全基因组关联研究
广义线性混合模型
计算机科学
混合模型
算法
线性模型
对数线性模型
基因组
生物
联想(心理学)
数据挖掘
遗传关联
遗传学
统计
数学
机器学习
基因型
基因
单核苷酸多态性
哲学
认识论
作者
Christoph Lippert,Jennifer Listgarten,Ying Liu,Carl Kadie,Robert I Davidson,David Heckerman
出处
期刊:Nature Methods
[Springer Nature]
日期:2011-09-04
卷期号:8 (10): 833-835
被引量:1155
摘要
We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).
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