Discover Community Create You

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

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

The plant genome - 1 Mar 2025

Ali Baber, Mary-Huard Tristan, Charcosset Alain, Moreau Laurence, Rincent Renaud

Abstract excerpt

Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Environment
Share to Gene Ontology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Markers

Share to Genome, Plant

Share to Genomics

Share to Models, Genetic

Share to Phenotype

Share to Zea mays

Source

Open in PubMed Open full text

Identifiers

PMID: 39779652
DOI: 10.1002/tpg2.20553
PMCID: PMC11711123

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.PMID 39779652Read ↗

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.Semantic proximity Read ↗

Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize.Semantic proximity Read ↗

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grainSemantic proximity Read ↗

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.Semantic proximity Read ↗

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data.Semantic proximity Read ↗

GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.Semantic proximity Read ↗

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain.Semantic proximity Read ↗

Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines.Semantic proximity Read ↗

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

Genomic prediction models for traits differing in heritability for soybean, rice, and maize.Semantic proximity Read ↗

Transcriptome-Based Prediction of Complex Traits in Maize.Semantic proximity Read ↗

Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding.Semantic proximity Read ↗

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