TAILIEUCHUNG - Báo cáo sinh học: "Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers | Moser et al. Genetics Selection Evolution 2010 42 37 http content 42 1 37 GSE Ge n et i cs Selection Evolution RESEARCH Open Access Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers 12 Gerhard Moser Mehar S Khatkar Ben J Hayes Herman W Raadsma Abstract Background At the current price the use of high-density single nucleotide polymorphisms SNP genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value DGV on five milk production traits an overall conformation trait a survival index and two profit index traits APR ASI . Methods Dense SNP genotypes were available for 42 576 SNP for 2 114 Holstein bulls and 510 cows. A subset of 1 847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression RR and partial least squares regression PLSR were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP namely subsets of the highest ranked SNP for each individual trait or a single subset of evenly spaced SNP where SNP were selected based on their rank for ASI APR or minor allele frequency within intervals of approximately equal length. Results RR and PLSR performed very similarly to predict DGV with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP DGV predictions for production traits which have a higher heritability were more accurate than for survival which has a low heritability. The gain in accuracy using subsets that included the highest ranked

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