报告摘要:

Genome-wide association studies (GWAS) suggests that a complex disease is typically 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 ignore relevant information revealed by the Big Data in genomics: (1) Accumulating evidence suggests that different complex diseases are genetically correlated, i.e., multiple diseases share common risk genetic bases, which is known as pleiotropy. (2) SNPs are not equally important and functionally annotated genetic variants have revealed a consistent pattern of enrichment. Thus, we proposed a novel statistical approach, named GPA, to performing integrative analysis of multiple GWAS datasets and functional annotation. Hypothesis testing procedures were developed to facilitate statistical inference of pleiotropy and enrichment of functional annotation. A computationally efficient EM algorithm was also available to handle millions of SNP markers.

We applied our approach to perform systematical analysis of psychiatric disorders. Not only did GPA identify many weak signals, but also revealed interesting genetic architectures of these disorders. The pleiotropy effect was very strong between bipolar disorder (BPD) and Schizophrenia (SCZ). The SNPs in the central nervous system genes were highly enriched for both BPD and SCZ. These results deepened our understanding of genetic etiology for psychiatric disorders. In summary, PGA can serve as an effective tool for integrative data analysis in the era of Big Data in genomics.