ArticleG3 (Bethesda, Md.)2017
A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction.
Article in G3 (Bethesda, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms.Plants (Basel, Switzerland) · 2022Review
- Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction.The plant genome · 2021Article
- A zero altered Poisson random forest model for genomic-enabled prediction.G3 (Bethesda, Md.) · 2021Article
- Prediction of count phenotypes using high-resolution images and genomic data.G3 (Bethesda, Md.) · 2021Article
- A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data.G3 (Bethesda, Md.) · 2020Article
- Application of Genomic Big Data in Plant Breeding:Past, Present, and Future.Plants (Basel, Switzerland) · 2020Review
- A singular value decomposition Bayesian multiple-trait and multiple-environment genomic model.Heredity · 2019Article
- An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm.The plant genome · 2018Article
- Prediction of Multiple-Trait and Multiple-Environment Genomic Data Using Recommender Systems.G3 (Bethesda, Md.) · 2018Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.
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Registered trials
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