title: Association analysis in a variance components framework.
abstract: Association analyses conducted in a variance components framework can include information from all available individuals but remain unbiased in the presence of familiality or linkage. Models that include both linkage and association parameters provide different estimates of the effect of a single locus and can be used to distinguish causal polymorphisms from other types of variation. We examine some of these models and their properties in a blind analysis of the simulated Genetic Analysis Workshop 12 data sets.
authors.affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
authors.forename: G R
authors.initials: GR
authors.lastname: Abecasis
authors.forename: L R
authors.initials: LR
authors.lastname: Cardon
authors.forename: W O
authors.initials: WO
authors.lastname: Cookson
authors.forename: P C
authors.initials: PC
authors.lastname: Sham
authors.forename: S S
authors.initials: SS
authors.lastname: Cherny
mesh_terms: D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D002896:Chromosomes, Human, Pair 6; D002899:Chromosomes, Human, Pair 9; D014644:Genetic Variation; D005828:Genetics, Population; D006801:Humans; D008126:Lod Score; D008957:Models, Genetic; D010641:Phenotype; D011110:Polymorphism, Genetic