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A molecular marker-assisted backcross breeding strategy for improving multiple complex traits in rice The China-EU workshop Jianlong Xu The Institute of Crop Sciences, Chinese Academy of Agricultural Sciences E-mail: [email protected]

A molecular marker-assisted backcross breeding strategy ... · A molecular marker-assisted backcross breeding strategy for improving ... P & Zn def., BPH, ... A case study -develop

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Page 1: A molecular marker-assisted backcross breeding strategy ... · A molecular marker-assisted backcross breeding strategy for improving ... P & Zn def., BPH, ... A case study -develop

A molecular marker-assisted backcrossbreeding strategy for improvingmultiple complex traits in rice

The China-EU workshop

Jianlong Xu

The Institute of Crop Sciences, ChineseAcademy of Agricultural Sciences

E-mail: [email protected]

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Actual yield in farmer field is much lower thantheoretical yield due to all kinds of biotic and abioticstresses. Future breeding will focus on: 1)improvingyield potential;2)reduce differences between actualyield and theoretical yield.

Yield potential Actual yield

Improving yield potential Filling yield gaps

Yield potential

02468

101214t/ha

Actual yield

Inbredindica rice

Hybridindica rice

Inbredjaponica rice

Difference > 35%

Goff and Salmeron 2004 Scientific American 291(2) 42-49

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Besides biotic stresses such as diseases and insects,drought, salinity, extreme low or high temperature, etc.are most important abiotic stresses affecting rice yield.

In the past, less efforts have been taken in rice breedingfor stress tolerance. So there is slow advance in stresstolerance breeding compared with other traits.

Drought

SalinityHigh temperature

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Most agronomic important traits are quantitatively inherited. Awide range of segregating populations derived from bi-parentalcrosses, including RILs, DHs, F2 and its derived populations, andBC or testcross populations, have been used for QTL mapping. Andmany major important QTLs have been cloned in rice. Contrastly,slow progresses have been made so far in MAS-based breeding forcomplex traits, mainly due to the following two aspects.

(1) Segregation populations derived from bi-parents can’t identifyfavorable alleles for the target traits. So we don’t have informationabout favorable alleles for the target trait which will be used inmolecular breeding.(2) QTL mapping is separate from practical breeding. Owing toQTL mapping results are seriously dependent on geneticbackground. So QTL information from mapping populations can’tbe directly applied in MAS-breeding.

Most agronomic important traits are quantitatively inherited. Awide range of segregating populations derived from bi-parentalcrosses, including RILs, DHs, F2 and its derived populations, andBC or testcross populations, have been used for QTL mapping. Andmany major important QTLs have been cloned in rice. Contrastly,slow progresses have been made so far in MAS-based breeding forcomplex traits, mainly due to the following two aspects.

(1) Segregation populations derived from bi-parents can’t identifyfavorable alleles for the target traits. So we don’t have informationabout favorable alleles for the target trait which will be used inmolecular breeding.(2) QTL mapping is separate from practical breeding. Owing toQTL mapping results are seriously dependent on geneticbackground. So QTL information from mapping populations can’tbe directly applied in MAS-breeding.

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So, integration of QTL mapping with MAS-basedbreeding in the same genetic background has beenstrongly recommended for complex quantitative traits byTanksley and Nelson (1996). So far, AB-QTL method hasbeen widely used in QTL identification from germplasm.However, there are still some defects:

(1) Relative high expenses resulting from phenotyping andgenotyping for a large mapping population.

(2) Favorable alleles can not be mined using populationsderived from bi-parents.

So, integration of QTL mapping with MAS-basedbreeding in the same genetic background has beenstrongly recommended for complex quantitative traits byTanksley and Nelson (1996). So far, AB-QTL method hasbeen widely used in QTL identification from germplasm.However, there are still some defects:

(1) Relative high expenses resulting from phenotyping andgenotyping for a large mapping population.

(2) Favorable alleles can not be mined using populationsderived from bi-parents.

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With the development of sequencing technologies and the sharp

decreased sequencing cost, genome wide association (GWS) has

been recently used for QTL mapping and allele mining from

germplasm resources and made good progresses. However, there

are still some problems with this method.

(1) Wide variations in plant height and heading date of a natural

population seriously affect growth and development for some

early and dwarf entries, thus resulting in inaccurate phenotyping

for those parts of entries.

(2) There is population structure effect on QTL association

mapping.

(3) GWS and MAS-based breeding is still separate.

With the development of sequencing technologies and the sharp

decreased sequencing cost, genome wide association (GWS) has

been recently used for QTL mapping and allele mining from

germplasm resources and made good progresses. However, there

are still some problems with this method.

(1) Wide variations in plant height and heading date of a natural

population seriously affect growth and development for some

early and dwarf entries, thus resulting in inaccurate phenotyping

for those parts of entries.

(2) There is population structure effect on QTL association

mapping.

(3) GWS and MAS-based breeding is still separate.

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Germplasm holds a large of genetic variation for improvingagricultural crops. However, in the past favorable genes fromgermplasm have not been efficiently used in plant breeding dueto linkage drag. Although backcross is effective to simplequalitative traits, it has not been successful to improvequantitative traits by backcross breeding procedure.

Here we demonstrate a new breeding strategy of backcrosscombined molecular marker technology to efficiently identifyQTL and improve multiple complex traits based on designedQTL pyramiding (DQP).

Germplasm holds a large of genetic variation for improvingagricultural crops. However, in the past favorable genes fromgermplasm have not been efficiently used in plant breeding dueto linkage drag. Although backcross is effective to simplequalitative traits, it has not been successful to improvequantitative traits by backcross breeding procedure.

Here we demonstrate a new breeding strategy of backcrosscombined molecular marker technology to efficiently identifyQTL and improve multiple complex traits based on designedQTL pyramiding (DQP).

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RP x donors (many) F1s x RP BC1F1s x RP

~25 BC2F1s/donor x RP

x

BC2F3-5 bulk populations

BC3F1s x RP

1, 2, 3, 4, 5, 6, ……

BC3F2-3 bulk populations

Self and bulkharvest

Selection for target traitsand backcrossing

BC4F1s

BC4F2s

x

x

Self and bulkharvest

1, 2, 3, 4, 5, 6, ……

Screening for target traits such as tolerances to drought, salinity,high temperature, anaerobic germ., P & Zn def., BPH, etc.

Strategy of integration of QTL mining with QTL-designed pyramidingusing backcross introgression lines in elite background

Confirmation of the selected traits by replicated phenotypingthen genotyping of trait-specific lines (ILs)

Crosses made between sister ILshaving unlinked desirable

QTLs for target ecosystem

DQP & MAS for pyramiding desirableQTLs and against undesirable donor

segments for target ecosystem

Develop multiple stress tolerant lines for different ecosystems and releaseNILs for individual genes/QTLs for functional genomic studies

Screening for target traits such as tolerances to drought, salinity,high temperature, anaerobic germ., P & Zn def., BPH, etc.

QTL identification and allele mining

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A case study-develop trait-specific introgression lines (ILs)

using Huang-Hua-Zhan (HHZ)as a recurrent parent

A case study-develop trait-specific introgression lines (ILs)

using Huang-Hua-Zhan (HHZ)as a recurrent parent

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Huang-Hua-Zhan(HHZ):

◆ High yield, high grain quality and wideadaptation

◆ Registered by more than 5 provinces inChina

◆ Extended area up to 3 million hectare inChina so far

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Name

Cote D

’ivoir

Mali

Rw

anda

Senegal

Nigeria

Mozam

bique

Tanzania

Uganda

Bangladesh

Indonesia

Lao P

DR

Pakistan

Sri Lanka

Vietnam

Philippines

HHZ 2 1 3 1 1 3 1 2 2 1 2 1 1 1 2

Zhongzu14 2 1 4 1 1 1

Zhong-Hua 1 2 2 3 4 2 1 1 1 1

KCD1 2 6 1 2 1 1 1

RC8 1 6 1 3 1 1 1

List of the promising widely adaptable inbreds identified fromadaptation yield trials in SSA, SEA and SA

RC8 1 6 1 3 1 1 1

Weed Tolerant 1 1 2 4 2 1

HUA-565 2 3 3 1

FFZ 1 3 6 2 4 1 1 1

SAGC-4 2 3 2 1 1 1

WX763 2 1 1 1

SAGC-2 1 3 1 1

SIMAO 1 3

08fan4 3 1

08fan2 1 3 1 1

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Two batches of 16 populations with the recurrent parent, Huang-Hua-Zhan (HHZ) and 16 donors from 9 different countries

Batch Pop. Donor Country of origin Gen.(10 DS)

1 HHZ5 OM1723 Vietnam (I) BC1F5

1 HHZ8 Phalguna India (I) BC1F5

1 HHZ9 IR50 IRRI (I) BC1F5

1 HHZ11 IR64 IRRI (I) BC1F5

1 HHZ12 Teqing China (I) BC1F5

1 HHZ15 PSB Rc66 Philippines (I) BC1F5

1 HHZ17 CDR22 India (I) BC1F51 HHZ17 CDR22 India (I) BC1F5

1 HHZ19 PSB Rc28 Philippines (I) BC1F5

2 HHZ1 Yue-Xiang-Zhan China (I) BC1F4

2 HHZ2 Khazar Iran (J) BC1F4

2 HHZ3 OM1706 Vietnam (I) BC1F4

2 HHZ6 IRAT352 CIAT (upland) BC1F4

2 HHZ10 Zhong 413 China (I) BC1F4

2 HHZ14 R644 China (I) BC1F4

2 HHZ16 IR58025B IRRI (I) BC1F4

2 HHZ18 Bg304 Sri Lanka (I) BC1F4

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The Introgression Breeding Procedure for Batch 1

Develop first batch of 8 HHZ BC1F2 populations

DT screen SUB screen

3 SUBT plants

311 progeny tested for all target traits

Randompopulations

109 DT plants

Yield traits

QTL/Allelicdiversity

discovery fortarget traits

82 HY plants

ST screen

120 ST plants

06WS

08WS

09DS

1st roundselection

2nd roundselection

108 PYT under DT, low input, NC

Dissection of geneticrelationships betweentarget trait and non-

target traits

QTL/Allelicdiversity

discovery fortarget traits

68 promising ILs

153 DT 171 SUB212 Yield 211 ST

09WS 512 trait-specific ILs

10DS

10WS/11DS 68 replicatedyield trials

Used as parents for designedQTL pyramiding

2 NCT in11WS

3 Demo

3rd roundselection

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Pop. SelectionSeedgeneration

Target traitNo of

selectedlines

Total noof lines ofeach trait

No. of linesof eachgeneration

HHZ5

1stselection(08WS)

BC1F3

Yield 11

47 47DT 21

ST 15

yield

yield-yield 21

36DT-yield 6

ST-yield 9HHZ5

2ndselection(09DS)

BC1F4 114

ST-yield 9

DT

yield-DT 6

51DT-DT 27

ST-DT 18

ST

yield-ST 6

27DT-ST 0

ST-ST 21

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Some introgression lines (HHZ background) and three checkSome introgression lines (HHZ background) and three checkvarieties were grown in 2010 wet season at IRRIvarieties were grown in 2010 wet season at IRRI

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Yield potential of promising 9 HHZ ILs in 2010 WS (S. Peng)

Grain yield and yield components of GSR lines compared withIR72, NSICRc158 and Mestizo7, IRRI, 2010WS.

DesignationGrain Yield

(t/ha) RankGrain

Filling % Panicle/m2Spikelets per

panicleTotal

spikelets/m2

IR72 5.96 abcde 6 72.9 def 357.8a 86.8 hi 31096.2 de

NSIRCRc158 5.86 bcde 7 80.0 abc 354.7 ab 98.0 fgh 34754.2 cde

Mestizo7 5.68 bcde 10 70.2 def 315.6 abcd 116.3 de 36748.7 cd

HHZ5-DT8-DT1-Y1 5.55 cde 12 75.7 abcd 285.4 d 127.8 bc 36462.6 cd

HHZ5-SAL10-DT1-DT1 6.14 abcd 4 73.9 cde 300.5 cd 121.5 bcd 36313.9 cd

HHZ5-SAL10-DT2-DT1 5.47 de 14 68.3 ef 281.8 d 124.2 bcd 34931.0 cde

HHZ5SAL10-DT3-Y2 5.69 bcde 9 76.0 abcd 276.6 d 131.9 ab 36474. 8 cd

HHZ8-SAL6-SAL3-Y2 6.55 ab 2 73.8 de 309.4 abcd 139.1 a 43039.4 ab

HHZ8-SAL9-DT2-Y1 5.78 bcde 8 81.5 a 308.3 bcd 100.9 fg 31089.6 de

HHZ8-SAL12-Y2-DT1 6.43 abc 3 72.2def 324.5 abcd 107.1 ef 34696.6 cde

HHZ12-DT10-SAL1-DT1 6.75 a 1 80.2 ab 323.4 abcd 117.5 cde 38118.6 bc

HHZ12-Y4-DT1-Y1 5.57 cde 11 75.6 abcd 347.4 abc 127.5 bcd 44300.9 a

Best line in WS: HHZ12-DT10-SAL1-DT1, high-yielding; resistant todrought, has aromatic properties, excellent phenotypic stand.

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HHZ8-SAL12-Y2-DT1

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DT HHZ5-Sal14-Sal2-Y2 APO (check)APO

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HHZ ILs HHZ ILsCK

Field evaluation of salinity tolerance in Infanta, Philippines

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QTL mapping for the target traitsusing trait-specific ILs

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Principle of using selected ILs and molecularmarkers to identify QTL

QTL detectionTaken allele frequency of the random population as an expected value,a significant deviation (excess or deficiency) of donor allele frequencyat single locus in the selected IL population from the expected levelimplies a positive selection favoring the donor allele (in excess), ornegative selection against the donor allele (in deficiency). Significantdeviation loci are considered as QTLs affecting the selected traits.

Gene action at putative QTLs● Excess of the donor homozygote additive gene action● Excess of the heterozygote overdominance gene action● Excess of both the donor homozygote and heterozygote partial

or complete dominance gene action

QTL detectionTaken allele frequency of the random population as an expected value,a significant deviation (excess or deficiency) of donor allele frequencyat single locus in the selected IL population from the expected levelimplies a positive selection favoring the donor allele (in excess), ornegative selection against the donor allele (in deficiency). Significantdeviation loci are considered as QTLs affecting the selected traits.

Gene action at putative QTLs● Excess of the donor homozygote additive gene action● Excess of the heterozygote overdominance gene action● Excess of both the donor homozygote and heterozygote partial

or complete dominance gene action

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0

0.2

0.4

0.6

0.8

1

FA FH FB

Comparison of allele segregation between DT-selected ILpopulation and the random population

Drought selected F2

0

0.2

0.4

0.6

0.8

1

Random F2

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ST-ILs selected from fourintrogression populations in

Minghui86 background at theoverall growth stage

Minghui86/Shennong265 (40)

Minghui86/Zaoxian14 (33)

Minghui86/Gayabyeo (37)

ST-ILs selected from fourintrogression populations in

Minghui86 background at theoverall growth stage

Minghui86/Zaoxian14 (33)

Minghui86/Y134 (40)

Page 24: A molecular marker-assisted backcross breeding strategy ... · A molecular marker-assisted backcross breeding strategy for improving ... P & Zn def., BPH, ... A case study -develop

0.330.310.64<.000123.42.44Bin3,13RM231–0.290.340.050.000117.931.06Bin2,62RM240

0.570.220.780.000068.517.80Bin2,32LT620.250.030.28<.0001102.60.560.250.81<.000168.410.07Bin2,22RM290.380.631.00<.000124.043.24Bin1,81LT44

0.460.080.540.0000104.834.64Bin1,61LT35

Diff.Randompop.

ST-ILs

PX2

Frequency ofintrogressionPX2

Minghui8686/Shennong265 (15)Minghui86/Gayabyeo (13)

Physicalposition

/MbBinChr.Marker

0.650.000.650.000216.90.490.200.690.000056.62.57Bin1,11LT3

QTLs for ST detected in Minghui86/Gayabyeo and Minghui86/Shennong265 ILs

Frequency ofintrogression

ST-ILs Randompop.

Diff.

0.390.130.51<.000151.69.93Bin12,212LT365

0.500.090.59<.0001100.817.31Bin11,311RM2090.300.160.46<.000128.40.75Bin11,111LT3260.330.140.46<.000138.817.68Bin10,310LT319

0.540.240.780.000058.73.53Bin10,110LT3050.290.250.540.000615.05.46Bin9,19RM4440.340.270.610.000119.218.40Bin8,38LT268 0.770.000.77<.000124.0

0.480.330.80<.000141.14.49Bin8,18LT253–0.340.420.080.000118.620.50Bin6,46LT207

0.530.310.84<.000154.10.200.150.350.000615.00.63Bin6,16LT1860.520.280.80<.000161.726.37Bin5,65RM260.350.290.64<.0001187.56.99Bin5,25RM1690.310.230.54<.000121.5–0.380.490.110.000021.231.49Bin4,64LT1500.450.500.95<.000132.421.14Bin4,54LT140

0.280.200.490.000021.216.70Bin3,33LT97–0.310.420.110.000119.69.81Bin3,23RM7

0.330.310.64<.000123.42.44Bin3,13RM231

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0.310.100.41<.000147.136.06Bin3,53

–0.310.340.030.000216.834.94Bin2,62

0.390.070.46<.0001146.610.07Bin2,22

0.290.230.530.000117.727.11Bin1,51

1.310.151.47<.000164.717.89Bin1,41

2.74Bin1,11

RM85

RM266

RM29

RM246

Mo18

Mo3

0.020.510.53<.000145.50

0.160.030.19<.000121.7

QTLs for ST detected in Minghui86/Zaoxian14 and Minghui86/Y134 ILs

Diff.Randompop.

ST-ILs

PX2

Frequency ofintrogressionPX2

Minghui8686/Y134 (10)Minghui86/Zaoxian14 (9)

Physicalposition

/MbBinChr.Marker

Frequency ofintrogression

ST-ILs Randompop.

Diff.

19.71Bin12,412

20.52Bin10,310

18.63Bin9,39

0.59Bin9,19

29.26Bin7,77

12.32Bin7,37

–0.801.080.280.000614.763.40Bin6,16

0.510.030.54<.0001110.2826.91Bin5,65

1.310.611.920.001712.7015.55Bin5,45

6.99Bin5,25

0.290.280.56<.000120.52.02Bin4,14

0.310.100.41<.000147.136.06Bin3,53

RM519

RM147

RM189

RM296

RM248

Mo233

Mo192

Mo185

Mo173

RM169

RM518

RM85

–0.460.670.21<.000136.42–0.020.360.34<.000127.06

0.150.030.18<.000128.90

0.220.110.330.000415.47

–0.360.510.15<.000120.19

0.150.030.18<.000121.67

0.200.030.22<.000133.3

0.180.040.21<.000124.18

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ST-QTLs detected in at least the two differentST-IL populations

Gayabyeo Shennong265 Zaoxian14 Y134

Bin2.2 √ √ √ √Bin1.1 √ √ √Bin6.1 √ √ √Bin2.6 √ √Bin4.6 √ √Bin4.6 √ √Bin5.2 √ √Bin5.4 √ √Bin5.6 √ √Bin8.3 √ √Bin9.1 √ √Bin10.3 √ √

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QTLs affecting high yield (HY), drought tolerance (DT) and salinity tolerance (ST)detected in two pyramiding populations by frequency distortion of genotypes

Pop. Locus Ch. Posi. HY DT ST

X2

P Geneaction

X2

P Geneaction

X2

P Geneaction

IL3/IL4(DTP2)

F2

RM486 1 153.5 18.75 0 OD 27.34 0 OD 25.87 0 OD

OSR14 2 6.9 7.76 0.0206 PD

RM471 4 53.8 13.46 0.0011 OD

RM584 6 26.2 7.74 0.0208 OD

RM3 6 74.3 7.67 0.0216 AD 13.66 0.001 OD

RM2 7 8.08 0.0175 OD

RM547 8 58.1 19.97 0 OD 27.89 0 OD 30.97 0 ODRM547 8 58.1 19.97 0 OD 27.89 0 OD 30.97 0 OD

RM21 11 85.7 10.78 0.0045 AD

RM4A 12 5.2 11.93 0.0025 OD

IL5/IL6(STP1)

F2

RM297 1 155.9 10.45 0.0053 AD 6.49 0.0389 AD 9.93 0.0069 AD

RM324 2 66 6.31 0.0426 PD

RM55 3 168.2 6.51 0.0385 PD

RM3 6 74.3 13.44 0.0012 AD 9.48 0.0087 AD 7.7 0.0212 AD

RM444 9 3.3 56.43 0 PD

RM434 9 57.7 30.82 0 AD

RM4A 12 5.2 6.29 0.043 OD

RM519 12 62.6 8.19 0.0166 OD

RM235 12 91.3 12.67 0.0017 PD

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RM582 RM57266.4RM31271.6RM2478.4

RM594.9RM488101.4

RM246115.2

RM302147.8RM212148.7RM486153.5RM297155.9

Chr1

RM764.0

RM25179.1

RM411127.9

RM55 RM186168.2

RM227182.1

Chr3RM33521.5

RM47153.8

Chr4

RM1220.0

RM31118.8

RM87129.2

Chr5

1

1 11

4

2 2 2

3 3 3

2 1 1

1

1 1 3

1 1 1

4

2RM6

OSR14 RM1106.9

RM52158.4RM324 RM42466.0RM29068.0RM26270.2RM34182.7

RM47592.5

RM6154.7

Chr21 1

4

3

21 3 4

1 2 3 4

QTLs for HY identified in pyramiding populations

QTLs for DT identified in pyramiding populations

RM213186.4

RM4692.2RM190 RM5887.4RM58710.7RM51020.8RM225 RM584RM225

26.2

RM27640.3

RM374.3

Chr6RM236.0RM43243.5

RM1890.4

RM248116.6

Chr7RM408RM5060.0RM4075.7

RM54758.1

RM22380.5

RM21090.3

RM80103.7

RM447124.6

Chr8RM2960.0RM4443.3

RM56647.7

RM43457.7RM25766.1RM10873.3RM55376.7

Chr9RM2860.0

RM2185.7

RM206102.9

Chr11RM4A5.2

RM51962.6RM31365.5

RM23591.3

RM12 RM17109.1

Chr12

2

1

2 2 3 3 3

4 4

2

1 2 2 2

1

3 4

3

14

1

2

2 3

34

3

1 2 3 4

1 2 3 4

QTLs for DT identified in pyramiding populations

QTLs for ST identified in pyramiding populations

Distributions of QTLsaffecting HY, DT and ST

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Bacterial blight (BB) resistance and geneticdissection using abiotic trait-specific ILs

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Evaluation of BBEvaluation of BBresistance of 512 HHZresistance of 512 HHZILs against 14 strainsILs against 14 strainsofof XooXoo races, 2010 WSraces, 2010 WSat IRRIat IRRI

HHZ PSBRc66 HHZ15-SAL13-Y2 HHZ15-DT7-SAL1

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Xoo races R% MR/MS% S%

P1 76.4 13.2 10.4

P2 4.7 9.7 85.7

P3b 4.9 48.8 46.4

P3c 4.9 27.5 67.6

P4 22.4 59.2 18.4

P5 78.4 12.6 9.0

P6 5.1 9.9 85.1

Summary of the reactions of the 512 HHZ ILs to the 14 Xoo races

P6 5.1 9.9 85.1

P7 46.6 42.8 10.6

P8 31.0 54.0 15.0

P9a 12.3 16.2 71.5

P10 12.1 15.7 72.2

P9c 4.7 6.6 88.7

P9b 5.1 25.0 69.8

P9d 50.8 33.6 15.6

Average 25.7±25.9 26.8±17.3 47.6±31.4

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PXO61

PXO86

PXO79

PXO34

0PX

O71

PXO11

2PX

O99

PXO145

PXO280

PXO33

9

PXO34

1

PXO34

7PXO349

PXO36

3average

HHZ 9.8 21.2 13.1 25.7 10.4 2.4 29.6 5.0 8.6 28.8 8.4 26.6 15.2 24.6 16.4

PSBRC66 6.4 18.5 16.4 21.4 11.6 0.7 13.0 2.6 8.8 4.1 7.0 12.0 3.4 17.3 10.2

HHZ15-SAL13-Y2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HHZ15-SAL-13-Y3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HHZ15-DT7-SAL1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

Ten HHZ ILs with broad resistance to all 14 races of bacterialblight pathogen, Xanthomonas oryzae

pv oryzae from N. VeraCruz

HHZ15-DT7-SAL1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HHZ15-DT7-SAL3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HHZ15-DT7-SAL6 0.2 0.2 0.2 0.2 9.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.9

PSBRC28 2.8 20.3 21.6 24.0 11.2 3.9 22.4 4.7 9.2 26.0 8.5 23.7 22.9 21.9 21.9

HHZ19-SAL-14-Y3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HHZ19-DT8-SAL2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

HHZ19-SAL12-SAL4 0.2 0.7 0.9 0.5 0.2 0.2 0.2 0.2 0.4 0.2 0.3 0.9 0.2 0.2 0.4

HHZ19-SAL14-SAL4 0.2 1.0 0.6 0.3 0.2 0.2 0.2 0.2 0.3 0.2 0.7 0.2 0.2 0.2 0.3

HHZ19-SAL15-SAL2 0.2 3.8 2.2 0.6 0.4 0.4 0.8 0.5 0.3 0.3 0.3 0.6 0.4 0.4 0.8

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2 3 4 5 61

RM4952.8

RM42819.3RM15136.2RM24357.3RM113RM29478.4

RM49379.7RM992.4RM594.9RM488101.4RM246115.2RM473A120.2RM443122.7

RM212148.7RM6154.7

1

3

1

1

1

2

1

1

1

1

1

2

5

3

2

41

Xa29

RM1090.0RM1544.8RM1106.9RM21114.4RM27917.3RM42328.7RM17447.5RM7149.8RM43858.4RM42466.0RM2968.9RM47592.5

RM106123.2RM263127.5RM526136.3

RM6154.7

RM207RM406186.4RM48190.2RM207191.2

2

4

1

1

1

2

2

2

6

1

1

5

3

RM23115.7

RM54535.3

RM21867.8

RM282100.6

RM411127.9RM16131.5

RM426157.3RM186168.2RM448171.2RM520191.3RM416191.6RM130208.2RM148224.2RM85231.0

1

1

1

1

3

RM5378.5

RM11976.1

RM27394.4RM25299.0

RM127150.1

1

1 1

Xa2 Xa1

Xa12

Xa14

RM1220.0

RM1328.6

RM43743.4RM16957.9

RM16378.7RM44092.7

RM26RM31118.8RM274126.6RM87129.2RM163138.7

1

3

1

1

2

1

1

1

4

2

1

xa5

RM5080.0RM1972.2RM1907.4RM51020.8RM20425.1RM21726.2RM40240.3RM54942.7RM12143.8RM13651.2RM374.3

RM45499.3RM162108.3RM528121.6RM30125.4RM439139.9 1

6

1

1

1

1

1 410

1

Xa7

Xa27(t)

Xa33(t)

At least 51 loci conferring resistance to 14 races of BB resistance inAsia identified in 512 HHZ ILs

8 10

9

117

RM48190.2RM207191.2

RM148224.2RM85231.0

RM227286.0

1

1

RM295RM436

0.0

RM18030.1RM50133.3RM54234.7RM44539.3RM43243.5RM1147.0RM18261.0RM23488.2RM1890.4RM47893.8RM42996.9RM420RM473

115.3

1

1

1

2

3

5

1

2

3124.6

xa8

RM4075.7RM1529.4

RM12657.0RM32RM72

60.9

RM51580.5

RM256101.5RM80103.7RM477121.8RM447124.6RM264128.6

2

1

1

5

6

1

xa13

RM2960.0RM52413.2

RM10532.1

RM43457.7RM10873.3RM10782.4RM18990.7

RM245112.3

RM105’186.6

1

1

1

2 1

2

RM4740.0RM24415.0

RM18458.3RM25870.8RM258'78.8RM294A87.1

1

2

7

11

RM55240.6RM53655.1

RM22977.8

RM254110.0RM224120.1 6

1

RM123 7

Xa3, Xa26Xa21

Xa22Xa4

Xa36

Xa10

Xa23

Xa30(t)

Xa34(t)

12

RM4150.0RM203.2RM1920.9RM11732.3

RM51962.6

RM17109.1 1

1

3

1

1Xa32(t)

Xa25(t)

12 17 1915985 11 represent QRLs detected in HHZ5、HHZ8、HHZ9、HHZ11、HHZ12、HHZ15、HHZ17 and HHZ19;The digitsstand for the number of strains effective by the QTL;“” means chromosomal locations of BB-QTLs previously mapped.

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Resequencing 512 HHZ-ILs for GWS analysisSo far, re-sequencing have been finished for 512 HHZ-ILs and now is being analyzedfor genotyic data. Phenotyping has been finished in multiple environments (IRRI,Hainan, Hangzhou, etc.) . GWS will be used to identify QTL and mine favorablealleles for the target traits using the compound population.

HHZ5:HHZ/OM1723

HHZ8: HHZ/Phalguna

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IL mappingAB-QTLTranditional

mapping

Main-effect QTL 10(2)3.8 (303) 16.5

Epistatic QTL 3.5(2)5.5 (7) Large

Droughttolerance

17.4

Large

Submergencetolerance

Comparison of QTL mapping efficiency among tranditional

mapping, AB-QTL and IL mapping

Epistatic QTL 3.5(2)5.5 (7) Large

Population size 200±200± 31.6

Phenotyping LargeLarge Less

Large

26

Less

Multiple allele mining NoNo Yes Yes

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Pyramiding of high yield (HY), drought andsalinity tolerance (DT, ST) using the

selected ILs

Pyramiding of high yield (HY), drought andsalinity tolerance (DT, ST) using the

selected ILs

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Development of HY-, DT- andST-ILs for QTL mapping

SN89366 Bg94-1 GH122 YJ7 JXSM

Feng-Ai-Zhan 1 (FAZ1) Backcross & selfingwith HY selection

Pyramiding of QTLsfor HY, DT and ST

IL1 IL2× IL3 IL4× IL5 IL6× IL7 IL8×

For DT For ST

F1 F1 F1 F1

F2 populations

Pop. 1 Pop. 2 Pop. 3 Pop. 4 Pop. 5BC3F5

HY & DT ILs HY & ST ILs

DT screening ST screening

HY &DT ILs

FAZ1/SN89366 (IL1)

FAZ1/Bg94-1 (IL2)

FAZ1/GH122 (IL3)

FAZ1/YJ7 (IL4)

FAZ1/SN89366 (IL5)

FAZ1/Bg94-1 (IL6)

FAZ1/JXSM (IL7)

FAZ1/BG94-1 (IL8)

HY &ST ILs

60 randomplants

~30 HYplants

~30 DTplants

~30 STplants

Confirmed or cross-testing ofselected ILs for QTL mapping

New breeding lines with HY, DT and/or ST

Promising lines for RYT

QTL mapping QTL mapping

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Selected pop. Intercrossor

repeatedscreening

trait

No. ofselected

lines

Line # Yield of introgression line (g) Salt tolerance of introgression line at the seedling stage

Traitvalue

Checkof

highervalue

parent

±%comp.with

check

No. of survival days Score of salt toxicity of leaves

Traitvalue

Check ofhigherparent

±%compcheck

Traitvalue

Check ofhigherparent

±%compcheck

DT selected (30)

HY 1 QP49 43.5 30.1 44.8 10 8.8 13.6 4.5 5.5 18.2

ST 10

QP47 31.8 30.1 5.5 11 8.8 20.6 4.5 5.5 18.2

QP48 29.8 30.1 -0.9 11 8.8 22.9 4.5 5.5 18.2

QP63 24.3 30.1 -19.3 12 8.8 36.4 4.5 5.5 18.2

QP60 26.3 30.1 -12.6 12 8.8 31.8 4 5.5 27.3

QP61 28.8 30.1 -4.3 11 8.8 30.3 4 5.5 27.3

QP36 28 30.1 -7 11 8.8 29.5 4 5.5 27.3

Promising pyramiding lines selected from intercross or repeatedscreening for HY and ST from IL1x IL2 population

QP36 28 30.1 -7 11 8.8 29.5 4 5.5 27.3

QP37 28.2 30.1 -6.3 11 8.8 29.7 5 5.5 9.1

HY selected (30)

HY 2QP163 38.6 30.1 28.4 9.6 8.8 9.1 5 5.5 9.1

QP167 36.6 30.1 21.8 11.4 8.8 29.5 4 5.5 27.3

ST 7

QP171 35.8 30.1 18.9 10 8.8 17.1 4.5 5.5 18.2

QP169 32.1 30.1 6.7 12 8.8 33 4.5 5.5 18.2

QP168 25.4 30.1 -15.6 13 8.8 51.1 4 5.5 27.3

QP166 28.3 30.1 -6 11 8.8 29.1 4 5.5 27.3

QP164 23 30.1 -23.4 11 8.8 25.7 4 5.5 27.3

QP170 17.4 30.1 -42.2 11 8.8 25.1 4.5 5.5 18.2

QP165 24.5 30.1 -18.7 11 8.8 20.6 4 5.5 27.3

ST selected (33) HY 2QP327 36.6 30.1 21.6 NA NA NA NA NA NA

QP337 34.9 30.1 15.9 NA NA NA NA NA NA

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Based on phenotypic and QTL information of trait-specific ILs, a new line withHY, DT and ST was developed by pyramiding of different target QTLs

Zhong-Guang-Lv 1(HY, DT & ST)

RYT in Yunnan province in 2011

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Zhong-Guang-You 2

RYT in Guangxi province in 2010-11

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Molecular recurrent selection systems for improvingmultiple complex traits based on trait-specific

ILs and dominant male sterile (DMS) line

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Jiafuzhan (Rr, sterile)

Jiafuzhan (rr, fertile)

Development of a DMS line in HHZ background

Spontaneous mutation

X Jiafuzhan (rr, fertile)

Jiafuzhan (1Rr sterile : 1rr fertile)

X Jiafuzhan (rr, fertile)

X HHZ (rr)

F1 (1Rr sterile : 1rr fertile)

X HHZ (rr), backcross 4-5 times

HHZ (1Rr sterile : 1rr fertile)

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Composition of the molecular RS (MRS) populations:

30-50 ILs/PLs carrying favorable QTL alleles from differentdonors plus the DMS line in the same genetic backgrounds (HHZ)

Ovals or boxes ofdifferent colorsrepresent different ILscarrying genes/QTLsfor different targettraits

Development of RSpopulation is stillunder the way

MRS population in HHZ GB

Bulk harvestseeds fromfertile plantsto be screenedfor target traits

Ovals or boxes ofdifferent colorsrepresent different ILscarrying genes/QTLsfor different targettraits

Development of RSpopulation is stillunder the way

Bulk harvestseeds fromfertile plantsto be screenedfor target traits

Bulk harvestseeds fromsterile plantsfor next roundof RS

HHZ MSline

Each fertile individual has even chance to pollinate with DMS plants,ensuring all possible recombinations produced inside the RS population

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50% fertile plants

RS populations based on trait-specificILs and a DMS line in the same GB

Irrigated(YP)

Abioticstresses

Bioticstresses

Trait-improvedlines

NewILs/PLs

Continuedintrogression

breeding/DQP50% DMS plants

Combine DMS-line based RS system with whole genome selection

RILs

GSmodel

Trait screening

New MRSpopulation for

next round

New lines with multipletraits by pyramiding

Trait-improvedlines

RYT and NCTunder different

target Es

Farmers in dif.target Es

Continuationof MRS

GSmodel

GS

GS

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◆ Most donors contributed performance enhancing alleles formost traits regardless of their own performances;

◆ Appropriate screening is the key to identify promising BCprogenies with improved target traits;

◆ Extremely selected ILs are best materials for main-effect QTLdetection and also for pyramiding of non-allelic favorablealleles from various donors to improve multiple complextraits

◆ Abiotic stress tolerance and biotic resistance may share partialgenetic overlap, thus being beneficial to rice breeding formultiple resistance

DMS-based molecular recurrent selection systems are higheffective for improving multiple complex traits by pedigree orGS

Summary◆ Most donors contributed performance enhancing alleles for

most traits regardless of their own performances;

◆ Appropriate screening is the key to identify promising BCprogenies with improved target traits;

◆ Extremely selected ILs are best materials for main-effect QTLdetection and also for pyramiding of non-allelic favorablealleles from various donors to improve multiple complextraits

◆ Abiotic stress tolerance and biotic resistance may share partialgenetic overlap, thus being beneficial to rice breeding formultiple resistance

DMS-based molecular recurrent selection systems are higheffective for improving multiple complex traits by pedigree orGS

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AcknowledgementResearch Team

Z.K. LiY.M. GaoB.Y. FuL.H. ZhuY. Li. ZhouX.Q. ZhaoT. Q. ZhengJauhar Ali (IRRI)

Funding:Chinese Ministry of AgricultureThe Generation Challenge ProgramChinese Ministry of Science &TechnologyThe BMGF GSR project

Z.K. LiY.M. GaoB.Y. FuL.H. ZhuY. Li. ZhouX.Q. ZhaoT. Q. ZhengJauhar Ali (IRRI)

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Thank You forYour Attention!