Comparison of Different Methods for Handling Linkage Disequilibrium in Genetic-Association Analyses via Simulation

Multiple makers exhibiting strong linkage disequilibrium (LD) in a single genomic region and a phenotype of interest generate very compelling statistical associations in the large-scale genetic-association studies. LD, especially strong LD, between variations at neighboring loci can not only make it difficult to discern markers associated with phenotype, but also create difficulties for distinguishing the functionally relevant variations from nonfunctional variations. In this paper, we compared 5 different methods, Boosting, LASSO, Ridge regression, Stepwise and Single locus analysis, for identifying real functional variations in the circumstance of LD exiting in the variations at different loci via simulation. We found that in the case of strong LD between 20 loci, Ridge regression performs the best while in the case of degenerated LD between 500 and 1000 loci, Boosting outperforms other methods.