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Drought is one of the most important factors affecting plant growth and productivity. The previous results on drought tolerance (DT) genetic system in soybean indicated a complex of genes not only few ones were involved in the trait. This study is featured with a relatively thorough identification of QTL-allele/candidate-gene system using an efficient restricted two-stage multi-locus multi-allele genome-wide association study, on two comprehensive DT indicators, membership index values of relative plant weight (MPW) and height (MPH), instead of a single biological characteristic, in a large sample (564 accessions) of the Chinese cultivated soybean population (CCSP). Based on 24,694 multi-allele markers, 75 and 64 QTL with 261 and 207 alleles (2–12/locus) were detected for MPW and MPH, explaining 54.7% and 47.1% of phenotypic variance, respectively. The detected QTL-alleles were organized into a QTL-allele matrix for each indicator, indicating DT is a super-trait conferred by two (even more) QTL-allele systems of sub-traits. Each CCSP matrix was separated into landrace (LR) and released cultivar (RC) sub-matrices, which showed significant differentiation in QTL-allele constitutions, with 58 LR alleles excluded and 16 new ones emerged in RC. Using the matrices, optimal crosses with great DT transgressive recombinants were predicted. From the detected QTL, 177 candidate genes were annotated and validated with quantitative Real-time PCR, and grouped into nine categories, with ABA and stress responders as the major parts. The key point of the above results is the establishment of relatively full QTL-allele matrices composed of numerous gene functions jointly conferring DT, therefore, demonstrates the complexity of DT genetic system and potential of CCSP in DT breeding.

Background The rheological properties of wheat dough exert a profound influence on the baking process and the quality of the final food products. Previous studies have partially elucidated the genetic basis of wheat flour rheological characteristics; however, multi-locus genome-wide association studies (ML-GWAS) investigating these traits under multi-year single-location field trials remain scarce and have yielded highly variable results. Therefore, the identification of stable genetic loci associated with dough rheological properties has become particularly critical. Results This study conducted a comprehensive analysis of a diverse natural population comprising 273 wheat varieties (lines) using a 55 K SNP array and an ML-GWAS model. This approach identified with high precision 239 quantitative trait nucleotides (QTNs) significantly associated with wheat flour dough rheological properties. Among these QTNs, 13 mono-effect QTNs were consistently detected across at least two environments and two distinct models, with only 3 classified as major-effect QTNs. Additionally, 7 pleiotropic QTNs were identified. Eleven of the mono-effect QTNs exhibited significant differences in phenotypic traits, while 3 pleiotropic QTNs demonstrated highly significant differences across all associated traits. Within a 4 Mb flanking region surrounding these major-effect and pleiotropic QTNs, a total of 606 genes were co-annotated. Gene annotation indicated these genes primarily participate in critical biological functions, including protein modification and degradation, and the regulation of seed development and maturation. Subsequent Gene Ontology (GO) enrichment analysis rigorously screened 18 promising candidate genes, and their expression profiles were analyzed throughout wheat tissue development and endosperm maturation. Ultimately, 6 SNP loci and 9 key candidate genes strongly linked to wheat flour dough rheological properties were identified. Conclusions This study employed ML-GWAS combined with the 55 K SNP array to analyze six dough rheological traits in 273 wheat germplasm accessions. SNP loci and candidate genes associated with dough rheological properties were identified, advancing molecular breeding technology in wheat and thereby establishing a theoretical foundation for genetic improvement and the cultivation of high-quality varieties.

In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in high-quality breeding of bread wheat.

Background Fruit branch number (FBN) is an important architectural trait of Upland cotton ( Gossypium hirsutum L.). In this study, FBN was investigated in 340 Upland cotton accessions across five environments. The restricted two-stage multi-locus genome-wide association study was employed for the first time to dissect the genetic architecture of FBN. Results A total of 37,010 high-quality single-nucleotide polymorphism markers (SNPs) were used to generate 6,749 single-SNP and 6,151 multi-SNP linkage disequilibrium block markers. A total of 78 quantitative trait loci (QTLs) for FBN were detected, with four QTLs consistently detected across different environments. Three QTLs, qFBN-A13 , qFBN-D03-2 , and qFBN-D06 , were further validated through single-marker/haplotype analysis. Gene Ontology (GO) enrichment analysis for genes near the three QTLs revealed that 22 significantly enriched terms (34 underlying genes), including “regulation of DNA-templated transcription,” “oxygen binding,” and “heme binding.” RNA-seq analysis further revealed that seven genes ( GH_A13G1575 , GH_D03G1306 , GH_D03G1319 , GH_D06G0408 , GH_D06G0424 , GH_D06G0436 , and GH_D06G0442 ) exhibited high expression levels in stem tissue, suggesting their potential roles in fruit branch formation. Additionally, 18 QTL-by-environment interactions (QEIs) were detected, with five QEIs validated by analyzing phenotypic differences among genotypes across various environments. The GO enrichment analysis of genes near these five QEIs identified 22 significantly enriched terms (28 underlying genes), including “oxidoreductase activity, acting on metal ions,” “copper ion binding,” and “protein neddylation.” Five genes ( GH_A06G1857 , GH_A06G1859 , GH_A06G1884 , GH_D07G0658 , and GH_D09G1041 ) showed high expression levels in the stem, indicating their potential involvement in fruit branch formation through gene–environment interactions. Conclusion In total, 12,905 SNPLDB markers were constructed from 37,010 high-quality SNPs. A total of 78 QTLs and 18 QEIs were detected. Three stable QTLs for FBN were validated through single-marker/haplotype analysis. Five QEIs for FBN were validated through phenotypic difference analysis. Seven QTL-related genes and five QEI-related genes may contribute to FBN.

Kernel moisture content at the harvest stage (KMC) is an important trait that affects the mechanical harvesting of maize grain, and the identification of genetic loci for KMC is beneficial for maize molecular breeding. In this study, we performed a multi-locus genome-wide association study (ML-GWAS) to identify quantitative trait nucleotides (QTNs) for KMC using an association mapping panel of 251 maize inbred lines that were genotyped with an Affymetrix CGMB56K SNP Array and phenotypically evaluated in three environments. Ninety-eight QTNs for KMC were detected using six ML-GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, PLARmEB, PKWmEB, and ISIS EM-BLASSO). Eleven of these QTNs were considered to be stable, as they were detected by at least four ML-GWAS models under a uniformed environment or in at least two environments and BLUP using the same ML-GWAS model. With qKMC5.6 removed, the remaining 10 stable QTNs explained <10% of the phenotypic variation, suggesting that KMC is mainly controlled by multiple minor-effect genetic loci. A total of 63 candidate genes were predicted from the 11 stable QTNs, and 10 candidate genes were highly expressed in the kernel at different time points after pollination. High prediction accuracy was achieved when the KMC-associated QTNs were included as fixed effects in genomic selection, and the best strategy was to integrate all KMC QTNs identified by all six ML-GWAS models. These results further our understanding of the genetic architecture of KMC and highlight the potential of genomic selection for KMC in maize breeding.

Common cutworm (CCW) is an omnivorous insect causing severe yield losses in soybean crops. The seedling-stage mini-tray identification system with the damaged leaf percentage (DLP) as an indicator was used to evaluate antixenosis against CCW in the Chinese soybean landrace population (CSLRP) under three environments. Using the innovative restricted two-stage multi-locus genome-wide association study procedure (RTM-GWAS), 86 DLP QTLs with 243 alleles (2–11/QTL) were identified, including 66 main-effect loci with 203 alleles and 57 QTL-environment interaction loci with 172 alleles. Among the main-effect loci, 12 large-contribution loci (R2 ≥ 1%) explained 25.45% of the phenotypic variation (PV), and 54 small-contribution loci (R2 < 1%) explained 16.55% of the PV. This indicates that the CSLRP can be characterized with a DLP QTL-allele system complex that has not been found before, except for a few individual QTLs without alleles involved. From the DLP QTL-allele matrix, the recombination potentials expressed in the 25th percentile of the DLP of all possible crosses were predicted to be reduced by 41.5% as the maximum improvement and 14.2% as the maximum transgression, indicating great breeding potential in the antixenosis of the CSLRP. From the QTLs, 62 candidate genes were annotated, which were involved in eight biological function categories as a gene network of the DLP. Changing from susceptible to moderate plus resistant varieties in the CSLRP, 26 QTLs had 32 alleles involved, in which 19 genes were annotated from 25 QTL-alleles, including eight increased negative alleles on seven loci and 11 decreased positive alleles on 11 loci, showing the major genetic constitution changes for the antixenosis enhancement at the seedling stage in the CSLRP.

In soybeans (Glycine max (L.) Merr.), their growth periods, DSF (days of sowing-to-flowering), and DFM (days of flowering-to-maturity) are determined by their required accumulative day-length (ADL) and active temperature (AAT). A sample of 354 soybean varieties from five world eco-regions was tested in four seasons in Nanjing, China. The ADL and AAT of DSF and DFM were calculated from daily day-lengths and temperatures provided by the Nanjing Meteorological Bureau. The improved restricted two-stage multi-locus genome-wide association study using gene–allele sequences as markers (coded GASM-RTM-GWAS) was performed. (i) For DSF and its related ADLDSF and AATDSF, 130–141 genes with 384–406 alleles were explored, and for DFM and its related ADLDFM and AATDFM, 124–135 genes with 362–384 alleles were explored, in a total of six gene–allele systems. DSF shared more ADL and AAT contributions than DFM. (ii) Comparisons between the eco-region gene–allele submatrices indicated that the genetic adaptation from the origin to the geographic sub-regions was characterized by allele emergence (mutation), while genetic expansion from primary maturity group (MG)-sets to early/late MG-sets featured allele exclusion (selection) without allele emergence in addition to inheritance (migration). (iii) Optimal crosses with transgressive segregations in both directions were predicted and recommended for breeding purposes, indicating that allele recombination in soybean is an important evolutionary drive. (iv) Genes of the six traits were mostly trait-specific involved in four categories of 10 groups of biological functions. GASM-RTM-GWAS showed potential in detecting directly causal genes with their alleles, identifying differential trait evolutionary drives, predicting recombination breeding potentials, and revealing population gene networks.

Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from \"Dongnong L13\" and \"Henong 60\" in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

The main stem node number (MSN) is a trait related to geographic adaptation, plant architecture and yield potential of soybean. The QTL-allele constitution of the Chinese Cultivated Soybean Population (CCSP) was identified using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) procedure, from which a QTL-allele matrix was established and then separated into submatrices to explore the genetic structure, evolutionary differentiation, breeding potential and candidate genes of MSN in CCSP. The MSN of 821 accessions varied from 8.8 to 31.1, with an average of 16.3 in Nanjing, China (32.07° N, 118.62° E), where the MSNs of all the materials could be evaluated in a standardized manner. Among the six geo-seasonal subpopulations, the MSN varied from 21.7 in a southern summer-autumn-sowing subpopulation (SA-IV) down to 13.5 in a northeastern spring-sowing subpopulation (SP-I). The materials were genotyped with restriction site-associated DNA-sequencing. Totally 142 main-effect QTLs (73.24% new) with 560 alleles contributing 72.98% to the phenotypic variance were identified. The evolutionary QTL-allele changes in MSN from SA-IV through SP-I showed that inheritance (78.93% of alleles) was the primary factor influencing the evolution of this trait, followed by allele emergence (19.64% alleles), allele exclusion (1.43% alleles), and recombination among retained alleles. In the evolutionary changes, 70 QTLs, including 12 newly emerged QTLs, with 118 alleles were involved. An increase potential of 2–8 nodes was predicted and 112 candidate genes were annotated and preliminarily verified with χ2-tests in the CCSP. The RTM-GWAS showed powerful in detecting QTL-allele system, assessing evolution factors, predicting optimal crosses and identifying candidate genes in a germplasm population.

Drought tolerance (DT) is one of the major challenges for world soybean production. A nested association mapping (NAM) population with 403 lines comprising two recombinant inbred line (RIL) populations: M8206 × TongShan and Zheng-Yang × M8206 was tested for DT using polyethylene-glycol (PEG) treatment under spring and summer environments. The population was sequenced using restriction-site-associated DNA sequencing (RAD-seq) filtered with minor allele frequency (MAF) = 0.01, 55,936 single nucleotide polymorphisms (SNPs) were obtained and organized into 6137 SNP linkage disequilibrium blocks (SNPLDBs). The restricted two-stage multi-locus genome-wide association studies (RTM-GWAS) identified 73 and 38 QTLs with 174 and 88 alleles contributed main effect 40.43 and 26.11% to phenotypic variance (PV) and QTL–environment interaction (QEI) effect 24.64 and 10.35% to PV for relative root length (RRL) and relative shoot length (RSL), respectively. The DT traits were characterized with high proportion of QEI variation (37.52–41.65 %), plus genetic variation (46.90–58.40%) in a total of 88.55–95.92% PV. The identified QTLs–alleles were organized into main-effect and QEI-effect QTL–allele matrices, showing the genetic and QEI architecture of the three parents/NAM population. From the matrices, the possible best genotype was predicted to have a weighted average value over two indicators (WAV) of 1.873, while the top ten optimal crosses among RILs with 95th percentile WAV 1.098–1.132, transgressive over the parents (0.651–0.773) but much less than 1.873, implying further pyramiding potential. From the matrices, 134 candidate genes were annotated involved in nine biological processes. The present results provide a novel way for molecular breeding in QTL–allele-based genomic selection for optimal cross selection.