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

Towards holistic phenotype prediction beyond genotypic data.

Journal of experimental botany - 15 Apr 2026

Jighly Abdulqader, Joukhadar Reem, Varshney Rajeev K, Spangenberg German

Abstract excerpt

Genomic selection (GS) has revolutionized breeding programmes by enabling the prediction of phenotypes based on genetic data. However, GS often only explains a portion of the phenotypic variation. This review explores the potential of integrating various data types beyond genomics to enhance the prediction ability of phenotypes. We categorize data integration strategies into five categories: eliminate,...

Read the complete abstract on PubMed

Topics

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

Share to Genomics
Share to Genotype
Share to Phenotype

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Plant Breeding

Share to Prediction Algorithms

Share to Selection, Genetic

Source

Identifiers

PMID: 41656352
DOI: 10.1093/jxb/erag068

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Towards holistic phenotype prediction beyond genotypic data.PMID 41656352Read ↗

Machine learning for predicting phenotype from genotype and environment.Semantic proximity Read ↗

Integrating genomic selection with a genotype plus genotype x environment (GGE) model improves prediction accuracy and computational efficiency.Semantic proximity Read ↗

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

GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Semantic proximity Read ↗

Predicting the direction of phenotypic differenceSemantic proximity Read ↗

Genomic selection: Essence, applications, and prospects.Semantic proximity Read ↗

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.Semantic proximity Read ↗

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

Enhancing winter wheat prediction with genomics, phenomics and environmental data.Semantic proximity Read ↗

FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms.Semantic proximity Read ↗

GS4PB: An R Shiny application to facilitate a genomic selection pipeline for plant breeding.Semantic proximity Read ↗

Overview of Genomic Prediction Methods and the Associated Assumptions on the Variance of Marker Effect, and on the Architecture of the Target Trait.Semantic proximity Read ↗

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