Propensity score‐based nonparametric test revealing genetic variants underlying bipolar disorder

Abstract

Association analysis has led to the identification of many genetic variants for complex diseases. While assessing the association between genes and a disease, other factors can play an important role. The consequence of not considering covariates (such as population stratification and environmental factors) is well-documented in genetic studies. We introduce a nonparametric test of association that adjusts for covariate effects. Specifically, the adjustment is realized through weights that are constructed from genomic propensity scores that summarize the contribution of all covariates. The benefit of our test is demonstrated through an important data set on bipolar disorder (BD) collected by the Wellcome Trust Case Control Consortium. When compared to other tests, our test identified an unreported region with three single nucleotide polymorphisms (SNPs) on chromosome 16 that show strong evidence of association (P-value <5 × 10(-7)). This region is near the RPGRIP1L gene known to be associated with BD. A haplotype block including these three SNPs was further discovered to be strongly associated with BD. It is also interesting to note that our nonparametric test did not reveal strong signals at two SNPs that were detected by a covariate-adjusted parametric test. This suggests that different methods of covariate adjustment can complement each other. Thus, we recommend using both parametric and nonparametric testing. Additionally, we performed simulation studies to compare our proposed test with the unadjusted test and an adjusted parametric test. Our finding underscores the importance of accommodating and controlling for covariate effects in discovering genetic variants associated with complex disorders.