A Computational Framework for Scalable Epistasis Analysis on High-Dimensional Genomic Data.

University Of North Carolina Charlotte, Charlotte NC

Investigators

Linked publications, trials & patents

Abstract

ABSTRACT Data tsunami in genomic medicine calls for robust and scalable methods for genome scale analysis of a large number of genetic variants regarding to their complicated impact on various human traits. A systematic analytical framework in this study presents a new opportunity to rigorously assess the low and high order effect of numerous genetic variants on a wide range of traits. The specific aims of our proposed project are as follows: Aim 1. Develop covariate-aware models for pairwise epistatic analysis on a categorical trait; Aim 2. Develop scalable methods for multilocus and multi-trait epistatic analysis; and Aim 3. Extensively evaluate the methods using genomic datasets and deploy a computational frame- work with a Web-portal service. This study has several innovations: 1) we will provide a ?one-stop-shop? for comprehensive epistasis analysis of large-scale genomic datasets; 2) we will develop robust and scalable methods for dissecting main and epistatic effect of genetic variants on one or more traits of different types; 3) we will apply these methods to a variety of genomic datasets at different scales. The framework from our study will be flexible to include prospective large- scale genomic and epigenomic datasets in the future.