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Generalized multi‐SNP mediation intersection–union test
Author(s) -
Zhong Wujuan,
Darville Toni,
Zheng Xiaojing,
Fine Jason,
Li Yun
Publication year - 2022
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13418
Subject(s) - genome wide association study , computer science , leverage (statistics) , likelihood ratio test , computational biology , biology , statistics , mathematics , genetics , machine learning , single nucleotide polymorphism , gene , genotype
To elucidate the molecular mechanisms underlying genetic variants identified from genome‐wide association studies (GWAS) for a variety of phenotypic traits encompassing binary, continuous, count, and survival outcomes, we propose a novel and flexible method to test for mediation that can simultaneously accommodate multiple genetic variants and different types of outcome variables. Specifically, we employ the intersection–union test approach combined with the likelihood ratio test to detect mediation effect of multiple genetic variants via some mediator (e.g., the expression of a neighboring gene) on outcome. We fit high‐dimensional generalized linear mixed models under the mediation framework, separately under the null and alternative hypothesis. We leverage Laplace approximation to compute the marginal likelihood of outcome and use coordinate descent algorithm to estimate corresponding parameters. Our extensive simulations demonstrate the validity of our proposed methods and substantial, up to 97%, power gains over alternative methods. Applications to real data for the study of Chlamydia trachomatis infection further showcase advantages of our methods. We believe our proposed methods will be of value and general interest in this post‐GWAS era to disentangle the potential causal mechanism from DNA to phenotype for new drug discovery and personalized medicine.