Tag SNP selection based on clustering according to dominant sets found using replicator dynamics

Tag SNP selection is an important problem in genetic association studies. A class of algorithms to perform this task, among them a popular tool called Tagger, can be described as searching for a minimal vertex cover of a graph. In this article this approach is contrasted with a recently introduced clustering algorithm based on the graph theoretical concept of dominant sets. To compare the performance of both procedures comprehensive simulation studies have been performed using SNP data from the ten ENCODE regions included in the HapMap project. Quantitative traits have been simulated from additive models with a single causative SNP. Simulation results suggest that clustering performs always at least as good as Tagger, while in more than a third of the considered instances substantial improvement can be observed. Additionally an extension of the clustering algorithm is described which can be used for larger genomic data sets.