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

Ke Li1, Zilong Zhao1, Wei Wang2

  • 1Frontiers 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.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|March 6, 2026
PubMed
Summary

Improving maize kernel protein content is crucial for nutrition and feed efficiency. Meta-QTL analysis successfully pinpointed 67 regions, significantly narrowing down candidate genes for enhanced protein traits.

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Area of Science:

  • Plant Genetics
  • Agricultural Science
  • Bioinformatics

Background:

  • Maize kernel protein content is insufficient for nutritional needs, impacting feed efficiency and plant-based diets.
  • Previous quantitative trait loci (QTL) studies identified many regions but lacked precision due to broad confidence intervals and variability.
  • Identifying reliable candidate genes for protein enhancement in maize has been challenging.

Purpose of the Study:

  • To conduct a comprehensive meta-QTL (MQTL) analysis to refine the genetic architecture of maize kernel protein content.
  • To identify high-precision MQTLs and core candidate genes for improving protein accumulation in maize.
  • To provide a foundation for functional genomics and marker-assisted breeding for enhanced maize protein.

Main Methods:

  • Integrated data from 25 QTL studies, comprising 258 initial QTLs for maize kernel protein content.
  • Constructed a high-density consensus genetic map using 23 genetic maps and 19,836 markers.
  • Performed MQTL analysis, integrated genome-wide association studies (GWAS) data, and conducted homology analysis across cereal crops.

Main Results:

  • Identified 67 MQTLs, reducing confidence interval size by an average of 2.51-fold.
  • Achieved high mapping precision with 18 MQTLs having physical intervals <1 Mb.
  • Validated MQTLs by colocation with 15 GWAS signals and identified 54 core candidate genes, including homologs with known roles in protein accumulation.

Conclusions:

  • Meta-QTL analysis is effective in refining complex trait architecture and increasing mapping precision for maize kernel protein content.
  • The identified MQTLs and candidate genes provide valuable resources for future functional studies and genetic improvement of maize protein.
  • This study lays the groundwork for developing maize varieties with improved nutritional value and enhanced feed efficiency.