A maximum likelihood approach to functional mapping of longitudinal binary traits

Wang Chenguang, Li Hongying, Wang Zhong, Wang Yaqun, Wang Ningtao, Wang Zuoheng and Wu Rongling
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Wang Chenguang: Beijing Forestry University and Johns Hopkins University - Sidney Kimmel Comprehensive Cancer Center
Li Hongying: University of California at San Diego
Wang Zhong: Beijing Forestry University and The Pennsylvania State University - Center for Statistical Genetics
Wang Yaqun: The Pennsylvania State University - Center for Statistical Genetics
Wang Ningtao: The Pennsylvania State University - Center for Statistical Genetics
Wang Zuoheng: Yale University - Division of Biostatistics
Wu Rongling: Beijing Forestry University and The Pennsylvania State University

Statistical Applications in Genetics and Molecular Biology, 2012, vol. 11, issue 6, 25

Abstract: Despite their importance in biology and biomedicine, genetic mapping of binary traits that change over time has not been well explored. In this article, we develop a statistical model for mapping quantitative trait loci (QTLs) that govern longitudinal responses of binary traits. The model is constructed within the maximum likelihood framework by which the association between binary responses is modeled in terms of conditional log odds-ratios. With this parameterization, the maximum likelihood estimates (MLEs) of marginal mean parameters are robust to the misspecification of time dependence. We implement an iterative procedures to obtain the MLEs of QTL genotype-specific parameters that define longitudinal binary responses. The usefulness of the model was validated by analyzing a real example in rice. Simulation studies were performed to investigate the statistical properties of the model, showing that the model has power to identify and map specific QTLs responsible for the temporal pattern of binary traits.

Keywords: Binary trait; dynamic trait; functional mapping; maximum likelihood estimate (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1515/1544-6115.1675

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