Abstract
Among the large of number of statistical methods that have been proposed to identify gene-gene interactions in case-control genome-wide association studies (GWAS), gene-based methods have recently grown in popularity as they confer advantage in both statistical power and biological interpretation. All of the gene-based methods jointly model the distribution of single nucleotide polymorphisms (SNPs) sets prior to the statistical test, leading to a limited power to detect sums of SNP-SNP signals. In this paper, we instead propose a gene-based method that first performs SNP-SNP interaction tests before aggregating the obtained p-values into a test at the gene level. Our method called AGGrEGATOr is based on a minP procedure that tests the significance of the minimum of a set of p-values. We use simulations to assess the capacity of AGGrEGATOr to correctly control for type-I error. The benefits of our approach in terms of statistical power and robustness to SNPs set characteristics are evaluated in a wide range of disease models by comparing it to previous methods. We also apply our method to detect gene pairs associated to rheumatoid arthritis (RA) on the GSE39428 dataset. We identify 13 potential gene-gene interactions and replicate one gene pair in the Wellcome Trust Case Control Consortium dataset at the level of 5%. We further test 15 gene pairs, previously reported as being statistically associated with RA or Crohn’s disease (CD) or coronary artery disease (CAD), for replication in the Wellcome Trust Case Control Consortium dataset. We show that AGGrEGATOr is the only method able to successfully replicate seven gene pairs.
Acknowledgments
I acknowledge Maud Marchal for reading through the manuscript.
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