Qijian Tian

Maize (Zea mays L.) stalk cell wall composition is a key determinant of forage digestibility, lodging resistance, and biomass utilization efficiency. Although previous genome-wide association studies (GWAS) have identified loci associated with lignin (LIG), cellulose (CEL), and hemicellulose (HC), advances in genomic resources provide an opportunity to revisit existing phenotypic datasets at substantially higher resolution. Here, we re-analyzed a maize association panel consisting of 341 diverse inbred lines using an expanded genotype dataset containing 10.77 million SNPs, two derived compositional indices (CEL/HC and [LIG/(CEL + HC)], and six complementary GWAS models. Across all traits and models, we identified 855 unique significant SNPs associated with 579 candidate genes. Among the traits examined, LIG/(CEL + HC) yielded the greatest number of associations, suggesting that indices representing the relative balance among cell wall components may better capture the genetic architecture of cell wall composition than individual component measurements alone. Integration of multiple GWAS models with functional enrichment, haplotype, and selective sweep analyses prioritized three biologically relevant candidate genes encoding a MYB58 transcription factor, the glycosyltransferase Xt9, and a putative xyloglucan 6-xylosyltransferase. Haplotype analysis revealed significant effects of Xt9 and the xyloglucan 6-xylosyltransferase on cell wall composition, while selective sweep analysis identified Xt9 as a target of repeated selection during maize domestication, ecological adaptation, and modern breeding. Although these candidate genes provide promising targets for future investigation, the associations identified here are based on a single association panel and require functional and independent population validation. Collectively, our results demonstrate how high-density genotyping combined with complementary GWAS models can refine candidate associations and generate testable hypotheses from existing phenotypic datasets.

Background: Anthocyanin accumulation is a developmentally regulated trait shaped by complex interactions between metabolic and transcriptional networks. However, dissecting the regulatory mechanisms underlying anthocyanin biosynthesis is often complicated by confounding variation in plant growth and environmental conditions. Methods: Here, we used a time-resolved multi-omics approach to investigate anthocyanin accumulation in maize seedlings by comparing an anthocyanin-rich inbred line PH19401 with an anthocyanin-deficient line YPX across five developmental stages. Results: Untargeted metabolomic and transcriptomic profiling revealed progressive divergence between the two lines beginning at early development stages. Using a dual-line, intersection-based filtering strategy, we identified a refined set of metabolites and genes closely associated with anthocyanin accumulation. These candidates were enriched in pathways related to phenylpropanoid metabolism, energy metabolism, and redox regulation. Weighted gene co-expression network analysis (WGCNA) identified two transcriptional modules that showed opposing associations with anthocyanin content. The positively associated module was centered on MYB transcription factors, consistent with canonical regulation of flavonoid biosynthesis, whereas the negatively associated module was enriched in genes involved in primary metabolism and signaling. Integration of transcriptomic and metabolomic datasets further revealed coordinated relationships between MYB hub genes and flavonoid intermediates, linking transcriptional regulation with metabolic output. Together, these results support a model in which anthocyanin accumulation is associated with activation of MYB-centered transcriptional programs and broader metabolic reprogramming that enhances precursor supply and redox balance, while competing transcriptional programs favor primary metabolism. Conclusions: This study provides a systems-level perspective on anthocyanin biosynthesis in maize seedlings and establishes an analytical framework for dissecting developmentally regulated metabolic traits.