title: A composite-likelihood approach for identifying polymorphisms that are potentially directly associated with disease.
abstract: If a linkage signal can be fully accounted for by the association of a particular polymorphism with the disease, this polymorphism may be the sole causal variant in the region. On the other hand, if the linkage signal exceeds that explained by the association, different or additional directly associated loci must exist in the region. Several methods have been proposed for testing the hypothesis that association with a particular candidate single-nucleotide polymorphism (SNP) can explain an observed linkage signal. When several candidate SNPs exist, all of the existing methods test the hypothesis for each candidate SNP separately, by fitting the appropriate model for each individual candidate SNP. Here we propose a method that combines analyses of two or more candidate SNPs using a composite-likelihood approach. We use simulations to demonstrate that the proposed method can lead to substantial power increases over the earlier single SNP analyses.
authors.affiliation: Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic, Rochester, MN 55905, USA. biernacka.joanna@mayo.edu
authors.forename: Joanna M
authors.initials: JM
authors.lastname: Biernacka
authors.forename: Heather J
authors.initials: HJ
authors.lastname: Cordell
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide