Jia Wen1, Colby T Ford2,3, Daniel Janies2, Xinghua Shi4. 1. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA. 2. Department of Bioinformatics and Genomics, College of Computing and Informatics. 3. School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. 4. Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA.
Abstract
MOTIVATION: Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified. RESULTS: In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness. AVAILABILITY AND IMPLEMENTATION: The software is available at github.com/shilab/parEBEN.
MOTIVATION: Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified. RESULTS: In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness. AVAILABILITY AND IMPLEMENTATION: The software is available at github.com/shilab/parEBEN.
Authors: Andrew Anand Brown; Alfonso Buil; Ana Viñuela; Tuuli Lappalainen; Hou-Feng Zheng; J Brent Richards; Kerrin S Small; Timothy D Spector; Emmanouil T Dermitzakis; Richard Durbin Journal: Elife Date: 2014-04-25 Impact factor: 8.140