ArticleG3 (Bethesda, Md.)2016
Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction.
Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction.Frontiers in genetics · 2026Article
- Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).G3 (Bethesda, Md.) · 2023Article
- Multi-environment Genomic Selection in Rice Elite Breeding Lines.Rice (New York, N.Y.) · 2023Article
- Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.Frontiers in genetics · 2023Article
- Genomic selection for salinity tolerance in japonica rice.PloS one · 2023Article
- High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings.G3 (Bethesda, Md.) · 2022Article
- Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Frontiers in genetics · 2022Article
- Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction.The plant genome · 2021Article
- Utilization of eye health services and diabetic retinopathy: a cross-sectional study among persons living with diabetes visiting a tertiary eye care facility in Ghana.BMC health services research · 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
- Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction.Frontiers in plant science · 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 Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction.G3 (Bethesda, Md.) · 2017Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size ([Formula: see text]) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size ([Formula: see text]). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment [Formula: see text] interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data.
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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.