title: cuGWAM: Genome-wide association multifactor dimensionality reduction using CUDA-enabled high-performance graphics processing unit.
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 approximately 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.
authors.affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-gu, Seoul 151-742, Korea. intellims@gmail.com
authors.forename: Min-Seok
authors.initials: MS
authors.lastname: Kwon
authors.forename: Kyunga
authors.initials: K
authors.lastname: Kim
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D055106:Genome-Wide Association Study / Q000379:methods*; D058011:Multifactor Dimensionality Reduction / Q000379:methods*; D012984:Software