The complete compositional epistasis detection in genome-wide association studies
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
BMC Genetics 2013, 14:7 doi:10.1186/1471-2156-14-7Published: 19 February 2013
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.
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.htmlâˆ–#GBOOST webcite.
This work demonstrates that the complete compositional epistasis detection is computationally feasible in GWAS.