While genomewide association studies (GWAS) have been successful in mapping genetics of a range of complex traits, it has been difficult to translate the associations into mechanistic understandings. In the first part of my talk, I will describe a recently developed method to identify risk factors of complex traits. While a trait may have a large number of risk variants at the DNA level, their effects are likely mediated by a smaller number of intermediate traits such as cellular phenotypes. Identifying causal risk factors is thus a promising approach to translate GWAS into actionable targets. Mendelian Randomization (MR) is a framework to address this problem, using genetic variants of an exposure trait as "natural randomization" to estimate its effect on an outcome. However, current MR methods make strong assumptions that are violated when SNPs act on outcome not through the exposure, known as pleiotropic effects. We propose a method, CAUSE to deal with pleiotropy, by explicitly modeling hidden factors that would confound the relationship of exposure and outcome. We show in simulations and GWAS that CAUSE significantly reduces false discoveries while maintaining power.
In the second part, I will talk about our method for using rare variants to study complex trait genetics. Comparing with common variants, the focus of GWAS, rare variants are usually not in linkage disequilibrium, making it easier to detect causal variants and genes. However, the power of identifying rare variants is low. We described our approach to addressing this challenge, in the context of de novo mutations. Our method combines information of variants at the level of genes, and leverages functional information of variants. This method enabled the discovery of a number of risk genes of autism.