Statistical inference of genetic pathway analysis in high dimensions

• 成果类型: Article
• 署名作者: Liu, Yang; Sun, Wei; Reiner, Alexander P.; Kooperberg, Charles; He, Qianchuan
• 署名单位: University System of Ohio; Wright State University Dayton; Fred Hutchinson Cancer Center
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asz033
• 发表日期: 2019
• 页码: 651664
• 关键词: metaanalysis; cholesterol; RISK

摘要

Genetic pathway analysis has become an important tool for investigating the association between a group of genetic variants and traits. With dense genotyping and extensive imputation, the number of genetic variants in biological pathways has increased considerably and sometimes exceeds the sample size n. Conducting genetic pathway analysis and statistical inference in such settings is challenging. We introduce an approach that can handle pathways whose dimension p could be greater than n. Our method can be used to detect pathways that have nonsparse weak signals, as well as pathways that have sparse but stronger signals. We establish the asymptotic distribution for the proposed statistic and conduct theoretical analysis on its power. Simulation studies show that our test has correct Type I error control and is more powerful than existing approaches. An application to a genome-wide association study of high-density lipoproteins demonstrates the proposed approach.