What can we do with dairy cattle genomics other than predict more accurate breeding values?

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

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

Dairyproducers

DNAlaboratories

samples

samples

samples

genotypes

nominationsevaluations


Reliabilities for young bulls

0

250

500

750

1000

1250

1500

0 10 20 30 40 50 60 70 80 90 100

Bu

lls (

no

.)

Protein reliability (%)

GPTATraditionalPA


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 chipPotentially 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 DPRBased 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 DPR192 Low (<-1.8 DPR)

Find 384 SNPs in genes controllingreproduction

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-IlluminaDeep SNP Discovery

AngusHolsteinLimousin

JerseyNelore

BrahmanRomagnola

Gir

BFGLGenome Assemblies

NeloreWater Buffalo

PfizerLight SNP Discovery

AngusHolsteinJersey

HerefordCharolais

SimmentalBrahmanWaygu

PartnersDeep SNP Discovery

N’DamaSahiwal

SimmentalHanwoo

Blonde d’AquitaineMontbeliard


• 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

NCSU, November 23, 2010 (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


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

What can we do with dairy cattle genomics other than predict more accurate breeding values?

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