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March 2012 AIPL Update

March 2012 AIPL Update. Topics. Genomics overview April 2012 changes Accounting for bias Selection index adjustments Cow adjustments Calving traits update. Whole-genome selection. Use many markers to track inheritance of chromosomal segments

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March 2012 AIPL Update

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  1. March 2012 AIPL Update

  2. Topics • Genomics overview • April 2012 changes • Accounting for bias • Selection index adjustments • Cow adjustments • Calving traits update

  3. Whole-genome selection • Use many markers to track inheritance of chromosomal segments • Estimate the impact of each segment on each trait • Combine estimates with traditional evaluations to produce genomic evaluations (GPTA) • Select animals shortly after birth using GPTA • Very successful worldwide

  4. Illumina genotyping arrays BovineSNP50 54,001 SNPs (version 1) 54,609 SNPs (version 2) 45,187 SNPs used in evaluation BovineHD 777,962 SNPs Only BovineSNP50 SNPs used >1,700 SNPs in database BovineLD 6,909 SNPs Allows for additional SNPs BovineSNP50 v2 BovineHD BovineLD

  5. Reliabilities for young Holsteins* 9000 50K genotypes 8000 3K genotypes 7000 6000 5000 Number of animals 4000 3000 2000 1000 0 40 45 50 55 60 65 70 75 80 Reliability for PTA protein (%) *Animals with no traditional PTA in April 2011

  6. Genotyped Holsteins *Traditional evaluation **No traditional evaluation

  7. What’s a SNP genotype worth? Pedigree is equivalent to information on about 7 daughters For the protein yield (h2=0.30), the SNP genotype provides information equivalent to an additional 34 daughters

  8. What’s a SNP genotype worth? And for daughter pregnancy rate (h2=0.04), SNP = 131 daughters

  9. High density update HD tests before and after GBR, ITA 342 HD animals with 636,967 SNPs 1,074 HD animals with 636K or 311K 1,510 HD animals with 311,725 SNPs REL gain above 50K for the 3 tests -0.5% decrease in REL using 342 HD +0.4% increase using 1,074 HD +1.0% increase using 1,510 HD

  10. Holstein prediction accuracy aPL = productive life,CE = calving ease and SB = stillbirth. b2011 deregressed value – 2007 genomic evaluation.

  11. April 2012 changes • Genotypes from GGP included • Revised weights used to combine information for genotyped animals • Reduces evaluations of top young animals compared with older animals • Reliabilities changed only slightly, but would decrease if DGV weights were reduced more • Some regressions lower than expected, and the revised weights helped bring those into compliance with validation tests

  12. April 2012 changes, cont’d • Reliabilities revised to agree more precisely with observed reliabilities from truncated data • Published reliabilities for young Holstein animals were adjusted: • -3 percentage points for yield traits • +3 to 6 percentage points for fitness traits • -7 to 10 percentage points for calving traits • +1 percentage point for type traits

  13. April 2012 changes, cont’d • Traditional PL evaluations for females less than 48 months of age not used in the genomic evaluation • Eliminated large differences between genomic and traditional PL for some bulls • Traditional DPR evaluations for genotyped cows less than 36 months of age now excluded from genomic evaluation • Similar edit for CCR

  14. Some sources of bias • Pre-selection bias • Affects domestic and international evaluations • Preferential treatment of bull dams • Results in inflated PTA

  15. Expected value of Mendelian sampling no longer equal to 0 Key assumption of animal models References: Patry, Ducrocq 2011 GSE 43:30 Vitezica et al 2011 Genet Res (Camb) pp. 1–10. Bias from pre-selection

  16. Bulls born in 2008, progeny tested in 2009, with daughter records in 2012, were pre-selected: 3,434 genotyped vs. 1,096 sampled Now >10 genotyped per 1 marketed Potential for bias: 178 genotyped progeny 32 sons progeny tested Pre-selection bias now beginning

  17. 1-Step to incorporate genotypes Flexible models, many recent studies Foreign data not yet included Multi-step GEBV, then insert in AM Same trait (Ducrocq and Liu, 2009) Or correlated trait (Mantysaari and Stranden, 2010; Stoop et al, 2011) Foreign genotyped bulls included National methods to reduce bias

  18. Multi-step genomic methods Direct genomic value (DGV) Sum of effects for 45,187 genetic markers Does not include polygenic effect (USA) Does include the polygenic effect (CAN, others) Model: y = Xb + Zg + poly + e Combined genomic evaluation (GPTA) Include phenotypes not used in estimating DGV Selection index includes 3 terms per animal: (DGV + poly), traditional PTA, and subset PTA GPTA = w1(DGV + poly) + w2 PTA + w3 SPTA

  19. Combined GPTA • GPTA = w1(DGV + poly) + w2PTA + w3SPTA • (DGV + poly) = contribution from SNP effects • PTA = contribution from the traditional evaluation • SPTA = subset PTA estimated using pedigree relationships among the genotyped animals • Terms combined using theoretical weights based on reliabilities • Weights average 0.99 for DGV, 0.12 for EBV, and -0.11 for SBV

  20. Selection index examples Dam not genotyped, low GREL GPTA = .99 (DGV+poly) + .41 PTA - .40 SPTA Dam not genotyped, high GREL GPTA = .99 (DGV+poly) + .11 PTA - .10 SPTA Dam is genotyped GPTA = 1.00 (DGV+poly) + .00 PTA - .00 SPTA

  21. Proposal: Shift weight from DGV to SPTA Dam not genotyped, low GREL GPTA = .90 (DGV+poly) + .41 PTA - .31 SPTA Dam not genotyped, high GREL GPTA = .90 (DGV+poly) + .11 PTA - .01 SPTA Dam is genotyped GPTA = .90 (DGV+poly) + .10 PTA - .00 SPTA

  22. Results of shifting DGV weight Similar to adding more polygenic variance but easier computation Some genomic REL higher with .90 weight, but lower if < .80 weight Regressions of future on past data higher if DGV weight lower Highest animals have lower GPTAs with .90 weight

  23. Convert and exchange DYDg National GEBV and DYDg unbiased Can’t deregress GEBV without G Exchange similar to simple GMACE Other countries need DYD anyway Deregress, reregress EBVs in MACE Countries deregress MACE EBV Avoid bias by exchanging DYDg International bias reduction

  24. 6,743 bulls with no USA daughters Corr (National EBV, MACE EBV) .77 before adding foreign data .995 after adding foreign data Few foreign bulls in JE reference population, so hard to test gain in REL of young bull GEBV Foreign data in 1-Step: results

  25. 1-Step vs multi-step Data cutoff in August 2008

  26. Holstein convergence much slower JE took 11 sec / round including G HO took 1.6 min / round including G JE needed ~1000 rounds HO needed >5000 rounds All-breed model without genomics Replace software used since 1989 Correlations >.995 with traditional AM Preliminary larger analyses

  27. Bias in cow evaluations Top cows over-evaluated compared to top bulls Parent averages over-estimate eventual evaluations of bulls Unreasonable estimates of SNP effects in PAR reflect sex effect Adjustment of evaluations of genotyped cows implemented April 2010 Adjustment made genotyped cows not comparable to non genotyped cows

  28. Genomic evaluation Deregressed traditional evaluations used for estimation of SNP effects Predictor population consists of animals with both genotypes and traditional evaluations Cows can be predictors Increases size of predictor population Requires that cow and bull evaluations be comparable

  29. Adjustment of cow evaluations US industry requested adjustment of all cow evaluations to restore comparability Desirable to leave estimates of genetic trend unchanged Variability of cow evaluations to be reduced Industry requested proposal in February for possible implementation in April 2011 Industry partners collaborated in developing and distributing information on the new adjustment

  30. Method Adjustment for Milk, Fat, and Protein only Mendelian Sampling (MS) = PTA - PA Deregressed Value = MS/R DEcow = DEtot – DEpa R = DEcow/(DEtot + k) SD of Deregressed Values of cows and bulls compared AdjVar = SDbull/SDcow Varies with reliability

  31. Mean adjustment Calculate mean PA by birth year AdjMean = factor*(PA – PAmean) HO factor = -0.434 Devadj = AdjVar*Deregressed Value + AdjMean+ PA HO AdjVar = 0.3165 + 1.433 * Rcow Rcow = DEcow /(DEcow + k) PTAadj = R*Devadj + (1-R)*PAnew PAnew includes PTAadj of dam

  32. PTA milk for cows born in 2005 1500 1000 500 PTA 0 -500 -1000 -1500 -1000 -760 -520 -280 -40 200 440 680 920 PA ADJ No ADJ Difference

  33. Effect of reliability 600 500 400 300 200 100 0 PTA -100 -200 -300 -400 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 Reliability ADJ No ADJ Difference

  34. Additional adjustment for genomics DGV – direct genomic value, sum of SNP effects Further adjust so Mean PTA = Mean DGV All cow adjustment not able to remove bias in cows selected to be genotyped Reduce adjusted PTA by following amounts

  35. Possible further applications Additional breeds Ayrshire, Guernsey, Milking Shorthorn Additional traits Large SNP effects on PAR observed in type and functional traits Limitation with One-Step method Adjustment between traditional and genomic evaluations not possible

  36. Cow adjustment summary Improved adjustment of cow evaluations for use in genomic evaluations Accommodation of different population being genotyped with 3K chip Improved comparability of evaluations of genotyped and non genotyped cows Foreign cow evaluations not used in estimation of SNP effects

  37. Calving traits topics • Proposed changes • Multiple-trait evaluation • Interbull trend validation • Future research

  38. Calving traits • Sire-maternal grandsire threshold model y = HY + YS + PS + Ys + Ym + s + m + e • All parities combined into a single trait • Low heritabilities, 2 to 8%

  39. Interbull trend validation • Ensures evaluation results are in line with expectations • Must pass every two years • The US was failing the method 3 trend test • If we don’t pass, we get kicked out

  40. How do we fix it? • Wiggans et al. (2007) tried multiple-trait linear models • Poor correlations w/other countries • Did not implement • New approach – first and later parities evaluated separately and blended into a single PTA • Method may be too simple

  41. Results • Bulls ranked similarly • Reliabilities for bulls with little data decreased • Good correlations with MACE for high-reliability bulls • Poor correlations with test run results

  42. Decision • We don’t have a good explanation for the drop in correlations • May be way reliabilities are blended • Errors found in trend-testing code • Both models now pass validation • Continue with current model until we figure our correlation issue

  43. Ongoing research • Is there a link between use of sexed semen and stillbirths? • What about effects of age at first calf on calving traits? • What’s wrong with our correlations?

  44. Overall conclusions • No changes made to calving traits evaluations • All data are important • We are working on biases in both cow and bull evaluations • 1-step looks promising if we can get the calculations done

  45. Questions?

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