Total flower and pod numbers (TFPN) and effective pod numbers per plant (PNPP) are among the most important agronomic traits for soybean production. However, the underlying genetic mechanism remains unclear. In this study, we constructed a recombinant inbred line population derived from a cross between JY73 (high TFPN) and TJSLH (low TFPN) to map loci for the two traits. In total, six QTL for TFPN and five QTL for PNPP were identified, among which a QTL on chromosome 4, named qFPN4, explained 9.2% and 9.6% of the phenotypic variation of TFPN and PNPP, respectively. Analysis of residual heterozygous lines for qFPN4 indicated that TFPN or PNPP was controlled by a single dominant gene at this locus and delimited the QTL into a ~2.62 Mb interval which tightly linked to an Indel marker C1-5. This mapping result was further confirmed by bulked segregant analysis (BSA) of the near isogenic lines. The genome-sequencing-based BSA also identified eight candidate genes carrying nonsynonymous SNPs and/or Indels; two genes, Glyma.04G176600 and Glyma.04G178900, were nominated as the most promising genes for qFPN4 based on additional expression and function analysis. These results improve our understanding of the genetic mechanism of TFPN and PNPP and indicate the potential for soybean yield improvement.

Research on structural variations in the field of crop genetics has expanded with the rapid development of genome sequencing technologies. As an important aspect of genomic variations, structural variations have a profound impact on the genetic characteristics of crops and significantly affect their key agronomic traits, such as yield, quality, and disease and stress resistance—by changing the gene arrangement order, copy number, and the positions of regulatory elements. Compared with single-nucleotide polymorphisms, structural variations present a diverse range of types, including deletions, duplications, inversions, and translocations, and their impacts are more extensive and profound. However, research on structural variations in crops still faces many challenges, for example those relating to different ploidy levels, genome repetitiveness, and their associations with phenotypes. Nevertheless, breakthroughs in long-read sequencing technologies and the integration of multi-omics data offer hope for solving these problems. A deep understanding of the impact of structural variations on crops is of great significance for accurately analyzing the evolutionary history of crops and guiding modern crop breeding, and is expected to provide strong support for global food security and the sustainable development of agriculture.

Improving Soybean Breeding Efficiency Using Marker-Assisted Selection

Salinization of cultivated soils may result in either high salt levels or alkaline conditions, both of which stress crops and reduce performance. We sampled genotypes included in the Northeast China soybean germplasm population (NECSGP) to identify possible genes that affect tolerance to alkaline soil conditions. In this study, 361 soybean accessions collected in Northeast China were tested under 220 mM NaHCO₃:Na₂CO₃ = 9:1 (pH = 9.8) to evaluate the alkali-tolerance (ATI) at the seedling stage in Mudanjiang, Heilongjiang, China. The restricted two-stage multi-locus model genome-wide association study (RTM-GWAS) with gene–allele sequences as markers (6503 GASMs) based on simplified genome resequencing (RAD-sequencing) was accomplished. From this analysis, 132 main effect candidate genes with 359 alleles and 35 Gene × Environment genes with 103 alleles were identified, explaining 90.93% and 2.80% of the seedling alkali-tolerance phenotypic variation, respectively. Genetic variability of ATI in NECSGP was observed primarily within subpopulations, especially in ecoregion B, from which 80% of ATI-tolerant accessions were screened out. The biological functions of 132 candidate genes were classified into eight functional categories (defense response, substance transport, regulation, metabolism-related, substance synthesis, biological process, plant development, and unknown function). From the ATI gene–allele system, six key genes–alleles were identified as starting points for further study on understanding the ATI gene network.

The Impact of Marker-Assisted Selection on Soybean Yield and Disease Resistance