title: An evolutionary optimization strategy using graphics processing units to efficiently investigate gene-gene interactions in genetic association studies.
abstract: The analysis of gene-gene interactions related to common complex human diseases is complicated by the increasing scale of genetic association analysis. Concurrent with the advances in genetic technology that led to these large data sets, improvements have been made in parallel computing with graphics processing units (GPUs). The data-intensive nature of genetic association analysis makes this problem particularly suitable for improved computation with the powerful computing resources available in GPUs. In this study, we present a GPU-accelerated discrete optimization strategy to improve the computational efficiency of multi-locus association analysis. We implemented an adaptive evolutionary algorithm that takes advantage of linkage disequilibrium to reduce the need for exhaustive search for combinations of genetic markers. The proposed GPU algorithm was shown to have improved efficiency and equivalent power relative to the CPU version.
authors.affiliation: Bioinformatics Program, Department of Bioengineering, The University of Illinois at Chicago, IL 60607, USA. jfonta3@uic.edu
authors.forename: Joel B
authors.initials: JB
authors.lastname: Fontanarosa
authors.forename: Yang
authors.initials: Y
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D019143:Evolution, Molecular; D020869:Gene Expression Profiling / Q000379:methods*; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005828:Genetics, Population; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000235:genetics*
chemical_list: D020543:Proteome