Biostatistics Weekly Seminar

Genome-wide association studies (GWASs) have been successful at mapping common disease risk to specific genomic regions. However, with a few notable exceptions, the underlying causal mechanisms responsible for inherited disease risk are still unknown. In this talk I will present computational approaches that integrate total and splicing gene expression with large-scale GWASs to identify genes responsible for disease risk. In particular, I will introduce methods to test for association between gene or isoform-specific expression and complex trait using publicly available summary statistics. I illustrate its use with results integrating gene expression from 48 gene expression panels with summary data from 30 publicly-available GWASs and a recent large-scale GWAS in prostate cancer. Lastly, I will present recent work to statistically fine-map gene-disease associations in a Bayesian context to produce credible-sets of genes with motivation through lipids GWAS.

MRBIII, Room 1220
14 November 2018
1:30pm