Powerful statistical method to detect disease associated genes using publicly available GWAS summary data

To date, a large number of common variants underlying complex diseases have been identified via genome-wide association studies (GWAS) conducted in the last decade. More and more GWAS summary data have been posted for public access. We note that for most existing GWAS, the identified common variants are based on single-marker-based tests, which test one single nucleotide polymorphisms (SNP) at a time. A gene, rather than a SNP, is the basic functional unit of inheritance. Thus, results obtained at the gene level can be more readily extended to and integrated with downstream functional and pathogenic investigation. In this study, we propose a general gene based p value adaptive combination approach (GPA) that can integrate association evidence from GWAS summary statistics (which could be either p value or other test statistics) from continuous or binary traits. We conducted simulations to verify that the proposed method controls type I errors well, and performs favorably compared to single-marker analysis and other existing methods. We illustrated the utility of proposed methods through application to the GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. The proposed method identified novel glycemic associated genes which can improve our understanding of the mechanisms involved in β-cell function and glucose homeostasis.