Spatially Uniform ReliefF: Increasing the Power to Detect Epistasis in Genetic Association Studies

Background: Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low power for large datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights iteratively but does not fundamentally change the underlying ReliefF algorithm. To improve the power of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF). Results: SURF’s ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this power increase does not require an increase in