Evidence map›Paper›PMID 41792345›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2026

Meta-QTLs and candidate genes for kernel protein content in maize.

Ke Li, Zilong Zhao, Wei Wang, Zhuoyang Li, Fei Yi, Jinsheng Lai, Qiujie Liu, Hainan Zhao, Jian Chen

Abstract read
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In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Ke Li *Frontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China.
Zilong Zhao *Frontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China.
Wei WangState Key Laboratory of Plant Environmental Resilience, Engineering Research Center of Plant Growth Regulator, Ministry of Education and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, People's Republic of China.
Zhuoyang LiFrontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China.
Fei YiState Key Laboratory of Plant Environmental Resilience, Engineering Research Center of Plant Growth Regulator, Ministry of Education and College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, People's Republic of China.
Jinsheng LaiFrontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China.
Qiujie LiuFrontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China.
Hainan ZhaoFrontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China.
Jian ChenFrontiers Science Center for Molecular Design Breeding, State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, People's Republic of China. jianchen@cau.edu.cn.ORCID http://orcid.org/0000-0002-1764-8394

Funding

Biological Breeding-National Science and Technology Major Project 2023ZD04069Biological Breeding-National Science and Technology Major Project 2023ZD04074Biological Breeding-National Science and Technology Major Project 2023ZD04076
6 · The paper itself

Abstract

Maize is a major feed and food crop, yet its kernel protein content remains insufficient to meet nutritional needs. Enhancing protein content is critical for improving feed efficiency and supporting plant-based diets. Although numerous QTLs associated with protein content have been identified, their typically broad confidence intervals and variability across studies make it difficult to pinpoint reliable candidate genes. Therefore, we performed a comprehensive meta-QTL (MQTL) analysis by integrating data from 25 QTL studies, encompassing 258 initial QTLs related to maize kernel protein content. A high-density consensus genetic map was constructed by merging 23 genetic maps and incorporating 19,836 markers across IBM2_2008_Neighbors reference genetic map. A total of 67 MQTLs were identified, each integrating an average of 5 initial QTLs, with their confidence intervals reduced about 2.51-fold on average. Notably, 18 MQTLs had physical intervals shorter than 1 Mb, indicating high mapping precision. To validate the MQTLs, genome-wide association studies (GWAS) on maize protein content were integrated, and 15 MQTLs were colocated with GWAS signals. Further, based on expression patterns and homology analysis across cereal crops, 54 core candidate genes were identified, including Aaap54, Tar3 and EREB133, which their homolog in rice or soybean has been demonstrated to play an important role in regulating protein accumulation. These findings demonstrate the utility of meta-QTL analysis in refining complex trait architecture and provide a valuable foundation for functional genomics studies and the genetic improvement of maize kernel protein content.

Indexed as

Genes, PlantPlant ProteinsQuantitative Trait LociSeedsZea maysChromosome MappingGenetic Association StudiesGenetic MarkersGenome-Wide Association StudyPhenotypeGenetic MarkersPlant Proteins

Identifiers

PMID41792345

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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 OpenQuestion graph.