title: A maximum likelihood approach to functional mapping of longitudinal binary traits.
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.
authors.affiliation: Beijing Forestry University and Johns Hopkins University - Sidney Kimmel Comprehensive Cancer Center.
authors.forename: Chenguang
authors.initials: C
authors.lastname: Wang
authors.forename: Hongying
authors.initials: H
authors.lastname: Li
authors.forename: Zhong
authors.initials: Z
authors.lastname: Wang
authors.forename: Yaqun
authors.initials: Y
authors.lastname: Wang
authors.forename: Ningtao
authors.initials: N
authors.lastname: Wang
authors.forename: Zuoheng
authors.initials: Z
authors.lastname: Wang
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000706:statistics & numerical data; D005838:Genotype; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D015999:Multivariate Analysis; D016017:Odds Ratio; D012275:Oryza / Q000235:genetics*; D040641:Quantitative Trait Loci