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,Roger 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:1054
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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