1. WiggansANSC UMD(1) 2013 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350, USA george.wiggans@ars.usda.gov Genetic improvement program for dairy cattle 100 011110 1220020012 02121110111121 10111100112110002012200222011112021012002111221100211120220 00111100101101101022001100220110112002011010202221211221012202 2010011100011220221222112021120120201002022020002122 21122011101210011121110211211002010210002200020221 2010002011000022022110221121011211101222200120111 12220020002002020201222110022222220022121111220 21002111120011011101120020222000111201101021211 1121211102022100211201211001111102111211020002 122000101101110202200221110102011121111011221 202102102121101102212200121101121101202201100 01 22200210021100011100211021101110002220021121 2 21212110002220102002222120012211212101110112 11 200201102020012222220021110 22001120 211122 10101121211 202111 2112 12112121 10120 1021 01 11220 012 10 0 21 00 2 2 11 12 0 21 1 2 12001 0 12

2. WiggansANSC UMD(2) 2013 USDA-ARS-AIPL Animal Improvement Programs Laboratory

3. WiggansANSC UMD(3) 2013 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

4. WiggansANSC UMD(4) 2013 U.S. dairy population and milk yield

5. WiggansANSC UMD(5) 2013 Dairy cattle traits evaluated by USDA YearTraitYearTrait 1926Milk & fat yields2000Calving ease 1 1978Conformation (type)2003Daughter pregnancy rate 1978Protein yield2006Stillbirth rate 1994Productive life2006Bull conception rate 2 1994Somatic cell score (mastitis) 2009Cow and heifer conception rates 1 Sire calving ease evaluated by Iowa State University (1978–99) 2 Estimated relative conception rate evaluated by DRMS@Raleigh (1986–2005)

6. WiggansANSC UMD(6) 2013 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

7. WiggansANSC UMD(7) 2013 Traditional evaluations 3X/year Yield Milk, Fat, Protein Type Stature, Udder characteristics, feet and legs Calving Calving Ease, Stillbirth Functional Somatic Cell, Productive Life, Fertility

8. WiggansANSC UMD(8) 2013 Use of evaluations l Bulls to sell semen from l Parents of next generation of bulls l Cows for embryo donation

9. WiggansANSC UMD(9) 2013 Embryo Transferred to Recipient Daughters Born (9 m later) Bull Receives Progeny Test (5 yrs) Lifecycle of bull Parents Selected Dam Inseminated Bull Born Semen collected (1yr) Daughters have calves (2yr later) Genomic Test

10. WiggansANSC UMD(10) 2013 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

11. WiggansANSC UMD(11) 2013 Genomic evaluation program steps l Identify animals to genotype l Sample to genotyping lab l Genotype sample l Genotype to Beltsville l Calculate genomic evaluation l Release monthly

12. WiggansANSC UMD(12) 2013 Genomic data flow DHI herd DNA laboratory AI organization, breed association DNA samples genotypes genomic evaluations nominations, pedigree data genotype quality reports genomic evaluations DNA samples genotypes DNA samples AIPL

13. WiggansANSC UMD(13) 2013 Genotyped Animals (April 2013) Chip Traditional evaluation? Animal sexHolsteinJersey Brown Swiss Ayrshire  50K YesBulls 21,904 2,855 5,381 639 Cows 16,0621,054110 3 NoBulls45,5373,8841,031 325 Cows 32,892660102 110 <50KYesBulls1911289 Cows 21,9809,1324650 NoBulls14,0261,355902 Cows 158,62218,722658105 ImputedYesCows2,71323710312 NoCows 1,183321128 All314,93837,9428,0801,213

14. WiggansANSC UMD(14) 2013 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

15. WiggansANSC UMD(15) 2013 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

16. WiggansANSC UMD(16) 2013 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

17. WiggansANSC UMD(17) 2013 Sire Animal A/B *B/B *A/A B/BA/B B/B A/B *A/A A/BA/A B/BA/B *B/B * A/B B/BA/B *A/A *B/B A/B A/A *B/B A/BA/A A/BA/A Parent-Progeny conflicts Sire Conflicts=0 *Tests=10 Conflict %=0% Conflict % Relationship MGS A/B A/A A/B* A/A* B/B* A/A* B/B* * * A/B B/B* A/B A/A B/B* A/B A/A* B/B MGS Conflicts=3 *Tests=10 Conflict %=30.0%

18. WiggansANSC UMD(18) 2013 Detecting Unreliable Genotypes 00.20.40.60.811.21.4 1.6 1.82.02.42.8 3.2 Conflicts (%) Accept Unreliable Genotype (Reject) 3.6 Reject

19. WiggansANSC UMD(19) 2013 Grandsire detection l The two methods of Maternal Grandsire confirmation and discovery are: − SNP conflict method (SNP) Check if animal and MGS have opposite homozygotes (duo test) If sire is genotyped some heterozygous SNP can be checked (trio test) − Common haplotype method (HAP) After imputation of all loci, determine maternal contribution by removing paternal haplotype Count maternal haplotypes in common with MGS Remove haplotypes from MGS and check remaining against maternal great grandsire (MGGS)

20. WiggansANSC UMD(20) 2013 Results by breed SNP MethodHAP Method MGS MGGS Breed% Confirmed Holstein 95 (98) † 9792 Jersey91 (92)95 Brown Swiss94 (95)9785 † 50K genotyped animals only.

21. WiggansANSC UMD(21) 2013 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

22. WiggansANSC UMD(22) 2013 Before clustering adjustment 86% call rate

23. WiggansANSC UMD(23) 2013 After clustering adjustment 100% call rate

24. WiggansANSC UMD(24) 2013 Automated QC reporting 6160 Genotypes Processed from LAB2013021811 PASS/FAIL,Count,Description PASS,1,Parent Progeny Conflict SNP >2% PASS,5,Low Call Rate SNP >10% PASS,0,HWE SNP PASS,0,Chips w/ >20 Conflicts PASS,0.3,No Nomination % PASS,0,Genotype Submitted with No Sample Sheet Row

25. WiggansANSC UMD(25) 2013 Pedigree: Parents, Grandparents, etc. Manfred O-Man Jezebel O-Style Teamster Deva Dima

26. WiggansANSC UMD(26) 2013 O-Style Haplotypes chromosome 15

27. WiggansANSC UMD(27) 2013 What’s a SNP genotype worth? For protein yield (h 2 =0.30), the SNP genotype provides information equivalent to an additional 34 daughters Pedigree is equivalent to information on about 7 daughters

28. WiggansANSC UMD(28) 2013 And for daughter pregnancy rate (h 2 =0.04), SNP = 131 daughters What’s a SNP genotype worth?

29. WiggansANSC UMD(29) 2013 Genomic evaluations are calculated for each breed separately Correlation GPTAs and other Breeds ’ GPTAs

30. WiggansANSC UMD(30) 2013 Reliability of Holstein predictions 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.

31. WiggansANSC UMD(31) 2013 Marketed HO bulls

32. WiggansANSC UMD(32) 2013 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 other countries l Use of more SNP from HD chips l Full sequencing – Identify causative mutations

33. WiggansANSC UMD(33) 2013 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?

34. WiggansANSC UMD(34) 2013 Computing environment l Computation server w 2.3–2.7 GHz CPU (32 cores, 64 threads) w 256 GB RAM w 5 TB local storage l Database server w 3.0 GHz CPU (8 cores) w 40 GB RAM w 2 TB local storage l Shared storage w 19 TB

35. WiggansANSC UMD(35) 2013 Programming languages l C w Database interface including data editing l FORTRAN w Calculation of genetic merit estimates l SAS w Data preparation, checking, and delivery

36. WiggansANSC UMD(36) 2013 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

37. WiggansANSC UMD(37) 2013 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

38. WiggansANSC UMD(38) 2013 Council on Dairy Cattle Breeding l CDCB assuming responsibility for receiving data, computing, and delivering U.S. evaluations l USDA will continue research and development to improve evaluation system l CDCB and USDA employees collocated in Beltsville

39. WiggansANSC UMD(39) 2013 Questions?