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

• Published in: Statistical applications in genetics and molecular biology Vol. 16; no. 1; pp. 75 - 81
• Main Authors: Bureau, Alexandre, Croteau, Jordie
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 01.03.2017; Walter de Gruyter GmbH
• Subjects: Computer Simulation; conditional likelihood; Family Health - statistics & numerical data; Genetic Association Studies - methods; Genotype; Graphical user interface; Humans; Likelihood Functions; Logistic Models; Mathematical models; Maximum likelihood estimates; Models, Genetic; nuclear families; Parameter estimation; Phenotype; polytomous logistic model; Population (statistical); Regression analysis; Robustness (mathematics); Software development tools
• ISSN: 2194-6302; 1544-6115; 1544-6115
• DOI: 10.1515/sagmb-2016-0035

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 , an extension to polytomous phenotypes of the package, a flexible software tool for genetic association analysis in nuclear families. Like , is written in C++ and runs from the command line or from a Java graphical user interface. Most options remain available in , 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.