Accurate liability estimation improves power in ascertained case-control studies

Omer Weissbrod; Christoph Lippert; Dan Geiger; David Heckerman

doi:10.1038/nmeth.3285

Accurate liability estimation improves power in ascertained case-control studies

Omer Weissbrod, Christoph Lippert, Dan Geiger, David Heckerman

Computer Science

Technion - Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	332-334
Number of pages	3
Journal	Nature Methods
Volume	12
Issue number	4
DOIs	https://doi.org/10.1038/nmeth.3285
State	Published - 31 Mar 2015

All Science Journal Classification (ASJC) codes

Biotechnology
Biochemistry
Molecular Biology
Cell Biology

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10.1038/nmeth.3285

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title = "Accurate liability estimation improves power in ascertained case-control studies",

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.",

author = "Omer Weissbrod and Christoph Lippert and Dan Geiger and David Heckerman",

note = "Funding Information: This work was supported by the Israeli Science Foundation. This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. The MS and ulcerative colitis data sets were filtered by A. Gusev. We thank N. Zaitlen and S. Rosset for helpful discussions.",

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doi = "10.1038/nmeth.3285",

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AU - Lippert, Christoph

AU - Geiger, Dan

AU - Heckerman, David

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PY - 2015/3/31

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N2 - 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.

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JO - Nature Methods

JF - Nature Methods

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Accurate liability estimation improves power in ascertained case-control studies

Abstract

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