Integrative QTL mapping and candidate gene analysis for main stem node number in soybean

Main stem node number (MSNN) is a key yield-related quantitative trait that directly affects the number of branches and seeds per soybean plant. In this study, a QTL mapping using SLAF sequencing and candidate gene analyses were used to determine the detailed genetic basis of MSNN across a diverse set of soybean line. This study investigated the variation characteristics of MSNN in 325 recombinant inbred lines (RILs) obtained from the hybridization of Qihuang 34 and Dongsheng 16. The phenotypic analysis revealed prominent transgressive segregation and continuous variation in MSNN, with a normal distribution observed for MSNN in the RIL population. A genetic map including 6297 SLAF markers was developed which spanned 2945.26 cM, with an average genetic distance of 0.47 cM between adjacent markers. QTL mapping identified five significant QTLs associated with MSNN, were located on chromosomes 6 (qMSNN6.1), 17 (qMSNN17.1), 18 (qMSNN18.1), and 19 (qMSNN19.1 and qMSNN19.2) with LOD values ranging from 3.89 to 37.92, explaining 3.46-43.56% of the phenotypic variance. Among the five QTLs, qMSNN19.2 recorded the highest LOD value, 37.92, indicated a stable environment QTL explaining 43.56% of the variance. Candidate gene mining revealed 64 genes located in the QTL qMSNN19.2, with selections made based on biological processes like regulation of stem cell division and plant hormone signaling. Additionally, specific SNP variations in candidate genes were identified for KASP marker development, offering potential targets for enhancing soybean MSNN traits. The findings of this study can assist the soybean breeding programs for developing cultivars with desirable MSNN.

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Publication Details

Journal
BMC Plant Biology
Published
2025-04-03
DOI
https://doi.org/10.1186/s12870-025-06457-2
Citations
12
Primary Topic
Soybean genetics and cultivation
Type
article
Field-Weighted Citation Impact
21.00

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article

Integrative QTL mapping and candidate gene analysis for main stem node number in soybean

Rongqiang Yuan, Xiulin Liu, Sobhi F. Lamlom, Bixian Zhang et al.
12 citations
BMC Plant Biology
Soybean genetics and cultivation
21.00
article

Integrative QTL mapping and candidate gene analysis for main stem node number in soybean

Rongqiang Yuan, Xiulin Liu, Sobhi F. Lamlom, Bixian Zhang, Kezhen Zhao, Chunlei Zhang, Fengyi Zhang, Hong Ren, Xueyang Wang, Jianqiang Sun, Lijuan Qiu, Bire Zha
article en
12 citations

Abstract

Main stem node number (MSNN) is a key yield-related quantitative trait that directly affects the number of branches and seeds per soybean plant. In this study, a QTL mapping using SLAF sequencing and candidate gene analyses were used to determine the detailed genetic basis of MSNN across a diverse set of soybean line. This study investigated the variation characteristics of MSNN in 325 recombinant inbred lines (RILs) obtained from the hybridization of Qihuang 34 and Dongsheng 16. The phenotypic analysis revealed prominent transgressive segregation and continuous variation in MSNN, with a normal distribution observed for MSNN in the RIL population. A genetic map including 6297 SLAF markers was developed which spanned 2945.26 cM, with an average genetic distance of 0.47 cM between adjacent markers. QTL mapping identified five significant QTLs associated with MSNN, were located on chromosomes 6 (qMSNN6.1), 17 (qMSNN17.1), 18 (qMSNN18.1), and 19 (qMSNN19.1 and qMSNN19.2) with LOD values ranging from 3.89 to 37.92, explaining 3.46-43.56% of the phenotypic variance. Among the five QTLs, qMSNN19.2 recorded the highest LOD value, 37.92, indicated a stable environment QTL explaining 43.56% of the variance. Candidate gene mining revealed 64 genes located in the QTL qMSNN19.2, with selections made based on biological processes like regulation of stem cell division and plant hormone signaling. Additionally, specific SNP variations in candidate genes were identified for KASP marker development, offering potential targets for enhancing soybean MSNN traits. The findings of this study can assist the soybean breeding programs for developing cultivars with desirable MSNN.

BMC Plant BiologyVol. 25(1)
Shenyang Agricultural University (CN), Heilongjiang Academy of Sciences (CN), Ministry of Agriculture (EE), Heilongjiang Provincial Academy of Agricultural Sciences (CN), Institute of Crop Sciences (CN), Heilongjiang University (CN), Alexandria University (EG)
Natural Science Foundation of Heilongjiang Province, National Science and Technology Major Project
Openalex Percentile: Top 1%
Soybean genetics and cultivation
21.00
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