Genome-wide association analysis with matched samples discloses additional novel risk loci

Abstract

Genome-wide association studies have identified many candidate causal variants associated with common complex diseases and traits, but most of them have been drawn from nonrandomized case/control designs. In nonrandomized experiments, the results drawn from two different groups can be misleading because the units exposed to one group generally differ systematically from the units exposed to the other group. Propensity score is widely used to group case and control units for a more direct and significant comparison even with nonrandomized experiments. This propensity score matching can help with prioritizing additional uncovered variants on disease risk via sub-group analysis in genome-wide association studies. The aim of this work is to propose a post-hoc association test based on the subsets of samples. For that purpose, this paper presents a new paradigm for a post-hoc genome-wide association test when the sample size of controls are larger than that of cases: selecting control samples by equating the distribution of covariates in the case and control groups and re-performing association analysis upon these matched samples. We demonstrated the feasibility of this approach by applying it to 2752 type II diabetes patients in 8842 Korean population. Genome-wide association approach with matched samples is able to disclose 9 additional novel variants and 7 out of 9 have not identified from the association test of whole control samples. The process described here can successfully be combined with other types of case/control studies with large covariate information. This indicates that there a possibility of obtaining additional candidate causal variants responsible for common diseases through genome-wide association analysis with matched samples.