Evidence map›Paper›PMID 39765629›Full record

ArticleBiology2024

Genome-Wide Association Study and Genomic Prediction of Soft Wheat End-Use Quality Traits Under Post-Anthesis Heat-Stressed Conditions.

Dipendra Shahi, Jia Guo, Sumit Pradhan, Muhsin Avci, Guihua Bai, Jahangir Khan, Byung-Kee Baik, Mohamed Mergoum, Md Ali Babar

Abstract read
In one paragraph

Article in Biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Evaluating reaction norm and fixed effect models for genomic selection of end-use quality in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  2. Review
  3. Mining QTLs and candidate genes in bread wheat associated with kernel hardness through GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Dipendra ShahiSchool of Plant, Environmental and Soil Sciences, Louisiana State Agricultural Center, Baton Rouge, LA 70803, USA.
Jia GuoInari Agriculture, 1281 Win Hentschel Blvd w1108, West Lafayette, IN 47906, USA.
Sumit PradhanDepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32611, USA.
Muhsin AvciDepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32611, USA.
Guihua BaiUSDA-ARS Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66506, USA.ORCID 0000-0002-1194-319X
Jahangir KhanPARC-Balochistan Agricultural Research and Development Center, Quetta 87300, Pakistan.ORCID 0000-0002-3065-8116
Byung-Kee Baik16USDA-ARS, Corn, Soybean and Wheat Quality Research Laboratory Unit, Wooster, OH 44691, USA.ORCID 0000-0002-2475-8348
Mohamed Mergoum0260 Redding Building, Department of Agronomy, 1109 Experiment St, Griffin, GA 30223, USA.
Md Ali BabarDepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32611, USA.ORCID 0000-0001-9951-6856

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wheat end-use quality is an important component of a wheat breeding program. Heat stress during grain filling impacts wheat quality traits, making it crucial to understand the genetic basis of wheat quality traits under post-anthesis heat stress. This study aimed to identify the genomic regions associated with wheat quality traits using genome-wide association studies (GWASs) and evaluate the prediction accuracy of different genomic selection (GS) models. A panel of 236 soft red facultative wheat genotypes was evaluated for end-use quality traits across four heat-stressed environments over three years. Significant phenotypic variation was observed across environments for traits such as grain yield (GY), grain protein (GP), grain hardness (GH), and flour yield (AFY). Heritability estimates ranged from 0.52 (GY) to 0.91 (GH). The GWASs revealed 136 significant marker-trait associations (MTAs) across all 21 chromosomes, with several MTAs located within candidate genes involved in stress responses and quality traits. Genomic selection models showed prediction accuracy values up to 0.60, with within-environment prediction outperforming across-environment prediction. These results suggest that integrating GWAS and GS approaches can enhance the selection of wheat quality traits under heat stress, contributing to the development of heat-tolerant varieties.

Indexed as

end-use qualitygenomic selectionGWASheat stress

Identifiers

PMID39765629
PMCPMC11727209

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.