title: A scalable and portable framework for massively parallel variable selection in genetic association studies.
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.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Division of Biostatistics, Department of Preventive Medicine, Los Angeles, CA 90089, USA. gary.k.chen@usc.edu
authors.forename: Gary K
authors.initials: GK
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D020641:Polymorphism, Single Nucleotide; D011381:Programming Languages; D011471:Prostatic Neoplasms / Q000208:ethnology / Q000235:genetics; D012984:Software