CuGWAM: Genome-wide Association Multifactor Dimensionality reduction using CUDA-enabled high-performance graphics processing unit. (1st January 2012)

• Record Type: Journal Article
• Authors: Kwon, Min-Seok; Kim, Kyunga; Lee, Sungyoung; Park, Taesung
• Is Part Of: International journal of data mining and bioinformatics. Volume 6:Number 5(2012)
• Page Start: 471
• Page End: 481
• Publication Date: 2012-01-01
• Subjects: MDR -- GWAS -- GP-GPU -- gene-gene interaction -- parallel computing; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.