Genome-wide association study (GWAS) has been a great success in the past decade, with thousands of regions in the human genome implicated for hundreds of complex diseases. However, significant challenges remain in both identifying new risk loci and interpreting
results, even for samples with tens of thousands of subjects. In this presentation, we describe our recent efforts to infer the genetic architecture of complex disease through random effects models, the development of functional annotations of the human genome,
and the integrated analysis of these annotations with GWAS results. The effectiveness of our methods will be demonstrated through their applications to a large number of GWASs to identify tissues/cell types that are relevant to a specific disease, to infer
shared genetic contributions to several diseases, and to improve genetic disease risk predictions. This is joint work with Qiongshi Lu, Yiming Hu, Jiming Jiang, Can Yang, Ryan Powels, Qian Wang, and others.