Identification of key candidate genes regulating hundred-grain weight in the maize inbred line Ye107 from Southern China
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Updated: May 7, 2026

Lignin Down-regulation of Zea mays via dsRNAi and Klason Lignin Analysis
Published on: July 23, 2014
Xiaoli Hong1,2, Ranjan K Shaw1, Fuyan Jiang1
1Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, 650205, China.
Key Message:
A potential candidate gene regulating maize hundred-grain weight was identified in the Ye107 inbred line through a genome-wide association study and QTL mapping, providing insights for high-yield maize breeding. Hundred-grain weight (HGW) is a key agronomic trait that significantly affects maize yield. Identifying HGW-related candidate genes is crucial for genetic improvement of maize. To overcome the limitations of traditional association panels in dissecting complex traits and to improve detection power and accuracy, this study constructed a multi-parent population (MPP). The MPP, consisting of 482 F7 recombinant inbred lines (RILs), was developed by crossing three elite maize inbred lines (D39, R-2-1-1 and YML1218) with a backbone inbred line, Ye107. The study integrated two complementary approaches: genome-wide association study (GWAS) for fine-mapping and quantitative trait loci (QTL) mapping, which is effective at detecting major-effect loci. Using these methods, 42 SNPs and 17 QTLs significantly associated with HGW were identified. Through phenotypic variation analysis, functional annotation, and qRT-PCR validation, five candidate genes, including Zm00001eb225640, Zm00001eb225650, Zm00002eb228310, Zm00004eb228320, and Zm00001eb378820 were identified. Among them, Zm00001eb378820, which encodes the transcription factor GRF13 (GRFTF13), was identified as a key candidate gene regulating HGW in maize. Genomic estimated breeding values (GEBVs) indicated that population pop3 (YML1218 × Ye107) exhibited higher values than other subpopulations, suggesting its potential for high-yield breeding. These findings offer critical genetic targets for increasing maize yield and highlight the advantage of integrating MPPs with multiple genetic analysis strategies to dissect complex quantitative traits.
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