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

Open Access Proceedings

Identifying rare variants using a Bayesian regression approach

Aimin Yan12*, Nan M Laird1 and Cheng Li12*

Author Affiliations

1 Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA

2 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Mailstop CLS11007, Boston, MA 02115, USA

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BMC Proceedings 2011, 5(Suppl 9):S99  doi:10.1186/1753-6561-5-S9-S99

Published: 29 November 2011

Abstract

Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Statistical methods that test variants individually are underpowered to detect rare variants, so it is desirable to perform association analysis of rare variants by combining the information from all variants. In this study, we use a Bayesian regression method to model all variants simultaneously to identify rare variants in a data set from Genetic Analysis Workshop 17. We studied the association between the quantitative risk traits Q1, Q2, and Q4 and the single-nucleotide polymorphisms and identified several positive single-nucleotide polymorphisms for traits Q1 and Q2. However, the model also generated several apparent false positives and missed many true positives, suggesting that there is room for improvement in this model.