Using Dairy Cattle Genomics to Predict Breeding Values and Identify Important Genes

John B. ColeJohn B. Cole
Animal Improvement Programs Laboratory
Agricultural Research Service, USDA
Beltsville, MD 20705-2350
john.cole@ars.usda.gov
What can we do with dairy cattle
genomics other than predict more
accurate breeding values?

NCSU, November 23, 2010 (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

Data and evaluation flow
Animal
Improvement
Programs
Laboratory,
USDA
AI
organizations,
breed
associations
Dairy
producers
DNA
laboratories
samples
samples
samples
genotypes
nominations
evaluations

Reliabilities for young bulls
0
250
500
750
1000
1250
1500
0 10 20 30 40 50 60 70 80 90 100
Bulls(no.)
Protein reliability (%)
GPTATraditional
PA

Genotyping options
• Illumina
• Infinium: 3K, 50K, 770K SNP
• GoldenGate: 384 to 1,536 SNP
• Affymetrix
• High-density product (650K) expected
in late 2010/early 2011
• We can impute from lower to
higher densities with high accuracy

• 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

• Whole-genome sequences on individuals
will be available in the next few years
•How will we store and use those data?
• Not feasible to calculate effects for
3,000,000,000 nucleotides
• Best application may be SNP discovery
What about whole-genome sequencing?

Materials
• 43,382 SNP from the Illumina BovineSNP50
• Genotypes from three breeds
• 1,455 Brown Swiss males and females
• 40,351 Holstein males and females
• 4,064 Jersey males and females
• Many phenotypes
• Yield (5)
• Health and fitness (7)
• Conformation (3 composites, 14-18 individual)

What else can we do with these data?
• Quantitative Genetics
• Validate theoretical predictions
• Understand genetic variation
• Functional Biology
• Fine-map recessives
• Relate phenotypes to genotypes
• Identify important genes in complex
systems
• Phylogeny

Predicted Mendelian sampling variance
Trait Breed Lower Expected Upper
DPR BS 0.09 1.45 1.57
HO 0.57 1.45 4.02
JE 0.09 0.98 1.27
Milk BS 35,335 215,168 507,076
HO 228,011 261,364 1,069,741
JE 150,076 205,440 601,979
NM$ BS 2,539 19,602 40,458
HO 16,601 19,602 87,449
JE 3,978 19,602 44,552

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

How good a cow can we make in theory?
A “supercow” constructed from the best haplotypes in the
Holstein population would have an EBV(NM$) of $7,515

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

O-Style Haplotypes (chromosome 15)

Fine-mapping Weavers
• 35,353 SNP on BTA4
• 69 Brown Swiss bulls with HD
genotypes
• 20 cases and 49 controls
• No affected animals!
• Microsatellite-mapped to the
interval 43.2–51.2 cM

Sliding-window analysis
BTA4_43-60Mb
0
50
100
150
200
250
300
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Mb
-log10p

Now what?
• We can’t find tissue from affected
animals…
• We could make embryos…
25%
ww
Ww WwX
50%
Ww
25%
WW Genotype

Dystocia Complex
• Markers on BTA 18 had the largest
effects for 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 shorter
PL and decreased NM$

Marker Effects for Dystocia Complex
ARS-BFGL-NGS-109285

Refined Location Using HD Data
ARS-BFGL-NGS-109285
141 HO and 69 BS with 17,702 SNP on BTA18

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
• Preliminary results also indicate an
effect on gestation length
• Confirmed by Christian Maltecca

Correlations among GEBV for NM, PL, SCE,
DCE, STAT, STR, BDep, RWid

Discovery of Fertility Genes
Candidates for a fertility SNP chip
Potentially important in physiological causes of infertility
The Illumina GoldenGate Genotyping Assay
uses a discriminatory DNA polymerase and ligase to
interrogate 96, or from 384 to 1,536, SNP loci simultaneously.
Blastoff: +3.4 DPR
(=~13.6 days open)
Milk +793

Experimental Approach
Identify 384 proven bulls with accurate estimates of
DPR
Based on two runs of the Illumina Golden Gate genotyping system (96 samples per
run x 4 = 384)
CDDR: Historical bulls (all available bulls in top and bottom 10%) and current bulls
(randomly selected from > 3 and <-3)
192 High (> 2.7 DPR
192 Low (<-1.8 DPR)
Find 384 SNPs in genes controlling
reproduction
Genotype each bull for all 384 SNPs
Analyze the data to find relationships

How Were Fertility Markers Selected?
Candidates for a fertility SNP chip
Potentially important in physiological causes of infertility
Genes that are well known to be involved in reproduction
(LH, FSH, genes involves in prostaglandin synthesis,
etc)
Genes that are higher in embryos that are more likely to
establish pregnancy (i.e. genes found that are
differentially regulated by CSF2 and IGF1)
Genes in the literature that are expressed in the uterus
and have been related to embryo survival
(Schellander, Germany

BFGL-Illumina
Deep SNP Discovery
Angus
Holstein
Limousin
Jersey
Nelore
Brahman
Romagnola
Gir
BFGL
Genome Assemblies
Nelore
Water Buffalo
Pfizer
Light SNP Discovery
Angus
Holstein
Jersey
Hereford
Charolais
Simmental
Brahman
Waygu
Partners
Deep SNP Discovery
N’Dama
Sahiwal
Simmental
Hanwoo
Blonde d’Aquitaine
Montbeliard

• Collection of genotypes from universities and
public research organizations
• 3K genotypes from cooperator herds need to
enter the national dataset for reliable imputation
• Encourage even more widespread sharing of
genotypes across countries
• Funding of genotyping necessary to predict SNP
effects for future chips
• Intellectual property issues
Unresolved genotyping issues

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

Conclusions
• To answer interesting questions we
need more data
• Genotypes AND phenotypes
• Big p, small n
• More complex methodology
• Can genomics be used to make better
on-farm decisions?
• Mate selection
• Identify animals susceptible to disease
• Pedigree discovery

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