Work

Marginal Screening of 2 × 2 Tables in Large-Scale Case-Control Studies

Year: 2018

Type: article

Abstract: Summary Assessing the statistical significance of risk factors when screening large numbers of 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 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.

Source: Biometrics

DOI: 10.1111/biom.12957

Authors: Ian W. McKeague, Min Qian

Institution: Columbia University

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Mental Health, National Institute of General Medical Sciences