|
Abstract:
|
Although there has been many advances in incorporating prior information into prioritization of phenotype/trait-associated variants in genome wide association studies (GWAS), functional annotation data rarely played more than an indirect role in assessing evidence for association. Furthermore, functional annotation information has not been utilized at all in the joint analysis of GWAS and molecular (i.e., expression) quantitative loci data. This is a significant barrier in understanding the regulatory roles of variants in expression and disease progression.
We develop a novel functional mediation framework that can utilize high dimensional, noisy data-driven prior information. We devise this model to specifically address the challenging issue of effectively utilizing prior information that are most consistent with the data. We investigate the statistical properties of the model and quantify statistical gains in terms of power, robustness, and consistency in settings when high dimensional and potentially noisy prior information is available. We develop applications of this methodology to mediation analysis of a Type 2 Diabetes GWAS.
|
