Genomic Selection in Dairy Cattle John B Cole

Genomic Selection in Dairy Cattle John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD john. cole@ars. usda. gov Cornell Department of Plant Breeding and Genetics (1) G. R. Wiggans 2011

Dairy Cattle l 9 million cows in US l Attempt to have a calf born every year l Replaced after 2 or 3 years of milking l Bred via AI l Bull semen collected several times/week. Diluted and frozen l Popular bulls have 10, 000+ progeny l Cows can have many progeny though super ovulation and embryo transfer Cornell Department of Plant Breeding and Genetics (2) G. R. Wiggans 2011

Data Collection l Monthly recording w Milk yields w Fat and Protein percentages w Somatic Cell Count (Mastitis indicator) l Visual appraisal for type traits l Breed Associations record pedigree l Calving difficulty and Stillbirth Cornell Department of Plant Breeding and Genetics (3) G. R. Wiggans 2011

Lifecycle of bull Parents Selected Dam Inseminated Embryo Transferred to Recipient Bull Born Genomic Test Semen collected (1 yr) Daughters Born (9 m later) Daughters have calves (2 yr later) Cornell Department of Plant Breeding and Genetics (4) Bull Receives Progeny Test (5 yrs) G. R. Wiggans 2011

Benefit of genomics l Determine value of bull at birth l Increase accuracy of selection l Reduce generation interval l Increase selection intensity l Increase rate of genetic gain Cornell Department of Plant Breeding and Genetics (5) G. R. Wiggans 2011

History of genomic evaluations l Dec. 2007 Bovine. SNP 50 Bead. Chip available l Apr. 2008 First unofficial evaluation released l Jan. 2009 Genomic evaluations official for Holstein and Jersey l Aug. 2009 Official for Brown Swiss l Sept. 2010 Unofficial evaluations from 3 K chip released l Dec. 2010 3 K genomic evaluations to be official l Sept. 2011 Infinium Bovine. LD Bead. Chip available Cornell Department of Plant Breeding and Genetics (6) G. R. Wiggans 2011

Chips l l l Bovine. SNP 50 w Version 1 54, 001 SNP w Version 2 54, 609 SNP w 45, 187 used in evaluations 50 KV 2 HD LD HD w 777, 962 SNP w Only 50 K SNP used, w >1700 in database LD w 6, 909 SNP w Replaced 3 K Cornell Department of Plant Breeding and Genetics (7) G. R. Wiggans 2011

Use of HD l l Currently only 50 K subset of SNP used Some increase in accuracy from better tracking of QTL possible Potential for across breed evaluations Requires few new HD genotypes once adequate base for imputation developed Cornell Department of Plant Breeding and Genetics (8) G. R. Wiggans 2011

LD chip l l 6909 SNP mostly from SNP 50 chip w 9 Y Chr SNP included for sex validation w 13 Mitochondrial DNA SNP w Evenly spaced across 30 Chr (increased density at ends) Developed to address performance issues with 3 K while continuing to provide low cost genotyping l Provides over 98% accuracy imputing 50 K genotypes l Included beginning with Nov genomic evaluation Cornell Department of Plant Breeding and Genetics (9) G. R. Wiggans 2011

Genomic evaluation program steps l Identify animals to genotype l Sample to lab l Genotype sample l Genotype to USDA l Calculate genomic evaluation l Release monthly Cornell Department of Plant Breeding and Genetics (10) G. R. Wiggans 2011

Steps to prepare genotypes l Nominate animal for genotyping l Collect blood, hair, semen, nasal swab, or ear punch w Blood may not be suitable for twins l Extract DNA at laboratory l Prepare DNA and apply to Bead. Chip l Do amplification and hybridization, 3 -day process l Read red/green intensities from chip and call genotypes from clusters Cornell Department of Plant Breeding and Genetics (11) G. R. Wiggans 2011

What can go wrong l l l Sample does not provide adequate DNA quality or quantity Genotype has many SNP that can not be determined (90% call rate required) Parent-progeny conflicts w Pedigree error w Sample ID error (Switched samples) w Laboratory error w Parent-progeny relationship detected that is not in pedigree Cornell Department of Plant Breeding and Genetics (12) G. R. Wiggans 2011

Lab QC l Each SNP evaluated for w Call Rate w Portion Heterozygous w Parent-progeny conflicts l Clustering investigated if SNP exceeds limits l Number of failing SNP is indicator of genotype quality l Target fewer than 10 SNP in each category Cornell Department of Plant Breeding and Genetics (13) G. R. Wiggans 2011

Parentage validation and discovery l Parent-progeny conflicts detected Animal checked against all other genotypes w Reported to breeds and requesters w Correct sire usually detected w l Maternal Grandsire checking SNP at a time checking w Haplotype checking more accurate w l Breeds moving to accept SNP in place of microsatellites Cornell Department of Plant Breeding and Genetics (14) G. R. Wiggans 2011

Imputation l l Based on splitting the genotype into individual chromosomes (maternal & paternal contributions) Missing SNP assigned by tracking inheritance from ancestors and descendents Imputed dams increase predictor population 3 K, LD, & 50 K genotypes merged by imputing SNP not on LD or 3 K Cornell Department of Plant Breeding and Genetics (15) G. R. Wiggans 2011

Recessive defect discovery l Check for homozygous haplotypes l Most haplotype blocks ~5 Mbp long l 7 – 90 expected, but 0 observed l 5 of top 11 haplotypes confirmed as lethal l Investigation of 936 – 52, 449 carrier sire carrier MGS fertility records found 3. 0 – 3. 7% lower conception rates Cornell Department of Plant Breeding and Genetics (16) G. R. Wiggans 2011

Haplotypes impacting fertility Breed BTA chromosome Location, Mbases Carrier frequency, % Holstein 5 62– 68 4. 5 1 93– 98 4. 6 8 92– 97 4. 7 Jersey 15 11– 16 23. 4 Brown Swiss 7 42– 47 14. 0 Cornell Department of Plant Breeding and Genetics (17) G. R. Wiggans 2011

Data and evaluation flow Requester (Ex: AI, breeds) no s ple ev am s mi alu na ati tio on ns s Genomic Evaluation Lab Dairy producers samples sa s mp pe y t o les n ge DNA laboratories Cornell Department of Plant Breeding and Genetics (18) G. R. Wiggans 2011

Collaboration l Full sharing of genotypes with Canada w l Trading of Brown Swiss genotypes with Switzerland, Germany, and Austria w l CDN calculates genomic evaluations on Canadian base Interbull may facilitate sharing Agreements with Italy and Great Britain provide genotypes for Holstein w Negotiations underway with other countries Cornell Department of Plant Breeding and Genetics (19) G. R. Wiggans 2011

Number of New Genotypes 6000 5000 4000 3000 2000 1000 0 09/10 11/10 01/11 03/11 50 K and HD Cornell Department of Plant Breeding and Genetics (20) 05/11 07/11 09/11 11/11 3 K and LD G. R. Wiggans 2011

Genotyped Holsteins Date 04 -10 08 -10 12 -10 04 -11 05 -11 06 -11 07 -11 08 -11 09 -11 10 -11 11 -11 12 -11 Bulls* 9, 770 10, 430 11, 293 12, 152 12, 429 15, 379 15, 386 16, 519 16, 812 16, 834 17, 288 Cows* 7, 415 9, 372 12, 825 11, 224 11, 834 12, 098 12, 219 14, 380 14, 415 14, 573 14, 716 17, 236 Young animals** Bulls Heifers 16, 007 8, 630 18, 652 11, 021 21, 161 18, 336 25, 202 36, 545 26, 139 40, 996 27, 508 45, 632 28, 456 50, 179 29, 090 52, 053 30, 185 56, 559 31, 865 61, 045 32, 975 65, 330 33, 861 68, 051 All animals 41, 822 49, 475 63, 615 85, 123 91, 398 100, 617 106, 240 112, 042 117, 971 124, 315 129, 855 136, 436 *Traditional evaluation **No traditional evaluation Cornell Department of Plant Breeding and Genetics (21) G. R. Wiggans 2011

Sex Distribution August 2010 November 2011 Females 39% Males 38% Males 61% Females 62% All genotypes Cornell Department of Plant Breeding and Genetics (22) G. R. Wiggans 2011

Calculation of genomic evaluations l l l Deregressed values derived from traditional evaluations of predictor animals Allele substitutions random effects estimated for 45, 187 SNP Polygenic effect estimated for genetic variation not captured by SNP Selection Index combination of genomic and traditional not included in genomic Applied to yield, fitness, calving and type traits Cornell Department of Plant Breeding and Genetics (23) G. R. Wiggans 2011

Holstein prediction accuracy Traita Biasb b REL (%) REL gain (%) Milk (kg) − 64. 3 0. 92 67. 1 28. 6 Fat (kg) − 2. 7 0. 91 69. 8 31. 3 Protein (kg) 0. 7 0. 85 61. 5 23. 0 Fat (%) 0. 0 1. 00 86. 5 48. 0 Protein (%) 0. 0 0. 90 79. 0 40. 4 − 1. 8 0. 98 53. 0 21. 8 SCS 0. 0 0. 88 61. 2 27. 0 DPR (%) 0. 0 0. 92 51. 2 21. 7 Sire CE 0. 8 0. 73 31. 0 10. 4 − 1. 1 0. 81 38. 4 19. 9 1. 5 0. 92 21. 8 3. 7 − 0. 2 0. 83 30. 3 13. 2 PL (months) Daughter CE Sire SB Daughter SB a PL=productive life, CE = calving ease and SB = stillbirth. b 2011 deregressed value – 2007 genomic evaluation. Cornell Department of Plant Breeding and Genetics (24) G. R. Wiggans 2011

Reliabilities for young Holsteins* Number of animals 9000 50 K genotypes 8000 3 K genotypes 7000 6000 5000 4000 3000 2000 1000 0 40 45 50 55 60 65 70 75 80 Reliability for PTA protein (%) *Animals with no traditional PTA in April 2011 Cornell Department of Plant Breeding and Genetics (25) G. R. Wiggans 2011

Holstein Protein SNP Effects Cornell Department of Plant Breeding and Genetics (26) G. R. Wiggans 2011

Use of genomic evaluations l Determine which young bulls to bring into AI service l Use to select mating sires l Pick bull dams l Market semen from 2 -year-old bulls Cornell Department of Plant Breeding and Genetics (27) G. R. Wiggans 2011

Use of LD genomic evaluations l Sort heifers for breeding w Flush w Sexed semen w Beef bull l Confirm parentage to avoid inbreeding l Predict inbreeding depression better l Precision mating considering genomics(future) Cornell Department of Plant Breeding and Genetics (28) G. R. Wiggans 2011

Application to more traits l l l Animal’s genotype is good for all traits Traditional evaluations required for accurate estimates of SNP effects Traditional evaluations not currently available for heat tolerance or feed efficiency Research populations could provide data for traits that are expensive to measure Will resulting evaluations work in target population? Cornell Department of Plant Breeding and Genetics (29) G. R. Wiggans 2011

Impact on producers l l l Young-bull evaluations with accuracy of early 1 st crop evaluations AI organizations marketing genomically evaluated 2 year-olds Genotype usually required for cow to be bull dam Rate of genetic improvement likely to increase by up to 50% Studs reducing progeny-test programs Cornell Department of Plant Breeding and Genetics (30) G. R. Wiggans 2011

Why Genomics works in Dairy l Extensive historical data available l Well developed genetic evaluation program l Widespread use of AI sires l Progeny test programs l High valued animals, worth the cost of genotyping l Long generation interval which can be reduced substantially by genomics Cornell Department of Plant Breeding and Genetics (31) G. R. Wiggans 2011

Summary l l l Extraordinarily rapid implementation of genomic evaluations Chips provide genotypes of high accuracy Comprehensive checking insures quality of genotypes stored Young-bull acquisition and marketing now based on genomic evaluations Genotyping of many females because of lower cost low density chips Cornell Department of Plant Breeding and Genetics (32) G. R. Wiggans 2011

Cornell Department of Plant Breeding and Genetics (33) G. R. Wiggans 2011