  To date, genome-wide association studies (GWAS) have successfully identified
tens of thousands of genetic variants among a variety of traits/diseases,
shedding a light on the genetic architecture of complex diseases. Polygenicity
of complex diseases, which refers to the phenomenon that a vast number of risk
variants collectively contribute to the heritability of complex diseases with
modest individual effects, have been widely accepted. This imposes a major
challenge towards fully characterizing the genetic bases of complex diseases.
An immediate implication of polygenicity is that a much larger sample size is
required to detect risk variants with weak/moderate effects. Meanwhile,
accumulating evidence suggests that different complex diseases can share
genetic risk variants, a phenomenon known as pleiotropy. In this study, we
propose a statistical framework for Leveraging Pleiotropic effects in
large-scale GWAS data (LPG). LPG utilizes a variational Bayesian
expectation-maximization (VBEM) algorithm, making it computationally efficient
and scalable for genome-wide scale analysis. To demon- strate the advantage of
LPG over existing methods that do not leverage pleiotropy, we conducted
extensive simulation studies and also applied LPG to analyze three au- toimmune
disorders (Crohn's disease, rheumatoid arthritis and Type 1 diabetes). The
results indicate that LPG can improve the power of prioritization of risk
variants and accuracy of risk prediction by leveraging pleiotropy. The software
is available at http- s://github.com/Shufeyangyi2015310117/LPG.
