RT Journal Article
SR Electronic
T1 Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 022673
DO 10.1101/022673
A1 Jiangwei Xia
A1 Yang Wu
A1 Huizhong Fang
A1 Wengang Zhang
A1 Yuxin Song
A1 Lupei Zhang
A1 Xue Gao
A1 Yan Chen
A1 Junya Li
A1 Huijiang Gao
YR 2015
UL http://biorxiv.org/content/early/2015/07/17/022673.abstract
AB Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNPs).In this study, we investigate an approach to increase the efficiency of genomic prediction by using genome-wide markers. The approach is a feature selection based on genomic best linear unbiased prediction (GBLUP),which is a statistical method used to predict breeding values using SNPs for selection in animal and plant breeding. The objective of this study is the choice of kinship matrix for genomic best linear unbiased prediction (GBLUP).The G-matrix is using the information of genome-wide dense markers. We compare three kinds of kinships based on different combinations of centring and scaling of marker genotypes. And find a suitable kinship approach that adjusts for the resource population of Chinese Simmental beef cattle. Single nucleotide polymorphism (SNPs) can be used to estimate kinship matrix and individual inbreeding coefficients more accurately. So in our research a genomic relationship matrix was developed for 1059 Chinese Simmental beef cattle using 640000 single nucleotide polymorphisms and breeding values were estimated using phenotypes about Carcass weight and Sirloin weight. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Another aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship in the Chinese Simmental beef cattle.We find that the feature selection of GBLUP using Xu’s and the Astle and Balding’s kinships model performed similarly well, and were the best-performing methods in our study. Inbreeding and kinship matrix can be estimated with high accuracy using ≥12,000s in Chinese Simmental beef cattle.