利用PC-Select提高全基因组关联研究的效能并避免群体分层带来的混杂效应。
Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.
作者信息
Tucker George, Price Alkes L, Berger Bonnie
机构信息
*Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.
†Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
出版信息
Genetics. 2014 Jul;197(3):1045-9. doi: 10.1534/genetics.114.164285. Epub 2014 Apr 29.
Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.
在全基因组关联研究中，使用单核苷酸多态性（SNP）的简化子集可提高检验效能，但这可能导致对群体分层的校正不足。我们提出一种使用主成分的混合方法，该方法在存在群体分层的情况下不会使统计量膨胀，并且比标准线性混合模型具有更高的检验效能。
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