Accurate liability estimation improves power in ascertained case-control studies

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Item Type:	Article
Title:	Accurate liability estimation improves power in ascertained case-control studies
Creators Name:	Weissbrod, O., Lippert, C., Geiger, D. and Heckerman, D.
Abstract:	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.
Keywords:	Biostatistics, Case-Control Studies, Genome-Wide Association Study, Multiple Sclerosis, Sample Size, Theoretical Models
Source:	Nature Methods
ISSN:	1548-7091
Publisher:	Nature Publishing Group
Volume:	12
Number:	4
Page Range:	332-334
Date:	April 2015
Official Publication:	https://doi.org/10.1038/nmeth.3285
PubMed:	View item in PubMed

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