Presentation on alternative uses of genomic information made at the 2nd International Workshop on Genomics Applied to Livestock in Aracatuba, Brazil.

1. John B. ColeJohn B. Cole
Animal Improvement Programs Laboratory
Agricultural Research Service, USDA
Beltsville, MD 20705-2350
john.cole@ars.usda.gov
Genomics Beyond EBVs

2. 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

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Traditional data flow
AIPL AI
organization
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
genetic
evaluations
genetic
evaluations
grade pedigree data,
genetic evaluations
test-day data
m
anagem
ent reports
test-day data,
pedigree data,
breeding data
component
percentage
somatic
cellscore
On-farm
computers
healthand
fitnessdata

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Genomic data flow
DHI herd
DNA laboratory
AI organization,
breed association
DNA samples
genotypes
genom
ic
evaluations
nom
inations,
pedigree
data
genotype
qualityreports
genom
ic
evaluations
DNA
sam
ples
genotypes
DNA
sam
ples
AIPL

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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

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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 (%)
Numberofanimals
3K genotypes
50K genotypes

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Genotyped Holsteins
Date
SNP Estimation* Young animals**
All
animalsBulls Cows Bulls Heifers
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

8. 2nd International Workshop on Genomics Applied to Livestock, Araçatuba, Brasil, February 27, 2012 (8) Cole
• 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

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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%

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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

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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

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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)

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Genotype parents and grandparents
Manfred
O-Man
Jezebel
O-Style
Teamster
Deva
Dima

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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

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Genomic relationship matrix
PGS PGD MGS MGD Sire Dam Bull
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
1HO9167 O-Style1HO9167 O-Style

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Difference (Genomic – Pedigree)
PGS PGD MGS MGD Sire Dam Bull
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
1HO9167 O-Style1HO9167 O-Style

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Bull–MGS relationships
Van Tassell (personal communication)

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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.

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O-Style haplotypes (chromosome 15)

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Recessive defect discovery

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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

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Marker effects for dystocia complex
ARS-BFGL-NGS-109285
Cole et al., 2009 (J. Dairy Sci. 92:2931–2946)

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Correlations in dystocia complex

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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

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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)

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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

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We have divergent populations
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 >12
%DBH
PercentofScores
Cole et al., 2005 (J. Dairy Sci. 88(4):1529–1539)

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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

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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

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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

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• 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

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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

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33
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