through combination of natural selection and selection by
farmers have some valuable characters that can be utilized for improvement of new
cultivars. These cultivars show intraspecific genetic diversity than the modern one and
hence could be used as a gene pool for different valuable characters like drought, heat
and cold stress.
The Genome wide association mapping (GWAS) are strongly depends on choice of
material, population size and number of markers to be used. Large population and
126
large number of molecular markers are important parameters to investigate genetic
diversity.
There is general agreement that two SSR markers per chromosome arm are needed for a
good result. However, factors like length of the chromosome, diversity of the species,
diversity of the particular sample, and cost and availability of different marker systems
need consideration as well.
127
REFERENCES
Abdollahi, J.G., K.M. Hashemi, S.B. Mosavi, V. Feiziasl, J. Jafarzadeh and E. karimi.
2012. Effects of nitrogen application on dry land wheat roots and shoot. Green. J.
Agri. Sci. 2: 188-194
Adams, M.L., E. Lombi, F.J. Zhao and S.P McGrath. 2002. Evidence of low selenium
concentrations in UK bread-making wheat grain. J. Sci. Food and Agri. 82: 1160–
1165.
Agrama, H.A., G.C. Eizenga and W. Yan. 2007. Association mapping of yield and its
components in rice cultivars. Mol Breeding. 19: 341-356. DOI: DOI
10.1007/s11032-006-9066-6
Ahmad, I., Inamullah, H. Ahmad and I. Muhammad. 2014. Evaluation of water use
efficiency, biomass production and selected root traits of wheat (Triticum
aestivum L.) under drought conditions. Int. J. Sci. Eng Res. 5: 1079-1085
Ahmad, M., M.C. Ghulam and M. Iqbal. 2007. Wheat Productivity, Efficiency, and
Sustainability: A Stochastic Production Frontier Analysis. Pak. Dev. Rev. 41: 643–
663
Alizade, A. 2002. Soil, water and plants relationship. 3rd Edn., Emam Reza University
Press, Mashhad, Iran, ISBN: 964-6582-21-4
Ali, Z., A. Salam, F.M. Azhar, and I.A. Khan. 2007. Genotypic variation in salinity
tolerance among spring and winter wheat (Triticum aestivum L.) accessions. S.
Afr. J. Bot. 73: 70—75.
128
Ali, Z., A. Salam, F.M. Azhar, I.A. Khan, A.A. Khan, S. Bahadur, T. Mahmood, A.
Ahmad and R. Trethowan. 2012. The response of genetically distinct bread
wheat genotypes to salinity stress. Plant Breeding. 131: 707—715
Altman, A. 2003. From Plant tissue culture to biotechnology: Scientific Revolutions,
abiotic stress tolerance and forestry. In vitro Cell. Dev. Biot. Plant. 39: 75-84
Anikster, Y., J. Manisterski, D.L. Long and K.J. Leonard. 2005. Leaf rust and stem rust
resistance in Triticum dicoccoides populations in Israel. Plant Dis. 89: 55–62
Anonymous. 2005. Report of screening of advance lines against yellow and leaf rusts
under national Wheat diseases Screening Nursery (2004-05). Crop Diseases
Research Programme, Institute of Plant and Environmental Protection, National
Agricultural Research Centre, Pakistan Agricultural Research Council, Park
Road, Islamabad.
Anonymous. 2006. Agricultural Statistics of Pakistan. Ministry of Food, Agriculture
and Livestock, Islamabad, Pakistan
Anonymous. 2007. Agricultural Statistics of Pakistan. Ministry of Food, Agriculture and
Livestock, Government of Pakistan, Islamabad.
Anonymous. 2009. Agriculture: Economic Survey of Pakistan 2008-09. Government of
Pakistan. pp. 21.
Anonymous. 2009. Economic Survey, Economic Affairs Division, Govt. Pakistan.,
Islamabad.
129
Araus, J.L., M.P. Salfer, C. Royo and M.D. Serett. 2008. Breeding for yield potential and
stress adaptation in cereals. Crit. Rev. Plant Sci. 27: 377-412
Arjenaki, F.G., R. Jabbari and A. Morshedi. 2012. Evaluation of drought stress on
relative water content, chlorophyll content and mineral elements of wheat
(Triticum aestivum L.) varieties. Int. J. Agri. Crop. Sci. 4: 726-729
Ashfaq, M., A.S. Khan and Z. Ali. 2003. Association of morphological traits with grain
yield in wheat (triticum aestivum L.). Int .J. Agri. Bio. 4: 262–264
Asseng, S., J.T. Ritchie, A.J.M. Smucker and M.J. Robertson. 1998. Root growth and
water uptake during water deficit and recovering in wheat. Plant Soil. 201: 265-
273
Atta, B.M., M. Tariq and R.M. Trethowan. 2013. Relationship between root morphology
and grain yield of wheat in north-western NSW, Australia. AJCS 7: 2108-2115
Ayman, A.D., M. A.M. Atia, H.A. H. Ebtissam, A.H. Hashem and S.A. Sami. 2013. A
multidisciplinary approach for dissecting QTL controlling high yield and
drought tolerance-related traits in durum wheat. Int. J. Agri. Sci. Res.3: 99-116
Babu, V.R. and S. Kumar. 1975. Seed germination and early growth of wheat (Triticum
aestivum L.) cv. 1553 under the influence of salinity and plant growth hormones.
Ann. Arid Zone. 14: 221-228.
Baker, B., P. Zambryski, B. Staskawicz and S.P. Dinesh-Kumar. 1997. Signaling in Plant-
Microbe Interactions. Science. 276: 726-733
130
Balla, K., L. Karsai, S. Bencze, T. Kiss and O. Veisz. 2012. Study of yield components
under heat stress conditions in wheat. Tagung der Vereinigung der Pflanzenzüchter
und Saatgutkaufleute Österreichs. 99–101
Bashir, M. 1988. Field crop diseases. CDRI, NARC, PARC, Islamabad.
Beltrano, J. and G.R. Marta. 2008. Improved tolerance of wheat plants (Triticum estivum
L.) to drought stress and rewatering by the arbuscular mycorrhizal fungus
Glomus claroideum: Effect on growth and cell membrane stability. Braz. J. Plant
Physiol. 20: 29-37
Bennett, D., A. Izanloo, M. Reynolds, H. Kuchel, P. Langridge and T. Schnurbusch.
2012. Genetic dissection of grain yield and physical grain quality in bread wheat
(Triticum aestivum L.) under water-limited environments. Theor. Appl. Gene. 125:
255–271
Bhatti, I.M. and A.H. Soomro. 1996. Agricultural inputs and field crop production in
Sindh. Agricultural Research Sindh, Hyderabad
Bhatti, I.M. and M.M. Jiskani. 1996. Modern Agricultural Guide. Agricultural Research
Sindh, Hyderabad.
Blake, N.K., B.R. Lehfeldt. M. Lavin and L.E. Talbert. 1999. Phylogenetic reconstruction
based on low copy DNA sequence data in an allopolyploid: The B genome of
wheat. Genome 42: 351–360.
131
Bradbury, P.J., Z. Zhang, D.E. Kroon, T.M. Casstevens, Y. Ramdoss and E.S. Buckler.
2007. TASSEL: software for association mapping of complex traits in diverse
samples. Bioinformatics. 23: 2633–2635
Breseghello, F and M.E. Sorrells. 2006b. Association mapping of kernel size and milling
quality in wheat (Triticum aestivum L.) cultivars. Genetics. 172: 1165–1177
Briggle, L.W. and B.C. Curtis. 1987. Wheat worldwide. In: Heyne, E.G. (ed). Wheat and
wheat improvement. 2nd Ed.American Society of Agronomy Inc. Madison,
Wisconsin, USA. 4-31
Brisson, N., P. Gate, D. Gouache, G. Charmet, F. Oury and F. Huard. 2010. Why are
wheat yields stagnating in Europe? A comprehensive data analysis for France.
Field Crops Res. 119: 201–212
Budak. H., M. Kantar, and K.Y. Kurtoglu. 2013. Drought Tolerance in Modern and Wild
Wheat. Sci. World. J. 4: 1-16
Cao, A., L. Xing, X. Wang, X. Yang and W. Wang. 2011. Serine/threonine kinase gene
Stpk-V, a key member of powdery mildew resistance gene Pm21, confers
powdery mildew resistance in wheat. Proc Natl Acad Sci USA 108: 7727-7732.
doi:10.1073/pnas.1016981108. PubMed: 21508323
Carter, J.R and R.P. Patterson. 1985. Use of relative water content as a selection tool for
drought tolerance in soybean. Fide Agron abstr 77th Annu Meeting, p 77
Castillo, A. and J.S. Heslop-Harrison. 1995. Physical mapping of 5S and 18S-25S rDNA
and repetitive DNA sequences in Aegilops umbellulata. Genome. 38: 91-96
132
Cattivelli, L., R. Fulvia, W.B. Franz, M. Elisabetta, M.M. Anna, F. Enrico, M. Caterina, T.
Alessandro and S.A. Michele. 2008. Drought tolerance improvement in crop
plants: An integrated view from breeding to genomics. Field Crops Research. 105:
1–14
Chakraborty, S. and A.C. Newton. 2011. Climate change, plant diseases and food
security: an overview. Plant Pathology. 60: 2–14
Champoux, M.C., G. Wang, S. Sarkarung, D.J. Mackill and J.C. O‘Toole. 1995. Locating
genes associated with root morphology and drought avoidance in rice via
linkage to molecular markers. Theor Appl Genet. 90: 969–981
Chao, S.M., J. Dubcovsky, J. Dvorak, M.C. Luo, S.P. Baenziger, R. Matnyazov, D.R.
Clark, L.E. Talbert, J.A. Anderson, S. Dreisigacker, K. Glover, J.L. Chen, K.
Campbell, P.L. Bruckner, J.C. Rudd, S. Haley, B.F. Carver, S. Perry, M.E. Sorrells
and E.D. Akhunov. 2010. Population- and genome-specific patterns of linkage
disequilibrium and SNP 180 variation in spring and winter wheat (Triticum
aestivum L.). Bmc Genomics 11. DOI: Artn 727 Doi 10.1186/1471-2164-11-727
Chao, S.M., W.J. Zhang, J. Dubcovsky and M. Sorrells. 2007. Evaluation of genetic
diversity and genome-wide linkage disequilibrium among US wheat (Triticum
aestivum L.) germplasm representing different market classes. Crop Science. 47:
1018-1030. DOI: DOI 10.2135/cropsci2006.06.0434
Chen, X. 2005. Epidemiology and control of stripe rust Puccinia striiformis f. sp. tritici on
wheat. Canad. J. Plant Path. 27: 314–337
133
Chester, K.S. 1946. The Nature and Prevention of the Cereal Rusts as Exemplified in the
Leaf Rust of Wheat. Waltham, MA: Chronica Botanica. 3: 5-9
Christopher, J., M. Christopher, R. Jennings, S. Jones, S. Fletcher, A. Borrell, A.M.
Manschadi, D. Jordan, E. Mace and G. Hammer. 2013. QTL for root angle and
number in a population developed from bread wheats (Triticum aestivum) with
contrasting adaptation to water-limited environments. Theor. Appl. Genet. 126:
1563–1574. DOI 10.1007/s00122-013-2074-0
Clark, L.J., A.H. Price, K.A. Steele and R.R. Whalley. 2008. Evidence from near-isogenic
lines that root penetration increases with root diameter and bending stiffness in
wheat. Funct Plant Biol. 35: 1163–1171
Clark, R.B and R.R. Duncan. 1992: Selection of plants to tolerate soil salinity, acidity,
and mineral deficiencies. Inter. Crop Sci. 1: 371–379
Comadran, J., W.T.B. Thomas. F.A.V. Eeuwijk, S. Ceccarelli. S. Grando, A.M. Stanca, N.
Pecchioni, T. Akar, A. Al-Yassin, A. Benbelkacem, H. Ouabbou, J. Bort, I.
Romagosa, C.A. Hackett and J.R. Russell. 2009. Patterns of genetic diversity and
linkage disequilibrium in a highly structured Hordeum vulgare association-
mapping population for the Mediterranean basin. Theor. Appl. Genet. 119: 175-
187. DOI: DOI 10.1007/s00122-009-1027-0
Corre-Hellou, G., N. Brisson, M. Launay, J. Fustec and Y. Crozat. 2007. Effect of root
depth penetration on soil nitrogen competitive interactions and dry matter
134
production in pea-barley intercrops given different oil nitrogen supplies. Field
Crops Res. 103: 76–85
Courtois, B., N. Ahmadi, F. Khowaja, A.H. Price, J.F. Rami and J. Frouin. 2009. Rice root
genetic architecture: meta-analysis from a drought QTL database. Rice 2: 115–128.
doi:10.1007/s12284-009-9028-9
Cuadrado, A., C. Ceoloni and N. Jouve. 1995a. Variation in highly repetitive DNA
composition of heterochromatin in rye studied by fluorescence in situ
hybridization. Genome. 38: 1061-1069
Cuadrado, A., F. Vitellozzi, N. Jouve and C. Ceoloni. 1997. Fluorescence in situ
hybridization with multiple repeated DNA probes applied to the analysis of
wheat-rye chromosome pairing. Theor. Appl. Genet. 94: 347-355
Cui, F., L. Jun, D. Anming, Z. Chunhua, W. Lin, W. Xiuqin, L. Sishen, B. Yinguang, L.
Xingfeng, F. Deshun, K. Lingrang and W. Honggang. 2011. Conditional QTL
mapping for plant height with respect to the length of the spike and internode in
two mapping populations of wheat. Theor. Appl. Genet. 122: 1517–1536. DOI
10.1007/s00122-011-1551-6
Curtis, B.C., S. Rajaram and H. GomezMacpherson. 2002. Bread Wheat Improvement
and Production. Rome, Italy: FAO: Plant Production and Protection Series.
Demirevska, K., L. Simova-Stoilova, V. Vassileva, I. Vaseva, B. Grigorova and U. Feller.
2008. Drought-induced leaf protein alteration in sensitive and tolerant wheat
135
varieties. Inst. of Plant Sci., Uni. of Bern, Switzerland. Gen. Appl. Plant Physiol., 34:
79-102
Dubcovsky, J and J. Dvorak. 2007. Genome plasticity: a key factor in the success of
polyploidy wheat under domestication. Science. 316: 1862–1866
Eckardt, N.A. 2010. Evolution of domesticated bread wheat. Plant cell. 22: 993-996
El-Hendawy, S.E., Y. Hu, G.M. Yakout, A.M. Awad, S.E. Hafiz and U. Schmidhalter.
2005. Evaluating salt tolerance of wheat genotypes using multiple parameters.
Eur. J. Agron. 22: 243–253
Ersoz, E., J. Yu and E. Buckler. 2009. Applications of Linkage Disequilibrium and
Association Mapping in Maize. A.L. Kriz, B.A. Larkins (eds.), Molecular Genetic
Approaches to Maize Improvement 173. Biotech. Agri. Forestry. 63: 173-195
Eslam, B.P. 2011. Evaluation of physiological indices for improving water deficit
tolerance in spring safflower. J. Agr. Sci. Tech. 13: 327-338
Evanno, G., S. Regnaut and J. Goudet. 2005. Detecting the number of clusters of
individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14:
2611-2620
Falush. D., M. Stephens and J.K. Pritchard. 2003. Inference of population structure using
multilocus genotype data: linked loci and correlated allele frequencies. Genetics,
164: 1567-1587
136
FAO (Food and Agriculture Organization of the United Nation). 1998. Food Outlook.
Global information and early warning system.www.fao.org /docrep/004/
w8387e07 .htm
Faris, J.D., S.S. Xu, X. Cai, T.L. Friesen and Y. Jin. 2008. Molecular and cytogenetic
characterization of a durum wheat–Aegilops speltoides chromosome translocation
conferring resistance to stem rust. Chromosome Res. 16: 1097-1105.
doi:10.1007/s10577-008-1261-3
FAS (Food and Agriculture Service) of USDA. 2005. World crop production summary.
http://www.fas.usda.gov/wap/circular/2005/05-08/WldSum.pdf
Feldman. M and A.A. Levy. 2005. Allopolyploidy Y a shaping force in the evolution of
wheat genomes. Cytogenet. Genome. Res. 109: 250-258
Feldman, M and M.E. Kidlev. 2007. Domestication of emmer wheat and evolution of
free threshing tetraploid wheat. Isreal. J. plant sci. 55: 207-212
Feldman, M and E.R. Sears. 1981. The wild gene resources of wheat. Sci Am. 244: 102–
112
Fida, M., A. Ijaz, U.K. Naqib, M. Khurram, N. Aysha, S. Salma and A. Khalid. 2011.
Comparative study of morphological traits in wheat and triticale. Pak. J. Bot. 43:
165-170
Fitter, A. 2002. Characteristics and functions of root systems, in the hidden half, 3rd
Edn, eds Y.Waisel, A. Eshel, and U.K afkafi (NewYork, NY:Marcel Dekker, Inc.),
15–32
137
Flowers, T.J and M.A Hajiabagheri. 2001. Salinity tolerance in Hordeum vulgare: ion
concentration in root cells to cultivars differing in salt tolerance. Plant Soil. 231: 1-
9
Forster, B.P., R.P. Ellis, J. Moir, V. Talame, M.C. Sanguineti, R. Tuberosa, D. This, B.
Teulat-Merah, I. Ahmed, S.A.E. Mariy, H. Bahri, M. El-Ouahabi, N. Zoumarou-
Wallis, M. El-Fellah and M. Ben-Salem. 2004. Genotype and phenotype
associations with drought tolerance in barley tested in North Africa. Ann. Appl.
Biol. 144: 157–168
Fowler, S., D. Cook and M.F. Thomashow. 2005. The CBF cold-response pathway. In:
Jenks MA, Hasegawa PM (eds) Plant abiotic stress. Blackwell, Oxford, UK
Foulkes, M.J., G.A. Slafer, W.J. Davies, P.M. Berry, R. Sylvester-Bradley, P. Martre, D.F.
Calderini, S. Giffiths and M.P. Reynolds. 2011. Raising yield potential of wheat.
(III) Optimizing partitioning to grain while maintaining lodging resistance. J.
Exp. Bot. 62: 469–486
Gale, M.D. and T.E. Miller. 1987. The introduction of alien genetic variation into wheat.
In: EG,H. Lupton (Ed.) Wheat Breeding. Its Scientific Basis, pp. 173-210.
Chapman and Hall, London
Gaut, B.S. 2002. Evolutionary dynamics of grass genomes. New Phytol. 154: 15–28
Gibson, L.R and G.M. Paulsen. 1999. Yield components of wheat grown under high
temperature stress during reproductive growth. Crop Sci. 39: 1841-1846
138
Gill, B.S., B. Friebe and F.F. White. 2011. Alien introgressions represent a rich source of
genes for crop improvement. Proc. Nat. Acad. Sci. USA. 108: 7657-7658
Gonzalez-Martinez, S.C., D. Huber, E. Ersoz, J.M. Davis and D.B. Neale. 2008.
Association genetics in Pinus taeda L. II. Carbon isotope discrimination. Heredity.
101: 19-26. DOI: Doi 10.1038/Hdy.2008.21
Grama, A and Z.K. Gerechter-Amitai. 1974. Inheritance of resistance to stripe rust
(Puccinia striiformis) in crosses between wild emmer (Triticum dicoccoides) and
cultivated tetraploid and hexaploid wheat. II. Triticum aestivum. Euphytica 23:
393–398
Gupta, P.K and R.K. Varshney. 2004. Cereal Genomics: An Overview. In Cereal
Genomics; Gupta, P.K., Varshney, R.K., Eds.; Kluwer Academic Press: Dordrecht,
The Netherlands, p. 639
Gupta, P.K., S. Rustgi and P.L. Kulwal. 2005. Linkage disequilibrium and association
studies in higher plants: Present status and future prospects. Plant Mol. Bio. 57:
461-485. DOI: DOI 10.1007/s11103-005-0257-z
Gustafson, P.O., O. Raskina, X. Ma and E. Nevo. 2009. Wheat evolution, domestication
and improvement. Wheat: Science and Trade, ed Carver BF (Wiley Blackwell,
Danvers, MA).Pp 5–30
Hafeez, A.S., M.H.N. Tahir and M.T. Hussain. 2003. Physiogenetic aspect of drought
tolerance in canola (Brassica napus). Int. J. Agri. Biol. 5: 611-614
139
Hafiz, M.A., M.S. Iqbal, M. Saeed, A. Yar, A. Ali, K.A. Sahi and M.A. Nadeen. 2004.
Drought tolerance studies of wheat genotypes. Pak. J. Bio. Sci. 7: 90-92
Hale, I., X. Zhang, D. Fu and J. Dubcovsky. 2013. Registration of wheat lines carrying
the partial stripe rust resistance gene Yr36 without the Gpc-B1 allele for high
grain protein content. American Soc. Agron. 7: 108-112
Hao, M., J. Luo, M. Yang, L. Zhang, Z. Yan, Z. Yuan, Y. Zheng, H. Zhang and D. Liu.
2011. Comparison of homoeologous chromosome pairing between hybrids of
wheat genotypes Chinese spring ph1b and Kaixian-luohanmai with rye.
Genome. 54: 959-964
Harper, J., I. Armstead, A. Thomas, C. James, D. Gasior, M. Bisaga, L. Roberts, I. King
and J. King. 2011. Alien introgression in the grasses Lolium perenne (perennial
ryegrass) and Festuca pratensis (meadow fescue): the development of seven
monosomic substitution lines and their molecular and cytological
characterization. Annals of Botany. 1-9
Hasterok, R., G. Jenkins, T. Langdon and R.N. Jones. 2002a. The nature and destiny of
translocated B- chromosome- specific satellite DNA of rye. Chromosome Res. 10:
83–86
Hayashi, K., N. Hashimoto, M. Daigen and I. Ashikawa. 2004. Development of PCR-
based SNP markers for rice blast resistance genes at the Piz locus. Theor. Appl.
Genet. 108: 1212–1220
140
Henry, A. 2012. IRRI‘s drought stress research in rice with emphasis on roots:
accomplishments over the last 50 years. Plant Root. 7: 5–19
Herrera, J.M., P. Stamp and M. Liedgens. 2007. Intramural variability in root grown of
spring wheat at low and high nitrogen supply. Europ. J. Agron. 26: 317-326
Heslop-Harrison, J.S.P. 2000. Comparative genome organization in plants: from
sequence and markers to chromatin and chromosomes. Plant cell: 12: 617-635
Heslop-Harrison, J.S.P and T. Schwarzacher. 2011a. Organisation of the plant genome in
chromosomes. Plant Journal. 66: 18-33
Heslop-Harrison, J.S and T. Schwarzacher. 2011b. Genetics and genomics of crop
domestication. In: plant biotechnology and agriculture: prospects for the 21st
century. Pp.01-24
Hirschorn, J.N and M.J. Daly. 2005. Genome-wide association studies for common
diseases and complex traits. Nat. Rev. Genet. 6: 95–108
Hollington, P.A. 2000. Technological breakthroughs in screening/breeding wheat
varieties for salt tolerance. In: S. K. Gupta, S. K.Sharma, and N. K. Tyagi, (eds.)
Salinity management in agriculture. 273—289. Proceedings of the National
Conference, Central Soil Salinity Research Institute, Karnal, India
Hovmøller, M.S., S. Walter and A.F. Justesen. 2010. Escalating threat of wheat rusts.
Science. 329: 369–369
Huang, J. and R.E. Redmann. 1995. Salt tolerance of Hordeum and Brassica species
during germination and early seedling growth. Can. J. Plant. Sci. 75: 815-819
141
Huang, S.X., A. Sirikhachornkit, J.D. Faris, X.J. Su, B.S. Gill, R. Haselkorn and P.
Gornicki. 2002. Phylogenetic analysis of the acetyl-CoA carboxylase and 3-
phosphoglycerate kinase loci in wheat and other grasses. Plant Mol. Biol. 48: 805–
820
Huang, X.Q., S.L.K. Hsam, F.J. Zeller, G. Wenzel and V. Mohler. 2000. Molecular
mapping of the wheat powdery mildew resistance gene Pm24 and marker
validation for molecular breeding. Theor. Appl. Genet. 101: 407–414
Hutchinson, J., T.E. Miller and S.M. Reader. 1983. C-banding at meiosis as a means of
assessing chromosome affinities in the Triticeae. Can. J. Genet. Cytol. 25: 319-323
Ibrahim, S.E., A. Schubert, K. Pillen and J. Leon. 2012. QTL analysis of drought tolerance
for seedling root morphological traits in an advanced backcross population of
spring wheat. Int. J. Agri Sci. 2: 619-629
Iftikhar, R., K. Ihsan, K. Muhammad, A.A. Muhammad and Smiullah. 2012. Study of
morphological traits affecting grain yield in wheat (Triticum aestivum L.) Under
Field Stress Condition. Middle-East J. of Sci. Res.11: 19-23
Ijaz, S. and I.A. Khan. 2009. Molecular characterization of wheat germplasm using
microsatellite markers. Genet. Mol. Res. 8: 809-815
Imtiaz, H., M. Burhanuddin and K.J.B. Mohommad. 2010. Evaluation of Physiochemical
properties of Wheat and Mungbean from Bangladesh. Int. J. Food Safety. 12: 104-
108
142
Inoue, T., S. Inanaga, Y. Sugimoto, P. An and A.E. Eneji. 2004. Effect of drought on ear
and flag leaf photosynthesis of two wheat cultivars differing in drought
resistance. Photosynthetica. 42: 559–565
Iqbal, M., A. Navabi, D.F. Salmon, R.C. Yang, and D. Spaner. 2007. Simultaneous
selection for early maturity, increased grain yield and elevated grain protein
content in spring wheat. Plant Breeding. 126: 244–250
Ismail, A.M. 2003. Effect of salinity on physiological responses of selected lines/variety
of wheat. Acta Agron. 51: 1-9
Izanloo, A., A.G. Condon, P. Langridge, M. Tester and T. Schnurbusch. 2008. Different
mechanisms of adaptation to cyclic water stress in two South Australian bread
wheat cultivars. J. Exp. Bot. 59: 3327-3346
Jagshoran, R.K. Sharma and S.C. Tripathi. 2004. New varieties and production. The
Hindu, Survey of Indian Agriculture. 33-35
Jarne, P and P.J.L. Lagoda. 1996. Microsatellites, from molecules to populations and
back. Trends Ecol. Evol. 11: 424–429.
Jauhar, P.P. 2006. Modern biotechnology as an integral supplement to conventional
plant breeding: the prospects and challenges. Crop Sci 46: 1841-1859.
doi:10.2135/crop sci 2005.07-0223
Ji, X.M., B. Shiran, J.L. Wan, D.C. Lewis, C.L.D. Jenkins, A.G. Condon, R.A. Richards
and R. Dolferus. 2010. Importance of pre-anthesis anther sink strength for
143
maintenance of grain number during reproductive stage water stress in wheat.
Plant Cell and Environ. 33: 926-942. DOI: DOI 10.1111/j.1365-3040.2010.02130.x
Jia, J., R. Zhou, P. Li, M. Zhao and Y. Dong. 2002. Identifying the alien chromosomes in
wheat–Leymus multicaulis derivatives using GISH and RFLP techniques.
Euphytica. 127: 201–207
Jones, C.A., J.S. Jacobesen and J.M. Wraith. 2003. The effects of P fertilization on drought
tolerance of Malt Barley. Western Nutrient Management Conference. Salt Lake
City, UT, 5: 88-93
Juchimiuk, J., B. Hering and J. Maluszynska. 2007. Multicolour FISH in an analysis of
chromosome aberrations induced by N-nitroso-N-methylurea and maleic
hydrazide in barley cells. J. Appl. Genet. 48: 99–106
Kadam, S., K. Singh, S. Shukla, S. Goel, P. Vikram, V. Pawar, K. Gaikwad, R. Khanna-
Chopra and N. Singh. 2012. Genomic associations for drought tolerance on the
short arm of wheat chromosome 4B. Funct Integr Genomics. 12: 447-64. DOI:
10.1007/s10142-012-0276-1
Kalimullah., S.J. Khan, M. Irfaq and H.U. Rahman. 2012. Gentetic variability, correlation
and diversity studies in bread wheat (triticum aestivum L.) germplasm. J. Ani and
Pl Sci. 22: 330-333
Kang, H., Y. Wang, G. Fedak, W. Cao and H. Zhang. 2011. Introgression of chromosome
3Ns from Psathyrostachys huashanica into wheat specifying resistance to stripe
rust. PLOS ONE. 6: e21802. doi:10.1371/journal.pone.0021802. PubMed: 21760909
144
Khakwani, A.A., M.D. Dennett and M. Munir. 2011. Drought tolerance screening of
wheat varieties by inducing water stress conditions. Songklanakarin J. Sci.
Technol. 33: 135-142
Khan, A.J., F. Azam and A. Ali. 2010. Relationship of morphological traits and grain
yield in recombinant inbred wheat lines grown under drought conditions. Pak. J. Bot. 42:
259-267
Khan, A.L., M. Hamayun, S.A. Khan, Z.K. Shinwari, M. Kamaran, Sang-Mo Kang, Jong-
Guk Kim and In-Jung Lee. 2011. Pure culture of Metarhizium anisopliae LHL07
reporgrams soybean to higher growth and mitigates salt stress. World J. Microb.
Biotech. 28: 1483-94
Khan, I.A., J.D. Procunier, D.G. Humphreys, G. Tranquilli, A.R. Schlatter, S. Marcucci-
Poltri, R. Frohberg and J. Dubcovsky. 2000. Development of PCR-based markers
for a high grain protein content gene from Triticum turgidum ssp. Dicoccoides
transferred to bread wheat. Crop Sci. 40: 518–524
Khan, M.A. 1987. Wheat variety development and longevity of rust resistance.
Government of the Punjab, Agriculture Department, Lahore. pp. 197
Khan, M.I., A.J. Khan, G.S.S. Khattak and F. Subhan. 2014. Genetic effects in controlling
grain filling duration in wheat crosses. J. Ani. Plant Sci. 24: 803-813
Khan, M.Q., S. Anwar and M.I. Khan. 2002. Genetic variability for seedling Traits in
wheat (Triticum aestivum L.) under moisture stress conditions. Asian J. Plant Sci.
1: 588-590
145
Khanzada, B., M. A. Yasin, S.A. Ahmed, S. M. Alam, M.U. Shirazi and R. Ansari. 2001.
Water relations in different Guar (Cyamopsis tetragonoloba L. Taub) genotypes
under water stress. Pak. J. Bot. 33: 279-287
Khattak, I.S., G. Hassan, I. Ahmad and M. Hassan. 2001. Evaluation of some elite
breeding lines of wheat under the rainfed conditions of Kohat. Sar. J. Agric. 17:
564-569
Khazaei, H., S. Mohammady, M. Zaharieva, P. Monneveux. 2009 carbon isotope
discrimination and water use efficiency in Iranian diploid, tetraploid and
hexaploidwheats grown under well-watered conditions. Genet Res crop Evol.
56:104–114
Kirk, P.M., P.F. Cannon, J.V. David and J.A. Stalpers. 2001. Ainsworth and Bisbys
Dictionary of the Fungi, ninth edition, pp. 569, 610, 624. Wallingford, UK: CAB
International
Kirkegaard, J.A, J.M. Lilley, G.N. Howe and J.M. Graham. 2007. Impact of subsoil water
use on wheat yield. Aust. J. Agric. Res. 58: 303–315
Kobayashi, F., M. Ishibashi and S. Takumi. 2008. Transcriptional activation of Cor/Lea
genes and increase in abiotic stress tolerance through expression of a wheat
DREB2 homolog in transgenic tobacco. Transgenic Res. 17: 755–767
doi:10.1007/s11248-007-9158-z. PMID:18034365
146
Kotati, B.L and M.C. Henard. 2001. France oilseeds and products. New EU directive
may boost French oilseed production. Report No.1083. United States Department
of Agriculture
Kuchel, H., K. Williams, P. Langridge, H.A. Eagles and S.P. Jefferies. 2007. Genetic
dissection of grain yield in bread wheat. II. QTL-by-environment interaction.
Theor. Appl. Genet. 115: 1015-1027
Kuhlmann, H. and P.B. Barraclough. 2007. Comparison between the seminal and nodal
root systems of winter wheat in their activity for N and K uptake. Z.
Pflanzenernaehr. Bodenk. 150: 24–30. doi: 10.1002/jpln.19871500106
Kumar, P., R.K. Yadava, B. Gollen, S. Kumar, R.K. Verma and S. Yadav. 2011.
Nutritional Contents and Medicinal Properties of Wheat: A Review. Life Sci. Med.
Res. 22: 1-10
Kumar, A and M. Sharma. 2011. Wheat genome phylogeny and improvement. AJCS. 5:
1120-1126
Kurt, J., Leonard and L. J. Szabo. 2005. Stem rust of small grains and grasses caused by
Puccinia graminis. Mol. Plant Path. 6: 99–111
Lanceras, J.C., G. Pantuwan, B. Jongdee and T. Toojinda. 2004. Quantitative trait loci
associated with drought tolerance at reproductive stage in rice. Plant Physiol. 135:
384–399
147
Laszlo, 2009. Possibilities and limits of breeding wheat (Triticum aestivum L.) for
drought tolerance. Ph.D thesis. PhD School of Plant Sciences. Istitute of Genetics
and Biotechnology
Li, D and K.S. Yap. 2011. Climate Change and Its Impact on Food and Nutrition
Security and Food Safety in China. World Rev Nutr diet. 102: 175-182
Li, X.H., A.L. Wang, Y.H. Xiao, Y.M. Yan, Z.H. He, R. Appels, W.J. Ma, S.L.K. Hsam and
F.J. Zeller. 2007. Cloning and characterization of a novel low molecular weight
glutenin subunit gene at the Glu-A3 locus from wild emmer wheat (Triticum
turgidum L. var. dicoccoides). Euphytica. 159: 181–190
Litt, M and J.A. Luty. 1989. A hypervariable microsatellite revealed by in vitro
amplication of a dinucleotide repeat within the cardiac muscle actin gene. Am. J.
Hum. Genet. 44: 397–401
Liu, L., L. Wang, J. Yao and Y. Zheng. 2010b. Association mapping of six agronomic
traits on chromosome 4A of wheat (Triticum aestivum L.). Mol. Plant Breed. 1: 1-10
Liu, W., M. Rouse, B. Friebe, Y. Jin, B. Gill and M.O. Pumphrey. 2011. Discovery and
molecular mapping of a new gene conferring resistance to stem rust, Sr53,
derived from Aegilops geniculata and characterization of spontaneous
translocation stocks with reduced alien chromatin. Chromosome Res. 19: 669–682
Lonbani M and A. Arzani. 2011. Morpho-physiological traits associated with terminal
drought stress tolerance in triticale and wheat. Agronomy Research. 9: 315–329
148
Lugojan, C and S. Ciulca. 2011. Analysis of excised leaves water loss in winter wheat. J.
Horti. Forest. Biotech. 15: 178-182
Louise, H.C., R.B. Steven, V.C. VonMark, F.B. Patrick and A.D. David. 2013. Root traits
contributing to plant productivity under drought. Frontiers in science. 4: 1-16
Luan, Y., X. Wang, W. Liu, C. Li and J. Zhang. 2010. Production and identification of
wheat-Agropyron cristatum 6P translocation lines. Planta. 232: 501-510. doi:
10.1007/s00425-010-1187-9
Maccaferri, M., M.C. Sanguineti, A. Demontis, A. El-Ahmed, L.G. del Moral, F. Maalouf,
M. Nachit, N. Nserallah, H. Ouabbou, S.R.C. Royo, C. Royos, D. Villegas and R.
Tuberosa. 2011. Association mapping in durum wheat grown across a broad
range of water regimes. J. Exp. Bot. 62: 409-438
Maluszynska, J., J. Juchimiuk and E. Wolny. 2003. Chromosomal aberrations in Crepis
capillaris cells detected by FISH. Folia Histochemet Cyto bio. l41: 101–104
Manole, V and B. Bazga. 2011. The impact of climate changes on agriculture and food
security. Quality- Access to Success. 38: 93-99
Manschadi, A.M., G.L. Hammer, J.T. Christopher and P. deVoil. 2008. Genotypic
variation in seedling root architectural traits and implications for drought
adaptation in wheat (Triticum aestivum L.). Plant Soil. 303: 115-129
Manschadi, A.M., J.T. Christopher, G.L. Hammer and P. deVoil. 2010. Experimental and
modelling studies of drought-adaptive root architectural traits in wheat
(Triticum aestivum L.). Plant Biosyst. 144: 458–462
149
Manschadi, A.M., J.T. Christopher, P. de Voil and G.L. Hammer. 2006. The role of root
architectural traits in adaptation of wheat to water limited environments. Funct.
Plant. Biol. 33: 823–837
Manske, G.G.B and P.L.G. Vlek. 2002. Root architecture-wheat as a model plant. In:
Waisel Y, Eshel A, Kafkafi U, editors. Plant roots: the hidden half. Marcel Dekker,
New York, USA. 249–259
Manske, G.G.B., J.I. Ortiz-Monasterio, M. Van Ginkel, R.M. Gonzalez and S. Rajaram.
2000. Traits associated with improved P-uptake efficiency in CIMMYT‘ semi
dwarf spring bread wheat grown on an acid andisol in Mexico. Plant Soil. 221:
189–204
Manzoor, H., H.A. Lal, M. Rafiq, M.Z. Aslam, A.H. Tariq, M. Aslam, M. Arshad and A.
Saeed. 2010. Mairaj-08: New Wheat (Triticum aestivum) variety released for
general cultivation under normal and late planting in Punjab province (Pakistan).
Int. J. Agri and Bio. ISSN Print: 1560–8530; ISSN Online: 1814–9596
Marais, G.F., Z.A. Pretorius, C.R. Wellings, B. McCallum and A.S. Marais. 2005. Leaf
rust and stripe rust resistance genes transferred to common wheat from Triticum
dicoccoides. Euphytica. 143: 115–123
Marambe, B and T. Ando. 1995. Physiological basis of salinity tolerance of sorghum
seeds during seed germination. J. Agro. Crop. Sci. 174: 291-296
150
Matsuoka, Y. 2011. Evolution of polyploid Triticum Wheats under cultivation: the role of
domestication, natural hybridization and allopolyploid speciation in their
diversification. Plant Cell Physiol. 52: 750–764
McFadden, E.S. and E.R. Sears. 1946. The origin of Triticum spelta and its free threshing
hexaploid relatives. J. Hered. 37: 81–89
McIntosh, R.A., C.R. Wellings and R.F. Park. 1995. Wheat rusts: an atlas of resistance
genes. Kluwer Academic, Dordrecht, The Netherlands
McIntosh, R.A., Y. Yamazaki, K.M. Devos, J. Dubcovsky, R. Rogers, R. Appels.
2012. Catalogue of gene symbols for wheat. In: KOMUGI, Integrated wheat
Science Database
Mello-Sampayo, T. and A.P. Canas. 1973. Suppressors of meiotic chromosome pairing
in common wheat. Proc. 4th Internat. Wheat Genet. Symp., University of
Missouri, Columbia, MO, U.S.A., pp. 709-713
Miller, T.E., S.M. Reader, K.A. Purdie and I.P. King. 1996. Fluorescent in situ
hybridization. A useful aid to the introduction of alien genetic variation into
wheat. Euphytica. 89: 113-119
Mohammady, S., A. Arminian, H. Khazaie, K. Marcin. 2009. Does water use efficiency
explain the relationship between carbon isotope discrimination and wheat grain
yield? Acta Agricul Scand. 59: 385-388
Monroy, A.F., A. Dryanova, B. Malette, D.H. Oren, M.F. Ridha, W. Liu, J. Danyluk, L.
W.C. Ubayasena, K. Kane, G.J. Scoles, F. Sarhan and P.J. Gulick. 2007. Regulatory
151
gene candidates and gene expression analysis of cold acclimation in winter and
spring wheat. Plant Mol Biol. 64: 409–423
Motomura, Y., K. Fuminori, C.M.I. Julio and T. Shigeo. 2013. A major quantitative trait
locus for cold-responsive gene expression is linked to frost-resistance gene Fr-A2
in common wheat. Breeding Science. 63: 58–67
Muhammad, A., M.U. Rehman and Y. Zafar. 2005. DNA finger printing studies of some
wheat (Triticum aestivum L.) genotypes using random amplified polymorphic
DNA (RAPD) analysis. Pak. J. Bot. 37: 271-277
Mujeeb-Kazi, A. and G.P. Hettel. 1995. Utilizing wild grass biodiversity in wheat
improvement. CIMMYT Research Report No. 2; pp. 1-140
Mukai, Y., Y. Nakahara and M. Yamamoto. 1993. Simultaneous discrimination of the
three genomes in hexaploid wheat by multicolor fluorescence situ hybridization
using total genomic and highly repeated DNA probes. Genome. 3: 489-494
Muller, I., B. Schmid and J. Weiner. 2000. The effect of nutrient availability on biomass
allocation patterns in 27 species of herbaceous plants. Perspect. Plant Ecol. Evol.
Syst. 3: 115–127 10.1078/1433-8319-00007
Muller, J. 1991. Determining leaf surface area by means of linear measurements in
wheat and triticale (brief report). Archiv Fuchtungsforsch. 21: 121–123
Munns, R. 2006. Approaches to increasing the salt tolerance of wheat and other cereals.
J. Exp. Bot. 57: 1025-1043
152
Munns, R. and A. Termant. 1986. Whole-plant response to salinity. Aust. J. plant physiol.
13: 143-160
Munoz-Romero, V., J. Benıtez-Vega, L. Lopez-Bellido, R.J. Lopez-Bellido. 2010.
Monitoring wheat root development in a rainfed vertisol: Tillage effect. Eur. J.
Agron. 33: 182–187
Nakashima, K., Z.K. Shinwari, S. Miura, Y. Sakuma, M. Seki, K. Yamaguchi-Shinozaki
and K. Shinozaki. 2000. Structural organization, expression and promoter activity
of an Arabidopsis gene family encoding DRE/CRT binding proteins involved in
dehydration- and high salinity responsive gene expression. Plant Molecular
Biology: 42: 657-665
Nevo, E. 2001. Evolution of genome–phenome diversity under environmental stress.
Proc Natl Acad Sci USA. 98: 6233– 6240
Nevo, E. 2004. Population genetic structure of wild barley and wheat in the near east
fertile crescent: regional and local adaptive patterns. In: Gupta PK, Varshney RK
(eds) Cereal genomics, chapter 6, pp 135–163
Nevo, E., J.G. Moseman, A. Beiles and D. Zohary. 1985. Patterns of resistance of Israeli
wild emmer wheat to pathogens I. Predictive method by ecology and allozyme
genotypes for powdery mildew and leaf rust. Genetica. 67: 209–222
Nguyen, H.T., R.C. Babu and A. Blum. 1997. Breeding for drought resistance in rice:
physiology and molecular genetics considerations. Crop Sci. 37: 1426–1434
Oerke, E.C. 2006. Crop losses to pests. J. Agri. Sci. 144: 31–43
153
Oliver, R.E., R.W. Stack, J.D. Miller and X. Cai. 2007. Reaction of wild emmer wheat
accessions to Fusarium head blight. Crop Sci. 47: 893–899
Oliveira, H.R., M.G. Campana, H. Jones, H.V. Hunt, F. Leigh, D.I. Redhouse, D.L. Lister
and M.K. Jones. 2012. Tetraploid Wheat Landraces in the Mediterranean Basin:
Taxonomy, Evolution and Genetic Diversity. PLoS ONE. 7: 37-63. Doi: 10.1371/
journal. pone.0037063
Ozkan, H. 2002. AFLP analysis of a collection of tetraploid wheat indicates the origin of
emmer and hard wheat domestication in southeast Turkey. Mol. Bio. Evol. 19:
1797-1801
Passioura J.B. 1983. Root and drought resistance. Agri. Water Manag. 7: 265
Pellegrineschi, A., M. Reynolds, M. Pacheco, R.M. Brito, R. Almeraya, K. Yamaguchi-
Shinozaki and D. Hoisington. 2004. Stress-induced expression in wheat of the
Arabidopsis thaliana DREB1A gene delays water stress symptoms under
greenhouse conditions. Genome. 47: 493-500. DOI: Doi 10.1139/G03-140
Peng, J.H., H. Zadeh, G. Lazo, J.P. Gustafson and S. Chao. 2004. Chromosome bin map
of expressed sequence tags in homoeologous group 1 of hexaploid wheat and
homoeology with rice and Arabidopsis. Genetics. 168: 609–623
Peleg, Z., T. Fahima, S. Abbo, T. Krugman, E. Nevo, D. Yakir and Y. Saranga. 2005.
Genetic diversity for drought resistance in wild emmer wheat and its
ecogeographical associations. Plant Cell Environ. 28: 176–191
154
Peng, J.H., Y. Ronin, T. Fahima, M.S. Röder, Y.C. Li, E. Nevo and A. Korol. 2003.
Domestication quantitative trait loci in Triticum dicoccoides, the progenitor of
wheat. Proc Natl Acad Sci USA. 100: 2489–2494
Ping, Y.Y., C. Xiao, X.H. He, A.K. M.R. Islam and X.Z. Yong. 2003. Identification of
Wheat-Barley 2H alien substitution lines. Acta Botanica Sinica. 45: 1096-1102
Pomeranz, Y. 1987. Modern cereal science and technology. VCH publishers.Inc.Pp 1-486
Price, A.H., K.A. Steele, B.J. Moore and R.G.W Jones. 2002. Upland rice grown in soil-
filled chambers and exposed to contrasting water-deficit regimes: II. Mapping
quantitative trait loci for root morphology and distribution. Field Crops Res. 76:
25–43.doi:10.1016/S0378-4290 (02) 00010-2
Pritchard, J.K., M. Stephens and P. Donnelly. 2000. Inference of population structure
using multilocus genotype data. Genetics: 155: 945-959
Qi, P.F., Y.W. Yue, H. Long, Y.M. Wei, Z.H. Yan and Y.L. Zheng. 2006. Molecular
characterization of alpha-gliadin genes from wild emmer wheat (Triticum
dicoccoides). DNA Seq. 17: 415–421
Quarrie, S.A., S.P. Quarrie, R. Radosevic, D. Rancic, A. Kaminska, J.D. Barnes, M.
Leverington, C. Ceoloni and D. Dodig. 2006. Dissecting a wheat QTL for yield
present in a range of environments: from the QTL to candidate genes. J. Exp.Bot.
57: 2627–2637
155
Rabnawaz, Inamullah, H. Ahmad, S.U. Din and M.S. Iqbal. 2013. Agromorphological
studies of local wheat varieties for variability and their association with yield
related traits. Pak. J. Bot. 45: 1701-1706
Ramya, P., A. Chaubal, K. Kulkarni, L. Gupta, N. Kadoo, H.S. Dhaliwal, P. Chhuneja,
M. Lagu and V. Gupta. 2010. QTL mapping of 1000-kernel weight, kernel length,
and kernel width in bread wheat (Triticum aestivum L.). J. Appl. Genet. 51: 421-429.
Ranjana, R., R. S. Purty, V. A. and S. C. Gupta. 2006. Transformation of tomato cultivars
‗Pusa Ruby‘ with bsp A gene from Populus tremula for drought tolerance. Plant
Cell, Tissue and Organ Culture: 84: 55-67
Reynolds, M., D. Bonnett, S.C. Chapman, R.T. Furbank, Y. Manes, D.E. Mather and
M.A.J. Parry. 2011. Raising yield potential of wheat. I. Overview of a consortium
approach and breeding strategies. J. Exp. Bot. 62: 439-452. DOI: Doi
10.1093/Jxb/Erq311
Richard, L.A. 1954. Diagnosis and improvement of saline and Alkali soils. USDA II and
b. 60. U.S. Govt. printing office, Washington, D.C
Richards, R.A. 1990. The effect of dawarfing genes in spring wheat in dry environment.
Agronomic charecteristics. Aust. j. Agric. 43: 517-527
Richards, R.A., G.J. Rebetzke, A.G. Condon and A. F. van Herwaarden. 2002. Breeding
opportunities for increasing the efficiency of water use and crop yield in temperate
cereals. Crop Sci. 42: 111-121
156
Riley, R., V. Chapman and G. Kimber. 1959. Genetic control of chromosome pairing in
intergeneric hybrids in wheat. Nature. 183: 1244-1246
Roder, M.S., V. Korzun, K. Wendehake, J. Plaschke, M.H. Tixier, P. Leroy and M.W.
Ganal. 1998b. A microsatellite map of wheat. Genetics: 149: 2007-2023
Roder, M.S., V. Korzun, K. Wendehake and J. Plaschke. 1998. A microsatellite map of
wheat. Genetics. 149: 2007-2023
Rowland, J and M.W. Perry. 2000. Wheat in forming systems. Chapter 6. In: WK
Anderson, JR. Garlinge, eds. The wheat book principles and practice. Agriculture
Western Australia. 109-130
Saint Pierre, C., J. Crossa, D. Bonnet, K. Yamaguchi-Shinozaki and M. Reynolds. 2012.
Phenotyping transgenic wheat for drought resistance. J. Exp. Bot. 63: 1799-1808
Saleem, U., I. Khaliq, T. Mahmood and M. Rafique. 2006. Phenotypic and genotypic
correlation coefficients between yield and yield components in wheat. J. Agric.
Res. 44: 1-8.
Salse, J., S.P. Bolot, M. Throude, V. Jouffe, B. Piegu, U.M. Quraishi, T. Calcagno, R.
Cooke, M. Delseny and C. Feuillet. 2008. Identification and Characterization of
Shared Duplications between Rice and Wheat Provide New Insight into Grass
Genome Evolution. The Plant Cell. 20: 11–24
Schlegel, R. and E. Weryszko. 1979. lntergeneric chromosome pairing in different
wheat-rye hybrids revealed by giemsa banding technique and some implications
on karyotype evolution in the genus Secale. Biol. Zbl. 93: 398-407
157
Schlotterer, C. 2004. The evolution of molecular markers-just a matter of fashion? Nat.
Rev. Genet. 5: 63–69. doi:10.1038/ nrg1249. PMID:14666112
Schonfeld, M.A., R.C. Johnson, B.F. Carver and D.W. Mornhigweg. 1988. Water
relations in winter wheat as drought resistance indicators. Crop Sci. 28: 526–531
Schweiger, P., R. Petrasek and W. Hartl. 2009. Root distribution of winter wheat
cultivars as affected by drought. In: International Symposium Root Research and
Applications, Root RAP, 2-4 September 2009, Boku, Vienna, Austria
Sears, E.R. 1966. Nullisomic tetrasomic combinations in hexaploid wheat. In: RILEY, R.
LEWIS, KR. Eds. Chromosome manipulations and plant genetics. Oliver and
Boyd
Seghatoleslami, M.J., M. Kafi and E. Majidi. 2008. Effect of deficit irrigation on yield,
WUE and some morphological and phenological traits of three millet species.
Pak. J. Bot. 4: 1555-1560
Semagn, K., A. Bjornstad, H. Skinnes, A.G. Maroy, Y. Tarkegne and M. William. 2006.
Distribution of DArT, AFLP, and SSR markers in a genetic linkage map of a
double-haploid hexaploid wheat population. Genome. 49: 545–555
Shalaby, E.F., E. Epstein and C.O. Qualset. 1993. The variation in salinity tolerance
among some wheat and triticale genotypes. J. Agro. Crop Sci. 171: 298-304
Shewry, P.R., S. Powers, J.M. Field, R.J. Fido, H.D. Jones, G.M. Arnold, J. West, P.A
Lazzeri, P. Barcelo, F. Barro, A.S. Tatham, F. Bekes, B. Butow and H. Darlington.
2006. Comparative field performance over three years and two sites of transgenic
158
wheat lines expressing HMW subunit transgenes. Theoretical and Applied Genetics.
113: 128–136
Smirnoff, N. 1993 .―The role of active oxygen in the response of plants to water deficit
and desiccation‖. New Phytol. 125: 27-58
Sivamani, E., B. Ahmed, J.M. Wraith, T. Al-Niemi, W.E. Dyer, T.H.D. Ho, R. Qu. 2000.
Improved biomass productivity and water use efficiency under water deficit
conditions in transgenic wheat constitutively expressing the barley HVA1 gene.
Plant Science. 155: 1–9
Shi, G.Y., W.X. Liao, L.F. Qin and L.L. Lu. 2009. PEG simulated water stress effects on
physiological and biochemistry indexes of germination of Toona sinensis seeds.
Journal of Forestry Sci and Tech. 4. 142-145
Schwarzacher, T. 2003. DNA, chromosomes, and in situ hybridization. Genome. 46: 953-
62
Schwarzacher, T., K. Anamathawat-Jonsson, G.E. Harrison, A.K.M.R. Islam, L.Z. Jia, I.P.
King, A.R. Leitch, T.E. Miller, S.M. Reader, W.J. Rogers, M. Shi and J.S. Heslop-
Harrison. 1992. Genomic in situ hybridization to identify alien chromosome
segments in wheat. Theor. Appl. Genet. 84: 778-786
Schwarzacher, T., N. Ali, H.K. Chaudhry, R. Graybosch, H.V. Kapalande, E. Kinski and
J.S. Heslop- Harrison. 2011. Flourescent in situ hybridization as a genetic
technology to analzing chromosomal organization of alien wheat recombinant
lines. IAEA-TECDOC-1664. 90: 121-128
159
Semagn, K., A. Bjornstad and M.N. Ndjiondjop. 2006. An overview of molecular marker
methods for plants. Afr. J. Biotechnol. 5: 2540–2568
Serraj, R., L. Krishnamurthy, J. Kashiwagi, J. Kumar and S. Chandra. 2004. Variation in
root traits of chickpea (Cicer arietinum L.) grown under terminal drought. Field
Crops Res. 88: 115–127
Shinozaki, K and K. Yamaguchi-Shinozaki. 2007. Gene networks involved in drought
stress response and tolerance. J. Exp. Bot. 58: 221-227. DOI: Doi
10.1093/Jxb/Erl164
Singh, R.P., D.P. Hodson, J. Huerta-Espino, Y. Jin, P.Njau, R. Wanyera, S.A. Herrera-
Foessel and R.W. Ward. 2008b. Will stem Rust destroy the worlds wheat crop
Skinner, D.Z. 2009. Post-acclimation transcriptome adjustment is a major factor in
freezing tolerance of winter wheat. Funct Integr Genomics. 9: 513–523
Snape, J.W., K. Butterworth, E. Whitechurch and A.J. Worland. 2001. Waiting for fine
times: genetics of flowering time in wheat. Euphytica. 119: 185–190
Somers, D.J., P. Isaac, and K. Edwards. 2004. A high-density microsatellite consensus
map for bread wheat (Triticum aestivum L.). Theor. Appl. Genet. 109: 1105-1114
Song, L., L. Jiang, H. Han, A. Gao and X. Yang. 2013. Efficient induction of wheat-
Agropyron cristatum 6P translocation lines and GISH detection. PLoS ONE. 8:
e69501. doi:10.1371/journal.pone.0069501
Soreng, R.J. 2009. Catalogue of New World Grasses (Poaceae). database (version
24/11/2009)
160
Sorrells, M and J. Yu. 2009. Linkage disequilibrium and association mapping in the
triticeae. C. Feuillet, G. J. Muehlbauer (eds.), Genetics and Genomics of the
Triticeae, Plant Genetics and Genomics: Crops and Models 7. DOI 10. 1007/978-387-
77489-3_22
Sruthi, N., M. Amita, S.G. Kulvinder and P.V. Vara Prasad. 2014. Variability of root
traits in spring wheat germplasm. PLoS ONE 9: e100317.
doi:10.1371/journal.pone. 0100317
Stanislaw, G., T. Maciej, Grzesiak, F. Wtadystaw, J. Stabryta. 2003. acta physiologiae
plantarum. 25: 26-37
Steliana, E.M. K., S.E. Georgescu, A.M. Maria, Z. Mihaela, O.H. Anca and C. Marieta.
2010. Genetic diversity using microsatellite markers in four Romanian
autochthonous sheep breeds. Rom. Biotech. Lett. 15: 5059-5065
Stone, P.J and M.E Nicolas. 1995. A survey of the effects of high temperature during
grain filling on yield and quality of 75 wheat cultivars. Australian Journal of
Agricultural Research. 46: 475–492
Syva¨nen, A.C. 2005. Toward genome-wide SNP genotyping. Nat. Genet. 37: 5–10
Tabor, H.K., N.J. Risch and R.M. Myers. 2002. Candidate-gene approaches for studying
complex genetic traits: practical considerations. Nat. Rev. Genet. 3: 391-A396. DOI:
Doi 10.1038/Nrg796
161
Tang, Z., Z. Yang and S. Fu. 2014. Oligonucleotides replacing the roles of repetitive
sequences pAs1, pSc119.2, pTa-535, pTa71, CCS1, and pAWRC.1 for FISH
analysis. J Appl. Genet. 55: 313-28. doi: 10.1007/s13353-014-0215-z
Tardieu, F. 2011. Any trait or trait-related allele can confer drought tolerance: just
design the right drought scenario. J. Exp. Bot. 1-7.doi:10.1093/jxb/err269
Tautz, D. 1989. Hypervariability of simple sequences as a general source for
polymorphic DNA markers. Nucleic Acids Res. 17. 6463–6471
Taylor, R.D and W.W. Koo. 2012. Outlook of the U.S. and World Wheat Industries,
2012-2021. Agribusiness and applied economics. 696
Teulat, B., P. Monneveux, J. Wery, C. Borries, I. Souyris, A. Charrier and D. This. 1997.
Relathionship between relative water content and growth parameters under
water stress in barley. A QTL study. New Phytology. 137: 99-107
Thomashow, M.F. 1999. Plant cold acclimation, freezing tolerance genes and regulatory
mechanisms. Annu Rev Plant Physiol Plant Mol Biol. 50: 571–599
Thyer, R. 2005. Biofuels become a global option. Ground Cover. Grains Research and
Development Corporation
Topping, D. 2007. Cereal complex carbohydrates and their contribution to human
health. J. Cereal Sci. 46: 220–229
Tunio, S.D., M.N. Korejo, A.D. Jarwar, and M.R. Waggan. 2006. Studies on indegenious
and exotic weed competition in wheat. Pak. J. Agri. Biol. 5: 1-8
USDA. 2006. National Nutrient Database for Standard Reference, Release 19
162
Wasson, A.P., R.A. Richards, R. Chatrath, S.C. Misra, S.V. Prasad and G.J. Rebetzke.
2012. Traits and selection strategies to improve root systems and water up take in
water-limited wheat crops. J. Exp.Bot. 63: 3485–3498.doi: 10.1093/jxb/ers111
Xie, C., Q. Sun, Z. Ni, T. Yang, E. Nevo and T. Fahima. 2003. Chromosomal location of a
Triticum dicoccoides-derived powdery mildew resistance gene in common
wheat by using microsatellite markers. Theor Appl Genet. 106: 341–345
Xie, W and E. Nevo. 2008. Wild emmer: genetic resources, gene mapping and potential
for wheat improvement. Euphytica. 164: 603–614
Wan, A., Z. Zhao, X. Chen, Z. He, S. Jin, Q. Jia, G. Yao, J. Yang, B. Wang, G. Li, Y. Bi and
Z. Yuan. 2004. Wheat stripe rust epidemic and virulence of Puccinia striiformis f.
sp. Tritici in China in 2002. Plant Dis. 88: 896-904
Wei, B., R. Jing, C. Wang, J. Chen, X. Mao, X. Chang and J. Jia. 2009. Dreb1 genes in
wheat (Triticum aestivum L.): development of functional markers and gene
mapping based on SNPs. Mol Breeding. 23: 13-22
Weining, S and P. Langridge. 1992. Identification and mapping of polymorphism in
cereals base on polymerase chain reaction. Theor. Appl. Genet. 82: 209-216
Winfield, M.O., C. Lu, I.D. Wilson, J.A. Coghill and K.J. Edwards. 2010. Plant responses
to cold: transcriptome analysis of wheat. J. Plant Biotech. 8: 749–771
Wu, J., X. Yang, H. Wang, H. Li and L. Li .2006. The introgression of chromosome 6P
specifying for increased numbers of florets and kernels from Agropyron
cristatum into wheat. Theoretical and Applied Genetics. 114: 13-20
163
Xiao-bo, R., L.X. jin, D.C. Liu, L.J. Wang and Y.L. Zheng. 2008. Mapping QTLs for pre-
harvest sprouting tolerance on chromosome 2D in a synthetic hexaploid wheat x
common wheat cross. J. Appl. Genet. 49: 333–341
Yadv, S., M. Irfan, A. Ahmad and S. Hayat. 2011. Causes of salinity and plant
manifestations to salt stress: A review. J. Environ. Biol. 32: 667-685
Yang, X.Q., S.Q. Zhang, Z.S. Liang and Y. Shan. 2004. Effects of water stress on
chlorophyll fluorescence parameters of different drought resistance waiter wheat
cultivars seedlings. Acta Bot. Boreal. Occident. Sin. 24: 812-816
Yu, J., G. Pressoir, W.H. Briggs, B.I. Vroh, M. Yamasaki, J. Doebley, M. Mcmullen, B.
Gaut, J.J. Holland, S. Kresovich and E. Buckler. 2006. A unified mixed-model
method for association mapping that accounts for multiple levels of relatedness.
Nat. Genet. 38: 203-208
Zahid, P., K. Hussain, S.S.H. Gill and A.A. Sheikh. 2003. Iron requirement of Barani
wheat. Int. J. Agri Biol. 5: 608-610
Zhang, L.Y., S. Marchand, N.A. Tinker and F. Belzile. 2009. Population structure and
linkage disequilibrium in barley assessed by DArT markers. Theor. Appl. Genet.
119: 43-52. DOI: DOI 10.1007/s00122-009-1015-4
Zhang, S., L. Shan and X. Deng. 2002. Change of water use efficiency and its relation
with root system growth in wheat evolution. Chinese Science Bulletin. 47: No. 22
164
Zhang, X., X. Shen, Y. Hao, J. Cai and H.W. Ohm. 2011. A genetic map of Lophopyrum
ponticum chromosome 7E, harboring resistance genes to Fusarium head blight and
leaf rust. Theor. Appl. Genet. 122: 263-270
Zhao, H.H., R.L. Fernando and J.C.M. Dekkers. 2007. Effects of population structure on
power and precision of regression-based linkage disequilibrium mapping of
QTL. J Anim Sci. 42: 85:63
Zhou, R., Z. Zhu, X. Kong, N. Huo, Q. Tian, P. Li, C. Jin, Y. Dong, J. Jia. 2005.
Development of wheat near-isogenic lines for powdery mildew resistance. Theor
Appl Genet. 110: 640–648
Ziegelhoffer, E., J. Leonard and M. Elliot. 2000. Cloning of the Arabidopsis WIGGUM
gene identifies a role for farnesylation in meristem development. PNAS. 97: 7633-
7638
Zillinsky, F.J. 1983. Common Diseases of Small Grain Cereals: A Guide to Identification.
01-154
165
ANNEXURES
Annexure 1
Mean sorted table of hundred wheat genotypes on the base of morphological traits
S No Variety FLA Variety SL Variety PL Variety PH
1 Pari -73 92.5 Marwat-01 16.7 C-591 48.0 C-518 119.0
2 Chenab 79 68.7 Sussi 16.7 Dirk 45.0 Local white 117.8
3 Rawal 87 67.1 Barani-83 15.0 C-228 43.7 Lasani-08 114.3
4 LYP -73 66.2 Shalimar 88 14.8 Barani-83 43.0 Bahalwapur-79 113.0
5 Dawar 96 64.6 Faisalabad-83 14.8 Bahalwapur-79 43.0 Saleem 2000 112.7
6 Nori -70 62.3 Noshera 96 14.8 Sutlag-86 42.7 Rawal 87 112.7
7 Margalla 99 58.5 Potohar-70 14.4 SA-75 42.7 WL-711 112.3
8 Wadanak 98 57.8 Pak-81 14.3 RWP-94 41.7 Margalla 99 110.6
9 Chakwal 86 57.6 10737 14.3 Sandal 41.7 Chakwal 86 110.5
10 Soghat 90 57.6 Wadanak 85 14.2 Punjab-76 41.6 Haider 2002 109.0
11 Suliman 96 56.9 Shahkar- 95 14.2 Soghat 90 41.3 10789 108.7
12 ZA- 77 55.3 Ksk 14.1 Uqab 2000 40.7 potohar-90 108.7
13 Indus 79 54.1 Bakhtawar 94 14.1 Ksk 40.5 Pari -73 107.7
14 10776 53.8 NIAB 83 14.0 Chenab 79 40.4 Abdaghar 97 106.2
15 Ksk 52.4 Tandojam-83 14.0 Punjab-88 40.3 10737 106.1
16 Zarlashta 90 52.1 FPD-08 14.0 10793 40.3 NIAB 83 106.0
17 Lr-230 51.6 Suliman 96 13.9 10748 39.8 Uqab 2000 105.8
18 Haider 2002 50.9 WL-711 13.8 Chenab-96 39.7 AS -2002 105.7
19 Manther 50.3 Anmol-91 13.8 ZA- 77 39.5 C-591 104.7
20 Chenab 70 49.5 Uqab 2000 13.7 Margalla 99 39.3 AUP 5000 104.7
21 Uqab 2000 49.4 Raskoh 13.7 C-273 39.0 10776 104.0
22 AS -2002 48.8 Haider 2002 13.7 Faisalabad-83 38.7 Sutlag-86 103.7
23 Faisalabad 85 48.6 Meraj-08 13.7 Zamindar-80 38.3 Chenab 70 103.4
24 Bakhtawar 94 47.7 SH-2003 13.6 C-518 38.3 Zarghoon-79 103.0
25 MH-97 47.6 Sonalika 13.5 Maxi pak 38.3 C-228 103.0
26 Abdaghar 97 46.7 Chenab 70 13.5 Iqbal-2000 38.1 Punjab-81 103.0
27 Maxi pak 45.7 10742 13.5 Shalimar 88 37.9 Wafaq-2008 102.7
28 10737 44.7 Punjab-88 13.5 Merco 2007 37.8 SA-42 102.7
29 Shalimar 88 44.6 Zarlashta 90 13.4 10776 37.8 Barani 70 102.4
30 Barani 70 44.5 Kohinoor-83 13.3 Haider 2002 37.7 Iqbal-2000 102.0
31 Merco 2007 43.2 Wardak-85 13.3 Zarlashta 90 37.6 Dawar 96 102.0
32 Kaghan 93 43.0 Kaghan 93 13.3 AUP 5000 37.3 Shalimar 88 101.5
33 Noshera 96 42.4 Indus 79 13.2 Sindh 81 37.2 Noshera 96 101.4
34 10792 42.2 Barani 70 13.2 Rawal 87 37.1 RWP-94 101.3
35 Raskoh 40.8 Wafaq-2008 13.2 Khyber 83 36.9 Kiran 101.3
36 Saleem 2000 40.7 Potohar-93 13.2 10792 36.8 Indus 79 101.0
37 Pirsabak 2008 40.6 Maxi pak 13.1 Wadanak 98 36.8 LU-26 100.3
38 Fakhri sarhad 40.5 Saleem 2000 13.1 Anmol-91 36.7 Khyber-79 100.3
39 NIAB 83 40.3 LYP -73 13.1 LU-26 36.7 Barani-83 100.0
40 Punjab-76 40.2 Fakhri sarhad 13.1 Shahkar- 95 36.7 Zarlashta 90 99.8
41 GA 2002 39.5 10748 13.1 potohar-90 36.7 Punjab-96 99.7
42 10789 37.7 Sariab-92 13.1 Abdaghar 97 36.5 10793 99.7
43 10748 36.6 Abdaghar 97 13.0 Indus 79 36.5 Mehran-89 99.0
166
44 Sindh 81 36.5 SA-42 13.0 Sussi 36.3 Marwat-01 98.7
45 Local white 35.4 Pirsabak-85 13.0 Raskoh 36.3 AUP-4008 98.7
46 Khyber 83 33.1 Kiran 13.0 Dawar 96 36.2 LYP -73 98.7
47 FPD-08 30.8 Mehran-89 13.0 SA-42 36.0 Raskoh 98.1
48 Lasani-08 30.3 Chenab 79 12.9 Fakhri sarhad 35.8 MH-97 97.9
49 Blue silver 30.1 LU-26 12.9 Punjab-81 35.7 Janbaz 97.7
50 C-518 29.4 AUP 5000 12.9 AS -2002 35.4 SH-2003 97.3
51 Dirk 29.2 Punjab-76 12.9 Bakhtawar 94 35.3 Wardak-85 97.3
52 Bahalwapur-79 29.0 Chakwal 86 12.8 Wadanak 85 35.3 C-250 97.0
53 Sandal 28.8 Dawar 96 12.8 Barani 70 35.3 Khyber 83 96.7
54 Mehran-89 28.8 Punjab-96 12.7 FPD-08 35.0 Kohinoor-83 96.3
55 10724 28.7 Merco 2007 12.7 LYP -73 34.8 ZA- 77 96.3
56 SA-42 28.5 Manther 12.7 C-250 34.7 Faisalabad-83 95.7
57 10742 28.4 MH-97 12.7 10724 34.7 Dirk 95.7
58 Sussi 28.3 Pari -73 12.7 Janbaz 34.7 Bakhtawar 94 95.3
59 Sariab-92 28.3 ZA- 77 12.7 AUP-4008 34.7 Faisalabad 85 95.0
60 C-273 28.2 Punjab-81 12.6 Pirsabak 2008 34.2 10748 94.8
61 Pirsabak-85 28.0 AS -2002 12.6 Wafaq-2008 33.7 Zamindar-80 94.0
62 RWP-94 28.0 Blue silver 12.5 Kaghan 93 33.3 Maxi pak 93.7
63 Wafaq-2008 27.7 10789 12.4 MH-97 33.2 Pak-81 93.7
64 WL-711 27.5 Faisalabad 85 12.4 GA 2002 33.2 Wadanak 98 93.4
65 Tandojam-83 27.2 C-273 12.3 Manther 33.1 Chenab-96 93.0
66 Pak-81 26.8 Lasani-08 12.3 Faisalabad 85 32.9 Pirsabak-85 93.0
67 AUP 5000 26.7 Mumal-2002 12.3 Local white 32.9 Mumal-2002 92.7
68 Kiran 26.2 SA-75 12.3 Saleem 2000 32.8 Manther 92.1
69 Janbaz 26.1 Lr-230 12.2 Mumal-2002 32.6 Blue silver 92.0
70 SA-75 25.9 Pirsabak 2008 12.2 10737 32.4 Sariab-92 92.0
71 10793 25.7 Soghat 90 12.2 Blue silver 32.3 Sandal 92.0
72 C-250 25.4 10793 12.2 Mehran-89 32.3 Sussi 91.3
73 Kohinoor-83 25.3 Rawal 87 12.1 SH-2003 32.0 Punjab-76 91.0
74 potohar-90 24.9 C-228 12.1 Chenab 70 31.7 Punjab-88 91.0
75 Potohar-70 24.7 GA 2002 11.9 Kohinoor-83 31.7 Nori -70 90.6
76 Khyber-79 24.6 Zamindar-80 11.9 10789 31.5 Tandojam-83 90.3
77 Wadanak 85 24.3 Iqbal-2000 11.9 Zarghoon-79 31.3 Merco 2007 90.1
78 Punjab-81 23.9 Sutlag-86 11.9 Wardak-85 31.3 Ksk 89.5
79 Wardak-85 23.7 Nori -70 11.8 Noshera 96 31.3 Shahkar- 95 89.0
80 AUP-4008 23.7 10776 11.8 Lr-230 31.1 FPD-08 86.3
81 Shahkar- 95 23.3 Zarghoon-79 11.8 Pak-81 31.0 Chenab 79 85.7
82 Potohar-93 23.2 Chenab-96 11.7 Nori -70 30.8 Potohar-93 85.7
83 Zarghoon-79 23.2 Khyber 83 11.7 Suliman 96 30.8 Potohar-70 85.7
84 Punjab-88 23.2 Bahalwapur-79 11.7 sonalika 30.7 Lr-230 85.5
85 Barani-83 22.5 Margalla 99 11.6 Pari -73 30.4 GA 2002 85.0
86 Meraj-08 22.3 Janbaz 11.3 Chakwal 86 30.4 Suliman 96 84.6
87 sonalika 22.2 Sindh 81 11.3 Potohar-93 30.3 Soghat 90 84.3
88 Marwat-01 21.8 RWP-94 11.2 10742 29.7 Meraj-08 84.0
89 Punjab-96 21.5 C-591 11.2 WL-711 29.7 Anmol-91 83.7
90 Faisalabad-83 21.1 C-250 11.1 NIAB 83 29.4 C-273 83.7
91 Anmol-91 20.4 Wadanak 98 10.8 Potohar-70 28.7 Kaghan 93 83.4
92 Zamindar-80 20.1 10792 10.7 Lasani-08 28.7 sonalika 80.6
93 SH-2003 19.3 Dirk 10.7 Marwat-01 28.3 Wadanak 85 80.3
94 C-591 19.2 Khyber-79 10.7 Khyber-79 28.3 10742 79.7
95 LU-26 19.2 C-518 10.7 Kiran 28.3 Pirsabak 2008 79.2
96 Iqbal-2000 17.5 potohar-90 10.7 Meraj-08 28.3 Fakhri sarhad 79.0
167
97 Chenab-96 16.7 10724 10.2 Punjab-96 27.5 Sindh 81 77.6
98 C-228 16.0 Local white 10.1 Tandojam-83 26.3 10724 75.7
99 Mumal-2002 15.2 AUP-4008 9.7 Sariab-92 23.3 SA-75 73.3
100 Sutlag-86 14.7 Sandal 8.1 Pirsabak-85 22.3 10792 68.6
Annexure 1: Mean sorted table of hundred wheat genotypes on the base of morphological traits
S No Variety NTP Variety DM Variety DH Variety AL
1 Barani 70 6.33 Bahalwapur-79 182.7 10776 147.0 Lr-230 8
2 Rawal 87 6.33 Meraj-08 182.3 10737 145.3 Uqab 2000 8
3 Pak-81 6.33 Potohar-93 181.0 Abdaghar 97 144.0 Local white 7
4 Chenab 79 6.00 10742 180.7 NIAB 83 143.7 Faisalabad 85 7
5 Soghat 90 6.00 C-518 180.0 10748 143.7 10776 7
6 10737 6.00 C-591 179.3 Bakhtawar 94 142.7 Pari -73 7
7 10789 6.00 AUP 5000 178.7 Uqab 2000 142.7 ZA- 77 7
8 Pirsabak 2008 6.00 C-228 178.3 Kaghan 93 142.7 Margalla 99 7
9 10793 6.00 Sutlag-86 178.3 Raskoh 141.7 10792 7
10 RWP-94 6.00 10724 178.3 Indus 79 141.3 Chenab 70 7
11 10724 6.00 Mehran-89 178.0 Wadanak 85 141.3 Dawar 96 7
12 AUP 5000 6.00 Rawal 87 177.7 Margalla 99 141.3 10737 7
13 Sandal 6.00 Chenab-96 177.7 Sindh 81 141.3 Abdaghar 97 7
14 MH-97 5.67 Wafaq-2008 177.7 Fakhri sarhad 140.7 Ksk 7
15 Faisalabad 85 5.67 10737 177.3 Chenab 70 140.3 NIAB 83 7
16 Saleem 2000 5.67 Chenab 70 177.0 Merco 2007 140.0 LYP -73 7
17 Chakwal 86 5.67 Blue silver 177.0 Barani 70 140.0 Zarghoon-79 6
18 Dawar 96 5.67 Dawar 96 176.7 Nori -70 140.0 Nori -70 6
19 10792 5.67 Punjab-88 176.7 AS -2002 140.0 Kaghan 93 6
20 Punjab-88 5.67 Punjab-81 176.7 10789 140.0 Maxi pak 6
21 C-591 5.67 RWP-94 176.7 sonalika 139.7 Rawal 87 6
22 Blue silver 5.67 Zamindar-80 176.3 MH-97 139.7 Sindh 81 6
23 WL-711 5.67 Sariab-92 176.3 Pari -73 139.7 Pirsabak 2008 6
24 Potohar-70 5.67 Sandal 176.0 Zarghoon-79 139.7 sonalika 6
25 Sussi 5.67 Soghat 90 175.7 Punjab-81 139.7 Manther 6
26 C-518 5.67 C-250 175.7 Manther 139.3 Suliman 96 6
27 Haider 2002 5.33 Potohar-70 175.7 Faisalabad 85 139.3 Fakhri sarhad 6
28 Local white 5.33 Khyber-79 175.7 C-228 139.3 Soghat 90 6
29 Zarlashta 90 5.33 Shahkar- 95 175.3 GA 2002 139.0 Haider 2002 6
30 GA 2002 5.33 SA-42 175.3 SA-42 139.0 MH-97 6
31 Shahkar- 95 5.33 Wardak-85 175.0 Zarlashta 90 138.7 Wadanak 85 6
32 Wafaq-2008 5.33 Marwat-01 174.7 ZA- 77 138.7 Raskoh 6
168
33 Barani-83 5.33 potohar-90 174.7 Suliman 96 138.3 Shalimar 88 6
34 Potohar-93 5.33 10793 174.3 Faisalabad-83 138.3 Punjab-81 6
35 C-273 5.33 Pak-81 174.3 10793 138.3 Punjab-76 6
36 Tandojam-83 5.33 Barani-83 174.0 Ksk 137.7 Khyber 83 6
37 Mehran-89 5.33 FPD-08 174.0 Rawal 87 137.7 10789 6
38 Janbaz 5.33 Janbaz 174.0 Khyber 83 137.7 Merco 2007 6
39 Manther 5.00 Zarghoon-79 173.7 LYP -73 137.3 Zarlashta 90 6
40 Indus 79 5.00 Faisalabad-83 173.0 Zamindar-80 137.3 Noshera 96 6
41 Abdaghar 97 5.00 Kohinoor-83 173.0 SA-75 137.3 AS -2002 6
42 Uqab 2000 5.00 10776 172.7 Shahkar- 95 137.0 Blue silver 6
43 Raskoh 5.00 Punjab-96 172.7 Punjab-88 136.7 Chenab-96 6
44 Punjab-76 5.00 Suliman 96 172.3 C-591 136.7 Wadanak 98 6
45 NIAB 83 5.00 Pirsabak-85 172.3 Punjab-76 136.3 10793 6
46 Shalimar 88 5.00 Iqbal-2000 172.0 Chakwal 86 136.0 10724 6
47 Khyber 83 5.00 Barani 70 171.7 Dawar 96 136.0 Sariab-92 6
48 Chenab 70 5.00 Chenab 79 170.7 WL-711 135.7 Potohar-93 5
49 Pari -73 5.00 GA 2002 170.3 Haider 2002 135.3 Iqbal-2000 5
50 Wadanak 98 5.00 Margalla 99 169.7 Wadanak 98 135.3 Shahkar- 95 5
51 Kaghan 93 5.00 Dirk 169.7 Noshera 96 135.3 10742 5
52 AS -2002 5.00 Lr-230 169.3 Khyber-79 135.3 Barani 70 5
53 Sindh 81 5.00 Indus 79 169.3 Lr-230 134.7 Potohar-70 5
54 10776 5.00 MH-97 169.3 Saleem 2000 134.7 AUP 5000 5
55 10748 5.00 Wadanak 98 169.3 Potohar-70 134.7 Sutlag-86 5
56 Zamindar-80 5.00 Sindh 81 169.3 Iqbal-2000 134.3 Lasani-08 5
57 Anmol-91 5.00 Zarlashta 90 169.0 Maxi pak 134.0 SA-42 5
58 Zarghoon-79 5.00 Khyber 83 169.0 Local white 134.0 GA 2002 5
59 C-228 5.00 Pari -73 169.0 SH-2003 134.0 Pak-81 5
60 Punjab-81 5.00 AS -2002 169.0 Chenab-96 134.0 Chenab 79 5
61 Sariab-92 5.00 C-273 169.0 Soghat 90 133.7 Meraj-08 5
62 10742 5.00 Nori -70 168.7 Potohar-93 133.7 C-591 5
63 SA-42 5.00 Kaghan 93 168.7 FPD-08 133.3 SH-2003 5
64 Marwat-01 5.00 Tandojam-83 168.7 Mumal-2002 132.7 LU-26 5
65 Kohinoor-83 5.00 Kiran 168.7 Marwat-01 132.7 Tandojam-83 5
66 Dirk 5.00 NIAB 83 168.3 Meraj-08 132.7 C-250 5
67 FPD-08 5.00 Noshera 96 168.0 Tandojam-83 132.0 Wardak-85 5
68 Wardak-85 5.00 10789 168.0 Dirk 132.0 10748 5
69 potohar-90 5.00 LU-26 168.0 potohar-90 132.0 Kiran 5
70 Lr-230 4.67 Chakwal 86 167.7 Shalimar 88 131.3 Indus 79 5
71 Maxi pak 4.67 ZA- 77 167.7 Kohinoor-83 131.3 Chakwal 86 5
169
72 Bakhtawar 94 4.67 Maxi pak 167.3 Chenab 79 131.0 Faisalabad-83 5
73 ZA- 77 4.67 Anmol-91 167.3 Anmol-91 131.0 RWP-94 5
74 Noshera 96 4.67 Haider 2002 167.0 Wardak-85 131.0 Wafaq-2008 5
75 SH-2003 4.67 LYP -73 167.0 C-518 131.0 Barani-83 5
76 Sutlag-86 4.67 Fakhri sarhad 167.0 AUP 5000 130.7 Bahalwapur-79 5
77 SA-75 4.67 10748 167.0 10792 130.3 Bakhtawar 94 5
78 AUP-4008 4.67 Uqab 2000 166.7 Sutlag-86 130.3 C-273 5
79 Merco 2007 4.33 Saleem 2000 166.7 Pak-81 130.0 WL-711 5
80 Wadanak 85 4.33 Sussi 166.7 Sandal 130.0 Zamindar-80 5
81 Margalla 99 4.33 Abdaghar 97 166.3 Punjab-96 129.7 Saleem 2000 5
82 LYP -73 4.33 WL-711 166.0 Wafaq-2008 129.7 Punjab-88 4
83 Fakhri sarhad 4.33 Faisalabad 85 165.7 10724 129.7 Sandal 4
84 Iqbal-2000 4.33 Lasani-08 165.7 Bahalwapur-79 129.7 Dirk 4
85 LU-26 4.33 SH-2003 165.0 AUP-4008 129.7 Pirsabak-85 4
86 Chenab-96 4.33 Manther 164.7 10742 129.3 FPD-08 4
87 Pirsabak-85 4.33 Punjab-76 164.7 Lasani-08 129.3 Kohinoor-83 4
88 Bahalwapur-79 4.33 AUP-4008 164.7 Kiran 129.3 Mumal-2002 4
89 Kiran 4.33 Shalimar 88 164.3 C-250 128.7 C-228 4
90 Meraj-08 4.33 SA-75 163.7 Sariab-92 128.7 Sussi 4
91 Sonalika 4.00 Sonalika 162.7 Pirsabak 2008 128.3 Punjab-96 4
92 Ksk 4.00 Bakhtawar 94 162.7 LU-26 128.3 AUP-4008 4
93 Nori -70 4.00 Wadanak 85 162.7 Blue silver 128.3 Janbaz 4
94 Punjab-96 4.00 Merco 2007 161.7 RWP-94 128.0 Anmol-91 4
95 Khyber-79 4.00 10792 161.7 Sussi 127.0 SA-75 4
96 Suliman 96 3.67 Ksk 160.7 Pirsabak-85 126.7 Marwat-01 4
97 Faisalabad-83 3.67 Pirsabak 2008 160.7 Barani-83 126.3 Mehran-89 4
98 C-250 3.67 Local white 160.0 Janbaz 123.0 Khyber-79 4
99 Lasani-08 3.67 Mumal-2002 160.0 C-273 122.3 potohar-90 4
100 Mumal-2002 3.33 Raskoh 159.0 Mehran-89 121.7 C-518 3
170
Annexure 1:Mean sorted table of hundred wheat genotypes on the base of morphological traits
S No Variety SPS Variety SD Variety GS Variety 1000GW
1 Margalla 99 24.0 Sandal 2.2 Chenab 79 99.3 Zarghoon-79 48.7
2 Barani 70 24.0 Margalla 99 2.1 Indus 79 94.7 Faisalabad 85 48.6
3 Zarlashta 90 23.3 10792 2.1 10748 93.7 Mumal-2002 48.6
4 Manther 22.7 10724 2.0 10789 91.5 Sutlag-86 48.4
5 Wadanak 85 22.7 AUP-4008 2.0 Saleem 2000 91.3 C-591 47.4
6 Rawal 87 22.7 Sindh 81 2.0 Chenab 70 90.4 Punjab-81 47.4
7 10748 22.7 Rawal 87 1.9 Zarlashta 90 89.1 Potohar-70 46.7
8 Maxi pak 22.3 Local white 1.9 Soghat 90 88.7 Punjab-96 46.6
9 ZA- 77 22.3 potohar-90 1.9 Wadanak 85 86.7 Zamindar-80 46.4
10 Uqab 2000 22.0 Wadanak 98 1.9 Lr-230 83.5 LU-26 44.8
11 AS -2002 22.0 Barani 70 1.8 Margalla 99 82.8 Marwat-01 44.7
12 Fakhri sarhad 22.0 Janbaz 1.8 Uqab 2000 81.7 Kohinoor-83 44.6
13 Sonalika 21.7 C-518 1.8 ZA- 77 80.2 Noshera 96 44.2
14 Merco 2007 21.7 Dirk 1.8 Noshera 96 80.1 Punjab-88 44.1
15 Faisalabad 85 21.7 Manther 1.8 Sindh 81 80.0 Pak-81 44.1
16 10737 21.7 Faisalabad 85 1.8 Kaghan 93 79.5 Bahalwapur-79 44.1
17 10792 21.7 10776 1.8 Bakhtawar 94 78.0 C-273 43.9
18 LYP -73 21.3 Zarlashta 90 1.8 Nori -70 77.2 Blue silver 43.7
19 Indus 79 21.0 AS -2002 1.8 Pari -73 75.9 Anmol-91 43.4
20 Raskoh 21.0 ZA- 77 1.8 Dawar 96 75.2 C-250 43.0
21 MH-97 21.0 Khyber 83 1.8 Haider 2002 75.0 Barani-83 42.8
22 Chenab 79 21.0 10748 1.7 NIAB 83 74.7 10789 42.7
23 Chakwal 86 21.0 C-250 1.7 Chakwal 86 74.4 WL-711 42.7
24 Ksk 20.7 Merco 2007 1.7 Pirsabak 2008 74.3 Lasani-08 42.6
25 Chenab 70 20.7 Maxi pak 1.7 AS -2002 73.7 Barani 70 42.3
26 10776 20.7 Khyber-79 1.7 Wadanak 98 73.6 AUP 5000 42.1
27 AUP 5000 20.7 Fakhri sarhad 1.7 Punjab-76 73.4 Potohar-93 42.0
28 Khyber 83 20.3 MH-97 1.7 10737 73.0 SA-42 41.9
29 Wadanak 98 20.3 Soghat 90 1.7 Rawal 87 72.6 10724 41.7
30 Dawar 96 20.3 Chenab 79 1.7 Barani 70 70.0 Iqbal-2000 41.6
31 Sindh 81 20.3 Pirsabak 2008 1.6 Barani-83 68.3 Pirsabak-85 41.3
32 potohar-90 20.3 LYP -73 1.6 Maxi pak 68.3 Sandal 41.2
33 Bakhtawar 94 20.0 Chakwal 86 1.6 Suliman 96 68.0 GA 2002 41.2
34 Soghat 90 20.0 C-273 1.6 Abdaghar 97 67.7 Wardak-85 41.1
35 Pari -73 20.0 Dawar 96 1.6 Manther 67.0 Haider 2002 40.4
36 Suliman 96 20.0 Wadanak 85 1.6 LYP -73 66.9 Faisalabad-83 40.1
37 Pirsabak 2008 20.0 Chenab-96 1.6 MH-97 65.9 SA-75 40.0
171
38 Potohar-93 20.0 Uqab 2000 1.6 Raskoh 65.4 Saleem 2000 39.9
39 C-273 20.0 Sonalika 1.6 Shahkar- 95 65.3 10748 39.7
40 Janbaz 20.0 AUP 5000 1.6 C-273 65.0 10792 39.6
41 10724 19.7 Sutlag-86 1.6 Faisalabad 85 64.7 NIAB 83 39.6
42 WL-711 19.7 Indus 79 1.6 Faisalabad-83 64.3 Bakhtawar 94 39.5
43 Haider 2002 19.3 Pari -73 1.6 Shalimar 88 62.3 C-228 39.4
44 Shalimar 88 19.3 C-591 1.6 Merco 2007 60.9 RWP-94 39.4
45 Kaghan 93 19.3 Bahalwapur-79 1.6 Kohinoor-83 60.7 Kiran 39.2
46 C-250 19.3 Zarghoon-79 1.6 Pak-81 60.7 FPD-08 39.2
47 Potohar-70 19.3 10789 1.6 sonalika 59.0 Chenab-96 39.1
48 Pirsabak-85 19.3 Nori -70 1.6 Lasani-08 58.7 Merco 2007 39.0
49 Dirk 19.3 RWP-94 1.5 Ksk 58.3 C-518 39.0
50 C-518 19.3 Raskoh 1.5 Marwat-01 58.0 LYP -73 38.9
51 Abdaghar 97 19.0 Chenab 70 1.5 RWP-94 57.3 Shahkar- 95 38.6
52 Local white 19.0 Blue silver 1.5 Khyber 83 55.3 Raskoh 38.6
53 Punjab-76 19.0 Potohar-93 1.5 Wardak-85 55.0 Kaghan 93 38.5
54 10789 19.0 10737 1.5 10776 54.5 10742 38.5
55 Sutlag-86 19.0 Punjab-76 1.5 Fakhri sarhad 54.5 AUP-4008 38.2
56 Blue silver 19.0 Lr-230 1.5 Chenab-96 53.3 Khyber-79 38.0
57 10742 19.0 10793 1.5 Punjab-88 53.3 Tandojam-83 37.9
58 Pak-81 19.0 Punjab-96 1.5 Potohar-70 52.3 Local white 37.8
59 Sussi 19.0 Lasani-08 1.5 10793 51.3 Lr-230 37.6
60 Wardak-85 19.0 Pirsabak-85 1.5 GA 2002 51.0 Wafaq-2008 37.6
61 Mehran-89 19.0 Punjab-81 1.5 C-250 50.3 Chenab 79 37.1
62 Anmol-91 18.7 Ksk 1.5 Sussi 50.3 10737 36.9
63 Chenab-96 18.7 Abdaghar 97 1.5 10792 50.1 Janbaz 36.8
64 Shahkar- 95 18.7 Mehran-89 1.5 SA-42 46.3 Suliman 96 36.7
65 Punjab-81 18.7 Kaghan 93 1.5 Mumal-2002 44.7 Khyber 83 36.5
66 SA-42 18.7 Suliman 96 1.4 Meraj-08 44.3 10793 36.4
67 AUP-4008 18.7 C-228 1.4 Mehran-89 42.7 Pirsabak 2008 36.4
68 Lr-230 18.3 SA-42 1.4 Sandal 42.3 Dirk 36.2
69 Nori -70 18.3 Zamindar-80 1.4 Pirsabak-85 42.0 Pari -73 35.8
70 Faisalabad-83 18.3 Wardak-85 1.4 C-228 41.7 Sussi 35.8
71 Zarghoon-79 18.3 WL-711 1.4 AUP 5000 41.7 SH-2003 35.7
72 10793 18.3 Bakhtawar 94 1.4 AUP-4008 41.7 ZA- 77 35.5
73 Bahalwapur-79 18.3 Haider 2002 1.4 Anmol-91 41.3 potohar-90 35.3
74 Lasani-08 18.3 10742 1.4 Punjab-81 41.3 Ksk 35.1
75 Khyber-79 18.3 Iqbal-2000 1.4 SH-2003 41.0 Shalimar 88 34.8
76 Barani-83 18.0 GA 2002 1.4 Sariab-92 40.7 Mehran-89 34.3
172
77 Tandojam-83 18.0 SA-75 1.4 WL-711 40.7 Punjab-76 34.3
78 Punjab-96 17.7 Anmol-91 1.4 Zarghoon-79 40.3 Meraj-08 34.0
79 SH-2003 17.7 Wafaq-2008 1.3 Wafaq-2008 40.3 Margalla 99 34.0
80 C-591 17.7 Potohar-70 1.3 Khyber-79 40.0 Manther 33.9
81 Wafaq-2008 17.7 Pak-81 1.3 SA-75 39.3 Soghat 90 33.8
82 Sandal 17.7 Mumal-2002 1.3 Sutlag-86 38.3 Abdaghar 97 33.3
83 Meraj-08 17.7 Shahkar- 95 1.3 10742 38.3 Fakhri sarhad 33.3
84 C-228 17.3 LU-26 1.3 Local white 37.7 sonalika 33.0
85 Punjab-88 17.3 Shalimar 88 1.3 10724 37.7 MH-97 33.0
86 RWP-94 17.3 Meraj-08 1.3 LU-26 36.3 Dawar 96 33.0
87 Marwat-01 17.3 SH-2003 1.3 Janbaz 35.7 Wadanak 98 32.9
88 Noshera 96 17.0 Punjab-88 1.3 Iqbal-2000 35.3 Chenab 70 32.5
89 Zamindar-80 17.0 Tandojam-83 1.3 C-591 33.0 10776 32.5
90 LU-26 17.0 Kiran 1.3 Tandojam-83 31.7 Rawal 87 32.3
91 SA-75 17.0 Sariab-92 1.3 potohar-90 31.0 Wadanak 85 32.2
92 Iqbal-2000 16.7 Faisalabad-83 1.2 Blue silver 30.7 Zarlashta 90 32.2
93 FPD-08 16.7 Kohinoor-83 1.2 Potohar-93 30.3 Indus 79 31.6
94 Kiran 16.7 Barani-83 1.2 Dirk 30.3 Sindh 81 31.5
95 NIAB 83 16.3 FPD-08 1.2 C-518 29.7 Nori -70 31.4
96 GA 2002 16.3 Saleem 2000 1.2 Bahalwapur-79 26.0 Maxi pak 31.2
97 Sariab-92 16.3 NIAB 83 1.2 FPD-08 25.7 Chakwal 86 31.0
98 Mumal-2002 16.0 Noshera 96 1.2 Kiran 21.0 Uqab 2000 31.0
99 Kohinoor-83 16.0 Sussi 1.1 Zamindar-80 20.7 Sariab-92 30.2
100 Saleem 2000 15.3 Marwat-01 1.0 Punjab-96 16.7 AS -2002 30.1
173
Annexure 1: Mean sorted table of hundred wheat genotypes on the base of morphological
traits S No Variety YPP Variety HI Variety TWP
1 Uqab 2000 11.2 C-273 49.7 Pari -73 15.5
2 Haider 2002 9.1 C-518 47.2 Rawal 87 25.6
3 Sutlag-86 8.9 Sutlag-86 45.9 Chenab 79 14.0
4 Rawal 87 8.7 Wardak-85 45.0 LYP -73 20.4
5 Wadanak 85 8.6 Dirk 43.7 Margalla 99 24.1
6 Barani 70 8.6 Faisalabad-83 43.6 Dawar 96 11.0
7 C-273 8.6 Chenab-96 43.5 Nori -70 11.5
8 Margalla 99 8.5 Punjab-88 43.0 Uqab 2000 32.7
9 Potohar-70 8.2 potohar-90 43.0 Soghat 90 9.3
10 Indus 79 7.9 Iqbal-2000 42.2 Suliman 96 10.7
11 Merco 2007 7.7 Sussi 42.1 Indus 79 23.6
12 Pak-81 7.7 Mehran-89 42.0 Haider 2002 21.1
13 Kohinoor-83 7.7 AUP-4008 41.9 Wadanak 98 10.8
14 AUP 5000 7.6 Lasani-08 41.9 Chakwal 86 8.9
15 NIAB 83 7.5 Sandal 41.9 ZA- 77 14.5
16 C-518 7.5 Tandojam-83 41.7 Lr-230 13.9
17 Punjab-81 7.1 Potohar-93 41.3 Barani 70 27.5
18 C-228 7.0 Janbaz 41.1 Faisalabad 85 17.7
19 Bakhtawar 94 6.9 Saleem 2000 41.0 10776 11.4
20 Iqbal-2000 6.8 Khyber-79 40.9 Bakhtawar 94 20.0
21 Chenab 79 6.8 Punjab-81 40.7 Ksk 7.9
22 SH-2003 6.8 Meraj-08 40.6 Zarlashta 90 12.8
23 Zamindar-80 6.7 Kohinoor-83 40.3 Maxi pak 17.4
24 Pirsabak-85 6.7 FPD-08 40.2 Chenab 70 13.9
25 10724 6.6 10789 39.5 Noshera 96 21.2
26 Noshera 96 6.6 10724 39.1 NIAB 83 23.6
27 10789 6.6 Kiran 38.7 Manther 7.0
28 Potohar-93 6.5 SA-42 38.7 MH-97 15.9
29 10793 6.5 Bahalwapur-79 38.6 Kaghan 93 15.8
30 Faisalabad 85 6.4 RWP-94 38.6 AS -2002 8.2
31 C-591 6.4 C-591 38.6 Merco 2007 17.6
32 LYP -73 6.4 Pirsabak-85 37.9 Shalimar 88 11.3
33 Meraj-08 6.4 Potohar-70 37.6 10789 17.1
34 Punjab-76 6.3 Soghat 90 37.6 Abdaghar 97 9.7
35 Khyber 83 6.3 Zarghoon-79 37.5 10737 13.6
36 Mehran-89 6.3 10742 37.4 Fakhri sarhad 13.0
174
37 Maxi pak 6.3 Mumal-2002 37.1 Punjab-76 16.5
38 AUP-4008 6.3 Blue silver 36.6 10792 20.8
39 Kaghan 93 6.2 Anmol-91 36.5 Raskoh 13.6
40 Khyber-79 6.2 Barani-83 36.0 Saleem 2000 9.6
41 10742 6.1 LU-26 35.6 Pirsabak 2008 9.6
42 LU-26 6.1 Suliman 96 35.6 AUP 5000 31.4
43 Sussi 6.0 Punjab-96 34.9 GA 2002 11.2
44 Bahalwapur-79 6.0 10793 34.7 Khyber 83 16.1
45 sonalika 6.0 Marwat-01 34.7 Sindh 81 15.5
46 Dirk 6.0 SH-2003 34.6 Local white 10.8
47 Shalimar 88 6.0 Shalimar 88 34.4 C-518 16.7
48 Shahkar- 95 5.9 Wafaq-2008 32.7 10748 18.2
49 Chenab 70 5.9 Pak-81 32.5 10724 19.3
50 Tandojam-83 5.8 Shahkar- 95 32.2 WL-711 20.7
51 Kiran 5.8 Chenab 79 32.1 Dirk 14.2
52 Barani-83 5.7 C-250 32.0 Potohar-70 23.7
53 10792 5.7 Pirsabak 2008 31.5 FPD-08 14.2
54 10748 5.7 GA 2002 30.7 Pak-81 22.9
55 Wardak-85 5.6 Fakhri sarhad 30.2 RWP-94 15.3
56 Anmol-91 5.6 Merco 2007 29.9 Mehran-89 15.0
57 Raskoh 5.6 C-228 29.4 Wadanak 85 25.1
58 MH-97 5.6 Local white 28.9 C-273 18.2
59 Zarghoon-79 5.6 Lr-230 28.7 Kohinoor-83 19.8
60 Lr-230 5.5 Faisalabad 85 28.2 Sandal 12.3
61 potohar-90 5.5 Zamindar-80 28.2 Bahalwapur-79 15.8
62 FPD-08 5.5 Haider 2002 28.1 Lasani-08 11.8
63 Pari -73 5.4 Khyber 83 27.4 10742 16.0
64 Chenab-96 5.4 Manther 27.3 Sariab-92 16.5
65 C-250 5.3 SA-75 27.0 Sussi 14.6
66 Punjab-88 5.2 Chenab 70 26.8 Pirsabak-85 16.2
67 Marwat-01 5.2 WL-711 26.6 Tandojam-83 14.0
68 WL-711 5.2 Dawar 96 26.6 10793 17.4
69 Blue silver 5.1 AUP 5000 26.3 SA-42 10.0
70 SA-75 5.1 Raskoh 26.2 Janbaz 11.9
71 Fakhri sarhad 5.0 10776 26.2 potohar-90 13.2
72 Saleem 2000 4.9 Margalla 99 26.1 Khyber-79 14.7
73 Lasani-08 4.9 Chakwal 86 26.0 Wafaq-2008 12.9
74 Janbaz 4.9 Maxi pak 26.0 Kiran 13.7
75 Sandal 4.8 Punjab-76 25.6 Zarghoon-79 17.1
175
76 Faisalabad-83 4.7 Kaghan 93 24.7 Potohar-93 16.0
77 Dawar 96 4.6 Wadanak 98 24.2 Punjab-81 18.2
78 RWP-94 4.6 Ksk 24.1 Blue silver 13.5
79 Pirsabak 2008 4.6 Zarlashta 90 23.5 C-250 16.2
80 Mumal-2002 4.5 Abdaghar 97 23.5 AUP-4008 15.4
81 Suliman 96 4.5 Bakhtawar 94 23.5 SA-75 16.4
82 Wafaq-2008 4.5 Noshera 96 23.5 Shahkar- 95 18.5
83 Local white 4.5 Sonalika 23.4 Wardak-85 12.4
84 Sindh 81 4.4 Pari -73 23.4 Marwat-01 14.8
85 10737 4.4 MH-97 23.3 SH-2003 18.5
86 Punjab-96 4.3 AS -2002 23.2 sonalika 15.8
87 Zarlashta 90 4.3 Nori -70 22.2 Meraj-08 16.4
88 ZA- 77 4.2 Rawal 87 22.2 Barani-83 15.0
89 Soghat 90 4.0 Wadanak 85 22.1 Iqbal-2000 16.6
90 Sariab-92 3.8 10737 21.9 Punjab-88 14.2
91 10776 3.7 NIAB 83 21.5 C-591 16.6
92 Abdaghar 97 3.7 Indus 79 21.4 C-228 23.2
93 GA 2002 3.7 Uqab 2000 21.1 Faisalabad-83 11.7
94 SA-42 3.6 LYP -73 20.7 LU-26 15.3
95 Wadanak 98 3.6 Barani 70 19.6 Anmol-91 15.8
96 Nori -70 3.4 ZA- 77 19.4 Zamindar-80 22.0
97 Chakwal 86 3.2 Sariab-92 19.3 Sutlag-86 23.2
98 Manther 3.1 10748 18.1 Punjab-96 12.5
99 Ksk 3.1 Sindh 81 17.5 Chenab-96 12.5
100 AS -2002 2.7 10792 16.2 Mumal-2002 14.8
176
Annexure 2
Sorted mean table of hundred wheat genotypes on the base of RWCN
S.No Varieties RWCN% S.No Varieties RWCN%
1 Margalla 99 99 51 Local white 88
2 Wafaq-2008 98 52 Maxi pak 88
3 Anmol-91 98 53 RWP-94 88
4 Mumal-2002 97 54 Punjab-88 86
5 C-518 97 55 sonalika 86
6 Uqab 2000 97 56 10724 85
7 Meraj-08 97 57 Manther 85
8 Nori -70 96 58 10742 84
9 Lasani-08 96 59 FPD-08 84
10 Punjab-81 96 60 10796 84
11 C-228 95 61 Sindh 81 82
12 LU-26 95 62 10737 82
13 10793 95 63 Barani-83 81
14 Pirsabak-85 95 64 10748 80
15 Kaghan 93 95 65 Fakhri sarhad 79
16 C-273 95 66 Suliman 96 77
17 Wadanak 98 95 67 Janbaz 76
18 SA-42 95 68 Abdaghar 97 76
19 Sandal 95 69 SH-2003 75
20 SA-75 94 70 Pari -73 75
21 NIAB 83 94 71 Shalimar 88 73
22 Sussi 93 72 Lr-230 73
23 Ksk 93 73 Wardak-85 73
24 Pak-81 93 74 Saleem 2000 72
25 Marwat-01 93 75 potohar-90 72
26 Punjab-76 93 76 Khyber 83 69
27 Indus 79 93 77 10724 69
28 Merco 2007 92 78 ZA- 77 69
29 Potohar-93 92 79 Zarlashta 90 68
30 MH-97 92 80 Chenab 70 67
31 Wadanak 85 92 81 Dirk 67
32 Faisalabad 85 92 82 Khyber-79 67
33 LYP -73 92 83 C-250 66
34 WL-711 92 84 Raskoh 65
35 Sutlag-86 92 85 C-591 64
177
36 Barani 70 91 86 Potohar-70 62
37 GA 2002 91 87 Faisalabad-83 61
38 Kiran 91 88 Chakwal 86 60
39 Rawal 87 91 89 Shahkar- 95 59
40 Chenab-96 90 90 Pirsabak 2008 58
41 Sariab-92 90 91 Blue silver 57
42 Noshera 96 90 92 Iqbal-2000 52
43 Tandojam-83 90 93 Punjab-96 51
44 10776 90 94 AUP-4008 50
45 Bakhtawar 94 90 95 Haider 2002 48
46 Soghat 90 89 96 AUP 5000 45
47 Bahalwapur-79 89 97 Zamindar-80 43
48 Chenab 79 89 98 Kohinoor-83 43
49 Mehran-89 88 99 AS -2002 39
50 Dawar 96 88 100 Zarghoon-79 34
178
Annexure 3
Sorted mean table of hundred wheat genotypes on the base of RWCS
S.No Varieties RWCS% S.No Varieties RWCS%
1 NIAB 83 93 51 SA-42 67
2 Tandojam-83 91 52 AUP 5000 67
3 Local white 91 53 Wadanak 85 66
4 Rawal 87 90 54 SA-75 65
5 Soghat 90 89 55 Khyber-79 65
6 Potohar-93 89 56 10793 63
7 Indus 79 88 57 MH-97 61
8 Punjab-81 88 58 Mehran-89 61
9 potohar-90 88 59 Noshera 96 60
10 Sindh 81 86 60 Blue silver 60
11 10742 85 61 Sariab-92 59
12 Zarghoon-79 85 62 Shalimar 88 58
13 Bahalwapur-79 85 63 Nori -70 56
14 Kaghan 93 85 64 C-518 55
15 Anmol-91 85 65 10776 55
16 C-591 85 66 Chenab-96 54
17 WL-711 84 67 Raskoh 54
18 Punjab-76 84 68 Ksk 54
19 Uqab 2000 84 69 LU-26 53
20 Margalla 99 84 70 Dawar 96 53
21 10724 84 71 Mumal-2002 53
22 Chenab 70 83 72 LYP -73 51
23 Meraj-08 83 73 Marwat-01 49
24 Wafaq-2008 83 74 Sutlag-86 47
25 Abdaghar 97 82 75 10748 47
26 Wardak-85 82 76 10724 46
27 Punjab-96 81 77 AUP-4008 46
28 Merco 2007 80 78 Sussi 44
29 Pak-81 79 79 sonalika 44
30 Kiran 78 80 RWP-94 44
31 Manther 78 81 Kohinoor-83 42
32 Maxi pak 78 82 AS -2002 39
33 Shahkar- 95 77 83 Dirk 37
34 Bakhtawar 94 76 84 Pirsabak-85 35
35 C-250 75 85 Pari -73 35
179
36 Barani 70 75 86 Lr-230 34
37 C-228 75 87 Janbaz 31
38 Pirsabak 2008 74 88 Potohar-70 30
39 Saleem 2000 73 89 Barani-83 30
40 Fakhri sarhad 73 90 GA 2002 28
41 Haider 2002 73 91 FPD-08 27
42 Sandal 72 92 ZA- 77 27
43 C-273 72 93 10737 25
44 Zarlashta 90 71 94 SH-2003 24
45 Lasani-08 70 95 Faisalabad-83 22
46 Punjab-88 69 96 Wadanak 98 22
47 Chenab 79 69 97 Khyber 83 20
48 Zamindar-80 69 98 Faisalabad 85 19
49 10796 68 99 Suliman 96 8
50 Iqbal-2000 68 100 Chakwal 86 7
180
Annexure 4
Sorted mean table of hundred wheat genotypes on the base of WLRN and WLRS
S.No Genotype WLRN Genotype WLRS Genotype WUE
1 Manther 6.3 1 Pirsabak-85 5.4 1 NIAB 83 1.7
2 Maxi pak 5.0 2 010724 5.3 2 C-273 1.6
3 Chenab 70 3.5 3 Fakhri sarhad 4.9 3 010742 1.5
4 Kiran 3.3 4 Shalimar 88 4.8 4 Kiran 1.5
5 LU-26 3.2 5 Merco 2007 4.7 5 ZA- 77 1.5
6 Punjab-81 3.1 6 Khyber-79 4.7 6 Punjab-76 1.5
7 Pari -73 3.1 7 Blue silver 4.1 7 AS -2002 1.5
8 Wardak-85 3.1 8 Kiran 3.3 8 Potohar-93 1.5
9 Dirk 3.1 9 010776 3.1 9 Zamindar-80 1.5
10 Pirsabak 2008 3.0 10 010724 3.1 10 Bakhtawar 94 1.5
11 Pak-81 3.0 11 Sutlag-86 3.0 11 Soghat 90 1.5
12 Sussi 3.0 12 SA-42 2.8 12 Iqbal-2000 1.5
13 Meraj-08 3.0 13 Wafaq-2008 2.8 13 Barani 70 1.4
14 Khyber 83 2.9 14 C-591 2.8 14 Lasani-08 1.4
15 Wafaq-2008 2.8 15 C-518 2.8 15 Mehran-89 1.4
16 Potohar-70 2.8 16 Abdaghar 97 2.7 16 Janbaz 1.4
17 Sandal 2.8 17 Rawal 87 2.7 17 Meraj-08 1.4
18 Kaghan 93 2.8 18 Soghat 90 2.7 18 Ksk 1.4
19 Wadanak 98 2.8 19 Punjab-81 2.6 19 C-518 1.4
20 Sariab-92 2.8 20 Dawar 96 2.6 20 Faisalabad-83 1.4
21 Tandojam-83 2.8 21 AS -2002 2.5 21 Pirsabak 2008 1.4
22 FPD-08 2.8 22 Potohar-70 2.5 22 Merco 2007 1.4
23 Anmol-91 2.8 23 Nori -70 2.5 23 Punjab-81 1.4
24 C-518 2.7 24 Sandal 2.4 24 Pirsabak-85 1.4
25 Uqab 2000 2.7 25 010748 2.4 25 Chakwal 86 1.4
26 Punjab-76 2.7 26 Chenab 70 2.4 26 Pak-81 1.4
27 Faisalabad 85 2.6 27 Kaghan 93 2.4 27 Tandojam-83 1.4
28 Bakhtawar 94 2.5 28 Zarlashta 90 2.4 28 Dirk 1.4
29 Dawar 96 2.5 29 Suliman 96 2.4 29 010724 1.3
30 AUP-4008 2.5 30 Sindh 81 2.4 30 010792 1.3
31 SA-42 2.5 31 Punjab-88 2.4 31 SH-2003 1.3
32 010793 2.5 32 C-273 2.3 32 Maxi pak 1.3
33 Pirsabak-85 2.5 33 Zarghoon-79 2.3 33 Noshera 96 1.3
34 C-273 2.4 34 Raskoh 2.3 34 Pari -73 1.3
35 Saleem 2000 2.4 35 Kohinoor-83 2.2 35 Wadanak 98 1.3
181
36 010742 2.3 36 Mehran-89 2.2 36 LU-26 1.3
37 Potohar-93 2.3 37 Bakhtawar 94 2.2 37 Dawar 96 1.3
38 Mumal-2002 2.3 38 Sariab-92 2.2 38 Rawal 87 1.3
39 Indus 79 2.3 39 Khyber 83 2.1 39 Chenab 70 1.3
40 Chenab-96 2.3 40 FPD-08 2.1 40 Nori -70 1.3
41 Fakhri sarhad 2.3 41 010742 2.0 41 010793 1.3
42 Raskoh 2.3 42 Chenab-96 2.0 42 Sussi 1.3
43 RWP-94 2.3 43 Sussi 2.0 43 Zarlashta 90 1.3
44 SA-75 2.3 44 ZA- 77 2.0 44 Khyber 83 1.3
45 C-250 2.2 45 Chakwal 86 2.0 45 C-250 1.2
46 Zarghoon-79 2.2 46 Pak-81 2.0 46 SA-75 1.2
47 Local white 2.2 47 LYP -73 1.9 47 Kohinoor-83 1.2
48 Lasani-08 2.2 48 Tandojam-83 1.8 48 C-591 1.2
49 Bahalwapur-79 2.2 49 Faisalabad 85 1.7 49 Indus 79 1.2
50 Sutlag-86 2.1 50 MH-97 1.7 50 Chenab 79 1.2
51 Mehran-89 2.1 51 AUP 5000 1.7 51 Wadanak 85 1.2
52 Shalimar 88 2.1 52 Pari -73 1.7 52 Raskoh 1.2
53 Zarlashta 90 2.1 53 Wardak-85 1.7 53 Sariab-92 1.2
54 Suliman 96 2.1 54 Punjab-76 1.7 54 010737 1.2
55 Sindh 81 2.1 55 010737 1.6 55 Marwat-01 1.2
56 Punjab-88 2.1 56 potohar-90 1.6 56 Shahkar- 95 1.2
57 Ksk 2.1 57 Janbaz 1.6 57 Fakhri sarhad 1.2
58 Lr-230 2.0 58 Uqab 2000 1.6 58 Haider 2002 1.2
59 010737 2.0 59 Saleem 2000 1.4 59 Sutlag-86 1.2
60 potohar-90 2.0 60 WL-711 1.4 60 Wafaq-2008 1.2
61 Janbaz 2.0 61 Punjab-96 1.4 61 WL-711 1.2
62 Zamindar-80 2.0 62 Bahalwapur-79 1.4 62 AUP-4008 1.2
63 Blue silver 2.0 63 Wadanak 85 1.4 63 Kaghan 93 1.2
64 AUP 5000 2.0 64 Potohar-93 1.4 64 010776 1.2
65 Marwat-01 2.0 65 SA-75 1.4 65 Abdaghar 97 1.2
66 C-228 2.0 66 Marwat-01 1.3 66 GA 2002 1.2
67 010776 2.0 67 Meraj-08 1.3 67 Anmol-91 1.2
68 010724 2.0 68 AUP-4008 1.3 68 C-228 1.2
69 Haider 2002 2.0 69 GA 2002 1.3 69 Mumal-2002 1.2
70 Wadanak 85 1.9 70 Anmol-91 1.3 70 Bahalwapur-79 1.2
71 WL-711 1.9 71 C-228 1.3 71 Saleem 2000 1.2
72 Soghat 90 1.8 72 Haider 2002 1.3 72 Margalla 99 1.2
73 Merco 2007 1.8 73 Local white 1.3 73 Faisalabad 85 1.2
74 Khyber-79 1.8 74 Lasani-08 1.3 74 010748 1.2
182
75 Kohinoor-83 1.8 75 Shahkar- 95 1.2 75 Barani-83 1.2
76 Margalla 99 1.8 76 Margalla 99 1.2 76 potohar-90 1.2
77 LYP -73 1.8 77 Zamindar-80 1.2 77 RWP-94 1.2
78 Abdaghar 97 1.7 78 C-250 1.2 78 LYP -73 1.1
79 Rawal 87 1.7 79 Ksk 1.2 79 SA-42 1.1
80 AS -2002 1.7 80 LU-26 1.2 80 Shalimar 88 1.1
81 NIAB 83 1.7 81 Lr-230 1.2 81 Blue silver 1.1
82 Barani-83 1.7 82 Wadanak 98 1.1 82 AUP 5000 1.1
83 Barani 70 1.7 83 SH-2003 1.1 83 Suliman 96 1.1
84 Faisalabad-83 1.7 84 Indus 79 1.1 84 Local white 1.1
85 GA 2002 1.6 85 Maxi pak 1.1 85 FPD-08 1.1
86 C-591 1.6 86 Sonalika 1.0 86 Zarghoon-79 1.1
87 MH-97 1.6 87 RWP-94 1.0 87 Potohar-70 1.1
88 Nori -70 1.6 88 Barani 70 1.0 88 Wardak-85 1.1
89 Punjab-96 1.5 89 Dirk 0.9 89 Uqab 2000 1.1
90 SH-2003 1.5 90 010793 0.9 90 Chenab-96 1.1
91 Sonalika 1.5 91 Iqbal-2000 0.9 91 Punjab-88 1.1
92 Shahkar- 95 1.4 92 Manther 0.9 92 Lr-230 1.1
93 Noshera 96 1.4 93 Mumal-2002 0.8 93 010724 1.1
94 Chakwal 86 1.4 94 Noshera 96 0.8 94 Sandal 1.1
95 010724 1.2 95 010792 0.6 95 Manther 1.0
96 Chenab 79 1.1 96 NIAB 83 0.3 96 MH-97 1.0
97 ZA- 77 1.0 97 Barani-83 0.3 97 Punjab-96 1.0
98 010748 0.9 98 Pirsabak 2008 0.3 98 Sindh 81 0.9
99 010792 0.9 99 Chenab 79 0.2 99 Khyber-79 0.9
100 Iqbal-2000 0.5 100 Faisalabad-83 0.2 100 sonalika 0.4
183
Annexure 4: Sorted tables of hundred wheat genotypes evaluated for different root traits
Genotype RFW Genotype SFW Genotype RDW Genotype SDW
AUP 5000 0.37 Saleem 2000 0.94 Soghat 90 0.102 Saleem 2000 0.632
Soghat 90 0.16 Zarlashta 90 0.83 NIAB 83 0.074 NIAB 83 0.581
NIAB 83 0.13 NIAB 83 0.76 Sutlag-86 0.061 Zarlashta 90 0.466
Faisalabad 85 0.09 Lr-230 0.76 C-273 0.061 Faisalabad 85 0.385
Rawal 87 0.09 Indus 79 0.76 Pirsabak-85 0.059 Chenab 79 0.369
Blue silver 0.08 Raskoh 0.72 Blue silver 0.057 10724 0.357
C-273 0.08 10742 0.72 AUP-4008 0.054 Abdaghar 97 0.338
Lasani-08 0.08 Manther 0.70 Zamindar-80 0.053 Khyber 83 0.315
AUP-4008 0.08 Bakhtawar 94 0.69 Sandal 0.050 GA 2002 0.289
Sutlag-86 0.08 Sindh 81 0.69 Bahalwapur-79 0.049 SA-42 0.280
potohar-90 0.08 Wadanak 85 0.66 Noshera 96 0.047 Sindh 81 0.279
Pirsabak-85 0.08 Chenab 79 0.65 Rawal 87 0.046 Uqab 2000 0.243
Bakhtawar 94 0.08 Pari -73 0.65 10779 0.044 Rawal 87 0.243
Indus 79 0.07 Noshera 96 0.63 Faisalabad 85 0.044 Kaghan 93 0.240
Noshera 96 0.07 Maxi pak 0.63 Bakhtawar 94 0.043 Noshera 96 0.229
Bahalwapur-79 0.07 Merco 2007 0.60 Fakhri sarhad 0.042 Wadanak 85 0.204
Maxi pak 0.07 10724 0.59 potohar-90 0.042 Blue silver 0.203
Punjab-76 0.07 sonalika 0.58 FPD-08 0.041 Lr-230 0.196
Zamindar-80 0.07 Abdaghar 97 0.56 Sariab-92 0.040 MH-97 0.196
Kohinoor-83 0.06 Rawal 87 0.54 Tandojam-83 0.040 10742 0.194
Zarlashta 90 0.06 Faisalabad 85 0.54 Kohinoor-83 0.038 Indus 79 0.187
Sandal 0.06 Chenab 70 0.53 Anmol-91 0.035 Bakhtawar 94 0.187
Anmol-91 0.06 Haider 2002 0.52 Indus 79 0.035 Pari -73 0.178
Barani-83 0.06 Tandojam-83 0.52 Kiran 0.034 Faisalabad-83 0.171
10779 0.06 MH-97 0.50 Janbaz 0.034 Tandojam-83 0.169
Chenab 79 0.06 Local white 0.49 Punjab-76 0.033 LU-26 0.168
Wardak-85 0.06 Sariab-92 0.49 10792 0.031 RWP-94 0.168
Lr-230 0.06 Blue silver 0.48 AUP 5000 0.030 Punjab-81 0.167
Fakhri sarhad 0.06 Uqab 2000 0.48 Zarlashta 90 0.029 Sariab-92 0.167
Tandojam-83 0.05 Khyber 83 0.47 Merco 2007 0.029 Barani 70 0.165
Sariab-92 0.05 GA 2002 0.47 C-250 0.029 Kohinoor-83 0.162
FPD-08 0.05 C-250 0.46 MH-97 0.029 Wafaq-2008 0.158
GA 2002 0.05 Barani 70 0.45 10724 0.028 Shalimar 88 0.155
Kiran 0.05 AUP-4008 0.44 Potohar-93 0.028 Potohar-70 0.150
Kaghan 93 0.05 Kaghan 93 0.43 Maxi pak 0.028 Manther 0.146
Janbaz 0.05 AUP 5000 0.43 Lasani-08 0.027 Punjab-76 0.146
C-250 0.05 RWP-94 0.43 Wardak-85 0.026 Raskoh 0.142
184
Marwat-01 0.05 SA-75 0.42 Kaghan 93 0.026 AUP-4008 0.140
Ksk 0.05 Punjab-81 0.42 Wadanak 85 0.025 10776 0.140
Merco 2007 0.05 ZA- 77 0.42 Abdaghar 97 0.025 sonalika 0.138
10792 0.05 Faisalabad-83 0.41 Chenab 79 0.025 Merco 2007 0.136
MH-97 0.05 Suliman 96 0.40 Faisalabad-83 0.025 Local white 0.136
10724 0.05 Potohar-70 0.39 Wafaq-2008 0.024 Anmol-91 0.134
Wadanak 85 0.04 LU-26 0.39 Khyber-79 0.024 AUP 5000 0.133
Sonalika 0.04 Wadanak 98 0.38 Mehran-89 0.024 Potohar-93 0.130
Punjab-96 0.04 AS -2002 0.37 Zarghoon-79 0.022 Sutlag-86 0.130
Khyber 83 0.04 Wafaq-2008 0.37 SA-42 0.022 10737 0.130
Manther 0.04 Soghat 90 0.37 10793 0.022 C-273 0.130
Khyber-79 0.04 Kohinoor-83 0.37 Sonalika 0.022 Zarghoon-79 0.129
Potohar-93 0.04 C-273 0.37 Pak-81 0.022 Wadanak 98 0.129
Dirk 0.04 10776 0.36 Nori -70 0.021 10793 0.125
Abdaghar 97 0.04 SA-42 0.34 C-591 0.020 ZA- 77 0.123
Wafaq-2008 0.04 Janbaz 0.33 Barani-83 0.020 Maxi pak 0.121
Sindh 81 0.04 10792 0.33 Chakwal 86 0.020 Barani-83 0.120
Mehran-89 0.03 Punjab-88 0.33 Khyber 83 0.020 Chenab 70 0.119
C-591 0.03 Potohar-93 0.33 GA 2002 0.020 Haider 2002 0.118
Faisalabad-83 0.03 10737 0.32 Iqbal-2000 0.020 Dirk 0.116
Uqab 2000 0.03 Dawar 96 0.31 Lr-230 0.019 SA-75 0.114
Raskoh 0.03 Zarghoon-79 0.30 LU-26 0.019 Khyber-79 0.113
Nori -70 0.03 Ksk 0.30 Manther 0.018 10792 0.112
Zarghoon-79 0.03 Barani-83 0.29 Punjab-81 0.018 Fakhri sarhad 0.111
ZA- 77 0.03 Sutlag-86 0.29 ZA- 77 0.018 potohar-90 0.110
AS -2002 0.03 10793 0.29 10742 0.018 C-250 0.108
10776 0.03 Chakwal 86 0.29 Ksk 0.018 Wardak-85 0.106
SA-42 0.03 Sussi 0.28 C-228 0.017 Lasani-08 0.104
Suliman 96 0.03 LYP -73 0.28 Dawar 96 0.017 Zamindar-80 0.104
Chenab-96 0.03 Shalimar 88 0.28 Chenab-96 0.017 10748 0.104
Pari -73 0.03 Anmol-91 0.28 Dirk 0.017 Punjab-88 0.102
Chenab 70 0.03 10748 0.28 Margalla 99 0.016 Chakwal 86 0.102
Saleem 2000 0.03 potohar-90 0.28 Punjab-88 0.016 Chenab-96 0.102
C-518 0.03 Bahalwapur-79 0.27 Chenab 70 0.016 Sussi 0.100
10793 0.03 C-518 0.27 10737 0.016 Kiran 0.098
Margalla 99 0.03 Lasani-08 0.27 Pari -73 0.016 Nori -70 0.096
10737 0.03 Margalla 99 0.27 AS -2002 0.016 Margalla 99 0.094
Iqbal-2000 0.03 Punjab-76 0.26 Shalimar 88 0.015 SH-2003 0.094
C-228 0.03 Wardak-85 0.26 LYP -73 0.015 Sandal 0.094
185
Punjab-81 0.03 Pak-81 0.26 10748 0.015 C-228 0.093
Barani 70 0.03 Dirk 0.25 WL-711 0.014 LYP -73 0.092
Wadanak 98 0.02 Sandal 0.25 Sindh 81 0.014 Punjab-96 0.091
LU-26 0.02 Fakhri sarhad 0.24 Wadanak 98 0.014 Bahalwapur-79 0.088
Shalimar 88 0.02 Khyber-79 0.23 Raskoh 0.013 Janbaz 0.086
10748 0.02 WL-711 0.23 Pirsabak 2008 0.013 Pak-81 0.086
Local white 0.02 C-591 0.22 C-518 0.013 Ksk 0.086
10742 0.02 Chenab-96 0.22 Shahkar- 95 0.012 WL-711 0.085
Chakwal 86 0.02 SH-2003 0.22 Uqab 2000 0.011 Mumal-2002 0.084
Dawar 96 0.02 Punjab-96 0.22 SA-75 0.011 Dawar 96 0.080
LYP -73 0.02 Zamindar-80 0.22 Meraj-08 0.011 Suliman 96 0.079
Punjab-88 0.02 Nori -70 0.21 Marwat-01 0.011 C-518 0.078
Shahkar- 95 0.02 Shahkar- 95 0.21 Suliman 96 0.011 10779 0.077
Sussi 0.02 Meraj-08 0.21 Sussi 0.011 Shahkar- 95 0.075
Pirsabak 2008 0.02 C-228 0.20 Saleem 2000 0.010 Meraj-08 0.075
Potohar-70 0.02 10779 0.19 Potohar-70 0.010 Soghat 90 0.073
Meraj-08 0.02 Kiran 0.19 10776 0.010 C-591 0.071
WL-711 0.02 Mumal-2002 0.19 Punjab-96 0.009 Pirsabak 2008 0.065
Haider 2002 0.01 Marwat-01 0.17 Haider 2002 0.009 Mehran-89 0.065
SA-75 0.01 FPD-08 0.15 RWP-94 0.009 Marwat-01 0.058
Mumal-2002 0.01 Pirsabak 2008 0.15 Local white 0.008 Pirsabak-85 0.057
SH-2003 0.01 Pirsabak-85 0.13 Mumal-2002 0.008 FPD-08 0.057
RWP-94 0.01 Iqbal-2000 0.12 SH-2003 0.007 AS -2002 0.053
Pak-81 0.01 Mehran-89 0.10 Barani 70 0.006 Iqbal-2000 0.052
186
Annexure 4: Sorted table of hundred wheat genotypes evaluated for different root traits
Genotype R:S Genotype R.D Genotype NNR Genotype NSR
Pirsabak-85 2.544 AS -2002 0.533 Meraj-08 4 Marwat-01 6.67
AUP 5000 0.862 Maxi pak 0.417 Iqbal-2000 3 AS -2002 6.33
Janbaz 0.754 Manther 0.407 10742 2.67 Chenab-96 6.33
Soghat 90 0.509 Haider 2002 0.400 Lasani-08 2.67 10724 6.33
FPD-08 0.466 Ksk 0.350 Sariab-92 2.67 AUP 5000 6.33
Lasani-08 0.424 Margalla 99 0.333 Pirsabak 2008 2.67 MH-97 6
Bahalwapur-79 0.403 Local white 0.300 Faisalabad-83 2.67 Kaghan 93 6
potohar-90 0.372 Kohinoor-83 0.297 GA 2002 2.67 Noshera 96 6
Wardak-85 0.354 Rawal 87 0.290 Barani 70 2.33 10792 6
Mehran-89 0.326 Merco 2007 0.283 LYP -73 2.33 C-273 6
Sandal 0.318 Lr-230 0.280 Zarghoon-79 2.33 Kiran 6
10779 0.313 Uqab 2000 0.267 Sonalika 2 Bakhtawar 94 5.67
Zamindar-80 0.303 MH-97 0.257 Chenab 79 2 Margalla 99 5.67
Kiran 0.283 Barani-83 0.247 Sutlag-86 2 NIAB 83 5.67
Sutlag-86 0.279 Sonalika 0.243 C-518 2 Saleem 2000 5.67
Punjab-76 0.265 GA 2002 0.240 Saleem 2000 2 Pari -73 5.67
Khyber-79 0.260 Lasani-08 0.240 Khyber-79 2 Faisalabad-83 5.67
Iqbal-2000 0.254 Raskoh 0.233 Noshera 96 2 Potohar-93 5.67
Fakhri sarhad 0.251 C-273 0.227 C-250 2 Lr-230 5.33
Anmol-91 0.251 Faisalabad 85 0.227 Uqab 2000 2 Wadanak 85 5.33
Marwat-01 0.250 ZA- 77 0.227 Suliman 96 2 Zarlashta 90 5.33
Barani-83 0.238 10793 0.227 SH-2003 2 GA 2002 5.33
Punjab-96 0.233 NIAB 83 0.223 Ksk 2 Chakwal 86 5.33
C-273 0.215 C-591 0.223 C-273 2 Shahkar- 95 5.33
AUP-4008 0.202 Chenab-96 0.217 AUP-4008 2 Punjab-88 5.33
Faisalabad 85 0.185 Marwat-01 0.217 Shalimar 88 1.67 10793 5.33
Kohinoor-83 0.178 Potohar-70 0.217 Khyber 83 1.67 Barani-83 5.33
Blue silver 0.163 Dirk 0.217 Chenab 70 1.67 Pirsabak-85 5.33
Rawal 87 0.161 Sindh 81 0.213 Haider 2002 1.67 Sussi 5.33
NIAB 83 0.161 Faisalabad-83 0.213 RWP-94 1.67 Janbaz 5.33
Pirsabak 2008 0.161 Sariab-92 0.213 MH-97 1.67 sonalika 5
Ksk 0.160 Sutlag-86 0.210 Punjab-76 1.67 Uqab 2000 5
C-591 0.157 Zarlashta 90 0.207 Punjab-88 1.67 Faisalabad 85 5
Nori -70 0.154 Wadanak 98 0.207 Pak-81 1.67 LYP -73 5
Noshera 96 0.152 Zarghoon-79 0.207 Barani-83 1.67 10737 5
Shahkar- 95 0.142 C-228 0.207 Shahkar- 95 1.67 Punjab-96 5
Sariab-92 0.141 Pirsabak-85 0.207 Punjab-81 1.33 Mumal-2002 5
187
10792 0.141 10748 0.203 Dirk 1.33 Zamindar-80 5
Dirk 0.141 Wafaq-2008 0.200 Lr-230 1.33 Zarghoon-79 5
GA 2002 0.123 Khyber-79 0.200 Indus 79 1.33 Wafaq-2008 5
Bakhtawar 94 0.122 Wardak-85 0.197 10737 1.33 Tandojam-83 5
Potohar-93 0.119 Soghat 90 0.197 Janbaz 1.33 FPD-08 5
C-228 0.118 Punjab-81 0.197 WL-711 1.33 Ksk 4.67
Margalla 99 0.114 LU-26 0.193 Dawar 96 1.33 Raskoh 4.67
Kaghan 93 0.113 10742 0.193 Fakhri sarhad 1.33 Local white 4.67
Wafaq-2008 0.112 Pak-81 0.193 10793 1.33 Chenab 79 4.67
Indus 79 0.112 Tandojam-83 0.193 Marwat-01 1.33 Wadanak 98 4.67
Chenab-96 0.112 Bahalwapur-79 0.193 Wafaq-2008 1.33 Sindh 81 4.67
Tandojam-83 0.109 WL-711 0.190 Sussi 1.33 10776 4.67
C-250 0.106 Potohar-93 0.190 Tandojam-83 1 LU-26 4.67
Khyber 83 0.105 FPD-08 0.190 Merco 2007 1 C-228 4.67
Maxi pak 0.104 Indus 79 0.187 Manther 1 Sariab-92 4.67
C-518 0.098 Shalimar 88 0.187 Margalla 99 1 Kohinoor-83 4.67
Shalimar 88 0.098 Nori -70 0.187 Zarlashta 90 1 Dirk 4.67
10793 0.096 Anmol-91 0.187 Soghat 90 1 Lasani-08 4.67
Zarghoon-79 0.095 Meraj-08 0.187 Nori -70 1 Wardak-85 4.67
SA-42 0.094 Pari -73 0.183 C-591 1 Meraj-08 4.67
MH-97 0.092 SA-42 0.180 Anmol-91 1 Mehran-89 4.67
Chenab 79 0.092 SA-75 0.177 Maxi pak 1 AUP-4008 4.67
10737 0.088 10779 0.173 Abdaghar 97 1 Indus 79 4.33
Lr-230 0.087 C-250 0.173 Pari -73 1 Abdaghar 97 4.33
10776 0.086 Abdaghar 97 0.167 SA-42 1 Rawal 87 4.33
Zarlashta 90 0.084 Sussi 0.167 potohar-90 1 ZA- 77 4.33
Faisalabad-83 0.083 Barani 70 0.163 Local white 1 10779 4.33
10748 0.079 potohar-90 0.163 NIAB 83 1 Pirsabak 2008 4.33
Uqab 2000 0.078 Zamindar-80 0.160 Sindh 81 1 Punjab-81 4.33
Merco 2007 0.078 RWP-94 0.153 Blue silver 1 Pak-81 4.33
Punjab-81 0.078 Punjab-88 0.153 Kaghan 93 1 Merco 2007 4
Wadanak 85 0.078 10724 0.150 Pirsabak-85 1 Punjab-76 4
10724 0.077 Chenab 79 0.147 Zamindar-80 1 Shalimar 88 4
Meraj-08 0.077 Punjab-96 0.147 Chenab-96 1 Dawar 96 4
AS -2002 0.076 Chenab 70 0.143 ZA- 77 0.67 10748 4
Mumal-2002 0.076 Noshera 96 0.140 C-228 0.67 SH-2003 4
Chakwal 86 0.076 10776 0.140 Potohar-70 0.67 Blue silver 4
LYP -73 0.075 Shahkar- 95 0.140 Bahalwapur-79 0.67 Sandal 4
Sonalika 0.075 Blue silver 0.140 Punjab-96 0.67 potohar-90 4
188
WL-711 0.072 Sandal 0.140 SA-75 0.67 Manther 3.67
ZA- 77 0.071 Chakwal 86 0.137 Potohar-93 0.67 Haider 2002 3.67
Suliman 96 0.071 Kaghan 93 0.137 10748 0.67 Barani 70 3.67
Abdaghar 97 0.069 Wadanak 85 0.133 10776 0.67 Anmol-91 3.67
LU-26 0.069 LYP -73 0.133 10779 0.67 C-250 3.67
Dawar 96 0.066 Fakhri sarhad 0.133 FPD-08 0.67 SA-75 3.67
Sussi 0.065 10737 0.133 Wadanak 98 0.67 SA-42 3.67
Manther 0.065 AUP 5000 0.130 Mumal-2002 0.67 Potohar-70 3.67
Wadanak 98 0.064 Kiran 0.127 10724 0.33 Khyber-79 3.67
SH-2003 0.063 AUP-4008 0.123 Raskoh 0.33 Maxi pak 3.33
Punjab-88 0.059 Iqbal-2000 0.120 AS -2002 0.33 Chenab 70 3.33
Sindh 81 0.052 Janbaz 0.120 Kiran 0.33 Soghat 90 3.33
Barani 70 0.052 Khyber 83 0.117 10792 0.33 Suliman 96 3.33
Chenab 70 0.050 Punjab-76 0.117 Mehran-89 0.33 Fakhri sarhad 3.33
Raskoh 0.048 Mehran-89 0.117 LU-26 0 Iqbal-2000 3.33
Pari -73 0.047 Pirsabak 2008 0.113 Kohinoor-83 0 C-591 3.33
Potohar-70 0.045 Mumal-2002 0.113 Bakhtawar 94 0 Sutlag-86 3.33
Local white 0.043 SH-2003 0.113 Wadanak 85 0 RWP-94 3.33
SA-75 0.036 C-518 0.113 Faisalabad 85 0 WL-711 3.33
Pak-81 0.033 Bakhtawar 94 0.103 Chakwal 86 0 Bahalwapur-79 3.33
10742 0.030 Dawar 96 0.093 Wardak-85 0 Khyber 83 3
Saleem 2000 0.027 Saleem 2000 0.087 Rawal 87 0 Nori -70 3
RWP-94 0.027 10792 0.083 Sandal 0 10742 2.67
Haider 2002 0.025 Suliman 96 0.067 AUP 5000 0 C-518 2
189
Annexure 4: Sorted table of hundred wheat genotypes evaluated for different root traits
Genotype R A Genotype TRL Genotype RDT Genotype MRL
MH-97 113 Pirsabak-85 56 Soghat 90 12 Abdaghar 97 30
Potohar-70 110 Chenab 79 53 10748 9 Punjab-96 29
Manther 107 10776 43 10724 9 Fakhri sarhad 29
Lr-230 103 Bakhtawar 94 42 Lasani-08 9 Chenab 79 26
C-518 100 NIAB 83 42 LYP -73 8 sonalika 24
10779 97 Faisalabad 85 41 Marwat-01 8 C-228 23
Pirsabak-85 97 Blue silver 41 Barani-83 8 Punjab-76 21
Lasani-08 97 Sutlag-86 39 Barani 70 7 Faisalabad 85 21
Sonalika 93 C-273 39 C-591 7 Anmol-91 21
Maxi pak 93 Kohinoor-83 38 Kohinoor-83 7 NIAB 83 20
Margalla 99 93 Noshera 96 38 Rawal 87 7 10776 20
Local white 93 Fakhri sarhad 36 10792 7 C-273 19
Shalimar 88 93 Anmol-91 36 Pirsabak 2008 7 10779 19
Pak-81 93 10779 35 AUP 5000 7 Zarlashta 90 19
Tandojam-83 93 Janbaz 35 Zarlashta 90 7 Kohinoor-83 17
Indus 79 90 10793 35 Potohar-93 7 Pirsabak-85 17
Haider 2002 90 Kiran 34 Potohar-70 7 10793 16
Faisalabad 85 90 Shahkar- 95 34 Bahalwapur-79 7 Shalimar 88 16
Khyber 83 90 Sariab-92 33 MH-97 6 Lasani-08 16
Sindh 81 87 Punjab-96 33 Punjab-76 6 Kiran 16
potohar-90 87 C-228 33 Kaghan 93 6 AUP-4008 16
Raskoh 83 Zarlashta 90 32 AS -2002 6 Rawal 87 16
Saleem 2000 83 AUP-4008 32 10776 6 LU-26 16
Chenab 70 83 Merco 2007 31 Zamindar-80 6 WL-711 16
Wadanak 85 80 Shalimar 88 31 10793 6 Faisalabad-83 15
Chenab 79 80 Nori -70 30 Punjab-81 6 Blue silver 15
Punjab-88 80 Punjab-76 30 Blue silver 6 C-518 15
GA 2002 77 Punjab-81 30 RWP-94 6 Shahkar- 95 15
LU-26 77 10724 30 Tandojam-83 6 Sariab-92 15
AUP 5000 77 WL-711 30 Pari -73 6 Barani-83 15
SA-75 77 Sonalika 29 Sariab-92 6 potohar-90 15
Bakhtawar 94 73 Manther 29 10742 6 Mehran-89 15
Zarlashta 90 73 Lasani-08 29 Pirsabak-85 6 10792 15
Barani 70 73 Kaghan 93 29 Kiran 6 Merco 2007 14
Soghat 90 73 Wafaq-2008 28 Wardak-85 6 Noshera 96 14
Nori -70 73 C-518 28 Wadanak 98 6 Sindh 81 14
Kaghan 93 73 10748 28 ZA- 77 6 Zamindar-80 14
190
Noshera 96 73 C-591 28 Fakhri sarhad 6 Punjab-81 14
Sandal 73 Faisalabad-83 27 SH-2003 6 SA-42 14
Wardak-85 73 Wadanak 98 27 Faisalabad-83 6 Khyber-79 14
Rawal 87 70 C-250 27 Zarghoon-79 6 Mumal-2002 14
Chakwal 86 70 Mehran-89 27 Punjab-88 6 RWP-94 14
10792 70 Indus 79 27 SA-42 6 Potohar-93 14
Pirsabak 2008 70 Soghat 90 27 C-273 6 Bakhtawar 94 14
FPD-08 70 10792 27 Ksk 5 Bahalwapur-79 14
Kiran 70 Tandojam-83 27 Maxi pak 5 Wadanak 85 13
Merco 2007 67 Dirk 26 NIAB 83 5 Sutlag-86 13
Ksk 67 Margalla 99 26 Chakwal 86 5 Sandal 13
Fakhri sarhad 67 Bahalwapur-79 26 Nori -70 5 Wadanak 98 13
10776 67 Raskoh 26 Sindh 81 5 AS -2002 13
Punjab-96 67 GA 2002 26 Punjab-96 5 Tandojam-83 13
Zarghoon-79 67 Saleem 2000 25 Anmol-91 5 C-591 13
Dirk 67 LU-26 25 C-250 5 FPD-08 13
Uqab 2000 63 SA-42 25 WL-711 5 Janbaz 13
ZA- 77 63 Potohar-93 25 Meraj-08 5 GA 2002 12
Shahkar- 95 63 Sindh 81 25 Faisalabad 85 5 Nori -70 12
10742 63 potohar-90 25 Chenab 79 5 Kaghan 93 12
Abdaghar 97 60 Barani-83 24 Dawar 96 5 Dirk 12
Pari -73 60 Zarghoon-79 24 Chenab-96 5 Wardak-85 12
AS -2002 60 Lr-230 23 Sutlag-86 5 Soghat 90 12
SH-2003 60 Chenab-96 23 Dirk 5 SH-2003 12
Anmol-91 60 FPD-08 23 Sandal 5 Manther 11
Kohinoor-83 60 Uqab 2000 23 C-518 5 Uqab 2000 11
Bahalwapur-79 60 AS -2002 23 Abdaghar 97 5 Iqbal-2000 11
Punjab-76 57 AUP 5000 23 10779 5 Zarghoon-79 11
NIAB 83 57 10742 23 C-228 5 C-250 11
10748 57 Punjab-88 22 FPD-08 5 Margalla 99 11
C-228 57 Khyber-79 22 Mehran-89 5 MH-97 11
10793 57 MH-97 22 Bakhtawar 94 4 Ksk 11
RWP-94 57 Chakwal 86 22 Margalla 99 4 Indus 79 11
WL-711 57 Abdaghar 97 22 GA 2002 4 Raskoh 11
LYP -73 53 Rawal 87 22 Suliman 96 4 Suliman 96 11
10737 53 Sandal 22 10737 4 LYP -73 11
Mumal-2002 53 Ksk 21 Iqbal-2000 4 Chenab-96 11
Zamindar-80 53 Wadanak 85 21 LU-26 4 ZA- 77 10
Iqbal-2000 53 Marwat-01 21 Shahkar- 95 4 Pirsabak 2008 10
191
Blue silver 53 Wardak-85 21 Wafaq-2008 4 10742 10
C-273 53 Zamindar-80 21 SA-75 4 Wafaq-2008 10
Sussi 53 Haider 2002 20 Pak-81 4 10748 10
Meraj-08 53 Mumal-2002 20 Khyber-79 4 10724 10
Janbaz 53 Pari -73 20 potohar-90 4 AUP 5000 10
Faisalabad-83 50 Meraj-08 20 AUP-4008 4 Local white 9
10724 50 Khyber 83 19 Lr-230 4 Maxi pak 9
Barani-83 50 Pirsabak 2008 19 Uqab 2000 4 Pari -73 9
Potohar-93 50 Sussi 19 Noshera 96 4 Haider 2002 9
Suliman 96 47 Local white 18 Janbaz 4 Saleem 2000 9
SA-42 47 ZA- 77 18 Wadanak 85 4 Punjab-88 9
Wadanak 98 43 LYP -73 18 Raskoh 4 Meraj-08 8
Dawar 96 43 Iqbal-2000 18 Mumal-2002 4 Lr-230 8
Chenab-96 43 RWP-94 18 sonalika 3 Khyber 83 8
Sariab-92 43 SH-2003 17 Manther 3 Dawar 96 8
Marwat-01 43 Suliman 96 17 Indus 79 3 Marwat-01 8
Khyber-79 43 10737 16 Haider 2002 3 Chakwal 86 8
Punjab-81 40 Maxi pak 15 Chenab 70 3 Pak-81 7
C-591 40 Dawar 96 14 Saleem 2000 3 Sussi 7
Sutlag-86 40 Pak-81 14 Shalimar 88 3 Barani 70 6
Mehran-89 40 Barani 70 14 Sussi 3 10737 6
AUP-4008 40 Potohar-70 13 Merco 2007 3 Potohar-70 6
C-250 37 SA-75 11 Local white 3 Chenab 70 5
Wafaq-2008 33 Chenab 70 11 Khyber 83 2 SA-75 4
![Mansehra Structure Plan[1]](https://img.dokumen.tips/doc/110x75/543ebc0eb1af9f880b8b47c8/mansehra-structure-plan1.jpg)
