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

Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.

The plant genome - 1 Jun 2026

Dhakal Rishap, Sniadower Guillermo, Silva Paula, Lado Bettina, Sandro Pablo, Rebollo Inés, Quincke Martin, Dawson Julie C, Gutiérrez Lucia, González Barrios Pablo

Abstract excerpt

Wheat (Triticum aestivum L.), a crucial cereal crop for global food security, faces growing challenges from climate change. Future production requires varieties that are resilient to environmental extremes and fluctuations. The goal of this study was to assess strategies to increase selection response through genomic selection in wheat by integrating genotypic-specific phenology-derived environmental covariates...

Topics

Join the communities discussing this publication.

Environment
Gene-Environment Interaction
Genome, Plant
Genomics
Models, Genetic

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Prediction Algorithms

Regression Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 42101087
DOI: 10.1002/tpg2.70247
PMCID: PMC13155077

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

Improving genomic prediction in wheat with random regression models with genotype-specific phenology-driven environmental covariates.PMID 42101087Read ↗

Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.Semantic proximity Read ↗

Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield.Semantic proximity Read ↗

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.Semantic proximity Read ↗

Genomic selection for multi-trait crop improvement in wheat (Triticum aestivum L.): a practical modeling approach.Semantic proximity Read ↗

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

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.Semantic proximity Read ↗

Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.Semantic proximity Read ↗

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.Semantic proximity Read ↗

Genomic prediction of agronomic traits in wheat using different models and cross-validation designs.Semantic proximity Read ↗

Genomic Prediction from Multi-Environment Trials of Wheat Breeding.Semantic proximity Read ↗

Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat.Semantic proximity Read ↗

Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi-environment genomic prediction models incorporating spectral and thermal information.Semantic proximity Read ↗

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