This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data
Identification of functional rare variants in genome-wide association studies using stability selection based on random collapsing
1 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
2 Division of Biostatistics, School of Medicine, New York University, New York, NY 10016, USA
3 Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
BMC Proceedings 2011, 5(Suppl 9):S56 doi:10.1186/1753-6561-5-S9-S56Published: 29 November 2011
Genome-wide association studies are a powerful approach used to identify common variants for complex disease. However, the traditional genome-wide association methods may not be optimal when they are applied to rare variants because of the rare variants’ low frequencies and weak signals. To alleviate the difficulty, investigators have proposed many methods that collapse rare variants. In this paper, we propose a novel ranking method, which we call stability selection based on random collapsing, to rank the candidate rare variants. We use the simulated mini-exome data sets of unrelated individuals from Genetic Analysis Workshop 17 for the analysis. The numerical results suggest that the selection based on a random collapsing method is promising for identifying functional rare variants in genome-wide association studies. Further research to examine the error control property of the proposed method is underway.