1. G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD george.wiggans@ars.usda.gov G.R. WiggansAlta Genetics meeting May 2010 (1) Genomic Evaluations

2. G.R. Wiggans Alta Genetics meeting May 2010 (2) How the system works l Studs and breeds nominate animals through AIPL web site l Hair, blood, semen, or extracted DNA sent to 1 of 4 Labs l Genotypes sent to AIPL monthly l Monthly evaluation updates released on the first Tuesday of most months l Official evaluations updated only at tri-annual traditional runs (except for C & P bulls)

3. G.R. Wiggans Alta Genetics meeting May 2010 (3) Pedigree & Nomination l Studs may submit pedigree and nominate in batch files l Pedigree for CAN, AUS, GBR automatically collected from web sites l Nomination expected by the time sample arrives at lab l Sample ID reported at nomination must match ID on sample at lab

4. G.R. Wiggans Alta Genetics meeting May 2010 (4) Conflict processing l Parent-progeny conflicts detected l Sex and breed checked l Conflicts reported to lab and requester for resolution l Pedigree changes automatically update genotype usability l Foreign pedigree updates not automatic

5. G.R. Wiggans Alta Genetics meeting May 2010 (5) Changes in April l Deviations of predictor cows adjusted to be like bulls with similar reliability to improve their contribution to accuracy l Genotypes of dams of genotyped animals imputed to add predictor animals l Sum of genomic relationships of each animal with the predictor animals used to improve estimation of Reliability

6. G.R. Wiggans Alta Genetics meeting May 2010 (6) Imputation l Determine an animal’s genotype from genotypes of its parents and progeny l Genotype separated into sire and dam contributions. Identifies the allele on each member of a chromosome pair

7. G.R. Wiggans Alta Genetics meeting May 2010 (7) O-Style Haplotypes chromosome 15

8. G.R. Wiggans Alta Genetics meeting May 2010 (8) Imputation (cont.) l Inheritance of haplotypes tracked l Accuracy of imputation improves with number of progeny l Crossovers during meiosis contribute to uncertainty

9. G.R. Wiggans Alta Genetics meeting May 2010 (9) Imputation Plans l Add separate genomic indicator code (probably 3) to cow format 105 to identify imputed cows. Already are identified in XML files. l 3K genotypes will be imputed to 50K, chip type code to be added to XML l No authorization to release evaluations on imputed bulls. 15 HO bulls have 5+ genotyped progeny whose dams are genotyped.

10. G.R. Wiggans Alta Genetics meeting May 2010 (10) Genotyped Holstein by run Run Date Old*Young** Total MaleFemaleMaleFemale 0904760027119690194321944 09067883304911459297425365 09088512372812137367028047 09108568396513288479730618 10018974434814061603133414 10029378508615328762037412 10049770741516007863041822 10059958794016594977244264 * Animals with traditional evaluation ** Animals with no traditional evaluation

11. G.R. Wiggans Alta Genetics meeting May 2010 (11) Cow Adjustment l Evaluations of elite cows biased upward l Cutoff studies showed little benefit from including cows as predictors l Reducing heritability would reduce the problem but industry is reluctant to do so l Adjustment of cow evaluations implemented

12. G.R. Wiggans Alta Genetics meeting May 2010 (12) SD of Cow Deviation from PA 0 500 1000 1500 2000 2500 0.40.61.02.5 Daughter Equivalent (progeny) Std. Dev of Dereg M.S. (Milk) Cow Bull

13. G.R. Wiggans Alta Genetics meeting May 2010 (13) Mean of Cow Deviation from PA -400 -200 0 200 400 600 800 1000 20002001200220032004200520062007 Birth year Milk (lbs.) Cow Bull Cow SD Adj

14. G.R. Wiggans Alta Genetics meeting May 2010 (14) Cow Adjustment Procedure l Deregressed Mendelian Sampling (MS) = (PTA-PA) / f(REL) l Adj. MS =.84*MS - 784 l Adj. PTA = f(REL)*(Adj. MS+ PA n ) + (1- (REL)*PA n ) f(REL) = fraction of PTA from own records and progeny

15. G.R. Wiggans Alta Genetics meeting May 2010 (15) Effect of Adjustment on Holstein BiasRegressionGain REL NoYesDiffNoYesDiffNoYesDiff Milk (lb)-75.3-27.947.4.93.90-.0329.532.53.0 Fat (lb)-5.7-2.92.8.98.97-.0134.037.13.1 Protein (lb)-0.20.81.0.90.97.0725.027.12.1 Fat (%)0.0.97.99.0249.852.42.6 Protein (%)0.0.87.88.0138.841.52.7

16. G.R. Wiggans Alta Genetics meeting May 2010 (16) Effect of Adjustment on Jersey BiasRegressionGain REL NoYesDiffNoYesDiffNoYesDiff Milk (lb)-44.081.5125.5.99.0010.819.68.8 Fat (lb)-7.37.915.2.78.84.069.418.28.8 Protein (lb)1.74.32.6.86.90.044.112.78.6 Fat (%)0.0.90.95.0529.937.67.7 Protein (%)0.0.87.93.0624.834.29.4

17. G.R. Wiggans Alta Genetics meeting May 2010 (17) l Increased reliability of genomic predictions l Genomic evaluations of the top cows, top young bulls, and top heifers decreased l Among bulls, foreign bulls with a high proportion of genotyped daughters had largest changes l Adjusted PTA reported in XML traditional fields Cow Adjustment Summary

18. G.R. Wiggans Alta Genetics meeting May 2010 (18) Reliability for young HO Bulls 0 500 1000 1500 2000 2500 3000 3500 4000 52535455565758596061626364656667686970717273747576777879 Milk REL Number of Bulls N = 15,226

19. G.R. Wiggans Alta Genetics meeting May 2010 (19) Reliabilities for HO born ≥ 2005 No Traditional EvaluationWith Traditional Evaluation TraitMaleFemaleMaleFemale N1522675367523191 Milk (lb)73.973.785.877.9 Protein (lb)73.973.785.877.8 PL (months)64.063.670.167.0 SCS69.769.578.173.0 DPR (%)61.661.266.564.6 PTAT70.470.178.374.5 Sire CE64.961.780.863.5 Daughter CE60.259.069.561.8 Sire SB59.858.766.259.6 Daughter SB58.357.664.959.6 Net Merit ($)68.668.377.872.0

20. G.R. Wiggans Alta Genetics meeting May 2010 (20) Accommodating chip diversity l Impute to higher density l Calculate effects for all high density SNP l Mechanism for accounting for loss in accuracy due to imputation error needed w Percent missing genotypes l Only observed genotypes stored in database l Evaluations labeled as to source of genotype

21. G.R. Wiggans Alta Genetics meeting May 2010 (21) Illumina 3K chip l SNP chosen w 3072, evenly spaced w Some Y specific SNP w 90 SNP for breed determination l Expect to impute genotypes for 43,382 SNP with high accuracy l Expect breeds to use 3K chip to replace microsatellites for parentage verification l Breeds allowed to genotype bulls for parentage only

22. G.R. Wiggans Alta Genetics meeting May 2010 (22) Proposed stud use of 3K chip l Accuracy adequate for first stage screening l HD genotyping reserved for bulls acquired. w Confirm ID w Second stage selection l Genotyping of more candidates l Genotype remaining CDDR predictor bulls to meet or exceed EuroGenomics reliabilities

23. G.R. Wiggans Alta Genetics meeting May 2010 (23) HD chip l Proposed 860K SNP include current 43,382 so can replace 50K chip in current evaluations l 3,000+ genotypes at HD may be adequate to support imputation of HD from current 50K SNP l Expected gain in Rel < 2 l May allow HO genotypes to contribute to accuracy of JE & BS genomic evaluations

24. G.R. Wiggans Alta Genetics meeting May 2010 (24) HD chip (Cont.) l Could share cost of HD genotyping with Europe to get more animals to improve accuracy of imputation l Trend is toward higher densities l Continued genotyping at 50K may be shortsighted l May allow reduction in polygenic effect giving increased accuracy

25. G.R. Wiggans Alta Genetics meeting May 2010 (25) Will data recording survive? l Progeny test no longer required to market bulls l In 2013, new entrants may have no data collection expense l Loss in accuracy of SNP effect estimates occurs over time l How much data is needed?

26. G.R. Wiggans Alta Genetics meeting May 2010 (26) What replaces the PT program? l G bulls will have thousands of daughters in their early traditional evaluations l Milk recording is justified for management information l Type data may come from breeder herds because they use G bulls l Data on new traits will have to be paid for

27. G.R. Wiggans Alta Genetics meeting May 2010 (27) Data into National Evaluations l Progeny test herds could become data supply herds l Data acquisition could be supported by a fee based on animals receiving a genomic evaluation l Plan must be perceived as fair by all industry players l Quality certification model could apply

28. G.R. Wiggans Alta Genetics meeting May 2010 (28) Questions l How can accuracy of evaluations from EuroGenomics be exceeded? l Should young bull purchases be based on 3K genotypes? l How will continued flow of data into genetic evaluations be assured?

29. G.R. Wiggans Alta Genetics meeting May 2010 (29) Questions from Bob

30. G.R. Wiggans Alta Genetics meeting May 2010 (30) Will there be a code on GPTA’s to distinguish between genotyped and imputed animals? l Genomic indicator code of 3 planned for imputed cows in format 105 in August l Already designated in XML files

31. G.R. Wiggans Alta Genetics meeting May 2010 (31) From which EU countries are cow proofs used in genomics? (All, health, type?) l Cow evaluations for milk, fat, and protein are collected from: w NLD w DEU w FRA w GBR w ITA w DNK

32. G.R. Wiggans Alta Genetics meeting May 2010 (32) What caused the DPR changes from Dec – Feb – April? Can it happen again? l The traditional DPR PAs for some foreign bulls were incorrect in Feb l May have been due to missing the dam or MGS l We have increased checking for missing pedigree

33. G.R. Wiggans Alta Genetics meeting May 2010 (33) What is the difference between selection index (US) and blending of proofs (CAN)? l Selection Index combines w Genomic w Traditional w Traditional computed using only genotyped animals l Theoretically justified l DGV includes all information when both parents genotyped l Used in most countries

34. G.R. Wiggans Alta Genetics meeting May 2010 (34) What is the difference between Selection Index (US) and Blending of proofs (CAN)? (Cont.) l Blending combines w Genomic w Traditional l Weighted by reliability l Simple to explain

35. G.R. Wiggans Alta Genetics meeting May 2010 (35) What are the criteria for an animal to be included in the reference population for genomics? l Traditional Rel > PA Rel

36. G.R. Wiggans Alta Genetics meeting May 2010 (36) What other factors can change SNP effect estimates, beyond adding new animals to the reference population? l New traditional evaluations – tri-annual runs l Insufficient iterations in previous run l Change in SNP used

37. G.R. Wiggans Alta Genetics meeting May 2010 (37) Why do genomic evaluations change? l Reference population animals are added l Changes in traditional PTA cause genomic evaluation to change particularly for high reliability bulls l Small changes due to filling in missing SNP genotypes when possible

38. G.R. Wiggans Alta Genetics meeting May 2010 (38) How much do the SNP effect estimates change Trait SNP effect differences from April to May Maximum MeanStd. Dev. Milk (lbs)0.420.3824.5 Fat (lbs)0.020.011.1 Protein (lbs)0.020.010.8 Fat (%)3.9E-53.4E-59.1E-4 Protein (%)1.9E-51.6E-54.1E-4

39. G.R. Wiggans Alta Genetics meeting May 2010 (39) Are reliability calculations different in the US vs. EU? And, why are reliability values similar with a large discrepancy in the number of predictor bulls? (9,000 vs. 17,000) l ~8,000 cows also contribute to accuracy l US Rel is adjusted to reflect gains from cutoff studies

40. G.R. Wiggans Alta Genetics meeting May 2010 (40) Can sire proofs be imputed? And, is this likely to happen? l Genotypes of bulls can be imputed l Only 15 non-genotyped HO bulls with 5+ genotyped progeny with genotyped dams l May be approved for bulls controlled by participating studs

41. G.R. Wiggans Alta Genetics meeting May 2010 (41) Will there be an adjustment made to type in August? l No

42. G.R. Wiggans Alta Genetics meeting May 2010 (42) Is it possible to estimate the variation in the offspring for $NM when two genotyped animals are mated? l Yes, sum absolute value of SNP effects weighed as: w Parents both 0 or 2, weight 0 w Parents 0 and 2, weight 1 w 1 or both parents 1, weight 2 l Weights are max difference in progeny genotypes

43. G.R. Wiggans Alta Genetics meeting May 2010 (43) Making genotyped and non- genotyped cows more comparable l High priority research area w Reduce h 2 w Add herd x dam interaction w Differential adjustment by herd

44. G.R. Wiggans Alta Genetics meeting May 2010 (44) Will AIPL continue to impute cows during each genomic evaluation? l Yes

45. G.R. Wiggans Alta Genetics meeting May 2010 (45) Explanation of changes in SHOTTLE evaluation from Jan. to April JanuaryApril TraitTraditionalGenomicTraditionalGenomic Milk (lb)1597178412921399 Protein (lb)45513840 PL (months)3.06.23.64.4 Net Merit ($)529729507551

46. G.R. Wiggans Alta Genetics meeting May 2010 (46) Why were cows in Advantage herds with no preferential treatment adjusted? l All genotyped cows were adjusted in the same way l The maternal component of PA was adjusted l Investigating if accuracy can be improved by adjusting each herd based on its own average PTA-PA

47. G.R. Wiggans Alta Genetics meeting May 2010 (47) What changes to the imputation process were made in May? l Maternal grandparents were checked for haplotypes where parents were not available l Current allele frequencies replaced base population frequencies for unknown genotypes

48. G.R. Wiggans Alta Genetics meeting May 2010 (48) How do you incorporate various chip sets (ex. 3K, 50K, 700K, 850K) into a single genomic evaluation? And, what level of imputing will take place? l Lower densities will be imputed to highest density l If the larger HD chip does not include all the SNP of the smaller one, then combined set must be imputed or some SNP ignored

49. G.R. Wiggans Alta Genetics meeting May 2010 (49) What will be the gain in accuracy from going from 50K to 850K? l <2 increase in reliability

50. G.R. Wiggans Alta Genetics meeting May 2010 (50) What are the biggest changes and challenges after March 2013 when anyone can get a genomic evaluation of a bull? l Maintaining support for data collection for genetic evaluations

51. G.R. Wiggans Alta Genetics meeting May 2010 (51) What are the relative weights given to SNP information for the GPTAs of 1 st lactation cows, 1 st crop bulls, and 2 nd crop bulls? l Proportional to daughter equivalents (DE) l DE = k*Rel/(100 – Rel) l Calculate DE at each stage of evaluation l DE G = DE total - DE PA

52. G.R. Wiggans Alta Genetics meeting May 2010 (52) How is a DGV calculated? l ∑ SNP effects + base l Polygenic effect not included

53. G.R. Wiggans Alta Genetics meeting May 2010 (53) Do SNP estimates change based on family? l No, SNP effect is change in PTA from having an A allele instead of a B allele (substitution effect)

54. G.R. Wiggans Alta Genetics meeting May 2010 (54) How can 99% Reliability bulls change between runs? l Traditional evaluations change l High reliability evaluations force SNP effects to adjust to equal evaluation l Possible because more SNP effects than predictor animals