adaptive ridge regression for rare variant detection自适应岭回归罕见的变异检测.pdfVIP

Adaptive Ridge Regression for Rare Variant Detection Haimao Zhan, Shizhong Xu* Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, United States of America Abstract It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances. Advances in high- throughput DNA sequencing technologies allow us to genotype rare causal variants and investigate the effects of such rare variants on complex traits. We developed an adaptive ridge regression method to analyze the collective effects of multiple variants in the same gene or the same functional unit. Our model focuses on continuous trait and incorporates covariate factors to remove potential confounding effects. The proposed method estimates and tests multiple rare variants collectively but does not depend on the assumption of same direction of each rare variant effect. Compared with the Bayesian hierarchical generalized linear model approach, the state-of-the-art method of rare variant detection, the proposed new method is easy to implement, yet it has higher statistical power. Application of the new method is demonstrated using the well-known data from the Dallas Heart Study. Citation: Zhan H, Xu S (2012) Adaptive Ridge Regression for Rare Variant Detection. PLoS ONE 7(8): e44173. doi:10.1371/journal.pone.0044173 Editor: Momiao Xiong, University of Texas School of Public Health, United States of America Received May 28, 2012; Accepted July 30, 2012; Published August 28, 2012 Copyright: 2012 Zhan, Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction