title: HLODs, trait models, and ascertainment: implications of admixture for parameter estimation and linkage detection.
abstract: Maximizing the homogeneity lod is known to be an appropriate procedure for estimating parameters of the trait model in an approximately 'ascertainment assumption free' (AAF) manner. We have investigated whether this same property also holds for the heterogeneity lod (HLOD). We show that, when the genetic models at linked and unlinked loci differ, HLODs are not AAF, and maximizing the HLOD yields parameter estimates that are for all practical purposes meaningless; indeed, the admixture parameter alpha does not even measure the proportion of linked families within the sample, as is commonly supposed. In spite of this, our results confirm a large body of evidence supporting the use of HLODs as robust tools for linkage detection, and suggest further that maximizing the HLOD over both alpha and parameters of the trait model can improve accuracy in estimation of the recombination fraction theta;. These findings have important implications for the optimal handling of nuisance parameters in linkage analysis, particularly when evaluating the evidence for or against linkage based on multiple independent heterogeneous sets of data.
authors.affiliation: Department of Biostatistics, Division of Statistical Genetics, Center for Statistical Genetics Research, University of Iowa, Iowa City 52240, USA. veronica-vieland@uiowa.edu
authors.forename: Veronica J
authors.initials: VJ
authors.lastname: Vieland
authors.forename: Mark
authors.initials: M
authors.lastname: Logue
mesh_terms: D000818:Animals; D015982:Bias; D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D018740:Genetic Heterogeneity; D008040:Genetic Linkage; D006801:Humans; D016013:Likelihood Functions; D008126:Lod Score; D008957:Models, Genetic