A scalable and portable framework for massively parallel variable selection in genetic association studies

Summary: 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. Contact: gary.k.chen@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online.