Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Bayesian multitrait kernel methods improve multienvironment genome-based prediction.

G3 (Bethesda, Md.) - 4 Feb 2022

Montesinos-López Osval Antonio, Montesinos-López José Cricelio, Montesinos-López Abelardo, Ramírez-Alcaraz Juan Manuel, Poland Jesse, Singh Ravi, Dreisigacker Susanne, Crespo Leonardo, Mondal Sushismita, Govidan Velu, Juliana Philomin, Espino Julio Huerta, Shrestha Sandesh, Varshney Rajeev K, Crossa José

Abstract excerpt

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets....

Topics

Join the communities discussing this publication.

Bayes Theorem
Genome
Genotype
Models, Genetic
Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Open in PubMed Open full text

Identifiers

PMID: 34849802
DOI: 10.1093/g3journal/jkab406
PMCID: PMC9210316

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Bayesian multitrait kernel methods improve multienvironment genome-based prediction.PMID 34849802Read ↗

Accounting for Correlation Between Traits in Genomic Prediction.Semantic proximity Read ↗

Multitrait genomic prediction method in approximate genome-based kernel model.Semantic proximity Read ↗

GWKBR: a novel method integrating machine learning and Bayesian inference framework to improve genomic prediction accuracy.Semantic proximity Read ↗

Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.Semantic proximity Read ↗

A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.Semantic proximity Read ↗

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.Semantic proximity Read ↗

KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency.Semantic proximity Read ↗

A Genomic Bayesian Multi-trait and Multi-environment Model.Semantic proximity Read ↗

A Multi-Trait Gaussian Kernel Genomic Prediction Model under Three Tunning Strategies.Semantic proximity Read ↗

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.Semantic proximity Read ↗

Weighted kernels improve multi-environment genomic prediction.Semantic proximity Read ↗

Multiple-trait genomic selection methods increase genetic value prediction accuracy.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.