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AGB – 691 (Seminar)

By:

Dr. Kush Shrivastava

PhD Scholar

IVRI

SEMINAR ON Submitted byKUSH SHRIVASTAVA

M.V.Sc. & A.H.Roll. No. 772

Submitted toDR. M. SWAMI

Officer-in-chargeSeminar/Synopsis

Major AdvisorDR. M.S. THAKUR

Assistant Professor

Course No. : AGB-691 Credit hr.: 0+1

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INTRODUCTION

Most of the traits considered in animal genetic improvement programmes are

quantitative, i.e. they are controlled by many genes together with environmental factors, and

the underlying genes have small effects on the phenotype observed. Milk yield and growth rate

in animals are typical examples of such quantitative traits (Kim and Park, 2001).

In classical genetic improvement programmes, selection is carried out based on

observable phenotypes of the individuals and/or their relatives. The potential benefits of using

markers linked to genes of interest in breeding programmes, thus moving from phenotype

based selection towards genotype-based selection, have been obvious for many decades

(Beuzen et al., 2000).

Molecular genetic markers are identifiable DNA sequences, found at specific locations of

the genome and transmitted by the standard laws of inheritance from one generation to the

next, they rely on a DNA assay, in contrast to morphological markers that are based on visible

traits, and biochemical markers that are based on proteins produced by genes.

The information provided to the breeder by the markers varies depending on the type

of marker system used. The molecular marker systems allow high-density DNA marker maps

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(i.e. with many markers of known location, interspersed at relatively short intervals throughout

the genome) to be constructed for a range of economically important livestock species, thus

providing the framework needed for eventual applications of marker assisted selection (MAS).

Using the marker map, putative genes affecting traits of interest can then be detected

by testing for statistical associations between marker variants and any trait of interest. These

traits might be genetically simple – controlled by one or a few genes (Young, 1999),

alternatively they could be genetically complex quantitative traits, involving many genes (i.e.

quantitative trait loci [QTL]) and environmental effects.

What is a QTL?

A quantitative trait locus (QTL) is a region of DNA that is associated with a particular

phenotypic trait. These QTLs are often found on different chromosomes. Knowing the number

of QTLs that explains variation in the phenotypic trait tells us about the genetic architecture of

a trait. It may tell us that milk yield for example is controlled by many genes of small effect, or

by a few genes of large effect.

QTL often interact in complex ways and their expression can also be influenced by non-

genetic factors therefore phenotypes observed are thus the combined results of the action of

large numbers of polygenes or quantitative trait loci (QTL) and environmental factors.

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Figure 1: Arrangement of chromosomes (number 1 to 10), blue circles on the chromosomes show the QTLs for any quantitative trait.

Quantitative Trait Loci (QTL) provides a way to associate segments of genome locations

with quantitative traits which represent the majority of economically important phenotypes

measured in livestock animals. Over the past 15 years or so, thousands of QTL have been

detected in pigs, cattle, chickens, and sheep. These data allow researchers to narrow down

genomic regions and identify the genetic factors that contribute to trait variations (Hocking,

2005; Rothschild et al., 2007).

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Types of genetic markers

As with genes, genetic markers are located on chromosomes, like the proverbial ‘beads

on a string’. Genetic makers are of two types, indirect and direct genetic marker. Figure 2 shows

a indirect genetic marker that is 'linked' to a gene.

Figure 2: Shows an indirect genetic marker (variants A and B) linked to a major gene (variants circle and triangle).

If we 'organize' this bull’s semen we get

Figure

3: Bull

semen after organization, showing non recombinants and recombinants.

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Notice that the bull can make 4 ‘types’ of sperm ,for example, marker A can end up

with either the circle or the triangle variant of the major gene. This is because of

'recombination', or 'crossing-over'. The recombination fraction above is 4/17 or about 24%. The

more loosely linked the marker and genes are - the more far apart they are - the higher the

recombination fraction. The maximum recombination fraction is 50% (Figure: 3).

Because of recombination we cannot be sure which marker variant is associated with

each gene variant in an animal - hence they are termed as 'indirect' genetic marker. We have to

record pedigree and make trait measurements in order to work with indirect markers.

If a marker is located within a major gene, it’s called the direct genetic marker and then

recombination is no longer the problem. In this case, knowledge of maker variants tells us

directly about the variants of the gene carried by the animal, as in figure 4, we only need to

measure the trait(s) involved for monitoring purposes.

Figure 4: A direct genetic marker.

Significant association of marker alleles with the phenotype of interest suggests linkage

of the marker to the gene of inetrest (QTL). Such a selection based on markers is called Marker

Assisted Selection (MAS) (Mackinnon and Georges, 1998).

A - Always circle, always good.B - Always triangle, always bad.

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How can a marker genotype be helpful in selection?

Selection of animals could be based on genetic marker information only. However, in

that case the effect of other genes not covered by the marker would be ignored. Optimal

selection should aim for QTL as well as for polygenes. Therefore an optimum selection

programme should be based on information from markers genotypes combined with

information on animals’ phenotype.

The success of MAS is influenced by the relationship between the markers and the

genes of interest. Dekkers (2004) distinguished three kinds of relationship:

(i) The molecular marker is located within the gene of interest.

(ii) The marker is in linkage disequilibrium (LD) with QTL throughout the whole population.

(iii) The marker is not in linkage disequilibrium (i.e. it is in linkage equilibrium [LE]) with QTL

throughout the whole population.

Figure 5: The optimal method of selection programme – incorporating the molecular information with the phenotypic observation.

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Linkage disequilibrium

In population genetics, linkage disequilibrium (LD) is the non-random

association of alleles at two or more loci, not necessarily on the same chromosome. It is not the

same as linkage, which describes the association of two or more loci on a chromosome with

limited recombination between them. Linkage disequilibrium describes a situation in which

some combinations of alleles or genetic markers occur more or less frequently in a population

than would be expected from a random formation of haplotypes from alleles based on their

frequencies.

Linkage disequilibrium relates to dependence of alleles at different loci and is

central to both QTL detection and MAS. Thus, a thorough understanding of LD and of the

factors that affect the presence and extent of LD in populations is essential for a discussion of

both QTL detection and MAS (Darvasi et al., 1993).

The level of linkage disequilibrium is influenced by a number of factors

including genetic linkage, selection, rate of recombination, rate of mutation, genetic drift, non-

random mating and population structure. For example, some organisms (such as bacteria) may

show linkage disequilibrium because they reproduce asexually and there is no recombination to

break down the linkage disequilibrium.

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- Indicates mutation

Figure 6: Shows linkage disequilibrium around an ancestral mutation. (Ardlie et al., 2002)

Chromosomal stretches derived from a common ancestor of all mutant

chromosomes are shown in red, new stretches formed by recombination are shown in blue.

Markers that are physically close (i.e., in red region of present day chromosome) tend to remain

associated with the ancestral mutation even as the recombination limits the extent of the

region of association over time.

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Effect of recombination on the decay of Linkage Disequilibrium over generations

Figure 7: Effect of recombination (r) on the decay of LD over generations. The rate of decay depends on

the rate of recombination between the loci. For tightly linked loci, any LD that has been created will

persist over many generations but, for loosely linked loci (r > 0.1), LD will decline rapidly over

generations.

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STATUS OF MAS IN LIVESTOCK & POULTRY

CATTLE

Olsen et al. (2002) carried out genome scan for QTL affecting milk production in

Norwegian dairy cattle. Suggestive QTL for one or several of the five milk traits (milk yield,

protein percentage, protein yield, fat percentage and fat yield) were detected on chromosomes

3, 5, 6, 11, 13, 18 and 20.

Table 1. The most likely positions of the putative QTL for milk production in the across-family genome scan and their corresponding significance levels.

Trait1 Chromosome

Position Marker - interval F- value P chromosome P genome2

MY 6 37 FBN12-BM143 3.74 0.019 NS18 39 INRA121-BM7109 4.58 0.003 0.08

F% 5 120 UW48-ETH152 4.90 0.009 NS6 41 FBN9-FBN13 11.76 <10–7 <10–5

FY 3 14 FCGR3-EAL 3.73 0.014 NS5 115 BM1819-UW48 3.69 0.018 NS11 83 HUJV174-ILSTS45 3.71 0.014 NS

P% 6 41 FBN9-FBN13 16.38 <10–7 <10–5

13 32 BMC1222-BMS1352 4.62 0.005 NSPY 18 79 ILSTS2-EAC 3.47 0.018 NS

20 66 BMS2361-UWCA26 3.05 0.037 NS1MY = milk yield, F% = fat percentage, FY = fat yield, P% = protein percentage, PY = protein yield.2NS = not significant.

The results of the study are depicted in table 1. The genome scan for quantitative trait

loci affecting milk production in Norwegian dairy cattle revealed putative QTL on chromosomes

(BTA) 3, 5, 6, 11, 13, 18 and 20 (Table 1). A highly significant QTL for protein (P genome < 10−5) and

fat (Pgenome < 10−5) percentages was found in the middle of chromosome 6, close to marker FBN9

(Figure 8). The analysis also suggested that a QTL affecting milk yield (P chromosome = 0.02) could be

positioned to the same region. For the other chromosomes, a putative QTL affecting milk yield

on BTA18 was the most significant, with a genome-wise significance level of 8%.

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Figure 8: Across-family QTL results for milk production traits on bovine chromosome 6 in the Norwegian

Dairy Cattle population. Markers are pointed out on the lower X-axis and map distances in cM from the

centromere are shown on the upper X-axis. F-values are shown on the Y-axis.

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Guillaume et al. (2008) estimated the efficiency of French marker assisted selection by a

simulation study. The data files of two different time periods were used: April 2004 and 2006.

The simulation method used the structure of the existing French MAS: same pedigree, same

marker genotypes and same animals with records, the details of the population used in the

simulation study are given in the table 2.

Table 2. Population structure of French MAS program in April 2004 and 2006April 2004 April 2006

Animals in pedigree 34318 55336Animals with records 23137 38859Genotyped animals 16629 27551Male candidates 1180 1689Sires1 72 79Dams1 793 1130Genotyped dams1 486 887Maternal grand – sires1 70 114Sires1 with more than 20 genotyped progeny daughters 11 12Progeny tested sire families with 30 sons or more 47 64

1 Of male candidates

The program simulated breeding values and new records based on this existing

structure and knowledge on the QTL used in MAS (variance and frequency). Reliabilities of

genetic values of young animals (less than one year old) obtained with and without marker

information were compared to assess the efficiency of MAS for evaluation of milk, fat and

protein yields and fat and protein contents (Table 3)

Table 3. Reliabilities (R2) of classical polygenic EBV* (POL) and MAS EBV (MAS) for male candidates from 2004 and 2006.

Trait April 2004 April 2006POL MAS Difference POL MAS Difference

Milk yield 0.294 0.327 + 0.033 0.313 0.361 + 0.048Fat yield 0.281 0.296 + 0.015 0.310 0.373 + 0.063

Protein yield 0.254 0.273 + 0.019 0.303 0.341 + 0.038Fat content 0.313 0.407 + 0.094 0.342 0.453 + 0.111

Protein content 0.214 0.301 + 0.087 0.342 0.418 + 0.076* EBV = Estimated Breeding Value

In 2004, the gain of reliability ranged from 0.015 for fat yield up to 0.094 for fat content.

Gain was relatively limited for yield traits (0.033, 0.015 and 0.019 for milk, fat, and protein

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yields, respectively) and larger for content traits (0.094 and 0.087 for fat and protein contents,

respectively). In 2006, the difference between MAS EBV and classical EBV was larger, especially

for yield traits (0.048, 0.063 and 0.038 for milk, fat and protein yields, respectively).

Table 4. Reliabilities of classical polygenic EBV (POL) and marker – assisted EBV (MAS) of candidates of 2004, depending on the status of their sires.

Trait Sires of candidates without genotyped progeny daughters

Sires of candidates with genotyped progeny daughters

POL MAS Difference POL MAS DifferenceMilk yield 0.266 0.302 +0.036 0.291 0.353 +0.062Fat yield 0.255 0.263 +0.008 0.277 0.312 +0.035Protein yield 0.243 0.265 +0.022 0.267 0.307 +0.040Fat content 0.269 0.384 +0.115 0.304 0.476 +0.172Protein content

0.200 0.301 +0.101 0.210 0.372 +0.162

In 2004, MAS and classical EBV were also compared with respect to the amount of

information available to estimate gametic effects of the sires (Table: 4). The improvement of

accuracy due to MAS is larger for all traits when a group of progeny daughters is also

genotyped.

In beef cattle

Abe et al. (2009) detected the QTLs affecting the muscle histochemical properties in F2

progeny of a cross between Japanese black and Limousin breed of beef cattle.

Table 5. Summary of chromosome wide significant QTL for muscle fiber traitsTraits Muscle fiber

typeChromosome

F- value1 Location (cM)

Flanking markers

Diameter (µm) Red 17 5.61 42 MB008 – IDVGA-40Composition (%)

Red 5 5.89 0 BMS1095 – BMS610Red 8 5.58 106 DIK1169 – BMS2629White 5 5.72 0 BMS1095 – BMS610

1All F – statistics are significant at 5% chromosome wide level.

The results provided evidence for a QTL affecting the diameter of the red muscle fiber

(<5 % chromosome wide level) at 42 cM on BTA17; a QTL affecting the composition of the red &

white muscle fibers (<5 % chromosome wide level) at 0 cM on BTA5 and a QTL affecting the

composition of the red muscle fiber (<5 % chromosome wide level) at 106 cM on BTA8.

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SHEEP

Maddox et al. (2001) developed medium-density linkage map of the ovine genome.

Marker data for 550 new loci were generated and merged with the previous sheep linkage map.

The new map comprises 1093 markers representing 1062 unique loci (941 anonymous loci, 121

genes) and spans 3500 cM (sex-averaged) for the autosomes and 132 cM (female) on the X

chromosome.

Wool traits in sheep

Purvis and Franklin (2004) published a review on major genes and QTL affecting wool

production and wool quality traits in sheep. The QTLs for these traits where summarized as:

Table 6. Putative QTL for wool production and quality traits from linkage association studies.Trait Breeds Description

(Chromosome no.)

Marker Reference

1. Fiber diameter Peppin Merino Chro. 1 KRTAP6 and KRTAP8 Parsons et al. (1994)2. Fiber diameter Merino x

RomneyLinked but not named Henry et al. (1998)

3. Fiber diameter INRA 401 Chro. 6Chro. 25

Segment OARE101 (20cM), Segment IDVGA8 to midpoint with IDVGA088

Ponz et al. (2001)

4. Staple length Romney Chro. 11 KRT1.2, B2A and B2c Rogers et al. (1994)5. Staple length INRA 401 Chro. 3

Chro. 7Chro. 25

Segment BMC1009 –OARVH34, Segment ILST005 (20cM), Segment IDGVA8 – IDVGA088

Ponz et al. (2001)

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Bidinost et al. (2008) identified QTL affecting wool production and wool quality in

Merino sheep. Greasy fleece weight and body weight were recorded at hogget and adult

shearing. Individual mid-side fleece samples were collected to measure seven wool traits which

are body weight at shearing (BW); greasy fleece weight (GFW); clean fleece weight (CFW); yield

(YLD); fiber diameter (FD); staple strength (SS); staple length (SL) and coefficient of variation of

fiber diameter (CV_FD). Specific regions of chromosomes 3, 4, 11 and 25 were analyzed in eight

half-sib families. The study was successful in detecting QTL for wool traits in the analysis of the

complete set of families, on three of the four chromosome regions studied (Table 7).

Table 7. QTL for wool traits in Merino sheeps.Chromosome Trait Position (cM) CI (cM) Family no.3 FD1

**,# 201 193 – 205 57

4 GFW2 *

28 23–35 45

4 CFW2 * 43 41–end 325 YLD1 *,# 39 Origin–52 225 YLD2 * 50 47–52 1

725 CV_FD2 * 53 51–end 1Greasy fleece weight, GFW; clean fleece weight, CFW; yield, YLD; fiber diameter, FD and coefficient of variation of fiber diameter, CV FD.Subscript 1: hogget 14 month of age; subscript 2: adult 23 month of age. CI: confidence interval of QTL position.* p < 0.05 chromosome-wide significance.** p < 0.01 chromosome-wide.# p < 0.05 genome-wide significance.

The F-statistics profile from interval mapping of sheep chromosomes 3, 4 & 25 is depicted in

Figure 9.

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Figure 9: F-statistics profile from interval mapping of sheep chromosomes 3, 4 and 25. Bar on the left represent markers position on each chromosome. Fiber diameter ( ), greasy fleece weight ( ), clean fleece weight ( ), yield1 ( ), yield2 ( ) and coefficient of variation of fiber diameter ( ). Subscript 1 = hogget 14 month of age; subscript 2 = adult 23 month of age. Chromosome-wide significance p = 0.05 (– – –). Experimental-wide significance p = 0.05 (· · ·) and information content (—).

The QTL related to fiber diameter on chromosome 3 was near OARVH34 marker. Two

QTL influencing adult fleece weight (clean and greasy) were detected on chromosome 4, near

MCM218 and BMS1237 markers & because of the short distance between these two markers

and the high genetic correlation between greasy fleece weight and clean fleece weight (r g = 0.8,

Safari et al., 2005), these QTL could be a single QTL with effect on both traits, whereas a single

QTL with an effect on CV_FD (coefficient of variation of fiber diameter) was detected on

chromosome 25 near marker BM1714.

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Dairy traits in sheep

Barillet et al. (2004) mapped QTL for milk production in dairy sheep. The QTL results for

production traits are reported in Table 8 which shows the significant QTL for production traits in

the three QTL mapping programmes (BC, GDD and DD):

Table 8. 11-BC = Sarda x Lacaune backcross design; 2- DD= Churra daughter design; 3- GDD= Lacaune or Manech granddaughter design. 2 First lactation for BC, all lactations for DD and GDD resource populations.

For the Sarda × Lacaune backcross ewes, the most significant results related to

production were on OAR 3, OAR 16 and OAR 20. These three QTL were found to segregate in

the same families and to affect milk, fat and protein yields. The most significant QTL detected in

this resource population was located on OAR 20 for fat content.

OAR Programme 1 Trait 2 Closest marker Position(cM)

P- value%

1 1 – BC Protein content MCM058 98 0.55 3 – GDD Protein content BMS2258 106 4.33 1 - BC

1 - BC1 - BC

Milk yieldFat yieldProtein yield

BMC1009BMC1009BMC1009

168168166

0.122.30.05

6 2 – DD2 – DD3 - GDD

Milk yieldProtein contentProtein yield

BM4311BM4311OARAE101

12812634

3.53.23.9

9 2-DD3-GDD

Fat contentFat content

RHJICSSM66

11512

5.0

16 1- BC1- BC1- BC

Milk yieldFat yield Protein yield

MAF214MAF214MAF214

343232

0.150.370.59

20 1- BC1- BC1- BC1- BC

Milk yieldFat yield Protein yieldFat content

BM1258BM1258BM1258OARH56

20230

0.340.681.040.05

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In the Churra sheep, a QTL was suggested in the telomeric region of OAR6 in the

neighbourhood of the casein region (marker BM4311) affecting both protein percentage and

milk yield.

For the Lacaune and Manech breeds, in agreement with dairy cattle results (Georges et

al., 1995; Spelman et al., 1996; Coppieters et al., 1998; Zhang et al., 1998; Boichard et al., 2003)

QTL for fat content, protein content and protein yield were found on OAR9, OAR 5 and OAR 6,

respectively (Table 8). The most significant QTL was detected for fat content on OAR 9. This QTL

was recently identified as a mutation in the DGAT1 gene on the homologous bovine

chromosome (BTA 14) (Grisart et al., 2002).

Carcass traits in sheep

Margawati et al. (2009) detected QTL affecting carcass traits in backcross family of

Indonesian Thin Tail Sheep. Four half-sib families were used to analyze QTL for carcass traits

(carcass weight, body length, chest width, leg circumference). Population of 393 ITT x Merino x

Merino backcross with carcass information (phenotype measurements) and their parents were

genotyped for 136 micro-satellite markers covering the 26 autosomal ovine chromosomes. The

results of significant chromosome wide threshold of permutation test on 3 phenotypes were

presented on Table 9.

Table 9. Significance chromosome wide threshold of permutation test for carcass traits.Traits Chromosome F – test statistics F test threshold

0.05 0.01Carcass weight 2 3.7 3.67* 4.65

14 4.62 2.97 4.44**23 3.32 3.05* 4.21

Carcass length 15 3.19 3.15* 3.1517 4.73 4.42 4.56**

Leg circumference1 4.35 4.32* 5.7615 3.67 3.26* 4.7517 3.63 3.39* 4.37

*Significant effect (p<0.05), **highly significant effect (p<0.01). There was not found significance for carcass chest circumference.

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Significant effect of QTL for carcass traits were identified for carcass weight located on 3

chromosomes (Table 9). Those significant effects were on chromosome 2 (p<0.05), highly

significance on chromosome 14 (p<0.01) and chromosome 23 (p<0.05). The QTL locations for

carcass length and leg circumference were identified on 2 and 3 chromosomes, respectively.

The QTL for carcass length was highly significance (p<0.01) identified on chromosome 17. The

QTL for carcass weight was detected on chromosome 2, it might be linked to meat production.

QTL for carcass traits were detected in the 3 parameters, each of those QTLs were flanked by 2

markers. Details of flanking marker of each detected QTL are presented in Table 10.

Table 10. QTL location and flanking markers for carcass traits.Traits Chromosome QTL Location (cM) Flanking markersCarcass weight 2* 264 OARHH30 – BMS1126

14** 8 BMS2213 – LSCV3023* 28 CSSM031 – MCM136

Carcass length 15* 80 IDVGA10 – BM084817** 104 BM7136 – TGLA322

Leg circumference1* 256 BM6506 – URB03015* 92 IDVGA10 – BM084817* 104 BM7136 – TGLA322

*Significant effect (p<0.05), **highly significant effect (p<0.01). There was no significance for carcass chest circumference.

PIGS

Pigs are reared for their meat (pork), from last decade breeders have tried to evaluate

various genes encoding for carcass traits in pigs, genome scan based on linkage mapping DNA

markers and candidate gene used in association tests are two main strategies to identify, to

map and to characterize trait loci influencing meat quality (Andersson et al., 2004; Plastow et

al., 2005; Van der Steen et al., 2005).

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Roberta Davoli and Silvia Braglia (2008) reviewed the advances in molecular genetics

that have led to the identification of genes and markers associated with meat quality in pig and

the main markers associated with meat & carcass quality in pigs applied in industry (Table 11).

Table 11. Main markers associated with meat and carcass quality in pigs applied in the industry.DNA marker/ Gene

Developer Trait First application Reference

MC4R ISU/PIC DG/FC/Lean 1998 Kim et al. (2000)RN-/rn+ (PRKAG3)

INRA/Uppsala/Kiel; ISU/PIC

MQ 1997/1999/2000 Milan et al. (2000); Ciobanu et al. (2001)

IGF2 Liege/Uppsala Lean 2002 Jeon et al. (1999); Nezer et al. (1999); Van Laere et al. (2003)

MQ (several genes)

PIC and ISU/PIC MQ 2001 Knap et al. (2002)

CAST ISU/PIC MQ 2003 Ciobanu et al. (2001); Meyers et al. (2007)

RL, DA PIC RL, DA 2003 Plastow et al. (2003)MQ: meat quality; FC: feed conversion; DG: daily gain; RL: reproductive longevity; ISU: Iowa State University; INRA: Institut National de la Recherche Agronomique, France.

Ya-lan et al. (2007) evaluated the effect and profitability of gene-assisted selection in

pig breeding system. The effect and profitability of using the quantitative trait loci (QTL)-linked

direct marker (DR marker) in gene-assisted selection (GAS) was evaluated. Three populations

(100, 200, or 300 sows plus 10 boars within each group) with segregating QTL were simulated

stochastically. Five economic traits were investigated, including number of born alive (NBA),

average daily gain to 100 kg body weight (ADG), feed conversion ratio (FCR), back fat at 100 kg

body weight (BF) and intramuscular fat (IMF). Selection was based on the estimated breeding

value (EBV) of each trait. The pigs selected for breeding were based on the EBV (estimated

breeding value) estimated by three genetic models: SBLUP (standard BLUP) model, QBLUP

(QTL-BLUP) model, and FBLUP (fixed-BLUP) model, the extra genetic gain was then calculated

for the FBLUP & QBLUP models.

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Table 12. The extra returns for GAS in three populations

Extra economic returns (RMB yuan)

Model

100 sow 200 sow 300 sow

P0= 0.1 P0= 0.3 P0= 0.5 P0= 0.1 P0= 0.3 P0= 0.5 P0= 0.1 P0= 0.3 P0= 0.5

QBLUP 3230647.3 559532.72 86018.29 5599108.49 2243969.4 2231599.7 6726772.5 3453732.4 4955329.35

FBLUP 3768478.9 2414493.2 614107.34 5354031.78 476440.35 3306397.2 6367686.5 4132929.7 5012847.05

P0: The initial frequency of the QTL’s favorable allele

The extra economic returns for GAS schemes reduced dramatically when P0 increased,

which was consistent with the pattern that the lower the P0 was, the higher the genetic gain of

the selected trait was, using GAS scheme (Table 12). Among the three populations, the 300-sow

nucleus population had the highest extra profit, and the 100-sow population had the lowest

one, because with the increase of the nucleus size, the total extra genetic gain for the

population increased, and the pigs sent to slaughter per 6-month period increased too.

CHICKEN

During the last ten years, many QTL have been detected in poultry affecting production

traits including growth rate, carcass traits and feed efficiency (Van Kaam et al., 1999a, 1999b;

Tatsuda et al., 2001; Sewalem et al., 2002; Carlborg et al., 2003; de Koning et al., 2003; Hocking

et al., 2003; Kerje et al., 2003), as well as disease resistance (Yonash et al., 2001; Yunis et al.,

2002; Zhu et al., 2003; Heifetz et al., 2009; Van der Laan et al., 2009).

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Jennen et al. (2005) confirmed the quantitative trait locus (QTL) regions found for

fatness traits in broilers. QTL analysis was performed on chromosomes 1, 3, 4, 15, 18, and 27.

The advanced intercross line (AIL) was created by random intercrossing in each generation from

generation 2 (G2) onwards until generation 9 (G9) was reached. QTL for abdominal fat weight

(AFW) and/or percentage abdominal fat (AF %) on chromosomes 1, 3 and 27 were confirmed in

the G9 population.

Table 13. QTL for abdominal fat weight (AFW), percentage abdominal fat (AF%), body weight at 5 (BW5) and 7 (BW7) weeks of age, and percentage intramuscular fat (IF%) in the G8/G9 population of chickens derived from a broiler × broiler cross. Positions are given in cM on the Haldane scale.

Trait Chromosome Position (cM) Marker bracket F- ratio P- value1

AFW 1 10 MCW0289 – MCW0297 1.559 0.23†82 MCW0018 – MCW0058 1.889 0.052c,†

3 0 MCW0116 – MCW0148 1.430 0.062c

AF % 1 11 MCW0289 – MCW0297 1.474 0.29†84 MCW0018 – MCW0058 1.732 0.11c,†

3 0 MCW0116 – MCW0148 1.364 0.086c

27 11 MCW0076 – ADL0376 1.848 0.016 c,†BW5 1 68 ADL0359 – MCW0018 2.135 0.018c,*

15 19 LEI0120 – MCW0052 2.027 0.0093 c,†27 9 MCW0076 – MCW0376 1.494 0.089 c,†

BW7 1 83 MCW0018 – MCW0058 2.282 0.0061 c,*3 0 MCW0116 – MCW0148 1.397 0.080c

IF % 1 114 MCW0018 – MCW0101 1.464 0.30†27 23 MCW0328 – ADL0376 1.750 0.036 c,†

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1 Chromosome-wise P-value; c chromosome-wise linkage; ∗ experiment-wise significant linkage; † experiment-wise suggestive linkage.

QTL were detected for at least one of the traits AFW, AF%, BW5, BW7, and/or IF% on

chromosomes 1, 3, 15, and 27. For chromosomes 4 and 18, test statistics did not exceed the

significance threshold for any of the traits measured in this experiment.

Figure 10: Test statistic values from the full sib QTL analysis for abdominal fat weight (AFW), percentage abdominal fat (AF%), body weight at the age of 5 and 7 weeks (BW5 and BW7), and percentage intramuscular fat (IF%) on chicken chromosome 1. Average thresholds for significance linkage at the 5% level (______) and for suggestive linkage (- - - -) are included. Map positions are given in cM on the Haldane scale.

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On chromosome 1 the QTL for AFW as well as AF% were confirmed (table 13).

Moreover, for both traits the analysis revealed two distinct peaks on this chromosome at a

distance of around 75 cM (Figure 10). Evidence for QTL for BW5 and/ or BW7 was found on

chromosomes 1, 3, 15 and 27 (Table 13), suggestive evidence of QTL for IF% was also found on

chromosome 1 and 27. The results of this study show the confirmation of QTL found in an

earlier generation in an AIL. Moreover, it was able to identify two distinct regions for fat

deposition on chromosome 1.

Liu et al. (2007) carried out mapping quantitative trait loci affecting body weight and

abdominal fat weight on chicken chromosome one. A total of 369 F2 individuals produced from

4 F1 families, their parents, and F0 birds were genotyped by 23 fluorescent microsatellite

markers on chromosome 1. A linkage map was constructed, and interval mapping was

conducted to identify putative QTL.

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Details of the markers flanking each QTL and the estimated location relative to the first

marker of the NEAURP linkage map (Figure 11) are presented in table 14.

Table 14. The QTL locations for BW and abdominal fat traits.

Position (cM)1 Trait F- ratio Flanking markers69 AFP 5.08† MCW0010-MCW0106183 AFW 4.67† LEI0068-MCW0297195 BW5# 11.54** MCW0297-LEI0146219 BW0 3.92† LEI0146-MCW0018231 BW3 4.01† LEI0146-MCW0018271 BW2 4.51† MCW0018-MCW0058339 BW4 11.43** ADL251-MCW0061343 BW1 3.22† MCW0061-LEI0088351 BW8 8.72* MCW0061-LEI0088523 BW8 6.94† LEI0079-ADL328528 BW9 14.29** LEI0079-ADL328534 BW11 18.72** LEI0079-ADL328534 BW12 28.12** LEI0079-ADL328536 CW 28.06** LEI0079-ADL328548 BW6 11.61** ADL0328-ROS0025548 AFP 11.91** ADL0328-ROS0025550 BW10 9.14** ADL0328-ROS0025550 AFW 7.39* ADL0328-ROS0025551 BW7 19.23** ADL0328-ROS0025553 BW4 9.31** ADL0328-ROS0025555 BW5 6.19* ADL0328-ROS00251QTL positions relative to the genetic map of Northeast Agricultural University resource population in Figure 1. †Suggestive linkage; *Chromosome wide significant, P < 0.05; **chromosome wide significant, P < 0.01.(BW– Body Weight, # Numbers following body weight indicates age in weeks; CW – Carcass weight; AFW – Abdominal Fat Weight; AFP – Abdominal Fat %, expressed as percentage of BW 12).

Figure 11: The linkage map of chromosome 1 of the Northeast Agricultural University (China) resource population.

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For BW, 10 QTL were identified at the 1% chromosome wide level, 2 QTL were identified

at the 5% chromosome wide level, and 5 QTL were identified at the suggestive level. For

abdominal fat traits, 1 QTL was identified at the 1% chromosome wide level, 1 QTL was

identified at the 5% chromosome wide level, and 2 QTL were identified at the suggestive level.

The test statistics for BW of 4 to 12 wk of age and CW peaked in the region 523 to 555

cM. The F-ratios of QTL mapping for BW11, BW12, and CW are shown in Figure 13. Test

statistics for AFW peaked at

550 cM and AFP peaked at

548 cM. They were close to

the QTL region associated with

BW. Furthermore,

test statistics for AFW and AFP

also peaked at 183 and 69 cM,

respectively. However, the

test statistics only reached

suggestive linkage level.

The F-ratios of QTL mapping for AFW and AFP are shown in Figure 12.

Figure 12: The F-ratio of QTL mapping for abdominal fat weight (AFW) and abdominal fat percentage (AFP). Triangles above the X-axis indicate the marker positions.

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Incorporating MAS in selection programmes

Figure 13: The F-ratio of QTL mapping for BW at 11 (BW11), BW at 12 wk (BW12), and carcass weight (CW).Triangles above the X-axis indicate the marker positions.

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Successful implementation of a MAS program requires a comprehensive integrated

approach that is closely aligned with business goals and markets. Components of such an

approach are illustrated in Figure 14 (Dekkers, 2004).

Figure 14: Components of an integrated system for the use of molecular genetic information in breeding programs for marker-assisted selection (MAS).

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CURRENT STATUS & FUTURE PROSPECTS

Identification of new genes affecting quantitative traits are rapidly increasing and lot of

theoretical and experimental results of QTL detection have accumulated (Zhi- Liang Hu et al.,

2010). The current status of QTL-marker associations detected in livestock species is available

on “The Animal Quantitative Trait Locus (QTL) database (AnimalQTLdb)” which is designed to

house all publicly available QTL data on livestock animal species for easily locating and making

comparisons within and between species. The database tools are also added to link the QTL

data to other types of genome information, such as RH maps, physical maps, and human

genome maps. The QTLdb is built on a RedHat Linux platform with MySQL (version 14.12) as the

backend relational database and Apache (2.2.13) as the web server (Zhi Liang Hu et al., 2010).

As per the 13th release of the database upto December 31, 2010, there were total 13825 QTL

reported for 1354 different traits in four livestock species (Table :15 ).

Table 15. Summary of the Animal QTLdb content in terms of the number of QTL in the collection, number of published papers the data were taken from, and number of traits they represent.

Species Number of QTL Number of publication Number of traits

Pig 6344 281 593

Cattle 4682 274 376

Chicken 2451 125 248

Sheep 348 47 137

Total 13825 727 1354

(Source - Animal QTLdb)

Although there are a large number of scientific reports on detection of QTL for livestock

(Bidanel and Rothschild, 2002; Bovenhuis and Schrooten, 2002; Hocking, 2003), most of these

were identified in experimental populations using crosses between breeds or lines (Andersson,

2001). They can, however, provide an important stepping stone for identification of LD markers

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for QTL that segregate within breeds using positional candidate gene approaches (Rothschild

and Soller, 1997; Zhao et al., 2003).

In spite of success in QTL research for complex traits in livestock species in the

last twenty years, location confidence intervals of many QTL are wide, possibly harbouring

hundreds of genes. This is the major obstacle to finding causative mutations underlying any QTL

identified. In addition, fine mapping techniques and positional cloning to reduce the location

confidence interval of the initial QTL are time-consuming, especially for livestock species

compared to plant and animal models. The level of expression of individual genes is dependent

on both environmental circumstances & other genes within the genome. Several groups

(Mootha et al., 2003; Hubner et al., 2005; Schadt et al., 2005; DeCook et al., 2006; Ghazalpour

et al., 2006) have proposed combining QTL detection programs and high throughput

transcriptome data to elucidate biological pathways associated with complex traits and their

underlying genetic determinants. This new integrative approach, known as "Genetical

Genomics (GG)" or "Integrative Genomics", treats the expression level of each gene present on

a microarray as a quantitative trait and use genetic markers to identify genomic regions that

regulate gene expression phenotypes. Such regions are named eQTL (expression Quantitative

Trait Loci). Independently of the context of QTL identification for a complex trait, the eQTL

identification approach was first applied by Brem et al. (2002) in order to understand the

genetic architecture of natural variations in gene expression in yeast (Mignon et al., 2009).

The transcription level of an individual gene has a heritable component (Gibson and

Weir, 2005), which may be population &/or environment specific. If the relevant tissues of

economic importance are profiled at appropriate stages of development, eQTL mapping can be

used to identify candidate genes for production trait QTL & to elucidate the gene regulatory

networks that lead to variation in the phenotypic traits (Sellner et al., 2007). eQTL studies in

livestock have just been initiated on a limited scale, Mignon et al. (2009) using transcriptomic

profile characterize the QTL regions on chicken chromosome 5, Ponsuksili et al. (2010)

identified eQTL of genes expressed in porcine M. longissimus dorsi and associated with meat

quality traits in pigs. Collection of multiple tissues from significant number of individuals for

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gene expression profiling is cost intensive & it becomes a major impediment in eQTL studies of

farm animals. However, high throughput techniques like microarray & RNA sequencing are just

beginning to become widely available for transcription profiling in livestock.

The traditional marker assisted selection (MAS) focuses only on those regions which are

relatively certain to influence the trait of interest & leaves most of the genome & much of the

unaccounted genetic variation. Meuwissen et al. (2001) predicted total genetic value using

genome-wide dense marker maps, this put forward the concept of whole genome selection.

Whole genome selection (WGS) approach uses DNA markers that are distributed throughout

the entire genome. Genes affecting most economically important traits are distributed

throughout the genome and there are relatively few that have large effects & many more genes

with progressively smaller effects. It uses genotypes of thousands of single nucleotide

polymorphisms (SNP) markers, to predict breeding values (Meuwissen, 2009). WGS allows SNPs

with smaller effects on target traits to be used more effectively & to account for a greater

proportion of genetic variation. Whole genome selection has been applied successfully to the

selection of young bulls to enter AI in the dairy industry which uses limited number of high

ranking sire (Moser et al., 2009).

Theo Meuwissen and Mike Goddard (2010) suggested the use of whole genome

resequencing for accurate prediction of genetic values for complex traits. Whole genome

sequence analysis is traditionally done by shotgun approaches using automated Capillary

Electrophoresis (CE) Systems. Once the initial sequence for a particular genome is available, it is

then possible to perform comparative sequencing called resequencing. The entire genome

sequence data can be used to predict the genetic value of an individual for complex traits,

therefore in field of animal breeding it can be of great practical benefit because most important

traits are complex, quantitative traits, i.e., traits that are affected by many genes and by the

environment. Meuwissen and Goddard (2010) in a simulation study concluded that when using

whole-genome sequence data, accuracies of prediction of genetic value were >40% increased

relative to the use of dense ~ 30K SNP chips. At equal high density, the inclusion of the

causative mutations yielded an extra increase of accuracy of 2.5–3.7%.

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CONCLUSION

Marker-assisted selection (MAS) is a complementary technology, for use in conjunction

with more established conventional methods of genetic selection, for livestock and poultry

improvement. Evaluation of MAS under simulation studies have shown promising results when

it is used in combination with the existing conventional breeding system (Lande et al., 1990;

Zhang et al., 1992; Dekkers, 2007). Marker assisted selection can be effectively applied for the

traits with low heritability, are difficult or expensive to measure (disease resistance), cannot be

measured until after selection has occurred (carcass data) and are currently not selected for

due to lack of available phenotypic data (tenderness).

However some constraints still remains by the direct application of MAS like cost of

genotyping the animals, development of QTL linked markers & moreover farmers interest in

using MAS over the conventional methods.

Opportunities for the application of marker-assisted selection exist, in particular for

gene-assisted selection and linkage disequilibrium markers-assisted selection and, to a lesser

degree, for linkage equilibrium markers-assisted selection because of greater implementation

requirements. Regardless of the strategy, successful application of marker-assisted selection

requires a comprehensive integrated approach with continued emphasis on phenotypic

recording programs to enable quantitative trait loci detection, estimation and confirmation of

effects, and use of estimates in selection.

Information on the genes that control commercially important traits is only just

emerging from the numerous studies that are underway. For those genes that have been

identified, the level of variation within the genes between individuals or populations is not

known, nor is the effect of specific variations on phenotypes. It is therefore premature to start

using DNA-based selection widely. However, some DNA tests for specific polymorphisms are

being offered commercially, e.g. the GeneSTAR tests for tenderness (based on variations in the

calpastatin gene) and marbling (based on variations in the thyroglobulin gene), and the Ingenity

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test for fat deposition (based on variations in the leptin gene). These tests can be used by

breeders and evaluated in their populations (Williams, 2005).

The opportunities to use marker –assisted selection will grow as more

associations between markers and traits are identified. However, the information from DNA

alone will not replace traditional system of selection based on performance in near future. With

recent advances in technology (transcriptomics and proteomics) and availability of whole

genome sequence data, there would be paradigm shift from simple genotype- phenotype

association studies to delineation of regulatory gene networks that underlie the expression of

trait.

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