Comparative transcriptome analysis identified candidate genes associated with kernel row number in maize

Kernel row number (KRN) is a crucial trait in maize that has a high impact on yield. However, KRN is a typical quantitative trait with only a few genes being verified so far. Here, two maize inbred lines with contrasting KRN were used to perform transcriptome analysis at five early ear developmental stages. Pairwise differential gene expression analyses were performed, and a total of 11,897 line-specific differentially expressed genes (DEGs) were detected between the two lines across the five development stages. Clustering analysis of line-specific DEGs revealed that the trends of gene expression changed significantly in the five stages, thus the five stages were further divided into two development phases: Phase I (V6-V8) and Phase II (V9-V10). Gene ontology enrichment analysis revealed that different transcriptional pathways were activated in the two phases. DEGs in Phase I were significantly enriched in morphogenesis and differentiation processes and hormone regulation. Of the 5,850 line-specific DEGs in Phase I, 2,132 genes were in known quantitative trait loci (QTLs) or flanking regions of quantitative trait nucleotides (QTNs), of which 92 were repeatedly detected in QTLs where QTNs also exist. The 92 high-probability candidate genes included development-related transcription factors (SBP-box and AP2/EREBP TFs) as well as genes involved in hormone homeostasis and signaling. Our study provides genetic resources for the elucidation of the molecular mechanisms of KRN development and reference for the cloning of candidate genes.

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Publication Details

Journal
PeerJ
Published
2025-03-31
DOI
https://doi.org/10.7717/peerj.19143
Primary Topic
Genetic Mapping and Diversity in Plants and Animals
Type
article
Field-Weighted Citation Impact
0.00

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article

Comparative transcriptome analysis identified candidate genes associated with kernel row number in maize

Senan Cheng, Baokun Li, Zhenghong Li, Yancui Wang et al.
PeerJ
Genetic Mapping and Diversity in Plants and Animals
article

Comparative transcriptome analysis identified candidate genes associated with kernel row number in maize

Senan Cheng, Baokun Li, Zhenghong Li, Yancui Wang, Xitong Xu, Shukai Wang, Yuxin Zhao, Dusheng Lu, Cuixia Chen
article en

Abstract

Kernel row number (KRN) is a crucial trait in maize that has a high impact on yield. However, KRN is a typical quantitative trait with only a few genes being verified so far. Here, two maize inbred lines with contrasting KRN were used to perform transcriptome analysis at five early ear developmental stages. Pairwise differential gene expression analyses were performed, and a total of 11,897 line-specific differentially expressed genes (DEGs) were detected between the two lines across the five development stages. Clustering analysis of line-specific DEGs revealed that the trends of gene expression changed significantly in the five stages, thus the five stages were further divided into two development phases: Phase I (V6-V8) and Phase II (V9-V10). Gene ontology enrichment analysis revealed that different transcriptional pathways were activated in the two phases. DEGs in Phase I were significantly enriched in morphogenesis and differentiation processes and hormone regulation. Of the 5,850 line-specific DEGs in Phase I, 2,132 genes were in known quantitative trait loci (QTLs) or flanking regions of quantitative trait nucleotides (QTNs), of which 92 were repeatedly detected in QTLs where QTNs also exist. The 92 high-probability candidate genes included development-related transcription factors (SBP-box and AP2/EREBP TFs) as well as genes involved in hormone homeostasis and signaling. Our study provides genetic resources for the elucidation of the molecular mechanisms of KRN development and reference for the cloning of candidate genes.

PeerJVol. 13
Shandong Agricultural University (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 92%
Genetic Mapping and Diversity in Plants and Animals
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