March 23, 2010 - Seyoung Kim

Title: Understanding the Genetic Basis of Complex Diseases via Genome-Phenome Association

Abstract:
Genome-wide association studies have recently become popular as a tool for identifying the genetic loci that are responsible for increased disease susceptibility by examining genetic and phenotypic variation across a large number of individuals.  The cause of many complex disease syndromes involves the complex interplay of a large number of genomic variations that perturb disease-related genes in the context of a regulatory network.  As patient cohorts are routinely surveyed for a large number of traits such as hundreds of clinical phenotypes and genome-wide profiling for thousands of gene expressions, this raises new computational challenges in identifying genetic variations associated simultaneously with multiple correlated traits. In this talk, I will present algorithms that go beyond the traditional approach of examining the correlation between a single genetic marker and a single trait.
Our algorithms build on a sparse regression method in statistics, and are able to discover genetic variants that perturb modules of correlated molecular and clinical phenotypes during genome-phenome association mapping. Our approach is significantly better at detecting associations when genetic markers influence synergistically a group of traits.