Detecting essential and removable interactions in genome-wide association studies

Abstract

Detection of disease gene interaction effects among the enormous array of single nucleotide polymorphism (SNP) combinations represents the next frontier in genome-wide association (GWA) studies. Here we propose a novel strategy on the basis of the pattern and nature of the interaction, which can be classified as essential (EI) or removable (RI). We provide an analytical framework, including the qualitative conditions for screening EIs/RIs and a RI-to-EI likelihood ratio score to quantitatively measure the effect. In analyzing six GWA data sets, we find that the scores follow an exponential distribution, except in the upper 10(-8) tail region in which the scores become irregular and unpredictable. Our approach is conceptually simple, computationally efficient and detects interactions that can be visualized and unequivocally interpreted.