Comparisons of Multi-Marker Association Methods to Detect Association Between a Candidate Region and Disease

The joint use of information from multiple markers may be more effective to reveal association between a genomic region and a trait than single marker analysis. In this article, we compare the performance of seven multi-marker methods. These methods include (1) single marker analysis (either the best-scoring single nucleotide polymorphism in a candidate region or a combined test based on Fisher's method); (2) fixed effects regression models where the predictors are either the observed genotypes in the region, principal components that explain a proportion of the genetic variation, or predictors based on Fourier transformation for the genotypes; and (3) variance components analysis. In our simulation studies, we consider genetic models where the association is due to one, two, or three markers, and the disease-causing markers have varying allele frequencies. We use information from either all the markers in a region or information only from tagging markers. Our simulation results suggest that when there is one disease-causing variant, the best-scoring marker method is preferred whereas the variance components method and the principal components method work well for more common disease-causing variants. When there is more than one disease-causing variant, the principal components method seems to perform well over all the scenarios studied. When these methods are applied to analyze associations between all the markers in or near a gene and disease status for an inflammatory bowel disease data set, the analysis based on the principal components method eads to biologically more consistent discoveries than other methods. Genet. Epidemiol. 34:201-212, 2010. (C) 2009 Wiley-Liss, Inc.