genomics beyond ebvs

John B. ColeJohn B. ColeAnimal Improvement Programs LaboratoryAgricultural Research Service, USDABeltsville, MD [email protected]

Genomics Beyond EBVs

2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (2) Cole

Whole-genome selection (2008)

• Use many markers to track inheritance of chromosomal segments

• Estimate the impact of each segment on each trait

• Combine estimates with traditional evaluations to produce genomic evaluations (GPTA)

• Select animals shortly after birth using GPTA

• Very successful worldwide


Traditional data flow

AIPL AIorganization

Milk testing laboratory DHI herd

Dairy records processing center

Breed association

registered pedigree data

lactation records

registered

pedigree data

registered

pedigree data

milk samples

bull status

geneticevaluations

genetic evaluations

grade pedigree data,

genetic evaluations

test-day data

management reports

test-day data,

pedigree data,

breeding data

com

pone

nt

perc

enta

ge

som

atic

cell

scor

e

On-farm computers

heal

th a

nd

fitne

ss d

ata


Genomic data flow

DHI herd

DNA laboratory AI organization, breed association

DNA samples

genotypes

genomic

evaluations

nominations,

pedigree datagenotype

quality reports genomic

evaluations

DNA samples

genotypes

DNA samples

AIPL


Illumina genotyping arrays

• BovineSNP50• 54,001 SNPs (version 1)

• 54,609 SNPs (version 2)

• 45,187 SNPs used in evaluation

• BovineHD• 777,962 SNPs

• Only BovineSNP50 SNPs used

• >1,700 SNPs in database

• BovineLD• 6,909 SNPs

• Allows for additional SNPs

BovineSNP50 v2

BovineLD

BovineHD


Reliabilities for young Holsteins*

*Animals with no traditional PTA in April 2011

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

40 45 50 55 60 65 70 75 80

Reliability for PTA protein (%)

Nu

mb

er o

f an

imal

s 3K genotypes

50K genotypes


Genotyped Holsteins

Date

SNP Estimation* Young animals**All

animalsBulls Cows BullsHeifers

04-10 9,770 7,415 16,007 8,630

41,822

08-10 10,430 9,372 18,652 11,021

49,475

12-10 11,293 12,825 21,161 18,336

63,615

04-11 12,152 11,224 25,202 36,545

85,123

08-11 16,519 14,380 29,090 52,053

112,042

09-11 16,812 14,415 30,185 56,559

117,971

10-11 16,832 14,573 31,865 61,045

124,315

11-11 16,834 14,716 32,975 65,330

129,855

12-11 17,288 17,236 33,861 68,051

136,436

01-12 17,681 17,418 35,404 74,072

144,575

02-12 17,710 17,679 36,597 80,845

152,831

*Traditional evaluation **No traditional evaluation


• Identify haplotypes in population using many markers

• Track haplotypes with fewer markers

• e.g., use 5 SNP to track 25 SNP • 5 SNP: 22020

• 25 SNP: 2022020002002002000202200

Imputation


Phenotypes

• Animal model (linear)• Yield (milk, fat, protein)

• Type (Ayrshire, Brown Swiss, Guernsey, Jersey)

• Productive life

• Somatic cell score

• Daughter pregnancy rate

Heritability

8.6%3.6%3.0%6.5%

Sire – maternal grandsire model (threshold) Service sire calving ease Daughter calving ease Service sire stillbirth rate Daughter stillbirth rate

25 – 40%7 – 54%

8.5%12%4%


What can we do beyond EBVs?

• Quantitative Genetics• Validate theoretical predictions• Understand genetic variation

• Functional Biology• Fine-map recessives• Relate phenotypes to

genotypes• Identify important genes in

complex systems


Predicted selection limits

Trait Breed Lower Upper Largest DGV

DPR BS 20 53 8

HO 40 139 8

JE 19 53 5

Milk BS 14,193 34,023 4,544

HO 24,883 77,923 7,996

JE 16,133 40,249 5,620

NM$ BS 3,857 9,140 1,102

HO 7,515 23,588 2,528

JE 4,678 11,517 1,556


What’s the best cow we can make?

A “supercow” constructed from the best haplotypes in the Holstein population would have an EBV(NM$) of $7,515

Cole and VanRaden, 2011 (J. Anim. Breed. Genet. 128:448-455)


Genotype parents and grandparents

Manfred

O-Man

Jezebel

O-Style

Teamster

Deva

Dima


Pedigree relationship matrix

PGS PGD MGS MGD Sire Dam Bull

Manfred 1.053 .090 .090 .105 .571 .098 .334

Jezebel .090 1.037 .051 .099 .563 .075 .319

Teamster .090 .051 1.035 .120 .071 .578 .324

Dima .105 .099 .120 1.042 .102 .581 .342

O-Man .571 .563 .071 .102 1.045 .086 .566

Deva .098 .075 .578 .581 .086 1.060 .573

O-Style .334 .319 .324 .342 .566 .573 1.043

1HO9167 O-Style1HO9167 O-Style


Genomic relationship matrix


Manfred 1.201 .058 .050 .093 .609 .054 .344

Jezebel .058 1.131 .008 .135 .618 .079 .357

Teamster .050 .008 1.110 .100 .014 .613 .292

Dima .093 .135 .100 1.139 .131 .610 .401

O-Man .609 .618 .014 .131 1.166 .080 .626

Deva .054 .079 .613 .610 .080 1.148 .613

O-Style .344 .357 .292 .401 .626 .613 1.157



Difference (Genomic – Pedigree)


Manfred .149 -.032 -.040 -.012 .038 -.043 .010

Jezebel -.032 .095 -.043 .036 .055 .004 .038

Teamster -.040 -.043 .075 -.021 -.057 .035 -.032

Dima -.012 .036 -.021 .097 .029 .029 .059

O-Man .038 .055 -.057 .029 .121 -.006 .060

Deva -.043 .004 .035 .029 -.006 .087 .040

O-Style .010 .038 -.032 .059 .060 .040 .114



Bull–MGS relationships

Van Tassell (personal communication)


Should we really care about inbreeding?

Cole and VanRaden, 2011 (J. Anim. Breed. Genet. 128:448-455)

Bank semen and embryos to preserve genetic diversity and select the best haplotypes. Chromosomal EBV will reflect the value of marker diversity.


O-Style haplotypes (chromosome 15)


Recessive defect discovery


Dystocia complex

• Markers on chromosome 18 have large effects on several traits:• Dystocia and stillbirth: Sire and

daughter calving ease and sire stillbirth

• Conformation: rump width, stature, strength, and body depth

• Efficiency: longevity and net merit

• Large calves contribute to reduced lifetimes and decreased profitability


Marker effects for dystocia complex

ARS-BFGL-NGS-109285

Cole et al., 2009 (J. Dairy Sci. 92:2931–2946)


Correlations in dystocia complex


Biology of the dystocia complex

• The key marker is ARS-BFGL-NGS-109285 at 57,125,868 Mb on BTA18

• Located in a cluster of CD33-related Siglec genes• Many Siglecs involved in leptin

signaling

• Recent results indicate effects on gestation length and calf birth weight


One SNP isn’t the whole story!

AIPL (http://aipl.arsusda.gov/Report_Data/Marker_Effects/marker_effects.cfm?Breed=HO&Trait=Sire_Calv_Ease)


What do we do next?

• Markers with large effects don’t explain that much variation

• What about groups of SNP?• Individual markers may not have

significant effects• Groups of markers may

collectively have significant effects


We have divergent populations

0

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1 2 3 4 5 6 7 8 9 10 11 12 >12

%DBH

Per

cent

of

Sco

res

Cole et al., 2005 (J. Dairy Sci. 88(4):1529–1539)


Gene set enrichment analysis-SNP

Gene pathways (G)GWAS results

Score increase is proportional to SNP test statistic

Nominal p-value corrected for multiple testing

Pathways with moderate effects

Holden et al., 2008 (Bioinformatics 89:1669-1683. doi:10.2527/jas.2010-3681)

SNP ranked by significance (L)

SNP in pathway genes (S)

Score increases for each Li in S

Permutation test and FDR

Includes all SNP, S, that are included in L

The more SNP in S that appear near the top of

L, the higher the Enrichment Score


We hope to identify regulatory networks

Fortes et al., 2011 (J. Animal Sci. 89:1669-1683. doi:10.2527/jas.2010-3681)

Candidate genes and pathways that affect age at puberty common to both breeds


Challenges in pathway analysis

• This is a new procedure for our lab

• There are many steps involving lots of data sources

• Positive results can be challenging to explain

• Negative results are not necessarily definitive


• Genotypes from universities and research organizations

• More widespread sharing of genotypes across countries

• Genotypes needed to predict SNP effects for future chips

• Annotation of the bovine genome

•http://www.innatedb.com/

• Intellectual property concerns

Unresolved issues in genomic research


Conclusions

• We need more data• Genotypes AND phenotypes• Big p, small n• More complex methodology• We are all systems biologists now

• Can genomics be used on the farm?• Mate selection• Identify animals susceptible to

disease• Pedigree discovery

2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (33) Cole33

iBMAC Consortium

Funding

• USDA/NRI/CSREES• 2006-35616-16697• 2006-35205-16888• 2006-35205-16701• 2008-35205-04687 • 2009-65205-05635

• USDA/ARS• 1265-31000-081D• 1265-31000-090D• 5438-31000-073D

• Merial• Stewart Bauck

• NAAB• Gordon Doak• Accelerated Genetics• ABS Global• Alta Genetics• CRI/Genex• Select Sires• Semex Alliance• Taurus Service

• Illumina (industry)• Marylinn Munson• Cindy Lawley• Diane Lince• LuAnn Glaser• Christian Haudenschild

• Beltsville (USDA-ARS) • Curt Van Tassell• Lakshmi Matukumalli• Steve Schroeder• Tad Sonstegard

• Univ Missouri (Land-Grant)• Jerry Taylor• Bob Schnabel• Stephanie McKay

• Univ Alberta (University)• Steve Moore

• Clay Center, NE (USDA-ARS)• Tim Smith• Mark Allan

• AIPL• Paul VanRaden• George Wiggans• John Cole• Leigh Walton• Duane Norman

• BFGL• Marcos de Silva• Tad Sonstegard• Curt Van Tassell

• University of Wisconsin• Kent Weigel

• University of Maryland School of Medicine

• Jeff O’Connell• Partners

• GeneSeek• DNA Landmarks• Expression Analysis• Genetic Visions

Implementation Team

genomics beyond ebvs

Science

international workshop

cole reliabilities

cole whats

traditional evaluations

cole genotype parents

bovinesnp50 snps

snps version

genomic evaluations