Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations

Many genome-wide association studies have been conducted to identify single nucleotide polymorphisms (SNPs) that are associated with particular diseases or other traits. The local false discovery rate (LFDR) estimated using semiparametric models has enjoyed success in simultaneous inference. However, semiparametric LFDR estimators can be biased because they tend to overestimate the proportion of the nonassociated SNPs. We address the problem by adapting a simple parametric mixture model (PMM) and by comparing this model to the semiparametric mixture model (SMM) behind an LFDR estimator that is known to be conservatively biased. Then, we also compare the PMM with a parametric nonmixture model (PNM). In our simulation studies, we thoroughly analyze the performances of the three models under different values of p₁, a prior probability that is approximately equal to the proportion of SNPs that are associated with the disease. When p₁ > 10%, the PMM generally performs better than the SMM. When p₁ <; 0:1%, the SMM outperforms PMM. When p₁ lies between 0.1 and 10 percent, both methods have about the same performance. In that setting, the PMM may be preferred since it has the advantage of supplying an estimate of the detectability level of the nonassociated SNPs.