在多品种群体中利用标记和性状信息通过最佳线性无偏预测进行遗传评估。
Genetic evaluation by best linear unbiased prediction using marker and trait information in a multibreed population.
作者信息
Wang T, Fernando R L, Grossman M
机构信息
Department of Animal Sciences, University of Illinois, Urbana 61801, USA.
出版信息
Genetics. 1998 Jan;148(1):507-15. doi: 10.1093/genetics/148.1.507.
Genetic evaluation by best linear unbiased prediction (BLUP) requires modeling genetic means, variances, and covariances. This paper presents theory to model means, variances, and covariances in a multibreed population, given marker and breed information, in the presence of gametic disequilibrium between the marker locus (ML) and linked quantitative trait locus (MQTL). Theory and algorithms are presented to construct the matrix of conditional covariances between relatives (Gv) for the MQTL effects in a multibreed population and to obtain the inverse of Gv efficiently. Theory presented here accounts for heterogeneity of variances among pure breeds and for segregation variances between pure breeds. A numerical example was used to illustrate how the theory and algorithms can be used for genetic evaluation by BLUP using marker and trait information in a multibreed population.
通过最佳线性无偏预测（BLUP）进行遗传评估需要对遗传均值、方差和协方差进行建模。本文提出了在存在标记位点（ML）和连锁数量性状位点（MQTL）之间的配子不平衡的情况下，根据标记和品种信息对多品种群体中的均值、方差和协方差进行建模的理论。提出了理论和算法，用于构建多品种群体中MQTL效应的亲属间条件协方差矩阵（Gv），并有效地获得Gv的逆矩阵。这里提出的理论考虑了纯品种间方差的异质性以及纯品种间的分离方差。通过一个数值例子来说明如何利用该理论和算法，在多品种群体中使用标记和性状信息通过BLUP进行遗传评估。
Zotero 插件