tailieunhanh - Báo cáo sinh học: " Genomic prediction when some animals are not genotyped"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Genomic prediction when some animals are not genotyped | Christensen and Lund Genetics Selection Evolution 2010 42 2 http content 42 1 2 RESEARCH GSE Ge n et i cs Selection Evolution Open Access Genomic prediction when some animals are not genotyped Ole F Christensen Mogens S Lund Abstract Background The use of genomic selection in breeding programs may increase the rate of genetic improvement reduce the generation time and provide higher accuracy of estimated breeding values EBVs . A number of different methods have been developed for genomic prediction of breeding values but many of them assume that all animals have been genotyped. In practice not all animals are genotyped and the methods have to be adapted to this situation. Results In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions BLUPs . The method is illustrated using a simulated data set. Conclusions The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated .