1. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (1) Dr. George R. Wiggans Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 301-504-8407 (voice) 301-504-8092 (fax) george.wiggans@ars.usda.gov Genomics and where it can take us

2. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (2) Genomics and SNPs l Genomics w Applies DNA technology and bioinformatics to sequence, assemble and analyze the function and structure of genomes l SNPs – Single nucleotide polymorphisms w Serve as markers to track inheritance of chromosomal segments l Genomic selection w Selection using genomic predictions of economic merit early in life

3. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (3) Why genomics works for dairy cattle l Extensive historical data available l Well-developed genetic evaluation program l Widespread use of AI sires l Progeny-test programs l High-value animals worth the cost of genotyping l Long generation interval that can be reduced substantially by genomics

4. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (4) History of genomic evaluations l BovineSNP50 BeadChip availableDec. 2007 l First unofficial evaluation releasedApr. 2008 l Official evaluations for Holsteins and JerseysJan. 2009 l Official evaluations for Brown SwissAug. 2009 l Monthly evaluation Jan. 2010 l Official 3K evaluationsDec. 2010 l BovineLD BeadChip availableSept. 2011 l Official evaluations for Ayrshires Apr. 2013 l Weekly evaluation Nov. 2014

5. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (5) Evaluation flow l Animal nominated for genomic evaluation by approved nominator l DNA source sent to genotyping lab (2014)  SourceSamples (no.)Samples (%) Blood 10,7274 Hair113,45539 Nasal swab2,9541 Semen3,4321 Tissue149,30151 Unknown12,3014

6. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (6) Evaluation flow (continued) l DNA extracted and placed on chip for 3-day genotyping process l Genotypes sent from genotyping lab to CDCB for accuracy review  

7. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (7) Laboratory quality control 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 SNPs indicates genotype quality l Target of <10 SNPs in each category

8. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (8) Before clustering adjustment 86% call rate

9. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (9) After clustering adjustment 100% call rate

10. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (10) Evaluation flow (continued) l Genotype calls modified as necessary l Genotypes loaded into database l Nominators receive reports of parentage and other conflicts l Pedigree or animal assignments corrected l Genotypes extracted and imputed to 61K l SNP effects estimated l Final evaluations calculated       

11. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (11) 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 SNPs in place of microsatellites

12. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (12) Evaluation flow (continued) l Evaluations released to dairy industry w Download from CDCB FTP site with separate files for each nominator w Weekly release of evaluations of new animals w Monthly release for females and bulls not marketed w All genomic evaluations updated 3 times each year with traditional evaluations 

13. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (13) Genotype chips ChipSNP (no.)ChipSNP (no.) 50K54,001GP219,809 50K v254,609ZLD11,410 3K2,900ZMD56,955 HD777,962ELD9,072 Affy648,875LD26,912 LD6,909GP326,151 GGP8,762ZL217,557 GHD77,068ZM260,914

14. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (14) 2014 genotypes by chip SNP density Chip SNP densityFemaleMale All animals Low 239,07129,631268,702 Medium9,09814,20223,300 High14028168 All248,309 43,861292,170

15. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (15) 2014 genotypes by breed and sex BreedFemaleMale All animals Female: male Ayrshire 1,4852091,69488:12 Brown Swiss9448,6419,58510:90 Guernsey1,7773332,11084:16 Holstein212,76530,883243,64887:13 Jersey31,3233,79335,11689:11 Milking Shorthorn21367:33 Normande0100:100 Crossbred130 100:0 All248,30943,861292,17085:15

16. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (16) Growth in bull predictor population BreedJan. 201512-mo gain Ayrshire 71129 Brown Swiss6,112336 Holstein26,7592,174 Jersey4,448245

17. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (17) Holstein prediction accuracy *2013 deregressed value – 2009 genomic evaluation TraitBias*Reliability (%) Reliability gain (% points) Milk (kg)−80.369.230.3 Fat (kg)−1.468.429.5 Protein (kg)−0.960.922.6 Fat (%)0.093.754.8 Protein (%)0.086.348.0 Productive life (mo)−0.773.741.6 Somatic cell score 0.064.929.3 Daughter pregnancy rate (%) 0.253.520.9 Sire calving ease 0.645.819.6 Daughter calving ease−1.844.222.4 Sire stillbirth rate 0.228.2 5.9 Daughter stillbirth rate0.137.617.9

18. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (18) Holstein prediction accuracy *2013 deregressed value – 2009 genomic evaluation TraitBias*Reliability (%) Reliability gain (% points) Final score0.158.822.7 Stature−0.268.530.6 Dairy form−0.271.834.5 Rump angle0.070.234.7 Rump width−0.265.028.1 Feed and legs0.244.012.8 Fore udder attachment −0.270.433.1 Rear udder height −0.159.422.2 Udder depth −0.375.337.7 Udder cleft−0.262.125.1 Front teat placement −0.269.932.6 Teat length−0.166.729.4

19. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (19) Reliability gains Reliability (%)Ayrshire Brown SwissJerseyHolstein Genomic37546170 Parent average2830 Gain 9243140 Reference bulls 6805,767 4,207 24,547 Animals genotyped1,7889,01659,923469,960 Exchange partnersCanadaCanada, Interbull Canada, Denmark Canada, Italy, UK Source: VanRaden, Advancing Dairy Cattle Genetics: Genomics and Beyond presentation, Feb. 2014

20. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (20) Gene tests (imputed and actual) l Bovine leucocyte adhesion deficiency (BLAD) l Complex vertebral malformation (CVM) l Deficiency of uridine monophosphate synthase (DUMPS) l Syndactyly (mulefoot) l Weaver Syndrome, spinal dismyelination (SDM), spinal muscular atrophy (SMA) l Red coat color l Polledness

21. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (21) Haplotypes affecting fertility l Rapid discovery of new recessive defects w Large numbers of genotyped animals w Affordable DNA sequencing l Determination of haplotype location w Significant number of homozygous animals expected, but none observed w Narrow suspect region with fine mapping w Use sequence data to find causative mutation

22. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (22) New fertility haplotype for Jerseys (JH2) l Chromosome 26 at 8.8–9.4 Mbp l Carrier frequency w 14–28% in decades before 1990 w Only 2.6% now l Estimated effect on conception rate of –4.0% ± 1.5% l Additional sequencing needed to find causative genetic variant

23. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (23) Parent ages for marketed Holstein bulls

24. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (24) Inbreeding for Holstein cows – Inbreeding – Expected future inbreeding

25. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (25) Marketed Holstein bulls Year entered AI Traditional progeny- tested Genomic marketed All bulls 20081,7681701,938 20091,4743461,820 20101,3883931,781 20111,2546481,902 20121,2397061,945 2013 9077471,654 2014 6617921,453

26. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (26) Active AI bulls that were genomic bulls

27. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (27) Genetic merit of marketed Holstein bulls Average gain: $19.42/year Average gain: $47.95/year Average gain: $87.49/year

28. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (28) Stability of genomic evaluations l 642 Holstein bulls w Dec. 2012 NM$ compared with Dec. 2014 NM$ w First traditional evaluation in Aug. 2014 w  50 daughters by Dec. 2014 l Top 100 bulls in 2012 w Average rank change of 9.6 w Maximum drop of 119 w Maximum rise of 56 l All 642 bulls w Correlation of 0.94 between 2012 and 2014 w Regression of 0.92

29. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (29) Improving accuracy l Increase size of predictor population w Share genotypes across country w Young bulls receive progeny test l Use more or better SNPs l Account for effect of genomic selection on traditional evaluations l Reduce cost to reach more selection candidates

30. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (30) New GHD version (Expected this month) l Around 143,000 SNPs expected l Include 16,248 among 60,671 SNPs currently used that are not on GHD l Many added SNPs have low to moderate minor allele frequency w Increasing to 85,000 SNP improves evaluation accuracy

31. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (31) Low-cost chip (announcement this week) l ~4,100 SNPs l Built-in validation l Single-gene tests l Lower imputation accuracy if neither parent genotyped l Imputation accuracy within 1% of LD chip if at least 1 parent genotyped

32. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (32) Mating programs l Match genotypes of parents to minimize genomic inbreeding l Avoid mating carriers l Consider nonadditive gene action l May attempt to increase variance to get outliers

33. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (33) December 2014 changes l Net merit update l Grazing index l Genomic mating program l Base change l Weekly evaluations l New computer programs for traditional evaluations l New definition of daughter pregnancy rate

34. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (34) Weekly evaluations l Released to nominators, breed associations, and dairy records processing centers at 8 am each Tuesday l Calculations restricted to genotypes that first became usable during the previous week l Computing time minimized by not calculating reliability or inbreeding l Fast approximations for reliability and inbreeding being developed

35. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (35) Managing data l Genotypes added at an increasing rate w Requires periodic adjustments to maintain acceptable processing times l When loading genotypes, most decisions made based on 1,000 SNPs l Approximations developed for weekly evaluations may be applied to monthly evaluations to reduce processing time

36. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (36) Future l Discovery of causative genetic variants w Do not have linkage decay w Added to chips as discovered w Used when enough genotypes exist to support imputation w Accelerated by availability of sequence data at a lower cost

37. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (37) Future (continued) l Evaluation of benefit from larger SNP sets as cost per SNP genotype declines l Application of genomics to more traits l Across-breed evaluation/evaluation of crossbreds l Accounting for genomic pre-selection l Genomic evaluation of Guernseys in collaboration with the UK

38. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (38) Application to more traits l Animal’s genotype 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?

39. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (39) What’s already planned l BARD project (Volcani Center, Israel) w A posteriori granddaughter design (APGD) w Identification of causative variants for economically important traits l International collaboration on sequencing w United States, United Kingdom, Italy, Canada w Participation in 1000 Bull Genomes project

40. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (40) Conclusions l Genomic evaluation has dramatically changed dairy cattle breeding l Rate of gain has increased primarily because of large reduction in generation interval l Genomic research is ongoing w Detect causative genetic variants w Find more haplotypes that affect fertility w Improve accuracy

41. WiggansGenetics in the Age of Genomics, Scottsdale, AZ, March 4, 2015 (41) Questions? Holstein and Jersey crossbreds graze on American Farm Land Trust’s Cove Mountain Farm in south-central Pennsylvania Source: ARS Image Gallery, image #K8587-14; photo by Bob Nichols