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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Stratify or adjust? Dealing with multiple populations when evaluating rare variants

Robert C Culverhouse12*, Anthony L Hinrichs3 and Brian K Suarez34

Author Affiliations

1 Department of Medicine, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, USA

2 Division of Biostatistics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, USA

3 Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, USA

4 Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110, USA

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BMC Proceedings 2011, 5(Suppl 9):S101  doi:10.1186/1753-6561-5-S9-S101

Published: 29 November 2011

Abstract

The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype.