1Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia2Department of Neurology, Peking University Third Hospital, Beijing, China3Institute for Glycomics, Griffith University, Queensland, Australia
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Genome-wide association studies (GWAS) have indicated potential to identify heritability of common complex phenotypes, but traditional approaches have limited ability to detect hiding signals because single SNP has weak effect size accounting for only a small fraction of overall phenotypic variations. To improve the power of GWAS, methods have been developed to identify truly associated genes by jointly testing effects of all SNPs. However, equally considering all SNPs within a gene might dilute strong signals of SNPs in real functional categories. Here, we observed a consistent pattern on enrichment of significant SNPs in eight functional categories across six phenotypes, with the highest enrichment in coding and both UTR regions while the lowest enrichment in the intron. Based on the pattern of SNP enrichment in functional categories, we developed a new approach for detecting gene associations on traits (DGAT) by selecting the most significant functional category and then using SNPs within it to assess gene associations. The method was found to be robust in type I error rate on simulated data, and to have mostly higher power in detecting associated genes for three different diseases than other methods. Further analysis indicated ability of the DGAT to detect novel genes. The DGAT is available byhttp://sparks-lab.org/server/DGAT.