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Open Access Methodology article

The complete compositional epistasis detection in genome-wide association studies

Xiang Wan1*, Can Yang2, Qiang Yang3, Hongyu Zhao2 and Weichuan Yu4

Author affiliations

1 Department of Computer Science and Institute of Theoretical and Computational Study, Hong Kong Baptist University

2 Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06520, USA

3 Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

4 Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

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Citation and License

BMC Genetics 2013, 14:7  doi:10.1186/1471-2156-14-7

Published: 19 February 2013

Abstract

Background

The detection of epistasis among genetic markers is of great interest in genome-wide association studies (GWAS). In recent years, much research has been devoted to find disease-associated epistasis in GWAS. However, due to the high computational cost involved, most methods focus on specific epistasis models, making the potential loss of power when the underlying epistasis models are not examined in these analyses.

Results

In this work, we propose a computational efficient approach based on complete enumeration of two-locus epistasis models. This approach uses a two-stage (screening and testing) search strategy and guarantees the enumeration of all epistasis patterns. The implementation is done on graphic processing units (GPU), which can finish the analysis on a GWAS data (with around 5,000 subjects and around 350,000 markers) within two hours. Source code is available at http://bioinformatics.ust.hk/BOOST.html∖#GBOOST webcite.

Conclusions

This work demonstrates that the complete compositional epistasis detection is computationally feasible in GWAS.

Keywords:
Compositional epistasis; SNP; Genome-wide association study; GPU