PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs
Tony Kam-Thong, Benno Puetz, Nazanin Karbalai, Bertram Müller-Myhsok and Karsten Borgwardt
Bioinformatics Volume 27, Number 13, i214-i221, 2011.

Abstract

Motivation: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene–gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested. Results: In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert–Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day. Availability:The program is available at http://www.mpipsykl.mpg.de/epigpuhsic/.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8837
Deposited By:Karsten Borgwardt
Deposited On:21 February 2012