MARKER ASSISTED SELECTION IN LIVESTOCK & POULTRY: MARKER ASSISTED SELECTION IN LIVESTOCK & POULTRY:

CURRENT STATUS & FUTURE PROSPECTSCURRENT STATUS & FUTURE PROSPECTS

MARKER ASSISTED SELECTION IN LIVESTOCK & POULTRY: MARKER ASSISTED SELECTION IN LIVESTOCK & POULTRY:

CURRENT STATUS & FUTURE PROSPECTSCURRENT STATUS & FUTURE PROSPECTS

PRESENTED BY:

Kush ShrivastavaM.V.Sc & A.H

PRESENTED BY:

Kush ShrivastavaM.V.Sc & A.H

Department Of Animal Breeding & GeneticsDepartment Of Animal Breeding & Genetics

College of Veterinary Science & Animal HusbandryCollege of Veterinary Science & Animal Husbandry

Department Of Animal Breeding & GeneticsDepartment Of Animal Breeding & Genetics

College of Veterinary Science & Animal HusbandryCollege of Veterinary Science & Animal Husbandry

Introduction

Conventional selection – based on individual records, pedigree or progeny performance or family performance.

Can be termed as phenotypic selection.

MAS – make use of DNA segments/ QTLs for selection

Types of markers

Morphological

Biochemical

Cytological

DNA-based and/or molecular

DNA DNA MarkersMarkers

Quantitative traitsQuantitative traits

Genotype

Phenotype

Environment

Quantitative Trait Loci

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.

Types of Genetic markers

Genetic markers are located on chromosomes, like the proverbial ‘beads on a string’.

No function and no impact on animal performance.

Easily identified in the laboratory.

Direct Genetic Marker

GENES

MARKER

A B

MARKERS

GENES

Indirect Genetic Marker

Selection based on markers

Genes Genes

Phenotype QTL

Estimated Breeding Value

Selection

Molecular

Genetic Markers

Environment

Individual observatio

n

Information from

relatives

Relationship between the markers and the genes of interest

The molecular marker is located within the gene of interest.

The marker is in linkage disequilibrium (LD) with QTL

throughout the whole population

The marker is not in linkage disequilibrium with QTL

throughout the whole population.

(Dekkers, 2004)

Linkage Disequilibrium

- Indicates mutation (Ardlie et al., 2002)

In Dairy cattleOlsen et al. (2002) , detected QTL for milk production chromosomes 3, 5, 6,

11, 13, 18 and 20 in Norwegian dairy cattle.

1MY = milk yield, F% = fat percentage, FY = fat yield, P% = protein percentage, PY = protein yield. 2NS = not significant.

Trait1 Chromosome Position Marker - interval F value P chromosome P genome2

MY 6 37 FBN12-BM143 3.74 0.019 NS

18 39 INRA121-BM7109 4.58 0.003 0.08

F% 5 120 UW48-ETH152 4.90 0.009 NS

6 41 FBN9-FBN13 11.76 <10–7 <10–5

FY 3 14 FCGR3-EAL 3.73 0.014 NS

5 115 BM1819-UW48 3.69 0.018 NS

11 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 NS

PY 18 79 ILSTS2-EAC 3.47 0.018 NS

20 66 BMS2361-UWCA26 3.05 0.037 NS

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.

Olsen et al. (2002)

In SheepMaddox et al. (2001) : developed medium-density linkage

map of the ovine genome.

Wool traits in sheepPurvis and Franklin (2004) reviewed major genes and QTL

affecting wool production & quality.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 Romney

Linked 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)

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

Chromosome Trait Position (cM) CI (cM) Family no.

3 FD1 **,# 201 193 – 205 5

7

4 GFW2 *

28 23–35 4

5

4 CFW2 * 43 41–end 3

25 YLD1 *,# 39 Origin–52 2

25 YLD2 * 50 47–52 1

725 CV_FD2 * 53 51–end 1

QTL for wool traits in Merino sheep

Greasy 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. Confidence interval of QTL position, CI.* p < 0.05 chromosome-wide significance.** p < 0.01 chromosome-wide.# p < 0.05 genome-wide significance.

F-statistics profile from interval mapping of sheep chromosomes 3, 4 and 25. Bar on the left represent markers position on each chromosome . 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 (—).

Bidinost et al. (2008)

- Fiber diameter

- Greasy fleece weight

- Clean fleece weight

- Yield 1

- Yield 2

- Coefficient of variation of fiber diameter

Carcass traits in sheepMargawati et al. (2009) detected QTL affecting carcass

traits in backcross family of Indonesian Thin Tail Sheep.

QTL location and flanking markers for carcass traits.

Traits Chromosome QTL Location (cM) Flanking markers

Carcass weight 2* 264 OARHH30 – BMS1126

14** 8 BMS2213 – LSCV30

23* 28 CSSM031 – MCM136

Carcass length 15* 80 IDVGA10 – BM0848

17** 104 BM7136 – TGLA322

Leg circumference1* 256 BM6506 – URB030

15* 92 IDVGA10 – BM0848

17* 104 BM7136 – TGLA322*Significant effect (p<0.05), **highly significant effect (p<0.01). There was no significance for carcass chest circumference.

In PigsRoberta Davoli and Silvia Braglia (2008), reviewed the

advances in pig molecular genetics & summarized the main markers that are utilized in pig 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 Ciobanu et al. (2001); Milan et al. (2000)

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.

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 3230647.30 559532.72 86018.29 5599108.49 2243969.43 2231599.75 6726772.58 3453732.47 4955329.35

FBLUP 3768478.93 2414493.20 614107.34 5354031.78 476440.35 3306397.27 6367686.56 4132929.78 5012847.05

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

In ChickenLiu et al. (2007) mapped quantitative trait loci affecting

body weight and abdominal fat weight on chicken chromosome 1.

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

The QTL locations for BW and abdominal fat traits.

Position (cM)1 Trait F- ratio Flanking markers

69 AFP 5.08† MCW0010-MCW0106

183 AFW 4.67† LEI0068-MCW0297

195 BW5 # 11.54** MCW0297-LEI0146

219 BW0 3.92† LEI0146-MCW0018

231 BW3 4.01† LEI0146-MCW0018

271 BW2 4.51† MCW0018-MCW0058

339 BW4 11.43** ADL251-MCW0061

343 BW1 3.22† MCW0061-LEI0088

351 BW8 8.72* MCW0061-LEI0088

523 BW8 6.94† LEI0079-ADL328

528 BW9 14.29** LEI0079-ADL328

534 BW11 18.72** LEI0079-ADL328

534 BW12 28.12** LEI0079-ADL328

536 CW 28.06** LEI0079-ADL328

548 BW6 11.61** ADL0328-ROS0025

548 AFP 11.91** ADL0328-ROS0025

550 BW10 9.14** ADL0328-ROS0025

550 AFW 7.39* ADL0328-ROS0025

551 BW7 19.23** ADL0328-ROS0025

553 BW4 9.31** ADL0328-ROS0025

555 BW5 6.19* ADL0328-ROS0025

1QTL positions relative to the genetic map of Northeast Agricultural University resource population .

†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.

Liu et al. (2007)

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.

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.

Liu et al. (2007)

MAS in commercial livestock & poultry populationFinding QTL – Marker association: Biggest challenge in

commercial populations.Long generation interval, unavailability of inbred lines,

cost of genotyping are other limiting factors in livestock species.

Researches in poultry: Mostly on experimental poultry populations.

Only existing example on commercial level - French Dairy Cattle MAS programme.

Two major designs are used – The Daughter Design & The Granddaughter Design (Weller et.al., 1990) .

DAUGHTER DESIGN (Weller et al., 1990)

Only a single family is shown, although in practice several families will be analysed jointly. The sire is assumed to be heterozygous for a QTL and a linked genetic marker. The two alleles of the marker locus are denoted “M” and “m”, and the two alleles of the QTL are denoted “A” and “a”. Alleles of maternal origin are denoted by question marks.

GRAND - DAUGHTER DESIGN (Weller et al., 1990)

The grandsire is assumed to be heterozygous for a QTL and a linked genetic marker. Only a single family is shown. The two alleles of the marker locus are denoted as “M” and “m”, and the two alleles of the QTL are denoted “A” and “a”. Alleles of maternal origin are denoted by question marks. Genotypes are not listed for the granddaughters because they were not genotyped.

The French MAS programme

Started in 2000 (Boichard et al., 2002).14 chromosomal regions selected

-According to previous studies (mainly French QTL program).

-Containing at least one QTL underlying production, milk.

-Composition, mastitis resistance (SCS), female fertility traced with 45 microsatellite markers.

Individual genotyped - 10,000 genotypes per year.

French MAS: The Starting Point(Boichard et al., 2003)

French MAS: First Results(2001- 2007)

60000 genotyped individuals

45 microsatellite markers (14 QTL regions ~20% of the

genome).

Most QTL confirmed: 30 to 40% of the genetic variance

is explained by 3-5 QTLs

8 traits evaluated each month (MAS BV)

Evaluation of efficiency of French MAS programme (Guillaume et al., 2008)

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

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

Incorporating MAS in selection programmes

(Dekkers ,2004)

Current status & future prospectsThe Animal Quantitative Trait Locus (QTL) database

(AnimalQTLdb) :

- It 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.(Zhi- Liang Hu et al., 2010)

Summary of the Animal QTLdb

(Source - Animal QTLdb, 13th release)

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

QTL on Cattle Chromosome X

Red QTL lines represent for significant and light blue QTL lines for suggestive statistical evidence. Green dots represents the QTL peak position.

DYST – Dystocia (maternal), BSE – Bovine Spongiform Encephalopathy, EY – Milk Energy Yield, FY – Milk Fat Yield, MSPD – Milking speed, MY – Milk Yield, NONR – Non return Rate, PY – Milk Protein yield, RLEGS – Rear Leg Set, SB – Still Birth (maternal).

eQTL ( Expression QTL):

- 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.

- Fine mapping techniques and

positional cloning are costly.

Combining QTL detection programs and high throughput

transcriptome data to elucidate biological pathways associated with

complex traits and their underlying genetic determinants is 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).

(Mignon et al., 2009)

Whole genome selection (WGS) :

- Meuwissen et al. (2001):

predicted total genetic value using genome-wide dense marker

maps.

- Genes affecting most

economically important traits are distributed throughout the

genome.

Whole genome resequencing:

- The entire genome sequence data can be used to predict

the genetic value of an individual for complex traits.

- Theo Meuwissen and Mike Goddard (2010) suggested the

use of whole genome resequencing for accurate prediction of genetic

values for complex traits.

- 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%.