ArticleHeredity2012
Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling.
Article in Heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Estimation of dynamic SNP-heritability with Bayesian Gaussian process models.Bioinformatics (Oxford, England) · 2020Article
- A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data.Bioinformatics (Oxford, England) · 2019Article
- Article
- Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Article
- Mapping Quantitative Trait Loci Underlying Function-Valued Traits Using Functional Principal Component Analysis and Multi-Trait Mapping.G3 (Bethesda, Md.) · 2015Article
- Mapping complex traits as a dynamic system.Physics of life reviews · 2015Review
- Article
- Functional multi-locus QTL mapping of temporal trends in Scots pine wood traits.G3 (Bethesda, Md.) · 2014Article
- A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes.Genetics · 2014Article
- Article
- Genetic linkage map construction and QTL identification of juvenile growth traits in Torreya grandis.BMC genetics · 2014Article
- Article
- A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model.Genetics · 2012Article
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Authors and funding
8 authors.
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Abstract
A novel hierarchical quantitative trait locus (QTL) mapping method using a polynomial growth function and a multiple-QTL model (with no dependence in time) in a multitrait framework is presented. The method considers a population-based sample where individuals have been phenotyped (over time) with respect to some dynamic trait and genotyped at a given set of loci. A specific feature of the proposed approach is that, instead of an average functional curve, each individual has its own functional curve. Moreover, each QTL can modify the dynamic characteristics of the trait value of an individual through its influence on one or more growth curve parameters. Apparent advantages of the approach include: (1) assumption of time-independent QTL and environmental effects, (2) alleviating the necessity for an autoregressive covariance structure for residuals and (3) the flexibility to use variable selection methods. As a by-product of the method, heritabilities and genetic correlations can also be estimated for individual growth curve parameters, which are considered as latent traits. For selecting trait-associated loci in the model, we use a modified version of the well-known Bayesian adaptive shrinkage technique. We illustrate our approach by analysing a sub sample of 500 individuals from the simulated QTLMAS 2009 data set, as well as simulation replicates and a real Scots pine (Pinus sylvestris) data set, using temporal measurements of height as dynamic trait of interest.
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