连锁不平衡
生物
遗传关联
基因座(遗传学)
关联映射
全基因组关联研究
贝叶斯概率
计算生物学
混淆
遗传学
混合模型
基因组
数量性状位点
统计能力
等位基因异质性
人口
等位基因
计算机科学
统计
机器学习
单倍型
单核苷酸多态性
人工智能
基因型
基因
数学
社会学
人口学
作者
Vincent Segura,Bjarni J. Vilhjálmsson,Alexander Platt,Arthur Korte,Ümit Seren,Quan Long,Magnus Nordborg
出处
期刊:Nature Genetics
[Springer Nature]
日期:2012-06-17
卷期号:44 (7): 825-830
被引量:755
摘要
Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.
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