ArticleStatistics in medicine2013
Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data.
Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records.Nature communications · 2024Article
- Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies.Genomics & informatics · 2022Article
- A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits.Bioinformatics (Oxford, England) · 2016Article
- Mapping complex traits as a dynamic system.Physics of life reviews · 2015Review
- Article
- A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data.Biometrics · 2014Article
- A Bayesian semiparametric model for bivariate sparse longitudinal data.Statistics in medicine · 2013Article
- Stochastic modeling of systems mapping in pharmacogenomics.Advanced drug delivery reviews · 2013Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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 mean-covariances are estimated by MCMC, using the Gibbs sampler and the Metropolis-Hastings algorithm. We derive the full conditional distributions for the mean and covariance parameters and compute Bayes factors to test the hypothesis about the existence of significant QTLs. We used the model to screen the existence of specific QTLs for age-specific change of body mass index with a sparse longitudinal data set. The new model provides powerful means for broadening the application of genetic mapping to reveal the genetic control of dynamic traits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.