Genetic dissection of starch and protein quality traits and their potential co-regulation with grain yield in maize

Kernel starch and protein contents are major determinants of maize grain quality and end-use value, whereas yield remains the primary breeding objective. These traits are genetically complex and interrelated, potentially sharing genetic effects while exhibiting trade-offs. Recent advances in quantitative genetics, genome-wide association studies, and multi-omics have generated vast but scattered datasets on maize quality traits. However, an integrated analysis of research trends, genetic architecture, and systematic mining of candidate genes underlying starch and protein contents and their relationships with grain yield is lacking.

This review aims to systematically summarize research progress and emerging trends in the genetic and molecular regulation of maize starch and protein contents, to mine and prioritize candidate genes for these quality traits, and to elucidate their genetic basis in relation to yield-related traits. By integrating bibliometric analysis with genetic and multi-omics evidence, the review provides a coherent framework and valuable resources to support the joint improvement of grain quality and yield in modern maize breeding.

The review highlights three major scientific concepts. First, bibliometric analyses reveal a transition from classical QTL mapping toward genome-wide and system-level approaches, reflecting a shift in maize quality research paradigms. Second, by integrating functionally characterized maize genes, QTLs/QTNs, multi-omics integrative networks, and maize orthologs of regulators from rice and wheat, the review summarizes experimentally supported regulatory relationships, identifies genomic hotspots, and generates prioritized candidate gene resources for further investigation. Third, this review identifies genomic regions and candidate genes that are co-localized with or jointly associated with grain quality and yield traits, providing candidate genetic resources for investigating potential multi-trait effects and quality-yield trade-offs. Together, these insights demonstrate the value of integrative analysis for gene resource mining and outline a conceptual and practical roadmap for multi-omics-assisted precision maize breeding.