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Increased Efficiency of Case-Control Association Analysis by Using Allele-Sharing and Covariate InformationSchmidt S. · Schmidt M.A. · Qin X. · Martin E.R. · Hauser E.R.
Center for Human Genetics, Duke University Medical Center, Durham, N.C., USA Corresponding Author
Silke Schmidt, PhD
Center for Human Genetics, Duke University Medical Center
Durham, NC 27710 (USA)
Tel. +1 919 684 0624, Fax +1 919 684 0925, E-Mail firstname.lastname@example.org
Objective: We compared the efficiency of case selection strategies for following up a genome-wide linkage screen of multiplex families. We simulated datasets under three models by which continuous environmental or clinical covariates may contribute to disease risk or linkage heterogeneity: (i) a quantitative trait locus (QTL) underlying a continuous disease risk factor, (ii) a gene-environment interaction model, (iii) a heterogeneity model defined by distinct covariate distributions in linked and unlinked families. Methods: Marker genotypes and covariate values were generated for affected sibling pair (ASP) families, according to the three models above. We evaluated two case selection strategies relative to a reference design, which compared all family probands to a sample of unrelated controls (‘all’). The first strategy ignored covariates and selected probands from families with NPL scores ≧0 (‘linked best’). The second strategy selected probands from families identified by an ordered subset analysis (OSA), which utilizes family-specific linkage and covariate information. Results: The ‘linked best’ design provided power very similar to the ‘all’ design under all three models. Under some QTL and heterogeneity models, the OSA design was both most powerful and most efficient. Conclusions: Incorporating allele sharing and covariate information from ASP families into a case-control study design can increase power and reduce genotyping cost.
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