Genome-wide survey identified superior and rare haplotypes for plant height in the north-eastern soybean germplasm of China

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.

A genome-wide association study prioritizes VRN1-2 as a candidate gene associated with plant height in soybean

Plant height is an important architectural trait that affects crop growth, yield, and stress resistance. Tremendous efforts have been dedicated to revealing the genetic basis or regulatory mechanism; however, the underlying molecular mechanism remains largely unknown primarily due to the lack of controlling genes. In this study, we conducted a single-nucleotide resolution genome-wide association study (GWAS) of plant height using a diverse soybean panel collected worldwide with 6.7 million genome-wide variants (SNPs and Indels). The GWAS of plant height identified three QTLs on chromosomes 10, 18, and 19, of which the one on chromosome 19 precisely co-localized with Dt1, known as a major stem growth habit-controlling gene. Other loci without reported genes for plant height were regarded to be new. A close investigation within QTL intervals proposed nine genes that were likely involved in the regulation of plant height according to the expression specificity in developing shoot tip meristems. VRN1-2 underlies the significant QTL on chromosome 10 was prioritized as the most promising candidate gene. VRN1-2 shows higher expression in Williams 82 with indeterminate growth habit than Dongnong50 with semi-determinate growth habit across vegetative (V2, V3) and reproductive (R1) growth stages. VRN1-2 carries non-synonymous variants in the coding region that were significantly associated with plant height variation. The GT allele conferring short plant height was likely subjected to artificial selection during domestication. These results provide a source of new loci and genes for further elaborating the regulatory mechanism of plant height and the key variants would facilitate soybean molecular breeding.

Identification of Quantitative Trait Loci for Node Number, Pod Number, and Seed Number in Soybean

Optimizing soybean yield remains a crucial challenge in meeting global food security demands. In this study, we report a comprehensive genetic analysis of yield-related traits in soybeans using a recombinant inbred line (RIL) population derived from crosses between 'Qihuang 34' (GH34) and 'Dongsheng 16' (DS16). Phenotypic analysis across two years (2023-2024) revealed significant variations between parental lines. Through high-density genetic mapping with 6297 SLAF markers spanning 2945.26 cM across 20 chromosomes, we constructed a genetic map with an average marker distance of 0.47 cM and 99.17% of gaps under 5 cM. QTL analysis identified ten significant loci across both years: in 2023, we detected six QTLs, including a major main stem node number (MSNN) QTL on chromosome 19 (LOD = 22.59, PVE = 24.57%), two seed number (SN) QTLs on chromosomes 14 and 18 (LOD = 2.52-2.85, PVE = 7.35% combined), and one pod number (PN) QTL on chromosome 20 (LOD = 4.68, PVE = 5.85%). The 2024 analysis revealed four major QTLs, notably a cluster on chromosome 19 harboring significant loci for MSNN (LOD = 37.92, PVE = 43.59%), PN (LOD = 18.16, PVE = 23.02%), and SN (LOD = 15.24, PVE = 19.59%). Within the stable chromosome 19 region, we identified seventeen candidate genes involved in crucial developmental processes. Gene expression analysis revealed distinct temporal patterns between parental lines during vegetative and reproductive stages, with GH34 showing dramatically higher expression of key reproductive genes Glyma.19G201300 and Glyma.19G201400 during the R1 stage. Our findings provide new insights into the genetic architecture of soybean stem node development and yield components, offering multiple promising targets for molecular breeding programs aimed at crop improvement.

GWAS analysis revealed genomic loci and candidate genes associated with the 100-seed weight in high-latitude-adapted soybean germplasm

KEY MESSAGE

In the present study, we identified 22 significant SNPs, eight stable QTLs and 17 potential candidate genes associated with 100-seed weight in soybean. Soybean is an economically important crop that is rich in seed oil and protein. The 100-seed weight (HSW) is a crucial yield contributing trait. This trait exhibits complex inheritance regulated by many genes and is highly sensitive to environmental factors. In this study, an integrated strategy of association mapping, QTL analysis, candidate gene and haplotype analysis was utilized to elucidate the complex genetic architecture of HSW in a panel of diverse soybean cultivars. Our study revealed 22 SNPs significantly associated with HSW through association mapping using five GWAS models across multiple environments plus a combined environment. By considering the detection of SNPs in multiple environments and GWAS models, the genomic regions of eight consistent SNPs within the ± 213.5 kb were depicted as stable QTLs. Among the eight QTLs, four, viz. qGW1.1, qGW1.2, qGW9 and qGW16, are reported here for the first time, and the other four, viz. qGW4, qGW8, qGW17 and qGW19, have been reported in previous studies. Thirty-two genes were detected as putative candidates within physical intervals of eight QTLs by in silico analysis. Twelve genes (out of total 32) showed significant differential expression patterns among the soybean accessions with contrasting HSW. Moreover, different haplotype alleles of 10 candidate genes are associated with different phenotypes of HSW. The outcome of the current investigation can be used in soybean breeding programs for producing cultivars with higher yields.

Identification of superior haplotypes and candidate gene for seed size-related traits in soybean (Glycine max L.)

Seed size is an economically important trait that directly determines the seed yield in soybean. In the current investigation, we used an integrated strategy of linkage mapping, association mapping, haplotype analysis and candidate gene analysis to determine the genetic makeup of four seed size-related traits viz., 100-seed weight (HSW), seed area (SA), seed length (SL), and seed width (SW) in soybean. Linkage mapping identified a total of 23 quantitative trait loci (QTL) associated with four seed size-related traits in the F2 population; among them, 17 were detected as novel QTLs, whereas the remaining six viz., qHSW3-1, qHSW4-1, qHSW18-1, qHSW19-1, qSL4-1 and qSW6-1 have been previously identified. Six out of 23 QTLs were major possessing phenotypic variation explained (PVE) ≥ 10%. Besides, the four QTL Clusters/QTL Hotspots harboring multiple QTLs for different seed size-related traits were identified on Chr.04, Chr.16, Chr.19 and Chr.20. Genome-wide association study (GWAS) identified a total of 62 SNPs significantly associated with the four seed size-related traits. Interestingly, the QTL viz., qHSW18-1 was identified by both linkage mapping and GWAS, and was regarded as the most stable loci regulating HSW in soybean. In-silico, sequencing and qRT-PCR analysis identified the Glyma.18G242400 as the most potential candidate gene underlying the qHSW18-1 for regulating HSW. Moreover, three haplotype blocks viz., Hap2, Hap6A and Hap6B were identified for the SW trait, and one haplotype was identified within the Glyma.18G242400 for the HSW. These four haplotypes harbor three to seven haplotype alleles across the association mapping panel of 350 soybean accessions, regulating the seed size from lowest to highest through intermediate phenotypes. Hence, the outcome of the current investigation can be utilized as a potential genetic and genomic resource for breeding the improved seed size in soybean.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01525-1.

Revolutionizing soybean genomics: How CRISPR and advanced sequencing are unlocking new potential

Soybean Glycine max L., paleopolyploid genome, poses challenges to its genetic improvement. However, the development of reference genome assemblies and genome sequencing has completely changed the field of soybean genomics, allowing for more accurate and successful breeding techniques as well as research. During the single-cell revolution, one of the most advanced sequencing tools for examining the transcriptome landscape is single-cell RNA sequencing (scRNA-seq). Comprehensive resources for genetic improvement of soybeans may be found in the SoyBase and other genomics databases. CRISPR-Cas9 genome editing technology provides promising prospects for precise genetic modifications in soybean. This method has enhanced several soybean traits, including as yield, nutritional value, and resistance to both biotic and abiotic stresses. With base editing techniques that allow for precise DNA modifications, the use of CRISPR-Cas9 is further increased. With the availability of the reference genome for soybeans and the following assembly of wild and cultivated soybeans, significant chromosomal rearrangements and gene duplication events have been identified, offering new perspectives on the complex genomic structure of soybeans. Furthermore, major single nucleotide polymorphisms (SNPs) linked to stachyose and sucrose content have been found through genome-wide association studies (GWAS), providing important tools for enhancing soybean carbohydrate profiles. In order to open up new avenues for soybean genetic improvement, future research approaches include investigating transcriptional divergence processes, enhancing genetic resources, and incorporating CRISPR-Cas9 technologies.

Identification of superior and rare haplotypes to optimize branch number in soybean

KEY MESSAGE

Using the integrated approach in the present study, we identified eleven significant SNPs, seven stable QTLs and 20 candidate genes associated with branch number in soybean. Branch number is a key yield-related quantitative trait that directly affects the number of pods and seeds per soybean plant. In this study, an integrated approach with a genome-wide association study (GWAS) and haplotype and candidate gene analyses was used to determine the detailed genetic basis of branch number across a diverse set of soybean accessions. The GWAS revealed a total of eleven SNPs significantly associated with branch number across three environments using the five GWAS models. Based on the consistency of the SNP detection in multiple GWAS models and environments, seven genomic regions within the physical distance of ± 202.4 kb were delineated as stable QTLs. Of these QTLs, six QTLs were novel, viz., qBN7, qBN13, qBN16, qBN18, qBN19 and qBN20, whereas the remaining one, viz., qBN12, has been previously reported. Moreover, 11 haplotype blocks, viz., Hap4, Hap7, Hap12, Hap13A, Hap13B, Hap16, Hap17, Hap18, Hap19A, Hap19B and Hap20, were identified on nine different chromosomes. Haplotype allele number across the identified haplotype blocks varies from two to five, and different branch number phenotype is regulated by these alleles ranging from the lowest to highest through intermediate branching. Furthermore, 20 genes were identified underlying the genomic region of ± 202.4 kb of the identified SNPs as putative candidates; and six of them showed significant differential expression patterns among the soybean cultivars possessing contrasting branch number, which might be the potential candidates regulating branch number in soybean. The findings of this study can assist the soybean breeding programs for developing cultivars with desirable branch numbers.