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Evaluating the Effects of QTN for Milk Fat Yield and their Impact on Accuracy and Bias of Genomic Prediction in New Zealand Holstein-Friesian Cows
Five SNPs were analyzed across 4,801 Holstein-Friesian cows, including three QTN for milk fat yield: DGAT1, GHR, and AGPAT6; a QTN for stature: PLAG1; and a control SNP with no effect on milk fat yield. Dominance was observed for DGAT1, AGPAT6 and PLAG1. A base model of 35,000 SNPs was run in GenSel using BayesB. In addition to the base model 1) SNP dosage was fit as a random covariate, or 2) SNP genotype was fit as a fixed covariate, or 3) SNP dosage was fit as a fixed covariate. Including these QTN as random covariates increased accuracy of direct genomic value prediction. Including QTN as fixed covariates slightly decreased accuracy and increased bias. Including DGAT1 as a fixed covariate decreased bias. These results suggest inclusion of QTN genotypes can potentially increase accuracy and decrease bias of DGV, although only slightly.
Keywords:
Milk fat
QTN
Prediction Accuracy
