Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction

• 成果类型: Article
• 署名作者: Dai, James Y.; Kooperberg, Charles; Leblanc, Michael; Prentice, Ross L.
• 署名单位: Fred Hutchinson Cancer Center
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass044
• 发表日期: 2012
• 页码: 929944
• 关键词: maximum-likelihood-estimation; family-based association; False Discovery Rate; fgfr2 gene; models; susceptibility; POWER

摘要

Several two-stage multiple testing procedures have been proposed to detect gene-environment interaction in genome-wide association studies. In this article, we elucidate general conditions that are required for validity and power of these procedures, and we propose extensions of two-stage procedures using the case-only estimator of gene-treatment interaction in randomized clinical trials. We develop a unified estimating equation approach to proving asymptotic independence between a filtering statistic and an interaction test statistic in a range of situations, including marginal association and interaction in a generalized linear model with a canonical link. We assess the performance of various two-stage procedures in simulations and in genetic studies from Women's Health Initiative clinical trials.