Abstract
Background: Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers provide opportunities to detect epistatic SNPs associated with quantitative traits and to detect the exact mode of an epistasis effect. Computational difficulty is the main bottleneck for epistasis testing in large scale GWAS. Results: The EPISNPmpi and EPISNP computer programs were developed for testing single-locus and epistatic SNP effects on quantitative traits in GWAS, including tests of three single-locus effects for each SNP (SNP genotypic effect, additive and dominance effects) and five epistasis effects for each pair of SNPs (two-locus interaction, additive × additive, additive × dominance, dominance × additive, and dominance × dominance) based on the extended Kempthorne model. EPISNPmpi is the parallel computing program for epistasis testing in large scale GWAS and achieved excellent scalability for large scale analysis and portability for various parallel computing platforms. EPISNP is the serial computing program based on the EPISNPmpi code for epistasis testing in small scale GWAS using commonly available operating systems and computer hardware. Three serial computing utility programs were developed for graphical viewing of test results and epistasis networks, and for estimating CPU time and disk space requirements. Conclusion: The EPISNPmpi parallel computing program provides an effective computing tool for epistasis testing in large scale GWAS, and the epiSNP serial computing programs are convenient tools for epistasis analysis in small scale GWAS using commonly available computer hardware.
| Original language | English (US) |
|---|---|
| Article number | 315 |
| Journal | BMC bioinformatics |
| Volume | 9 |
| DOIs | |
| State | Published - Jul 21 2008 |
Bibliographical note
Funding Information:This research is partially supported by the Minnesota Supercomputer Institute (LM, HBR), Digital Technology Center of the University of Minnesota (DD), National Research Initiative Grant no. 2008-35205-18846 from the USDA Cooperative State Research, Education, and Extension Service (LM), Cargill, Inc. (JRG), and the Agricultural Experiment Station of the University of Minnesota (YD). Supercomputer computing time was provided by the Minnesota Supercomputer Institute.