<p>Soybean seed width (SW) is a pivotal quantitative trait influencing both seed yield and appearance quality, controlled by a complex interplay of multiple genes and environmental factors. This research was undertaken to identify significant genetic loci and candidate genes associated with SW, thereby facilitating the development of molecular markers crucial for advancing soybean breeding programs. In this study, a four-way recombinant inbred line (FW-RIL) population, derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19),<sup>1</sup> alongside a diverse germplasm population (GP) comprising 455 soybean cultivars, served as the genetic material. Phenotypic measurements of SW were meticulously recorded for the FW-RILs across three distinct environments and for the GP across four environments. Subsequent linkage analysis in the FW-RIL population and genome-wide association studies (GWAS) in the GP were conducted to map the quantitative trait loci (QTLs) and quantitative trait nucleotides (QTNs) underlying SW. These analyses successfully identified a total of 51 QTLs and 103 QTNs associated with SW. Furthermore, detailed investigation of seven QTNs attenuation regions located within the consistently detected qSW-7–2 region was performed to predict potential candidate genes. This process led to the selection of three promising genes; Glyma.07G004700, Glyma.07G006300, and Glyma.07G013700 based on the integrated evidence from sequence variation analysis among parental lines, comprehensive haplotype analysis within the mapping populations, and relevant functional annotation. The comprehensive identification of these QTLs, QTNs, and particularly the three prioritized candidate genes, offers significant insights into the genetic control of soybean seed width and provides a robust foundation for the development of effective molecular markers to enhance the efficiency of marker-assisted selection for improved soybean yield.</p>

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Conditional QTL/QTN mapping for seed width and mining candidate genes based on soybean FW-RIL population

  • Xu Wang,
  • Bo Hu,
  • Hong Xue,
  • Ming Yuan,
  • Quanzhong Dong,
  • Wen-Xia Li,
  • Zhimin Dong,
  • Hailong Ning

摘要

Soybean seed width (SW) is a pivotal quantitative trait influencing both seed yield and appearance quality, controlled by a complex interplay of multiple genes and environmental factors. This research was undertaken to identify significant genetic loci and candidate genes associated with SW, thereby facilitating the development of molecular markers crucial for advancing soybean breeding programs. In this study, a four-way recombinant inbred line (FW-RIL) population, derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19),1 alongside a diverse germplasm population (GP) comprising 455 soybean cultivars, served as the genetic material. Phenotypic measurements of SW were meticulously recorded for the FW-RILs across three distinct environments and for the GP across four environments. Subsequent linkage analysis in the FW-RIL population and genome-wide association studies (GWAS) in the GP were conducted to map the quantitative trait loci (QTLs) and quantitative trait nucleotides (QTNs) underlying SW. These analyses successfully identified a total of 51 QTLs and 103 QTNs associated with SW. Furthermore, detailed investigation of seven QTNs attenuation regions located within the consistently detected qSW-7–2 region was performed to predict potential candidate genes. This process led to the selection of three promising genes; Glyma.07G004700, Glyma.07G006300, and Glyma.07G013700 based on the integrated evidence from sequence variation analysis among parental lines, comprehensive haplotype analysis within the mapping populations, and relevant functional annotation. The comprehensive identification of these QTLs, QTNs, and particularly the three prioritized candidate genes, offers significant insights into the genetic control of soybean seed width and provides a robust foundation for the development of effective molecular markers to enhance the efficiency of marker-assisted selection for improved soybean yield.