Epistasis detection on quantitative phenotypes by exhaustive enumeration using 
GPUs

Kam-Thong, T.; Pütz, B.; Karbalai, N.; Müller−Myhsok, B.; Borgwardt, K.

doi:10.1093/bioinformatics/btr218

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs

MPS-Authors

/persons/resource/persons75313

Borgwardt, K.
Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

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: ISMB/ECCB 2011), i214-i221. doi:10.1093/bioinformatics/btr218.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-4C93-E

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.