Polyunphased: an extension to polytomous outcomes of the Unphased package for family-based genetic association analysis

Bureau Alexandre () and Croteau Jordie
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Bureau Alexandre: Institut universitaire en santé mentale de Québec du Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2601 chemin de la Canardière, Québec, QC, G1J 2G3, Canada
Croteau Jordie: Institut universitaire en santé mentale de Québec du Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, 2601 chemin de la Canardière, Québec, QC, G1J 2G3, Canada

Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, issue 1, 75-81

Abstract: Polytomous phenotypes arise when a disease has multiple subtypes or when two dichotomous phenotypes are analyzed simultaneously. Few software programs offer the option to analyze such phenotypes in family studies, and none implements conditional polytomous logistic regression for within-family analysis robust to population stratification. We introduce Polyunphased, an extension to polytomous phenotypes of the Unphased package, a flexible software tool for genetic association analysis in nuclear families. Like Unphased, Polyunphased is written in C++ and runs from the command line or from a Java graphical user interface. Most Unphased options remain available in Polyunphased, including those handling missing parental genotypes while preserving robustness to population stratification, and the modelling options. Simulation studies confirmed the expected statistical behaviour of the maximum likelihood estimates of the association parameters of the conditional logistic regression model when the corresponding association parameters in the parental term of the likelihood function are set to 0, but revealed convergence problems when estimating these parental association parameters separately. The former approach is thus recommended with polytomous phenotypes.

Keywords: conditional likelihood; nuclear families; polytomous logistic model (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/sagmb-2016-0035

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