4.4 Article

Mapping Quantitative Trait Loci by Controlling Polygenic Background Effects

期刊

GENETICS
卷 195, 期 4, 页码 1209-+

出版社

GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.113.157032

关键词

bin genotype; epistasis; mixed model; quantitative trait locus; rice

资金

  1. U.S. Department of Agriculture National Institute of Food and Agriculture [2007-02784]

向作者/读者索取更多资源

A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive x additive, dominance x dominance, additive x dominance, and dominance x additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive x additive (18%), dominance x dominance (14%), additive x dominance (48%), and dominance x additive (15%) variances. Tiller number is contributed by additive (17%), additive x additive (22%), and dominance x additive (43%) variances. Grain number is mainly contributed by additive (42%), additive x additive (19%), and additive x dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive x additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据