Region-based interaction detection in genome-wide case-control studies

In genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects.

We propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test.

Simulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at http://bioinformatics.ust.hk/RRIntCC.html.

In this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed.