HCLC-FC: a novel statistical method for phenome-wide association studies

Abstract

The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC. Author summaryAs a complementary approach to genome-wide association studies, phenome-wide association studies (PheWAS) have been an efficient tool for testing associations between genetic variations and a wide range of phenotypes utilizing all available phenotypic information. For instance, the first PheWAS has demonstrated that rs3135388 on HLA-DRB1 associated with atrial fibrillation and multiple sclerosis. A challenging step in performing large-scale multiple testing of PheWAS is to control the false discovery rate (FDR). In this work, we propose a novel and powerful multivariate method, HCLC-FC, to test the association between a genetic variant with a large number of phenotypes simultaneously controlling FDR. Within each phenotypic category, a newly proposed method clusters phenotypes into different groups and the combined test statistic within each category based on the phenotypic clusters has an asymptotic distribution which avoids the computational burden of simulation. Furthermore, the newly developed FDR controlling process is based on p-values and does not depend on test statistics. Therefore, it is more general and can be applied to other multiple testing procedures to control FDR.