Abstract
We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.