A scalable and portable framework for massively parallel variable selection in genetic association studies.
basic_science · Level V
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- Record sourced from PubMed, PMID 22238272.
- Also identified by DOI 10.1093/bioinformatics/bts015 and PMC identifier 3289918.
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Abstract
The deluge of data emerging from high-throughput sequencing technologies poses large analytical challenges when testing for association to disease. We introduce a scalable framework for variable selection, implemented in C++ and OpenCL, that fits regularized regression across multiple Graphics Processing Units. Open source code and documentation can be found at a Google Code repository under the URL http://bioinformatics.oxfordjournals.org/content/early/2012/01/10/bioinformatics.bts015.abstract. Supplementary data are available at Bioinformatics online.
Medical subject headings
- Algorithms
- Genome-Wide Association Study
- Software