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

Open Access Proceedings

Detection of rare variant effects in association studies: extreme values, iterative regression, and a hybrid approach

Zhaogong Zhang12, Qiuying Sha1, Xinli Wang3 and Shuanglin Zhang1*

Author Affiliations

1 Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA

2 School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China

3 School of Technology, Michigan Technological University, Houghton, MI 49931, USA

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BMC Proceedings 2011, 5(Suppl 9):S112  doi:10.1186/1753-6561-5-S9-S112

Published: 29 November 2011

Abstract

We develop statistical methods for detecting rare variants that are associated with quantitative traits. We propose two strategies and their combination for this purpose: the iterative regression strategy and the extreme values strategy. In the iterative regression strategy, we use iterative regression on residuals and a multimarker association test to identify a group of significant variants. In the extreme values strategy, we use individuals with extreme trait values to select candidate genes and then test only these candidate genes. These two strategies are integrated into a hybrid approach through a weighting technology. We apply the proposed methods to analyze the Genetic Analysis Workshop 17 data set. The results show that the hybrid approach is the most powerful approach. Using the hybrid approach, the average power to detect causal genes for Q1 is about 40% and the powers to detect FLT1 and KDR are 100% and 68% for Q1, respectively. The powers to detect VNN3 and BCHE are 34% and 30% for Q2, respectively.