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Research paper | Open Access

Achieving high seed protein content in soybean by combining gene mining, genomic selection models, and genetic crosses

Xu Suna,bXinying Xua,bShicheng NingcFulin Shana,bBo Hua,bWen-Xia Lia,b( )Hailong Ninga,b( )
Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin 150000, Heilongjiang, China
Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin 150038, Heilongjiang, China
College of Veterinary Medicine, Northeast Agricultural University, Harbin 150038, Heilongjiang, China
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Abstract

Soybean (Glycine max L.) is a global staple crop valued for its seeds, which contain about 40% protein in total weight, making them a rich source of plant-based protein. Enhancing soybean seed protein content (SPC) has long been a central focus of breeding research. In this study, we used a recombinant inbred line (RIL) population derived from the cross Dongnong L13 × Henong 60 (RIL6013). We performed quantitative trait locus (QTL) mapping for SPC using a high-density genetic linkage map and IciMapping v4.2 software to identify and predict candidate genes associated with SPC. We employed the mrMLM method to detect significant quantitative trait nucleotides (QTNs) linked to SPC, followed by genomic selection (GS) based on these QTNs. We conducted simulation breeding, using genotypic data from significant QTNs and GS outcomes, to identify hybrid combinations for selecting high-protein soybean varieties. This analysis revealed 16 QTL associated with SPC, including a region containing the gene Glyma.02G250200 on chromosome 2. Genome-wide association study (GWAS) identified 37 significant QTNs, which we used as a single-nucleotide polymorphism (SNP) set for GS, together with the best linear unbiased prediction (BLUP) values of phenotypic data. We employed the five conventional statistical models BayesA, BayesB, BayesC, BayesLASSO, and GBLUP to perform genomic prediction. The prediction accuracy for all five GS models exceeded 0.65. Based on the five GS outcomes we devised five breeding schemes, followed by simulation breeding. The average genotypic values of the virtual progeny generated through these simulations were significantly higher than those of the parental populations. Simulation breeding identified 22 hybrid combinations optimal for high-protein selection. Of these, the line HN138 from the RIL6013 population was the parental line for multiple high-potential hybrid combinations and may thus represent the most suitable genetic background within RIL6013 for the breeding of cultivars with high protein content.

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The Crop Journal
Pages 1330-1339

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Cite this article:
Sun X, Xu X, Ning S, et al. Achieving high seed protein content in soybean by combining gene mining, genomic selection models, and genetic crosses. The Crop Journal, 2026, 14(4): 1330-1339. https://doi.org/10.1016/j.cj.2026.04.002

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Received: 08 December 2025
Revised: 31 March 2026
Accepted: 08 April 2026
Published: 06 May 2026
© 2026 Crop Science Society of China and Institute of Crop Science, CAAS.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).