title: Detection of rare variant effects in association studies: extreme values, iterative regression, and a hybrid approach.
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.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA. shuzhang@mtu.edu.
authors.forename: Zhaogong
authors.initials: Z
authors.lastname: Zhang
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha
authors.forename: Xinli
authors.initials: X
authors.lastname: Wang
authors.forename: Shuanglin
authors.initials: S
authors.lastname: Zhang