A0360
Title: Dealing with heterogeneity in large scale genomic studies
Authors: Iuliana Ionita-Laza - Columbia University (United States) [presenting]
Abstract: Genome-wide association studies (GWAS) for biomarkers can lead to clinically relevant discoveries. Numerous lines of evidence from both model organisms and human studies suggest that genetic associations can be highly heterogeneous, dynamic, and context-dependent. Despite twenty years of GWAS, most studies are based on statistical models that fail to account for such heterogeneity. Alternative approaches based on quantile regression (QR) models are discussed that naturally extend linear regression models to the analysis of the entire conditional distribution of a phenotype of interest. The advantages of QR are illustrated in the context of genetic discovery, showing improved power to identify heterogeneous genetic associations that may have larger effects on high-risk subgroups of individuals but with lower or no contribution overall and increased ability to adjust for subtle population structure. Potential applications to phenotype prediction are also discussed.
