cuGWAM: Genome-wide Association Multifactor Dimensionality reduction using CUDA-enabled high-performance graphics processing unit
- Authors
- Kwon, Min-Seok; Kim, Kyunga; Lee, Sungyoung; Park, Taesung
- Issue Date
- Sep-2012
- Publisher
- INDERSCIENCE ENTERPRISES LTD
- Keywords
- MDR; GWAS; GP-GPU; gene-gene interaction; parallel computing
- Citation
- INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, v.6, no.5, pp 471 - 481
- Pages
- 11
- Journal Title
- INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
- Volume
- 6
- Number
- 5
- Start Page
- 471
- End Page
- 481
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/12358
- DOI
- 10.1504/IJDMB.2012.049301
- ISSN
- 1748-5673
1748-5681
- Abstract
- Multifactor dimensionality reduction (MDR) method has been widely applied to detectgene-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 similar 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.
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