Detecting two-locus associations allowing for interactions in genome-wide association studies

Motivation: Genome-wide association studies (GWASs) aim to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single nucleotide polymorphisms (SNPs). Although traditional single-locus statistical tests have identified many genetic determinants of susceptibility, those findings cannot completely explain genetic contributions to complex diseases. Marchini and coauthors demonstrated the importance of testing two-locus associations allowing for interactions through a wide range of simulation studies. However, such a test is computationally demanding as we need to test hundreds of billions of SNP pairs in GWAS. Here, we provide a method to address this computational burden for dichotomous phenotypes. Results: We have applied our method on nine datasets from GWAS, including the aged-related macular degeneration (AMD) dataset, the Parkinson's disease dataset and seven datasets from the Wellcome Trust Case Control Consortium (WTCCC). Our method has discovered many associations that were not identified before. The running time for the AMD dataset, the Parkinson's disease dataset and each of seven WTCCC datasets are 2.5, 82 and 90 h on a standard 3.0 GHz desktop with 4 G memory running Windows XP system. Our experiment results demonstrate that our method is feasible for the full-scale analyses of both single- and two-locus associations allowing for interactions in GWAS.