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Genome-wide association studies (GWAS) have successfully identified a largenumber of genetic variants associated with traits and diseases. However, itstill remains challenging to fully understand functional mechanisms underlyingmany associated variants. This is especially the case when we are interested invariants shared across multiple phenotypes. To address this challenge, wepropose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrateGWAS datasets for multiple phenotypes and incorporate functional annotationswithin a unified framework. We conducted simulation studies to evaluate GGPA2.0. The results indicate that incorporating functional annotation data usingGGPA 2.0 does not only improve detection of disease-associated variants, butalso allows to identify more accurate relationships among diseases. We analyzedfive autoimmune diseases and five psychiatric disorders with the functionalannotations derived from GenoSkyline and GenoSkyline-Plus and the prior diseasegraph generated by biomedical literature mining. For autoimmune diseases, GGPA2.0 identified enrichment for blood, especially B cells and regulatory T cellsacross multiple diseases. Psychiatric disorders were enriched for brain,especially prefrontal cortex and inferior temporal lobe for bipolar disorder(BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfullyidentified the pleiotropy between BIP and SCZ. These results demonstrate thatGGPA 2.0 can be a powerful tool to identify associated variants associated witheach phenotype or those shared across multiple phenotypes, while also promotingunderstanding of functional mechanisms underlying the associated variants.

graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS Results with Integration of Functional Annotation Data