Cow Adjustment and Genomic Database Update

1. Cow AdjustmentandGenomic Database Update

2. Cow Adjustment

3. Gains in Reliability as of 2008

4. DGV vs Traditional PTA (Bulls) 2000 1500 1000 Milk (pounds) 500 0 -155 276 588 794 962 1104 1252 1403 1578 1867 -500 PA Milk (pounds) Bull DGV Bull Traditional PTA DGV – based on allele effects of all genotyped animals Traditional PTA – no genomics

5. 2500 2500 2000 2000 1500 1500 1000 1000 Milk (pounds) Milk (pounds) 500 500 0 0 -715 284 576 793 960 1104 1253 1414 1592 1936 -500 -715 284 576 793 960 1104 1253 1414 1592 1936 -500 -1000 -1000 PA Milk (pounds) PA Milk (pounds) Cow DGV Cow Traditional PTA Adjusted Traditional PTA Cow DGV Cow Traditional PTA DGV vs Traditional PTA (Cows) DGV – based on allele effects of all genotyped animals Traditional PTA – no genomics

6. Why reduce cow bias through adjustment? • high PTA values were causing the genomic predictions to suffer in accuracy • added information from genotyped cows was not increasing reliability

7. Which animals were adjusted? • All genotyped and imputed cows • All genotyped animals including bulls, were affected by the adjustment made to the maternal portion of the parent average • Brown Swiss adjustments were not implemented due to low numbers of genotyped cows

8. 2500 1000 800 2000 600 1500 Cow 400 Cow Milk (lbs.) Std. Dev of Dereg M.S. (Milk) Bull Cow SD Adj 1000 200 Bull 0 500 -200 0 2000 2001 2002 2003 2004 2005 2006 2007 0.4 0.6 1.0 2.5 -400 Birth year Daughter Equivalent (progeny) How was the adjustment made? Variance Adjustment Mean Adjustment

9. How was the adjustment made? • Deregressed Mendelian Sampling (MS) = (PTA-PA) / f(REL) • Adj. MS = .84*MS - 784 • Adj. PTA = f(REL)*(Adj. MS+ PAn) + (1- f(REL)*PAn) f(REL) = weight in PTA from own records and progeny

10. Effects of Cow Adjustment (Holstein)

11. Effects of Cow Adjustment (Jersey)

12. Example (Cow Milk PTA 1934  381) 2500 2000 1500 Milk (pounds) 1000 500 0 Progeny 1 Jan PA April PA Progeny2

13. The future • Investigate solutions to the problem of not being able to compare genotyped and non-genotyped cows • Reduce heritability • Add dam-herd interaction • Varying heritability by herd • Other • With 3K chip, adjustments may need to vary by sub-population

14. Genomic Database

15. Genotyped Holstein by run * Animals with traditional evaluation ** Animals with no traditional evaluation

16. Genotyped Jersey by run * Animals with traditional evaluation ** Animals with no traditional evaluation

17. Genotyped Brown Swiss by run * Animals with traditional evaluation ** Animals with no traditional evaluation

18. Genotype Processing • 2,000 New Genotypes a month • Four labs • GeneSeek • Genetic Visions • DNA LandMarks • GIVF

19. Genotype Processing: Nomination • All animals should be nominated by the time sample reaches lab • Goal: Parents and Grandparents on every genotyped animal • Minimum: Valid ID

20. Genotype Processing: Edits • Genotypes must pass 62 edits to be added to the database • Most common reasons a genotype fails • Low call rate • Parent / Progeny conflict • Possible split embryo / twin • Wrong gender • Breed Check • Switched samples

21. Continuous Updates • Pedigree changes update genotype usability daily • Harmonization with breed association important to maintain usability • Blanking a genotyped dam will make the genotype unusable • No automatic receipt of foreign pedigree updates

22. Low call rate • 80% on X chromosome • 90% on autosomal chromosomes (43,382) • Labs generally do not send genotypes with low call rates

23. Parent Progeny Conflict • Sire / Dam conflict • > 200 SNP conflicts • Sire / dam proposed if genotyped • Sire conflict - young animals from mixed flush • Sire conflicts represent $50,000 genotype cost

24. Split embryo / Identical twin / Clone • <1000 SNP differences considered identical • 98% similar, accounts for genotyping errors • Stored in clone table if valid • Animals registered as ETS or ETN (automatic) • Otherwise verification must come from requester

25. Wrong Gender • > 50 heterozygous SNP on X (not male) • < 50 heterozygous SNP on X (not female) • Homozygous X female • Common ancestor (source of X)

26. Source of X Bellwood BW Marshall Mara Elegant Patron Tanya Mary Jeff Mark Sam Sue Erin Patron 12144058 14860763

27. Breed Check • 622 SNP • Nearly monomorphic in 1 breed and have fewer than 30% of animals homozygous for that allele in another breed • An error when a higher number of conflicts for the reported breed than for another breed • > 10 SNP conflicts reported to requester, but remains usable • Higher conflicts for foreign animals

28. Switched Samples • Sample pair with reversed parents • Switched at the lab • Switched at the farm

29. Conflicts to Requester • Genotype conflicts reported to the requester immediately • Genotype remains stored in AIPL database as an unusable genotype until conflict is resolved • 2-3 days to fix conflicts before cutoff • Once a month all conflicts remaining in the system are sent to requesters

30. Issues Reported to Lab for QC • SNP that have call rate <90% • SNP that have high parent-progeny conflicts • SNP that deviate from HW equilibrium • Labs have the opportunity to re-cluster genotypes

31. Closing Thoughts • Currently 477 animals with failed or conflicted genotypes • Big increase in volume when 3K becomes available • Edits will remain with different thresholds for 3K data