English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs

Kam-Thong, T., Pütz, B., Karbalai, N., Müller−Myhsok, B., & Borgwardt, K. (2011). Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs. Bioinformatics, 27(13), i214-i221. doi:10.1093/bioinformatics/btr218.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Kam-Thong, Tony, Author
Pütz, Benno, Author
Karbalai, Nazanin, Author
Müller−Myhsok, Bertram, Author
Borgwardt, Karsten1, Author                 
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

Content

show
hide
Free keywords: -
 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/. Contact:  tony@mpipsykl.mpg.de

Details

show
hide
Language(s):
 Dates: 2011-07-012011
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btr218
ISSN: 1367-4811, 1367-4803
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 27 (13) Sequence Number: - Start / End Page: i214 - i221 Identifier: -