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

2. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (2) 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

3. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (3) 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

4. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (4) Traditional evaluations 3X/year l Yield w Milk, Fat, Protein l Type w Stature, Udder characteristics, feet and legs l Calving w Calving Ease, Stillbirth l Functional w Somatic Cell, Productive Life, Fertility

5. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (5) Use of evaluations l Bulls to sell semen from l Parents of next generation of bulls l Cows for embryo donation

6. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (6) Parents Selected Dam Inseminated Embryo Transferred to Recipient Bull Born Semen collected (1yr) Daughters Born (9 m later) Daughters have calves (2yr later) Bull Receives Progeny Test (5 yrs) Lifecycle of bull Genomic Test

7. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (7) 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

8. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (8) History of genomic evaluations l Dec. 2007BovineSNP50 BeadChip available l Apr. 2008First unofficial evaluation released l Jan. 2009Genomic evaluations official for Holstein and Jersey l Aug. 2009Official for Brown Swiss l Sept. 2010Unofficial evaluations from 3K chip released l Dec. 20103K genomic evaluations to be official l Sept. 2011 Infinium BovineLD BeadChip available

9. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (9) Cattle SNP Collaboration - iBMAC l Develop 60,000 Bead Illumina iSelect® assay w USDA-ARS Beltsville Agricultural Research Center: Bovine Functional Genomics Laboratory and Animal Improvement Programs Laboratory w University of Missouri w University of Alberta w USDA-ARS US Meat Animal Research Center l Started w/ 60,800 beads – 54,000 useable SNP

10. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (10) Participants  Illumina  Marylinn Munson  Cindy Lawley  Christian Haudenschild  BARC  Curt Van Tassell  Lakshmi Matukumalli  Tad Sonstegard  Missouri  Jerry Taylor  Bob Schnabel  Stephanie McKay  Alberta  Steve Moore  USMARC – Clay Center  Tim Smith  Mark Allan  USDA/NRI/CSREES  2006-35616-16697  2006-35205-16888  2006-35205-16701  USDA/ARS  1265-31000-081D  1265-31000-090D  5438-31000-073D  Merial  Stewart Bauck  NAAB  Godon Doak  ABS Global  Accelerated Genetics  Alta Genetics  CRI/Genex  Select Sires  Semex Alliance  Taurus Service iBMAC Consortium Funding Agencies

11. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (11) Chips l BovineSNP50 w Version 1 54,001 SNP w Version 2 54,609 SNP w 45,187 used in evaluations l HD w 777,962 SNP w Only 50K SNP used, w >1700 in database l LD w 6,909 SNP w Replaced 3K HD 50KV2 LD

12. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (12) Use of HD l Currently only 50K subset of SNP used l Some increase in accuracy from better tracking of QTL possible l Potential for across breed evaluations l Requires few new HD genotypes once adequate base for imputation developed

13. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (13) LD chip l 6909 SNP mostly from SNP50 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) l Developed to address performance issues with 3K while continuing to provide low cost genotyping l Provides over 98% accuracy imputing 50K genotypes l Included beginning with Nov genomic evaluation

14. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (14) Development of LD chip l Consortium included researchers from USA, AUS and FRA l Objective: good imputation performance in dairy breeds w Uniform distribution except heavier at chromosome ends w High MAF, avg MAF over 30% for most breeds w Adequate overlap with 3K

15. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (15) 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

16. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (16) Responsibilities of requester l Insure animal is properly identified eg HOCANF000123456789 l Enroll animal with breed association or insure pedigree on animal and dam reaches AIPL l Collect clean, clearly labeled DNA sample l Get sample to lab in time to be included in desired month’s results l Resolve parentage conflicts quickly

17. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (17) 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 BeadChip l Do amplification and hybridization, 3-day process l Read red/green intensities from chip and call genotypes from clusters

18. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (18) What can go wrong l Sample does not provide adequate DNA quality or quantity l Genotype has many SNP that can not be determined (90% call rate required) l 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

19. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (19) 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

20. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (20) Before clustering adjustment 86% call rate

21. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (21) After clustering adjustment 100% call rate

22. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (22) Parentage validation and discovery l Parent-progeny conflicts detected w Animal checked against all other genotypes w Reported to breeds and requesters w Correct sire usually detected l Maternal Grandsire checking w SNP at a time checking w Haplotype checking more accurate l Breeds moving to accept SNP in place of microsatellites

23. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (23) Checking facility l Labs place genotype files on AIPL server l Genotypes run through analysis procedures, but not added to database l Reports on missing nominations and QC data returned to Lab l Lab can w Detect sample misidentification w Improve clustering w Apply the same checks used by AIPL

24. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (24) Imputation l Based on splitting the genotype into individual chromosomes (maternal & paternal contributions) l Missing SNP assigned by tracking inheritance from ancestors and descendents l Imputed dams increase predictor population l 3K, LD, & 50K genotypes merged by imputing SNP not on LD or 3K

25. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (25) Recessive defect discovery l Check for homozygous haplotypes l Most haplotype blocks ~5Mbp 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

26. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (26) Breed BTA chromo- some Location, Mbases Carrier frequency, % Holstein 562–68 4.5 193–98 4.6 892–97 4.7 Jersey1511–1623.4 Brown Swiss 742–4714.0 Haplotypes impacting fertility

27. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (27) Data and evaluation flow Genomic Evaluation Lab Requester (Ex: AI, breeds) Dairy producers DNA laboratories samples genotypes nominations evaluations

28. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (28) Collaboration l Full sharing of genotypes with Canada w CDN calculates genomic evaluations on Canadian base l Trading of Brown Swiss genotypes with Switzerland, Germany, and Austria w Interbull may facilitate sharing l Agreements with Italy and Great Britain provide genotypes for Holstein w Negotiations underway with other countries

29. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (29) Number of New Genotypes 09/10 11/10 01/11 03/11 05/1107/1109/1111/11 50K and HD 3K and LD

30. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (30) Genotyped Holsteins Date Young animals** All animals Bulls*Cows* Bulls Heifers 04-10 9,770 7,415 16,007 8,630 41,822 08-1010,430 9,37218,65211,021 49,475 12-1011,29312,82521,16118,336 63,615 04-1112,15211,22425,20236,545 85,123 05-1112,42911,83426,13940,996 91,398 06-1115,37912,09827,50845,632100,617 07-1115,38612,21928,45650,179106,240 08-1116,51914,38029,09052,053112,042 09-1116,81214,41530,18556,559117,971 10-1116,83214,57331,86561,045124,315 11-1116,83414,71632,97565,330129,855 12-1117,28817,23633,86168,051136,436 *Traditional evaluation **No traditional evaluation

31. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (31) Sex Distribution Females 39% 61% All genotypes Males Females 38% 62% August 2010 November 2011

32. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (32) Calculation of genomic evaluations l Deregressed values derived from traditional evaluations of predictor animals l Allele substitutions random effects estimated for 45,187 SNP l Polygenic effect estimated for genetic variation not captured by SNP l Selection Index combination of genomic and traditional not included in genomic l Applied to yield, fitness, calving and type traits

33. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (33) Holstein prediction accuracy Trait a Bias b bREL (%)REL gain (%) Milk (kg)−64.30.9267.128.6 Fat (kg)−2.70.9169.831.3 Protein (kg) 0.70.8561.523.0 Fat (%) 0.01.0086.548.0 Protein (%) 0.00.9079.040.4 PL (months)−1.80.9853.021.8 SCS 0.00.8861.227.0 DPR (%) 0.00.9251.221.7 Sire CE 0.80.7331.010.4 Daughter CE−1.10.8138.419.9 Sire SB 1.50.9221.8 3.7 Daughter SB− 0.20.8330.313.2 a PL=productive life, CE = calving ease and SB = stillbirth. b 2011 deregressed value – 2007 genomic evaluation.

34. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (34) Reliabilities for young Holsteins* *Animals with no traditional PTA in April 2011 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 404550556065707580 Reliability for PTA protein (%) Number of animals 3K genotypes 50K genotypes

35. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (35) Holstein Protein SNP Effects

36. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (36) 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

37. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (37) 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)

38. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (38) Ways to increase accuracy l Automatic addition of traditional evaluations of genotyped bulls when reach 5 years of age l Possible genotyping of 10,000 bulls with semen in repository l Collaboration with more countries l Use of more SNP from HD chips l Full sequencing – Identify causative mutations

39. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (39) Application to more traits l Animal’s genotype is good for all traits l Traditional evaluations required for accurate estimates of SNP effects l Traditional evaluations not currently available for heat tolerance or feed efficiency l Research populations could provide data for traits that are expensive to measure l Will resulting evaluations work in target population?

40. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (40) Impact on producers l Young-bull evaluations with accuracy of early 1st­crop evaluations l AI organizations marketing genomically evaluated 2- year-olds l Genotype usually required for cow to be bull dam l Rate of genetic improvement likely to increase by up to 50% l Studs reducing progeny-test programs

41. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (41) 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

42. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (42) Summary l Extraordinarily rapid implementation of genomic evaluations l Chips provide genotypes of high accuracy l Comprehensive checking insures quality of genotypes stored l Young-bull acquisition and marketing now based on genomic evaluations l Genotyping of many females because of lower cost low density chips

43. G.R. Wiggans 2011 Cornell Department of Plant Breeding and Genetics (43)