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

Integrating molecular and physiological approaches to quantify genetic controls for wheat development and improve phenotyping.

Journal of experimental botany - 16 Sept 2026

Brown Hamish, McCallum John, Johnston Paul, Pither-Joyce Meeghan, Macknight Richard, Huth Neil, Moot Derrick, Zheng Bangyou, Zhao Zhigan, Hunt James, Wang Enli

Abstract excerpt

Disentangling genotype×environment (G×E) controls of flowering time requires phenotypes that link molecular regulation, developmental physiology, and environment. Here, we integrated time-resolved measurements of apical development, final leaf number, and expression of the flowering-time genes VRN1, VRN2, and VRN3 across contrasting temperature and photoperiod regimes in six wheat genotypes spanning a wide range...

Read the complete abstract on PubMed

Topics

Triticum
Phenotype
Photoperiod
Flowers
Plant Leaves
Gene Expression Regulation, Plant

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Temperature

Genotype

Plant Proteins

Gene Expression Profiling

Source

Open in PubMed Open full text

Identifiers

PMID: 42249571
DOI: 10.1093/jxb/erag278
PMCID: PMC13577759

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 24 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Integrating molecular and physiological approaches to quantify genetic controls for wheat development and improve phenotyping.PMID 42249571Read ↗

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase.Semantic proximity Read ↗

Transcriptomic Insights into Phenological Development and Cold Tolerance of Wheat Grown in the Field.Semantic proximity Read ↗

Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat.Semantic proximity Read ↗

Dissection of floral transition by single-meristem transcriptomes at high temporal resolution.Semantic proximity Read ↗

Linking phenology, harvest index, and genetics to improve chickpea grain yield.Semantic proximity Read ↗

Genetic dissection of a key dynamic growth period QTL for source-sink-related traits and its breeding application potential in wheat.Semantic proximity Read ↗

Image-based phenomic prediction can provide valuable decision support in wheat breeding.Semantic proximity Read ↗

Temperature response of wheat affects final height and the timing of stem elongation under field conditions.Semantic proximity Read ↗

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.Semantic proximity Read ↗

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops.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 ↗

Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments.Semantic proximity Read ↗

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