Marginal screening of 2 × 2 tables in large-scale case-control studies.

• 影响指数: 1.701
• 发表时间: 03 2019
• 来源期刊: Biometrics
• DOI: 10.1111/biom.12957
• PMID: 30039847
• 文章类型: Journal Article
• 中科院分区: Q3
• 方向: 综合性期刊
• 作者列表: McKeague IW,Qian M

关键词: Family-wise error rate; Genome-wide association studies; Multiple testing; Post-selection inference

Mesh: Case-Control Studies; Computer Simulation; Data Interpretation, Statistical; Genome-Wide Association Study / statistics & numerical data; Humans; Mass Screening / methods statistics & numerical data; Monte Carlo Method; Polymorphism, Single Nucleotide; Risk Factors; Stroke / genetics; Time Factors

Abstract

Assessing the statistical significance of risk factors when screening large numbers of 2 × 2 tables that cross-classify disease status with each type of exposure poses a challenging multiple testing problem. The problem is especially acute in large-scale genomic case-control studies. We develop a potentially more powerful and computationally efficient approach (compared with existing methods, including Bonferroni and permutation testing) by taking into account the presence of complex dependencies between the 2 × 2 tables. Our approach gains its power by exploiting Monte Carlo simulation from the estimated null distribution of a maximally selected log-odds ratio. We apply the method to case-control data from a study of a large collection of genetic variants related to the risk of early onset stroke.