Mixture models for linkage analysis of affected sibling pairs and covariates B. Devlin Bobby L. Jones Silviu-Alin Bacanu Kathryn Roeder 10.1184/R1/6586745.v1 https://kilthub.cmu.edu/articles/journal_contribution/Mixture_models_for_linkage_analysis_of_affected_sibling_pairs_and_covariates/6586745 <p>To determine the genetic etiology of complex diseases, a common study design is to recruit affected sib/relative pairs (ASP/ARP) and evaluate their genome-wide distribution of identical by descent (IBD)-sharing using a set of highly polymorphic markers. Other attributes or environmental exposures of the ASP/ARP, which are thought to affect liability to disease, are sometimes collected. Conceivably these covariates could refine the linkage analysis. Most published methods for ASP/ARP linkage with covariates can be conceptualized as logistic models in which IBD-status of the ASP is predicted by pair-specific covariates. We develop a different approach to the problem of ASP analysis in the presence of covariates, one that extends naturally to ARP under certain conditions.</p> 2012-04-01 00:00:00 clustering algorithms mixing distribution score statistics likelihood ratio asymptotic distributions