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Genome-wide association studies (GWAS) suggests that a complex disease istypically affected by many genetic variants with small or moderate effects.Identification of these risk variants remains to be a very challenging problem.Traditional approaches focusing on a single GWAS dataset alone ignore relevantinformation that could potentially improve our ability to detect thesevariants. We proposed a novel statistical approach, named GPA, to performingintegrative analysis of multiple GWAS datasets and functional annotations.Hypothesis testing procedures were developed to facilitate statisticalinference of pleiotropy and enrichment of functional annotation. We applied ourapproach to perform systematic analysis of five psychiatric disorders. Not onlydid GPA identify many weak signals missed by the original single phenotypeanalysis, but also revealed interesting genetic architectures of thesedisorders. We also applied GPA to the bladder cancer GWAS data with the ENCODEDNase-seq data from 125 cell lines and showed that GPA can detect cell linesthat are more biologically relevant to the phenotype of interest.

GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data