Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data.

ABSTRACT: Many phenomena of fundamental importance to biology and biomedicine arise as a dynamic curve, such as organ growth and HIV dynamics. The genetic mapping of these traits is challenged by longitudinal variables measured at irregular and possibly subject-specific time points, in which case nonnegative definiteness of the estimated covariance matrix needs to be guaranteed. We present a semiparametric approach for genetic mapping within the mixture-model setting by jointly modeling mean and covariance structures for irregular longitudinal data. Penalized spline is used to model the mean functions of individual quantitative trait locus (QTL) genotypes as latent variables, whereas an extended generalized linear model is used to approximate the covariance matrix. The parameters for modeling the me

SUBMITTER: Das K 

PROVIDER: S-EPMC3770845 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature