多元统计
人口分层
计算机科学
广义线性混合模型
全基因组关联研究
计算
算法
混合模型
遗传关联
计算生物学
人口
单核苷酸多态性
数据挖掘
生物
遗传学
机器学习
基因
人口学
社会学
基因型
作者
Xiang Zhou,Matthew Stephens
出处
期刊:Nature Methods
[Springer Nature]
日期:2014-02-16
卷期号:11 (4): 407-409
被引量:618
摘要
Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.
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