Replication of epistatic DNA loci in two case-control GWAS studies using OPE algorithm
© Goudey et al; licensee BioMed Central Ltd. 2011
Published: 21 November 2011
One of the limiting factors of current genome-wide association studies (GWAS) is the inability of current methods to comprehensively examine SNP interactions for a reasonable sized dataset. It is hypothesised that this limitation is one of the reasons that GWAS studies have not been able to have a greater impact [1, 2]. Many current methods for handling interactions are computationally expensive and do not scale to entire studies. Those methods that do scale often achieve this by pruning their datasets in some manner. This is commonly done by considering only those SNPs that show strong marginal effects, despite the fact that a strongly interacting pair may consist of SNPs with low effects individually.
Material and methods
In this presentation, we validate the robustness of a novel algorithm known as Optimal Pairwise Epistasis (OPE) for exhaustively examining all pairwise interactions in GWAS data. This method is based on the systematic evaluation of “binary genotype pairs” (BG-pairs), i.e. the pairs of complementary binary classification of genotype calls for an individual SNP, or a pair of SNPs. We can quantify the discrimination potential of BG-pairs using a family of statistics based on odds ratios.
Results and conclusion
The approach is computationally efficient: the dataset reported here as Study 1 (consisting of ~310K SNPs and 2200 samples ) takes 12 hour to process on a single CPU (compared to 149 hours of the recent BOOST algorithm ). The method can be highly parallelised with a recent GPU implementation reducing this processing time to less than 15 minutes.
- Cordell HJ: Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 2009, 10: 392–404.PubMed CentralView ArticlePubMedGoogle Scholar
- Moore JH, Asselbergs FW, Williams SM: Bioinformatics challenges for genome-wide association studies. Bioinformatics 2010, 26: 445–455. 10.1093/bioinformatics/btp713PubMed CentralView ArticlePubMedGoogle Scholar
- van Heel DA, Franke L, Hunt KA, Gwilliam R, Zhernakova A, Inouye M, Wapenaar MC, Barnardo MC, Bethel G, Holmes GK, et al.: A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21. Nat Genet 2007, 39: 827–829. 10.1038/ng2058PubMed CentralView ArticlePubMedGoogle Scholar
- Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NL, Yu W: BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 2010, 87: 325–340. 10.1016/j.ajhg.2010.07.021PubMed CentralView ArticlePubMedGoogle Scholar
- Dubois PC, Trynka G, Franke L, Hunt KA, Romanos J, Curtotti A, Zhernakova A, Heap GA, Adány R, Aromaa A, et al.: Multiple common variants for celiac disease influencing immune gene expression. Nat Genet 2010, 42: 295–302. 10.1038/ng.543PubMed CentralView ArticlePubMedGoogle Scholar
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