Elizabeth D. Schifano University of Connecticut
There is increasing interest in the joint analysis of multiple outcomes in genome-wide association studies (GWAS), especially for analysis of multiple secondary outcomes in case-control studies. We propose novel statistical testing and variable selection procedures using pseudolikelihoods to identify Single Nucleotide Polymorphism (SNP) sets (e.g., SNPs within a gene), as well as individual SNPs, associated with multiple outcomes measuring the same underlying trait. For multiple secondary outcomes, we use a weighted pseudolikelihood approach to account for case-control ascertainment in testing and variable selection, and additionally propose a weighted Bayesian Information Criterion for tuning parameter selection. We demonstrate the effectiveness of both procedures through theoretical and empirical analysis, as well as in application to investigate SNP associations with smoking behavior measured using multiple secondary smoking outcomes in a lung cancer case-control GWAS.
This is joint work with Tamar Sofer (University of Washington, Seattle), David C. Christiani (Harvard School of Public Health), and Xihong Lin (Harvard School of Public Health).