Title: cuGWAM: Genome-wide Association Multifactor Dimensionality reduction using CUDA-enabled high-performance graphics processing unit

Authors: Min-Seok Kwon; Kyunga Kim; Sungyoung Lee; Taesung Park

Addresses: Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-gu, Seoul 151-742, Korea ' Department of Statistics, Sookmyung Women's University, Yongsan-gu, Seoul 140-742, Korea ' Interdisciplinary program in Bioinformatics, Seoul National University, Gwanak-gu, Seoul 151-742, Korea ' Department of Statistics and Interdisciplinary program, Seoul National University, Gwanak-gu, Seoul 151-742, Korea

Abstract: Multifactor dimensionality reduction (MDR) method has been widely applied to detect gene-gene interactions that are well recognized as playing an important role in understanding complex traits. However, because of an exhaustive analysis of MDR, the current MDR software has some limitations to be extended to the genome-wide association studies (GWAS) with a large number of genetic markers up to ∼1 million. To overcome this computational problem, we developed CUDA (Compute Unified Device Architecture) based genome-wide association MDR (cuGWAM) software using efficient hardware accelerators. cuGWAM has better performance than CPU-based MDR methods and other GPU-based methods.

Keywords: MDR; multifactor dimensionality reduction; GWAS; genome-wide association studies; GP-GPU; gene-gene interactions; parallel computing; genetic markers; bioinformatics; graphics processing units; GPUs.

DOI: 10.1504/IJDMB.2012.049301

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.5, pp.471 - 481

Received: 10 May 2011
Accepted: 11 May 2011
Published online: 17 Dec 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article