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8/4/2019 QTL application in animal breeding
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TAMIL NADU VETERINARY AND ANIMAL SCIENCES UNIVERSITY
VETERINARY COLLEGE AND RESEARCH INSTITUTE
NAMAKKAL 637 002
AGB 602 MOLECULAR GENETICS IN ANIMAL BREEDING -2+1
Term paper
On
QUANTITATIVE TRAIT LOCI AND APPLICATIONS IN ANIMAL
BREEDING
SUBMITTED TO
Dr.N. MURALI, Ph.D.,
ASSOCIATE PROFESSOR
DEPARTMENT OF ANIMAL GENETICS AND BREEDING
NAMAKKAL.
SUBMITTED BY
A. RAMACHANDRAN
MVN 10001 (AGB)
DEPARTMENT OF ANIMAL GENETICS AND BREEDING
VETERINARY COLLEGE AND RESEARCH INSTITUTE
NAMAKKAL 637 002
2011
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QUANTITATIVE TRAIT LOCI
Introduction
Many genes responsible for polygenic inheritance of particular characteristics are
scattered around the genome. Their position is known as quantitative trait loci (QTL). It is
useful to know where they are for both medical and agricultural reason. In this case of diesessusceptibility, it is useful to identify the individual genes so that their normal function can be
identified and attempts made to design corrective medical treatments. In case of animal and
plant breeding it would be useful to identify young individuals with favourable alleles
without waiting for their expression at maturity. Those with favourable genotype could be
removed earlier from selective breeding programs, while potentially high quality types could
be cloned immediately (Winter et al., 2003)`
NEED FOR QTL STUDIES
Molecular genetics analyses of quantitative traits lead to the identification of broadly
two types of genetic markers ( causal mutations ) and indirect markers ( non functional
genetic markers that are linked to QTL ).Causal mutations are hard to find for quantities traits
and few examples are available. A gene with a large effect such as the halothane gene is very
much the exception. Nevertheless much research is now under way to identify possible genes
with useful effects on performance. The function of most of the genes so far detected is
unknown. By contrast indirect markers are abundant across the genome and their linkages
with QTLs have been established by evidence of empirical association of genotype with trait
phenotype. This form the basis for selection of individuals based on genetic marker rather
than phenotype, a process known as marker assisted selection ( MAS ).
The success of quantitative genetic approaches does, however, not mean that genetic
progress could not be enhanced if we could gain insight into the black box of quantitative
traits. By being able to study the genetic make-up of individuals at the DNA level, moleculargenetics has given us the tools to make those opportunities a reality. Molecular data is of
interest for use in genetic selection because genotype information has heritability equal to 1
(assuming no genotyping errors), it can be obtained in both sexes and on all animals, it can
be obtained early in life, and it may require the recording of less phenotypic information. The
eventual application of molecular genetics in livestock breeding programs depends on
developments in the following four key areas, which jointly culminate in the successful
implementation of strategies for marker-assisted selection (MAS):
i. Molecular genetics: identification and mapping of genes and genetic polymorphisms
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ii. QTL detection: detection and estimation of associations of identified genes and gametes
markers with economic traits
iii. Genetic evaluation: integration of phenotypic and genotypic data in statistical methods to
estimate breeding values of individual animals in a breeding population ::
iv. Marker-assisted selection: development of breeding strategies and programs for the use
of molecular genetic information in selection and mating programs. The objective of this
paper is to review the potential role and integration of each of these four key areas in genetic
improvement programs for livestock. ( Dekkers et al ).
THE LOCATION OF GENES - MAPPING FUNCTIONS.
Before genetic markers can be used to detect genes or QTLs, the
relative positions of the genetic markers on the genome need to be known. Determining the
relative position of markers is called "mapping" or "map development". The resulting mapgives an overview of the relative locations of markers on the entire genome. We need to
distinguish physical maps and linkage maps. On a physical map, distances between markers
are measured in base pairs. Thus the physical map is based on biochemical knowledge of the
genome. On the linkage map, the distance between markers is based on the recombination
frequency between the markers. Markers are close together when there is little recombination
between them, whereas markers are further apart Application of Molecular Genetics in
Animal Breeding 12-9 when there is substantial recombination between them. Markers are
placed on different chromosomes if they show 50% recombination.
QUANTITATIVE TRAITS: DETECTION OF QTL
Recall that a QTL is a gene or locus affecting a quantitative trait (Quantitative Trait Locus).
Detection of linkage between a marker gene and a QTL. There are a number of similarities
between the two. An important difference, however, is that the QTL genotype is not known.
Thus, in contrast to markers and qualitative traits, we cannot observe the genotype for genes
affecting quantitative traits. The search for QTL's in livestock has a history that goes back to
the 1950's and 1960's. Genetic markers used in these studies were protein polymorphisms or
blood groups. The approach followed in these studies was mostly that a number of (randomly
selected) individuals from a population were typed and it was tested if genotype means
differed significantly for the trait. If there was a significant effect of the marker genotype,
then it was concluded that a QTL was located close to the marker gene. However, the success
of these studies was limited and results were contradicting. Further, the limited number of
markers that was available hampered these studies. The principle of QTL mapping is simple:
trace chromosomal segments from parents to offspring and check if individuals that inherited
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alternative chromosomal segments differ with respect to the quantitative trait. There are a
number of different experimental designs that can be used to detect QTL:
1. Crosses between (inbred) lines or breeds.
2. Use of existing populations.
The type of design that is chosen for detecting QTL will depend upon the species and the aims
of
the experiment.
F2-cross: What is required for this design is two (inbred) lines or breeds that differ with
respect to the trait of interest. In livestock species, inbred lines, i.e. lines with an inbreeding
coefficient of 1, are not available. However, lines that differ considerably for a number of
interesting traits can also be used. In pigs, use has been made of crosses between the Chinese
Meishan breed and commercial pig breeds. The Meishan breed is characterised by a high
fertility and a high fatness. In poultry, crosses have been produced between the Red Jungle
Fowl and the White Leghorn or the Sasumadorri (Japanese breed) and the White Plymouth
Rock. These are crosses between unselected poultry breeds and heavily selected
commercial breeds. In dairy cattle crosses have been produced between the Holstein-Friesian
and Charolais breeds or between Jerseys and Holstein-Friesians. Characteristics of all these
crosses are that the breeds differ considerably for one or more traits. This difference is a
prerequisite, because in the F2-design, QTL will be detected that contribute to differences
between breeds. Lets consider a genetic marker M and a QTL Q, and the following cross
between two breeds:
Realize that in reality the QTL genotype can not be observed. To simplify things, it is assumed
that the breeds are fixed for alternative marker and QTL alleles: breed 1 is fixed for marker
allele M1 and QTL allele Q1 while breed 2 is fixed for marker allele M2 and QTL allele Q2.
The animals from breed 1 are crossed to those of breed 2 and the resulting F1 will be genetically
uniform; all animals have genotype M1M2 for the marker and Q1Q2 for the QTL, that is, they
all are heterozygous for the marker and the QTL. This is important because we indicated
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previously that in order to detect linkage (between a marker and the QTL) heterozygous
individuals are required. Crossing the F1-individuals among each other (F1 * F1) results in an
F2-population. The F1 individuals can produce four different types of gametes: M1Q1, M1Q2,
M2Q1 and M2Q2. The M1Q1 and the M2Q2 gametes are the parental or non-recombinant
gametes, whereas the M1Q2 and the M2Q1 gametes are the recombinant gametes. In the F2,
three different marker genotypes are represented: M1M1, M1M2 and M2M2. In case there is no
recombination between the marker and the QTL, i.e. the marker and the QTL are located closely
together on the genome, all M1M1 individuals will have genotype Q1Q1 and all M2M2
individuals will have genotype Q2Q2. In that case, the difference in for example backfat
thickness or fertility between M1M1 pigs and M2M2 pigs will reflect the difference between the
Q1Q1 and Q2Q2 genotypes. In case recombination occurs between the marker and the QTL,
then this contrast will be reduced because the animals with marker genotype M1M1 will alsocontain animals with QTL genotype Q1Q2 and Q2Q2. It can be derived that the observed
difference between the M1M1 and M2M2 genotypes is equal to 2(12q)a where q is the
recombination fraction between the marker and the QTL and a is the additive gene effect of the
QTL on the quantitative trait. Therefore, a difference between the marker genotypes provides
evidence for the presence of a QTL. Instead of looking at a single marker, in QTL mapping
experiments individuals are genotyped for many markers. Based on the available marker
information, the probability of being M1M1 or M2M2 is calculated for each F2 individual and at
every centiMorgan. This probability is subsequently used in an analysis were the difference
between M1M1 and M2M2 is tested statistically.
Result of the QTL analysis for backfat thickness of a cross between Meishan boars and
Dutch sows for chromosome 7 (De Koning, 2001). Arrows indicate the position of the
genetic markers.
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Analysis of a QTL-mapping experiment in pigs .In this experiment Meishan boars were crossed
to Dutch sows. In the F2, the differences were estimated between chromosomal segments
coming from the Meishan and from the Dutch pigs. This was done for each centiMorgan. For
back fat thickness, this analysis resulted QTL detection in a daughter design where differences
are quantified between daughters that inherited alternative marker alleles. in the profile shown in
Figure 12.10. The x-axis indicates the location on pig chromosome 7 and the arrows indicate the
location of the genetic markers. The y-axis shows the value of the test statistic and indicates the
probability for the presence of a QTL affecting back fat thickness. If the curve passes a certain
threshold then there is significant evidence for the presence of a QTL. As can be seen in Figure
12.10, there is significance evidence for QTL on chromosome 7 affecting back fat thickness and
the additive effect of the QTL (a) is 2.1 mm of back fat. The difference between pigshomozygous for this QTL is therefore 4.2 mm of back fat. (De Koning, 2001).
Daughter design:
As an alternative to a designed experiment involving crosses between lines or breeds,
existing populations can be used to map QTL. In this approach, the structure of breeding
populations is used to map genes that are segregating within these populations. An
experiment involving a cross between divergent populations is focussed primarily on finding
QTL that explain the
difference between populations, whereas an experiment within a population will reveal QTL
that explain genetic variation within a population. The principle underlying the detection of
QTL in an out bred population will be illustrated for a so-called daughter design, where sires
have a large number of progeny and analyses are performed within half sib families. The
basic idea of the design is to trace marker alleles from the sires to his offspring and to
determine whether offspring that have inherited alternative marker alleles from the sire differ
with respect to the quantitative trait
Identifying the gene:
In the past decade, a large number of QTL have been mapped using methods described above.
However, confidence intervals for the location of QTL typically are in the order of 50 cM. The
conclusions that can be drawn from the first QTL-mapping experiments are, therefore, that a
chromosomal region of 50 cM contains one, or possibly more, genes affecting a quantitative
trait. A region of 50 cM consist of roughly 50 million base pairs and may contain hundreds of
genes. This implies that finding the actual gene responsible for the effect is not a trivial exercise,
let alone finding the functional mutation. Therefore, after an initial QTL mapping study, in a
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next step the region needs to be narrowed down and candidate genes need to be identified.
Candidate genes can be identified by using comparative mapping. Comparative mapping refers
to the use of knowledge from other species, such as the human and the mouse. Genomes of
different species are very similar, which is called "conserved". In humans and mice much more
information is available on the location and function of genes. This information can be
transferred from one species to the other. Figure 12.12 shows the comparative map between
chicken and human. If e.g. a region on chicken chromosome 1 (GGA1) is identified as carrying
an important gene, then the corresponding region in the human genome can be scanned for
potential candidate genes. Obviously, this approach is limited by the current knowledge about
the function of genes.
THE CHICKEN-HUMAN COMPARATIVE MAP.
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Results from mapping studies
Although finding single genes or QTL is a tremendous task, in recent years a number of those
genes have been identified. In almost all of the cases these are so-called single-gene-traits, i.e.
traits that are determined by a single gene. Genes underlying quantitative traits are much more
difficult to identify, because they are not only affected by multiple genes, but also by
environmental factors. Table 12.2 gives an overview of single gene traits that have been
identified in livestock species. These will be discussed briefly. Further, a few other genes will be
briefly introduced, also some that have not yet been identified.
Overview of genes affecting traits in livestock were the responsible gene and the
functional mutation have been identified (Harlizius and Van der Lende, 2001)
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Quantitative Trait Loci: Several genes in different livestock species have been identified.
However, so far the vast majority of these genes are so called "single gene traits", i.e. traits
that are determined by a single major gene and where the effect of the environment is absent
or small. Recently, the research group led by Michel George in Liege has identified a gene
with an effect on milk production traits, i.e. a quantitative trait. The road towards finding the
gene and the effect of the gene on milk production traits will be described in the following
section.
SOTFWARE USED IN QTL MAPPING & LINKAGE ANALYSIS
An Alphabetic list of Genetic Analysis Software
Dendrome's QTL mapping software site
Pedros's directory ofbiomolecular research tools
POPGENE, a program for population genetics analysis
DnaSP - DNA Sequence Polymorphism
PLABQTL - a program for composite interval mapping of QTL
http://linkage.rockefeller.edu/soft/list.htmlhttp://s27w007.pswfs.gov/qtl/software.htmlhttp://www.public.iastate.edu/~pedro/research_tools.htmlhttp://www.ualberta.ca/~fyeh/index.htmhttp://www.bio.ub.es/~julio/DnaSP.htmlhttp://probe.nalusda.gov:8000/otherdocs/jqtl/jqtl1996-01/utz.htmlhttp://s27w007.pswfs.gov/qtl/software.htmlhttp://www.public.iastate.edu/~pedro/research_tools.htmlhttp://www.ualberta.ca/~fyeh/index.htmhttp://www.bio.ub.es/~julio/DnaSP.htmlhttp://probe.nalusda.gov:8000/otherdocs/jqtl/jqtl1996-01/utz.htmlhttp://linkage.rockefeller.edu/soft/list.html8/4/2019 QTL application in animal breeding
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LAMARC is a package of programs for computing likelihoods for samples of data
(sequences and electrophoretic polymorphisms) from populations
QTL Cartographer is a package of programs that will aid in locating the genes that
control quantitative traits using a molecular map of markers
MQTL : Software for simplified composite QTL interval mapping in multiple
environments
MSIM : Software for Automated Simulation of genetic markers and QTL
DISPAN, a software for the analysis of allozyme data.
Multimapper / Bayesian QTL mapping software for inbred lines (distributed as C source
code)
UTILIZATION OF KNOWN GENES AND QTL IN ANIMAL BREEDING
Use of marker information in Dairy cattle:
In dairy cattle, DNA information has been used to select against deleterious alleles such as
the BLAD (Bovine Leukocyte Adhesion Deficiency) or the CVM (Complex Vertebral
Malformation) mutation. Furthermore, selection has been performed for certain milk protein
genotypes. Marker information on coat color has also been used, particularly in red-and-
white breeding schemes. The most likely application of marker assisted selection in dairy
cattle is the screening of young bulls before they are progeny tested. Currently, bull-sires are
selected based on milk yield of their daughters. The selected bulls-sires are mated to bull-
dams to produce the next generation of young-bulls. Quite often, multiple ovulation and
embryo transfer (MOET) is applied to produce multiple sons from a single sire-dam
combination. The full-sib young bulls from such a mating have identical estimated breeding
values because their breeding value is estimated based on pedigree information only. Marker
information would make it possible to discriminate between full brothers and select the most
promising individuals to be progeny-tested.
The use of marker information in pig breeding:
At present, pig-breeding industry uses information from several loci or genetic markers to
support selection decisions .The first test to be applied was for the Halothane-gene. The
occurrence of pale, soft and exudative (PSE) meat is associated with the recessive allele at
the Halothane-locus. The test allows breeders to distinguish between animals carrying
alternative Halothane-alleles. There is some discussion on which allele to select for, because
the recessive allele reduces meat quality (PSE), but it improves lean meat content. Some
http://evolution.genetics.washington.edu/lamarc.htmlhttp://gnome.agrenv.mcgill.ca/tinker/mqtl.htmhttp://gnome.agrenv.mcgill.ca/tinker/msim.htmhttp://www.rni.helsinki.fi/~mjs/http://www.rni.helsinki.fi/~mjs/http://evolution.genetics.washington.edu/lamarc.htmlhttp://gnome.agrenv.mcgill.ca/tinker/mqtl.htmhttp://gnome.agrenv.mcgill.ca/tinker/msim.htmhttp://www.rni.helsinki.fi/~mjs/8/4/2019 QTL application in animal breeding
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companies adopted an approach in which the dam lines are free of the recessive allele, but the
allele is still present in the sire lines.
Incorporating QTL information in genetic improvement programs.
Strategies for selection on QTL information:
Once markers that are linked to QTL have been identified, their effects can be estimated
based on the association between phenotype and genotype and used to assign a 'molecular
score' to each selection candidate, which can be used to predict the genetic value of the
individual and used for selection. The constitution and method of quantification of the
molecular score depends on type of LD that is used and the method of marker use (see
below). In addition to a molecular score, individuals can also obtain a regular estimate of the
breeding value for the collective effect of all the other genes.
The following three selection strategies can then be distinguished:
1) select on the molecular score alone
2) two-stage selection, with selection on molecular score, followed by selection on regular
phenotype-based EBV
3) selection on an index of the molecular score and the regular EBV.
Selection on molecular score alone ignores information that is available on all the other
genes
that affect the trait and is expected to result in the lowest response to selection, unless all
genes that affect the trait are included in the molecular score. This strategy does, however,
not require additional phenotypes, other than those that are needed to estimate marker-
effects, and can be attractive when phenotype is difficult or expensive to record (e.g. disease
traits, meat quality, etc.). If both phenotypic and molecular information is available on
selection candidates, index selection is expected to result in the greater response to selection
than two-stage selection. The reason is similar to why two-trait selection using independent
culling levels is expected to give lower multiple-trait response than index selection; two-stage selection does not select individuals for which a low molecular score may be
compensated by a high phenotype-based EBV.
Use of molecular information to capitalize on QTL that segregate between breeds:
Breed or line crosses provide the most powerful populations to identify QTL, in particular if
the breeds are divergent for the main traits of interest. Such studies, however, identify QTL
that segregate between rather than within breeds. Nevertheless, this information can be used
for genetic improvement in a number of ways. If a large proportion of the breed difference in
the trait of interest is due to a small number of genes, introgression strategies can be used. If
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a larger number of genes is involved, marker-assisted selection within a synthetic line is the
preferred method of improvement.
Themost important use of genetic markers two types
1. Marker Assisted Selection (MAS)
2. Marker Assisted Introgression (MAI).
Marker Assisted Selection (MAS)
Traditional selection versus MAS: For many years, man has changed the genetic make
up of animals through selection without knowledge of the underlying genes. The basic
assumption was that many genes affected the quantitative trait (the infinitesimal model, see
Chapter 4). Thus, although the idea of genetic selection is to change allele frequencies in the
population, the actual alleles are not observed. Until recently, tools to identify the genesresponsible for genetic differences between individuals or populations were not available.
Developments in the area of molecular biology have changed this situation and have allowed
identifying genes. To understand how knowledge of genes underlying quantitative traits can
be used in animal breeding, it is useful to characterize present-day animal breeding.
1. Current approaches to estimate the genetic value of an individual rely on phenotypic
observations on the individual itself and/or its relatives. For almost all traits of interest to
animal breeders, differences in phenotypic observations are due to both genetic and
environmental effects (P = G + E). Therefore, the phenotype of an individual gives an
indication of its genetic potential. However, if environment plays an large role, then the
phenotypic record is not a reliable indicator of the breeding value of an animal. The relative
impact of genotype and environment on the phenotype is reflected by the heritability. A
heritability close to zero reflects the situation where almost all the differences in phenotype
are due to differences in the environment, whereas a heritability close to one indicates that
almost all the differences in phenotype are due to differences in the genotype of individuals.
Thus, based on the phenotype of an individual we can get an estimate of its breeding value.
Depending on heritability of the trait, the reliability of the estimated breeding value will be
larger or smaller.
2.In practice, we use not only the phenotype of the individual itself to estimate breeding
values, ut also phenotypes of its relatives. The reason is that relatives will in part carry the
same alleles. Information from relatives, in particular progeny, is especially useful when
heritability is low so that own phenotype does not give reliable information on the breeding
value. The resemblance between breeding values of relatives is quantified by the additive
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genetic relationship. The additive genetic relationship is not necessarily equal to the true
resemblance between breeding values, because there is uncertainty about which alleles are
transmitted to offspring (Mendelian sampling).
As a result of these two factors, accurate estimation of the breeding value of an animal is
possible only if heritability is high and a record on the phenotype of the individual itself is
available, or when a large number of records is available on its progeny. In general, the
requirement progeny records postpones the age at which the animal can be selected as a
parent, and therefore limits the attainable annual genetic progress.
When the genes and their effects on traits of interest are known, typing of animals at the
DNA level enables estimation of breeding values independent of phenotypic observations.
Marker genotypes can be obtained from blood samples that are taken as soon as the animal is
born and therefore can be used to estimate an animals breeding value before the animal has aphenotypic record. Further, the noise that is introduced by environmental effects which
currently makes it difficult to get a reliable estimated breeding value would be avoided, i.e.
markers assisted selection does not suffer from low h2.
MAS is expected to make a contribution especially for traits that are difficult to
improve by traditional selection. Such traits are:
Traits with low heritabilities: in that case the phenotype is a poor predictor of the
breeding value e.g. fertility traits.
The phenotype can be recorded in one sex only e.g. milk yield.
The trait is expressed late in life e.g. longevity.
The phenotype of a trait can not be recorded easily or is expensive to record e.g.
disease resistance
The animal needs to be sacrificed in order to record the phenotype e.g. meat quality.
The reason that MAS is expected to make a contribution to selection for these traits is that
MAS makes it possible to estimate breeding values independent of phenotypic observations.For the traits mentioned above, phenotypic observations cannot be obtained in time, are
difficult to obtain or are not very informative. For traits where traditional selection has been
applied very successfully e.g. growth in broilers, MAS is not likely to have a big impact.
Most traits of economic importance are quantitative traits controlled by a fairly large number
of genes. Some genes, however, especially those of large effect, might be detected and
localized in gene mapping studies. Once localized, information on the genotype of the QTL
itself or the genotypes of linked markers can be used to aid selection. It is very unlikely that
all genes affecting a trait will ever be detected and therefore unidentified genes affecting the
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trait will remain. Those unidentified genes are called "polygenes". The total genetic variation
is thus decomposed in a polygenic part and a part explained by QTL. Information on QTL
therefore adds to the accuracy of the estimated breeding value, and selection will be based on
both phenotypic and marker information.
Marker-assisted introgression
The aim of an introgression program is to introduce one or more favorable genes (target
genes) from a breed that is inferior for other performance characteristics (the donor breed)
into a high performance line that lacks the target genes (the recipient breed). This is done
through an initial F1 cross followed by multiple backcrosses to the recipient breed and one or
more generations of intercrossing The aim of the backcross generations is to maintain the
target gene(s) while ecovering the background genome of the recipient breed. The purpose
of the intcrcrosses is to fix the line for the target gene(s). The effectiveness of introgressionschemes is limited by the ability to identify backcross or intercross individuals that carry the
target gene(s) and by the ability to identify backcross individuals that have a high proportion
of the recipient genome, in particular in the region(s)
around the target gene(s). The latter affects the number of backcross generations required to
recover the recipient genome. Molecular genetics can enhance the effectiveness of both
phases of an introgression program. Effectiveness of the backcrossing phase can be increased
in two ways:
1. by identifying carriers of the target gene(s) (foreground selection),
2. by enhancing recovery of the donor genetic background (background selection).
Effectiveness of the intercrossing phase can also be enhanced through foreground
selection on the target gene(s).
Foreground selection relies on population-wide LD in the crossbred population between the
target gene(s) and linked markers. Ideally, the target gene can be identified directly through a
genetic test or even based on phenotype (e.g. the naked neck gene), in which case the LD will
be complete. If linked markers must be used, the effectiveness of foreground selection
depends on the number of target genes and on the confidence interval for the position of
those genes. The latter determines the size of the genomic region that must be introgressed.
Both factors have a large impact on the number of individuals that is required to find
individuals that are carriers for all target genes during the backcrossing phase and
homozygous during the intercrossing phase. For the introgression of multiple target genes,
gene pyramiding strategies can be used during the backcrossing phase to reduce the number
of individuals required .Alternatively, the requirement that selected backcross individuals
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must be heterozygous for all target genes could be relaxed. Although this will result in a
decline in the frequency of the target genes in the backcross population, it may still be large
enough to enable subsequent selection for these genes during the intercross phase. (Hospital
and Charcosset 1997).
MAIN PROBLEMS RELATED TO THE USE OF MOLECULAR GENETICS INTHE IMPROVEMENT
1.Direct use of a discovered QTL effect for selection across families is not possible.
2. By the time the information about the inferred genotypes is known, frequently the animals
involved in the study are not available as candidates for selection, because they will be too
old.
3. Advantage from within-family selection for a QTL bracketed by markers over BLUP or
phenotypic selection alone is frequently low and the methodology to exploit this information
for selection is complex and relatively inefficient.
4. There are statistical estimation errors, causing both false positive and false negative
effects,
particularly when the effect of the QTL is small.
5. There is a lack of consistency of the effect of the same QTL between studies, caused by
QTL by genetic background (epistasis) of QTL by environment interactions.
6. The net economic effect of the QTL may be lower than the effect on single traits, because
unfavourable effects on other traits.
7. Selection using QTL is more complex than phenotypic selection alone. QTL information
(whether the information on the QTL is direct or indirect), adds to the list of traits used as
selection criteria. Issues such as reduction of selection intensities and relative emphasis given
to each trait, make optimal selection more difficult, with a need for adequate relative weights
for the QTL, and the polygenic portions of the genetic variation for each trait at each
generation (year).
8. Short-term gains due to MAS may be at the expense of medium to long-term polygenicresponses for important traits.
CONCLUSION
In livestock, knowledge of effects of specific genes and gene combinations on important
traits could lead to their enhanced control to create new, more useful populations. The use of
specific gene information is not a panacea, but could help to increase rates of genetic
improvement, and open opportunities for using additive and non-additive genetic effects of
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domestic species, provided wise improvement goals are used and this new technology is
optimally used together with the so called traditional' or conventional' methods based on
phenotypic and genealogical information. Detecting genes related to disease and their
expression in humans from studies on the genome, could lead to the development of
therapies and the development of drugs for specific individuals, and enhanced early diagnosis
of individuals with high-risk genotypes, allowing for preventive or remedial actions, even
gene therapy. In animals, this knowledge could lead, in addition, to select against defective
genes.
The limited reports that are available in plants primarily focus on the introgression of known
genes or QTL regions and few results of a similar nature are available for Plant and mouse
studies on the introgression of QTL regions show that foreground selection based on markers
was effective in moving the targeted region into the recipient genome. However, theimprovement in performance of the recipient breed was generally less than expected based
on the initial QTL effect estimates . Apart from false positives or overestimation ofeffects in
the initial population, reasons suggested for the lower response includepresence ofepistatic
interactions among QTL and between QTL and the genetic background, and genotype by
environment interactions. Similar factors could reduce the realized gain from MAS in
synthetic or purebred populations. Given the uncertainties about the sustainability of marker
effects, it appears prudent to use molecular genetic information in a manner that does not
prevent progress toward the overall breeding goal that can be achieved through conventional
selection. A crucial concept in this regard is to apply MAS in selection space that is not or
under-utilized by conventional selection . A prime example is pre-selection on the basis of
markers among members of a full-sib family for further testing, prior to availability of
individual or progeny records. In such situations conventional selection has no basis for
selection because EBV are derived from pedigree information, which is the same for all
members of a full-sib family. Family members can, however, differ for the markers they
inherited, which then provides a basis for selection, instead of having to make a random
choice. An important decision for the application of MAS is which QTL or markers should
be used in selection. QTL mapping studies typically apply very stringent thresholds based on
genome-wide testing to reduce the rate of false positives.
Reference
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