Accurate liability estimation improves power in ascertained case-control studies

被引：0

作者：

Weissbrod, Omer ^{[1
]}

Lippert, Christoph ^{[2
]}

Geiger, Dan ^{[1
]}

Heckerman, David ^{[2
]}

机构：

[1] Technion Israel Inst Technol, Dept Comp Sci, IL-32000 Haifa, Israel

[2] Microsoft Res, eSci Grp, Los Angeles, CA USA

来源：

NATURE METHODS | 2015年 / 12卷 / 04期

基金：

英国惠康基金;

关键词：

LINEAR MIXED MODELS; ASSOCIATION; LOCI; STRATIFICATION; SUSCEPTIBILITY; HERITABILITY; RISK;

D O I：

10.1038/NMETH.3285

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in nonrandomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (liability estimator as a phenotype; https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and we demonstrate that this can lead to a substantial power increase.

引用

页码：332 / U78

页数：5

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