Adjustment of selection index coefficients and polygenic variance to improve regressions and reliability of genomic evaluations P. M. VanRaden, J. R. Wright*,

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Adjustment of selection index coefficients and polygenic variance to improve regressions and reliability of genomic evaluations P. M. VanRaden, J. R. Wright*, and T. A. Cooper Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville, MD Abstr. W56 INTRODUCTION  When genomic PTA were first computed in November 2007 few bulls’ ancestors were genotyped  To increase accuracy it was necessary to blend information from genotyped and non-genotyped ancestors in the current multi-step evaluation  Two possible solutions to blend: 1.Regressions for direct genomic values (DGV; sum of SNP effects) 2.Selection index (combine 3 terms by reliabilities computed from the amount of missing information)  DGV including polygenic effects (DGV + poly)  Traditional evaluation (PTA)  Subset evaluation estimated from pedigree relationships (SPTA) GPTA = w 1 (DGV + poly) +w 2 PTA + w 3 SPTA  Adjusting the weights may increase regressions and reliabilities and is relatively easy to do  In contrast, adjustment to the polygenic variance requires a re-estimation of marker effects so more computation is needed  Determine which alternative selection index weights are optimal to increase reliability and regression OBJECTIVE METHODS Choose a maximum weight w max such that adjusted w 1 = min (w 1, w max ) The difference w 1 – adjusted w 1 is added to w 3 so that the sum of weights = 1 The difference w 1 – adjusted w 1 is added to w 2 instead if adjusted w 3 would be positive METHODS (cont.) Regression and change in reliability of predicting future genomic (August 2011) on past (August 2008) by DGV weight for Holsteins 2012 SELECTION INDEX EXAMPLES (before change)  Dam not genotyped, low genomic reliability GPTA = 0.99 (DGV+poly) PTA SPTA  Dam not genotyped, high genomic reliability GPTA = 0.99 (DGV+poly) PTA SPTA  Dam is genotyped GPTA = 1.00 (DGV+poly) PTA SPTA (after weights shifted from DGV to SPTA)  Dam not genotyped, low genomic reliability GPTA = 0.90 (DGV+poly) PTA SPTA  Dam not genotyped, high genomic reliability GPTA = 0.90 (DGV+poly) PTA SPTA  Dam is genotyped GPTA = 0.90 (DGV+poly) PTA SPTA TraitWeight on Direct Genomic Value Expected Regression RegressionReliability change Milk Fat Protein Daughter Pregnancy Rate Somatic Cell Score Productive Life Sire Calving Ease Daughter Calving Ease Sire Stillbirth Daughter Stillbirth Overall conformation score Udder depth Regression and change in reliability of predicting future genomic (August 2011) on past (August 2008) by DGV weight for Jerseys TraitWeight on Direct Genomic Value Expected regression RegressionReliability change Milk Fat Protein Daughter Pregnancy Rate Somatic Cell Score Productive Life Overall conformation score Udder depth Regression and change in reliability of predicting future genomic (August 2011) on past (August 2008) by DGV weight for Brown Swiss TraitWeight on Direct Genomic Value Expected Regression RegressionReliability change Milk Fat Protein Daughter Pregnancy Rate Somatic Cell Score Productive Life Sire calving ease Daughter calving ease Overall conformation score Udder depth RESULTS CONCLUSIONS / APPLICATIONS  Theoretical selection index weights currently in use are close to ideal.  Index adjustments can help pass genomic validation tests by removing small biases in regression.  Maximum DGV weight values implemented beginning with the April 2012 evaluations were: Health traits 0.95 Yield (Jersey, Brown Swiss) 0.80 Calving traits0.75Yield (Holstein) 0.90 Type traits0.90  Genomic PTA for the highest young Holstein bulls decreased 45 kg for milk, 2 kg for fat, 1 kg for protein, 0.2 mo for productive life, 0.15 points final score, and $20 for net merit in April 2012 evaluations. RESULTS (cont.)