The quality of corn kernels is crucial for their nutritional value, making the enhancement of kernel quality a primary objective of contemporary corn breeding efforts. This study utilized 260 corn inbred lines as research materials and assessed three traits associated with grain quality. A genome-wide association study (GWAS) was conducted using the best linear unbiased estimator (BLUE) for quality traits, resulting in the identification of 23 significant single nucleotide polymorphisms (SNPs). Additionally, nine genes associated with grain quality traits were identified through gene function annotation and prediction. Furthermore, a total of 697 quantitative trait loci (QTL) related to quality traits were compiled from 27 documents, followed by a meta-QTL analysis that revealed 40 meta-QTL associated with these traits. Among these, 19 functional genes and reported candidate genes related to quality traits were detected. Three significant SNPs identified by GWAS were located within the intervals of these QTL, while the remaining eight significant SNPs were situated within 2 Mb of the QTL. In summary, the findings of this study provide a theoretical framework for analyzing the genetic basis of corn grain quality-related traits and for enhancing corn quality.

Maize kernel traits represent crucial agronomic characteristics that significantly determine yield potential. Analyzing the genetic basis of these traits is essential for yield improvement. In this study, we utilized 1283 maize inbred lines to investigate three kernel-related characteristics: kernel length (KL), kernel width (KW), and 100-kernel weight (HKW). We conducted a genome-wide association study (GWAS) on three kernel-related traits, resulting in the identification of 29 significantly associated SNPs and six candidate genes. Additionally, we compiled quantitative trait loci (QTL) information for 765 maize kernel-related traits from 56 studies, conducted a meta-analysis of QTL, and identified 65 meta-QTLs (MQTLs). Among the 23 MQTLs, we found 25 functional genes and reported candidate genes related to kernel traits. We identified 26 maize homologs across 19 MQTLs by utilizing 25 genes that affect rice grain traits. We compared the 29 significant SNPs detected with the physical locations of 65 MQTLs and found that 3 significant SNPs were located within these MQTL intervals, and another 10 significant SNPs were in proximity to these intervals, being less than 2 Mb away, although they were not included within the MQTL intervals. The results of this study provide a theoretical foundation for elucidating the genetic basis of maize kernel-related traits and advancing molecular marker-assisted breeding selection.

Multicellular organisms exhibit inherent cellular heterogeneity that cannot be captured by traditional high-throughput sequencing techniques, resulting in the unique cellular characteristics of individual cells being neglected. Single-cell transcriptome sequencing (scRNA-seq) technology can be used to determine the gene expression levels of each individual cell, facilitating the study of intercellular expression heterogeneity. This review provides a comprehensive overview of the development and applications of scRNA-seq technology in plant research. We highlight the significance of integrating single-cell multi-omics approaches to achieve a holistic understanding of plant systems. Additionally, we discuss the current challenges and future research directions for scRNA-seq technology in plant studies, aiming to offer valuable insights for its application across various plant species.