Analysis of quantitative trait loci for main plant traits in soybean

Densification of Genetic Map and Stable Quantitative Trait Locus Analysis for Amino Acid Content of Seed in Soybean (Glycine max L.)

Soybean, a primary vegetable protein source, boasts favorable amino acid profiles; however, its composition still falls short of meeting human nutritional demands. The soybean amino acid content is a quantitative trait controlled by multiple genes. In this study, an F2 population of 186 individual plants derived from the cross between ChangJiangChun2 and JiYu166 served as the mapping population. Based on the previously published genetic map of our lab, we increased the density of the genetic map and constructed a new genetic map containing 518 SSR (simple sequence repeats) markers and 64 InDel (insertion-deletion) markers, with an average distance of 5.27 cm and a total length of 2881.2 cm. The content of eight essential amino acids was evaluated in the F2:5, F2:6, and BLUP (best linear unbiased prediction). A total of 52 QTLs (quantitative trait loci) were identified, and 13 QTL clusters were identified, among which loci02.1 and loci11.1 emerged as stable QTL clusters, exploring candidate genes within these regions. Through GO enrichment and gene annotation, 16 candidate genes associated with soybean essential amino acid content were predicted. This study would lay the foundation for elucidating the regulatory mechanisms of essential amino acid content and contribute to germplasm innovation in soybeans.

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.

Identification of a Branch Number Locus in Soybean Using BSA-Seq and GWAS Approaches

The determination of the soybean branch number plays a pivotal role in plant morphogenesis and yield components. This polygenic trait is subject to environmental influences, and despite its significance, the genetic mechanisms governing the soybean branching number remain incompletely understood. To unravel these mechanisms, we conducted a comprehensive investigation employing a genome-wide association study (GWAS) and bulked sample analysis (BSA). The GWAS revealed 18 SNPs associated with the soybean branch number, among which qGBN3 on chromosome 2 emerged as a consistently detected locus across two years, utilizing different models. In parallel, a BSA was executed using an F2 population derived from contrasting cultivars, Wandou35 (low branching number) and Ruidou1 (high branching number). The BSA results pinpointed a significant quantitative trait locus (QTL), designated as qBBN1, located on chromosome 2 by four distinct methods. Importantly, both the GWAS and BSA methods concurred in co-locating qGBN3 and qBBN1. In the co-located region, 15 candidate genes were identified. Through gene annotation and RT-qPCR analysis, we predicted that Glyma.02G125200 and Glyma.02G125600 are candidate genes regulating the soybean branch number. These findings significantly enhance our comprehension of the genetic intricacies regulating the branch number in soybeans, offering promising candidate genes and materials for subsequent investigations aimed at augmenting the soybean yield. This research represents a crucial step toward unlocking the full potential of soybean cultivation through targeted genetic interventions.

Genetic Diversity Analysis of Soybean Collection Using Simple Sequence Repeat Markers

Soybean [Glycine max (L.) Merr.] is a nutrient-rich crop that offers a sustainable source of dietary protein and edible oil. Determining the level of genetic diversity and relationships between various genetic resources involved in breeding programs is very important in crop improvement strategies. This study evaluated 100 soybean accessions with diverse origins for 10 important agronomic traits, including plant height (PH), an important plant adaptation-related trait impacting yield, in conditions in southeastern Kazakhstan for 2 years. The comparison of different groups of PH (tall, middle, and short) using a t-test suggested that the group of plants with the tallest PH provided a higher yield (p < 0.001) in relatively dry field conditions. The genetic diversity of the accessions was estimated using 25 simple sequence repeat (SSR) markers previously known to be associated with plant height. The results showed a significant variation among different groups of origin for all measured agronomic traits, as well as high genetic diversity, with the PIC (polymorphism information content) varying from 0.140 to 0.732, with an average of 0.524. Nei's diversity index ranged between 0.152 and 0.747, with an average of 0.526. The principal coordinate analysis (PCoA) of the studied soybean collection showed that Kazakhstan accessions were genetically distant from European, East Asian, and North American cultivars. Twelve out of twenty-five SSR markers demonstrated significant associations with ten studied agronomic traits, including PH (p < 0.05). Six SSRs with pleiotropic effects for studied traits were selected, and their haplotypes with phenotypic effects were generated for each soybean accession. The obtained results can be used in soybean improvement programs, including molecular-assisted breeding projects.

Construction of a genetic map and QTL mapping of seed size traits in soybean

Soybean seed size and seed shape traits are closely related to plant yield and appearance quality. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiang Chun 2 and JiYu 166 were selected as the mapping population to construct a molecular genetic linkage map, and the phenotypic data of hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean under three generations of F2 single plants and F2:3 and F2:4 lines were combined to detect the QTL (quantitative trait loci) for the corresponding traits by ICIM mapping. A soybean genetic map containing 455 markers with an average distance of 6.15 cM and a total length of 2799.2 cM was obtained. Forty-nine QTLs related to the hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean were obtained under three environmental conditions. A total of 10 QTLs were detected in more than two environments with a phenotypic variation of over 10%. Twelve QTL clusters were identified on chromosomes 1, 2, 5, 6, 8, 13, 18, and 19, with the majority of the overlapping intervals for hundred-grain weight and seed width. These results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean seed weight and seed shape. Eighteen candidate genes that may be involved in the regulation of soybean seed size were screened by gene functional annotation and GO enrichment analysis.

Genome-wide association study of soybean (Glycine max [L.] Merr.) germplasm for dissecting the quantitative trait nucleotides and candidate genes underlying yield-related traits

Soybean (Glycine max [L.] Merr.) is one of the most significant crops in the world in terms of oil and protein. Owing to the rising demand for soybean products, there is an increasing need for improved varieties for more productive farming. However, complex correlation patterns among quantitative traits along with genetic interactions pose a challenge for soybean breeding. Association studies play an important role in the identification of accession with useful alleles by locating genomic sites associated with the phenotype in germplasm collections. In the present study, a genome-wide association study was carried out for seven agronomic and yield-related traits. A field experiment was conducted in 2015/2016 at two locations that include 155 diverse soybean germplasm. These germplasms were genotyped using SoySNP50K Illumina Infinium Bead-Chip. A total of 51 markers were identified for node number, plant height, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield using a multi-locus linear mixed model (MLMM) in FarmCPU. Among these significant SNPs, 18 were putative novel QTNs, while 33 co-localized with previously reported QTLs. A total of 2,356 genes were found in 250 kb upstream and downstream of significant SNPs, of which 17 genes were functional and the rest were hypothetical proteins. These 17 candidate genes were located in the region of 14 QTNs, of which ss715580365, ss715608427, ss715632502, and ss715620131 are novel QTNs for PH, PPP, SDPP, and TY respectively. Four candidate genes, Glyma.01g199200, Glyma.10g065700, Glyma.18g297900, and Glyma.14g009900, were identified in the vicinity of these novel QTNs, which encode lsd one like 1, Ergosterol biosynthesis ERG4/ERG24 family, HEAT repeat-containing protein, and RbcX2, respectively. Although further experimental validation of these candidate genes is required, several appear to be involved in growth and developmental processes related to the respective agronomic traits when compared with their homologs in Arabidopsis thaliana. This study supports the usefulness of association studies and provides valuable data for functional markers and investigating candidate genes within a diverse germplasm collection in future breeding programs.

Fine mapping and candidate gene analysis of proportion of four-seed pods by soybean CSSLs

Soybean yield, as one of the most important and consistent breeding goals, can be greatly affected by the proportion of four-seed pods (PoFSP). In this study, QTL mapping was performed by PoFSP data and BLUE (Best Linear Unbiased Estimator) value of the chromosome segment substitution line population (CSSLs) constructed previously by the laboratory from 2016 to 2018, and phenotype-based bulked segregant analysis (BSA) was performed using the plant lines with PoFSP extreme phenotype. Totally, 5 ICIM QTLs were repeatedly detected, and 6 BSA QTLs were identified in CSSLs. For QTL (qPoFSP13-1) repeated in ICIM and BSA results, the secondary segregation populations were constructed for fine mapping and the interval was reduced to 100Kb. The mapping results showed that the QTL had an additive effect of gain from wild parents. A total of 14 genes were annotated in the delimited interval by fine mapping. Sequence analysis showed that all 14 genes had genetic variation in promoter region or CDS region. The qRT-PCR results showed that a total of 5 candidate genes were differentially expressed between the plant lines having antagonistic extreme phenotype (High PoFSP > 35.92%, low PoFSP< 17.56%). The results of haplotype analysis showed that all five genes had two or more major haplotypes in the resource population. Significant analysis of phenotypic differences between major haplotypes showed all five candidate genes had haplotype differences. And the genotypes of the major haplotypes with relatively high PoFSP of each gene were similar to those of wild soybean. The results of this study were of great significance to the study of candidate genes affecting soybean PoFSP, and provided a basis for the study of molecular marker-assisted selection (MAS) breeding and four-seed pods domestication.

Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments

INTRODUCTION

The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear.

METHODS

To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining.

RESULTS

A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis.

DISCUSSION

The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.

A genome wide association study to dissect the genetic architecture of agronomic traits in Andean lupin (Lupinus mutabilis)

Establishing Lupinus mutabilis as a protein and oil crop requires improved varieties adapted to EU climates. The genetic regulation of strategic breeding traits, including plant architecture, growing cycle length and yield, is unknown. This study aimed to identify associations between 16 669 single nucleotide polymorphisms (SNPs) and 9 agronomic traits on a panel of 223 L. mutabilis accessions, grown in four environments, by applying a genome wide association study (GWAS). Seven environment-specific QTLs linked to vegetative yield, plant height, pods number and flowering time, were identified as major effect QTLs, being able to capture 6 to 20% of the phenotypic variation observed in these traits. Furthermore, two QTLs across environments were identified for flowering time on chromosome 8. The genes FAF, GAMYB and LNK, regulating major pathways involved in flowering and growth habit, as well as GA30X1, BIM1, Dr1, HDA15, HAT3, interacting with these pathways in response to hormonal and environmental cues, were prosed as candidate genes. These results are pivotal to accelerate the development of L. mutabilis varieties adapted to European cropping conditions by using marker-assisted selection (MAS), as well as to provide a framework for further functional studies on plant development and phenology in this species.

Physiological breeding for yield improvement in soybean: solar radiation interception-conversion, and harvest index

KEY MESSAGE

Efficiency of light interception, Radiation use efficiency and harvest index can be used as targets to improve grain yield potential in soybean. Grain yield (GY) production can be expressed as the result of three main efficiencies: light interception (Ei), radiation use (RUE), and harvest index (HI). Although dissecting GY through these three efficiencies is not entirely new, there is a lack of knowledge about the phenotypic variation, the genetic architecture, and the relative contribution of these three efficiencies on GY in soybean. This knowledge gap coupled with laborious phenotyping prevents the active consideration of these efficiencies into breeding programs. This study aims to reveal the phenotypic variation, heritability, genetic relationships, genetic architecture, and genomic prediction for Ei, RUE, and HI in soybean. We evaluated a maturity control panel of 383 Recombinant Inbred Lines (RILs) selected from the soybean nested association mapping (SoyNAM) population. Dry matter ground measured along with canopy coverage (CC) from UAS imagery were collected in three environments. Light interception was modeled through a logistic curve using CC as a proxy. The total above-ground biomass collected during the growing season and its respective cumulative light intercepted were used to derive RUE through linear models fitting. Additive-genetic correlations, genome-wide association (GWA) and whole-genome regressions (WGR) were performed to evaluate the relationship between traits, their association with genomic regions, and the feasibility of predicting these efficiencies with genomic information. Correlation analyses considered three groups: the entire data set, and the high- and low-yielding RILs to determine association as a function of the GY. Our results revealed moderate to high phenotypic variation for Ei, RUE, and HI with ranges of 8.5%, 1.1 g MJ-1, and 0.2, respectively. Additive-genetic correlation revealed a strong relationship of GY with HI and moderate with RUE and Ei when whole data set was considered, but negligible contribution of HI on GY when just the top 100 was analyzed. The GWA analyses showed that Ei is associated with three SNPs; two of them located on chromosome 7 and one on chromosome 11 with no previous quantitative trait loci (QTLs) reported for these regions. RUE is associated with four SNPs on chromosomes 1, 7, 11, and 18. Some of these QTLs are novel, while others are previously documented for plant architecture and chlorophyll content. Two SNPs positioned on chromosome 13 and 15 with previous QTLs reported for plant height and seed set, weight and abortion were associated with HI. WGR showed high predictive ability for Ei, RUE, and HI with maximum correlation ranging between 0.75 and 0.80. Future improvements in GY can be expected through strategies prioritizing Ei for short-term results when using high yielding germplasm and RUE for medium- and long-term outcomes. This work is a pioneer attempt to integrate traditional physiological traits into the breeding process in the context of physiological breeding.

Detecting QTL and Candidate Genes for Plant Height in Soybean via Linkage Analysis and GWAS

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600, involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding.

Novel target sites for soybean yield enhancement by photosynthesis

Photosynthesis plays an important role in plant growth and development. Increasing photosynthetic rate is a main objective of improving crop productivity. Chlorophyll fluorescence is an effective method for quickly evaluating photosynthesis. In this study, four representative chlorophyll fluorescence parameters, that is, maximum quantum efficiency of photosystem II, quantum efficiency of PSII, photochemical quenching, and non-photochemical quenching, of 219 diverse soybean accessions were measured across three environments. The underlying genetic architecture was analyzed by genome-wide association study. Forty-eight SNPs were detected to associate with the four traits and explained 10.43-20.41% of the phenotypic variation. Nine candidate genes in the stable QTLs were predicted. Great differences in the expression levels of the candidate genes existed between the high photosynthetic efficiency accessions and low photosynthetic efficiency accessions. In all, we uncover 17 QTLs associated with photosynthesis-related traits and nine genes that may participate in the regulation of photosynthesis, which can provide references for revealing the genetic mechanism of photosynthesis. These QTLs and candidate genes will provide new targets for crop yield improvement through increasing photosynthesis.

Identification of quantitative trait loci underlying five major agronomic traits of soybean in three biparental populations by specific length amplified fragment sequencing (SLAF-seq)

Flowering time, plant height, branch number, node numbers of main stem and pods per plant are important agronomic traits related to photoperiodic sensitivity, plant type and yield of soybean, which are controlled by multiple genes or quantitative trait loci (QTL). The main purpose of this study is to identify new QTL for five major agronomic traits, especially for flowering time. Three biparental populations were developed by crossing cultivars from northern and central China. Specific loci amplified fragment sequencing (SLAF-seq) was used to construct linkage map and QTL mapping was carried out. A total of 10 QTL for flowering time were identified in three populations, some of which were related to E1 and E2 genes or the other reported QTL listed in Soybase. In the Y159 population (Xudou No.9 × Kenfeng No.16), QTL for flowering time on chromosome 4, qFT4_1 and qFT4_2 were new. Compared with the QTL reported in Soybase, 1 QTL for plant height (PH), 3 QTL for branch number (BR), 5 QTL for node numbers of main stem, and 3 QTL for pods per plant were new QTL. Major E genes were frequently detected in different populations indicating that major the E loci had a great effect on flowering time and adaptation of soybean. Therefore, in order to further clone minor genes or QTL, it may be of great significance to carefully select the genotypes of known loci. These results may lay a foundation for fine mapping and clone of QTL/genes related to plant-type, provided a basis for high yield breeding of soybean.

QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs

Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.

Genome-wide association study and genomic selection for yield and related traits in soybean

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

Identification and validation of quantitative trait loci associated with seed yield in soybean

In soybean [Glycine max (L.) Merrill], the genetic analysis of seed yield is important to aid in the breeding of high-yielding cultivars. Seed yield is a complex trait, and the number of quantitative trait loci (QTL) involved in seed yield is high. The aims of this study were to identify QTL associated with seed yield and validate their effects on seed yield using near-isogenic lines. The QTL analysis was conducted using a recombinant inbred line population derived from a cross between Japanese cultivars 'Toyoharuka' and 'Toyomusume', and eight seed yield-associated QTL were identified. There were significant positive correlations between seed yield and the number of favorable alleles at QTL associated with seed yield in the recombinant inbred lines for three years. The effects of qSY8-1, a QTL promoting greater seed yield, was validated in the Toyoharuka background. In a two-year yield trial, the 100-seed weight and seed yield of Toyoharuka-NIL, the near-isogenic line having the Toyomusume allele at qSY8-1, were significantly greater than those of Toyoharuka (106% and 107%, respectively) without any change for days to flowering and maturity. Our results suggest that qSY8-1 was not associated with maturity genes, and contributed to the 100-seed weight.

Identification of QTNs and their candidate genes for flowering time and plant height in soybean using multi-locus genome-wide association studies

Flowering time (FT) and plant height (PH) are important agronomic traits in soybean. However, their genetic foundations are not fully understood. Thus, in this study, a total of 106,013 single nucleotide polymorphisms in 286 soybean accessions were used to associate with the first and full FT (FT1 and FT2) and PH in 4 environments and their BLUP values using 6 multi-locus genome-wide association study methods. As a result, 38, 43, and 27 stable quantitative trait nucleotides (QTNs) were identified, respectively, for FT1, FT2, and PH across at least 3 methods and/or environments. Among these QTNs for FT1, FT2, and PH, 31, 36, and 21 were found to have significant phenotype differences across 2 alleles; 22, 18, and 13 were consistent with the corresponding loci in previous studies; 13 and 8 genes, with more than average expression level, around 64 FT and 27 PH QTNs were predicted as their corresponding candidate genes. Among these candidate genes, GmPRR3b, and GmGIa for FT, and GmTFL1b for PH were known, while some were new, e.g., GmPHYA4, GmVRN5, GmFPA, and GmSPA1 for FT, and Glyma.02g300200, GmFPA, and Glyma.13g339800 for PH. All the validated QTNs were used to design the best cross-combinations in 2 FT directions. In each FT direction, the best 5 cross-combinations were predicted, such as Heihe 54 × Qincha 1 for early FT, and Yingdejiadou × Wuhuabayuehuang for late FT. This study provides solid foundations for genetic basis, molecular biology, and breeding by design of soybean FT and PH.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01230-3.

Genome-wide association studies of plant architecture-related traits and 100-seed weight in soybean landraces

BACKGROUND

Plant architecture-related traits (e.g., plant height (PH), number of nodes on main stem (NN), branch number (BN) and stem diameter (DI)) and 100-seed weight (100-SW) are important agronomic traits and are closely related to soybean yield. However, the genetic basis and breeding potential of these important agronomic traits remain largely ambiguous in soybean (Glycine max (L.) Merr.).

RESULTS

In this study, we collected 133 soybean landraces from China, phenotyped them in two years at two locations for the above five traits and conducted a genome-wide association study (GWAS) using 82,187 single nucleotide polymorphisms (SNPs). As a result, we found that a total of 59 SNPs were repeatedly detected in at least two environments. There were 12, 12, 4, 4 and 27 SNPs associated with PH, NN, BN, DI and 100-SW, respectively. Among these markers, seven SNPs (AX-90380587, AX-90406013, AX-90387160, AX-90317160, AX-90449770, AX-90460927 and AX-90520043) were large-effect markers for PH, NN, BN, DI and 100-SW, and 15 potential candidate genes were predicted to be in linkage disequilibrium (LD) decay distance or LD block. In addition, real-time quantitative PCR (qRT-PCR) analysis was performed on four 100-SW potential candidate genes, three of them showed significantly different expression levels between the extreme materials at the seed development stage. Therefore, Glyma.05 g127900, Glyma.05 g128000 and Glyma.05 g129000 were considered as candidate genes with 100-SW in soybean.

CONCLUSIONS

These findings shed light on the genetic basis of plant architecture-related traits and 100-SW in soybean, and candidate genes could be used for further positional cloning.

Mapping QTL/QTN and mining candidate genes for plant height and its response to planting densities in soybean [Glycine max (L.) Merr.] through a FW-RIL population

Plant height (PH) determines the morphology and seed yield of soybean, so it is an important breeding target, which is controlled by multiple genes and affected by plant density. In this research, it was used about a four-way recombinant inbred lines (FW-RIL) with 144 families constructed by double cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19) as experimental materials, with the purpose to map QTL/QTN associated with PH under densities of 2.2×105 plant/ha (D1) and 3×105 plant/ha (D2) in five environments. The results showed that response of PH to densities varied in accordance to genotypes among environments. A total of 26 QTLs and 13 QTNs were identified specifically in D1; 20 QTLs and 21 QTNs were identified specifically in D2. Nine QTLs and one QTN were discovered commonly in two densities. Fifteen QTLs and 9 QTNs were repeatedly detected by multiple statistical methods, densities, or environments, which could be considered stable. Eighteen QTLs were detected, as well as 7 QTNs underlying responses of PH to density increment. Six QTNs, co-located in the interval of QTL, were detected in more than two environments or methods with a longer genome length over 3000 kb. Based on gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, five genes were predicted as candidates, which were likely to be involved in growth and development of PH. The results will help elucidate the genetic basis and improve molecular assistant selection of PH.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01209-0.

Environmental and genetic regulation of plant height in soybean

BACKGROUND

Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.

RESULTS

In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80-26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19-1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.

CONCLUSIONS

Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies

100-seed weight (100-SW) in soybeans is a yield component trait and controlled by multiple genes with different effects, but limited information is available for its quantitative trait nucleotides (QTNs) and candidate genes. To better understand the genetic architecture underlying the trait and improve the precision of marker-assisted selection, a total of 43,834 single nucleotide polymorphisms (SNPs) in 250 soybean accessions were used to identify significant QTNs for 100-SW in four environments and their BLUP values using six multi-locus and one single-locus genome-wide association study methods. As a result, a total of 218 significant QTNs were detected using multi-locus methods, whereas eight QTNs were identified by a single-locus method. Among 43 QTNs or QTN clusters identified repeatedly across various environments and/or approaches, all of them exhibited significant trait differences between their corresponding alleles, 33 were found in the genomic region of previously reported QTLs, 10 were identified as new QTNs, and three (qHSW-4-1, qcHSW-7-3, and qcHSW-10-4) were detected in all the four environments. The number of seed weight (SW) increasing alleles for each accession ranged from 8 (18.6%) to 36 (83.72%), and three accessions (Yixingwuhuangdou, Nannong 95C-5, and Yafanzaodou) had more than 35 SW increasing alleles. Among 36 homologous seed-weight genes in Arabidopsis underlying the above 43 stable QTNs, more importantly, Glyma05g34120, GmCRY1, and GmCPK11 had known seed-size/weight-related genes in soybean, and Glyma07g07850, Glyma10g03440, and Glyma10g36070 were candidate genes identified in this study. These results provide useful information for genetic foundation, marker-assisted selection, genomic prediction, and functional genomics of 100-SW.

Genome-wide association study of soybean seed germination under drought stress

Drought stress, which is increasing with climate change, is a serious threat to agricultural sustainability worldwide. Seed germination is an essential growth phase that ensures the successful establishment and productivity of soybean, which can lose substantial productivity in soils with water deficits. However, only limited genetic information is available about how germinating soybean seeds may exert drought tolerance. In this study, we examined the germinating seed drought-tolerance phenotypes and genotypes of a panel of 259 released Chinese soybean cultivars panel. Based on 4616 Single-Nucleotide Polymorphisms (SNPs), we conducted a mixed-linear model GWAS that identified a total of 15 SNPs associated with at least one drought-tolerance index. Notably, three of these SNPs were commonly associated with two drought-tolerance indices. Two of these SNPs are positioned upstream of genes, and 11 of them are located in or near regions where QTLs have been previously mapped by linkage analysis, five of which are drought-related. The SNPs detected in this study can both drive hypothesis-driven research to deepen our understanding of genetic basis of soybean drought tolerance at the germination stage and provide useful genetic resources that can facilitate the selection of drought stress traits via genomic-assisted selection.

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

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.

Combined Linkage Mapping and BSA to Identify QTL and Candidate Genes for Plant Height and the Number of Nodes on the Main Stem in Soybean

Soybean is one of the most important food and oil crops in the world. Plant height (PH) and the number of nodes on the main stem (NNMS) are quantitative traits closely related to soybean yield. In this study, we used 208 chromosome segment substitution lines (CSSL) populations constructed using "SN14" and "ZYD00006" for quantitative trait locus (QTL) mapping of PH and NNMS. Combined with bulked segregant analysis (BSA) by extreme materials, 8 consistent QTLs were identified. According to the gene annotation of the QTL interval, a total of 335 genes were obtained. Five of which were associated with PH and NNMS, potentially representing candidate genes. RT-qPCR of these 5 candidate genes revealed two genes with differential relative expression levels in the stems of different materials. Haplotype analysis showed that different single nucleotide polymorphisms (SNPs) between the excellent haplotypes in Glyma.04G251900 and Glyma.16G156700 may be the cause of changes in these traits. These results provide the basis for research on candidate genes and marker-assisted selection (MAS) in soybean breeding.

Mapping developmental QTL for plant height in soybean [Glycine max (L.) Merr.] using a four-way recombinant inbred line population

Plant height (PH) is an important trait in soybean, as taller plants may have higher yields but may also be at risk for lodging. Many genes act jointly to influence PH throughout development. To map the quantitative trait loci (QTL) controlling PH, we used the unconditional variable method (UVM) and conditional variable method (CVM) to analyze PH data for a four-way recombinant inbred line (FW-RIL) population derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19). We identified 7, 8, 16, 19, 15, 27, 17, 27, 22, and 24 QTL associated with PH at 10 developmental stages, respectively. These QTL mapped to 95 genomic regions. Among these QTL, 9 were detected using UVM and CVM, and 89 and 66 were only detected by UVM or CVM, respectively. In total, 36 QTL controlling PH were detected at multiple developmental stages and these made unequal contributions to genetic variation throughout development. Among 19 novel regions discovered in our study, 7 could explain over 10% of the phenotypic variation and contained only one single QTL. The unconditional and conditional QTL detected here could be used in molecular design breeding across the whole developmental procedure.

Utilization of Interspecific High-Density Genetic Map of RIL Population for the QTL Detection and Candidate Gene Mining for 100-Seed Weight in Soybean

Seed-weight is one of the most important traits determining soybean yield. Hence, it is prerequisite to have detailed understanding of the genetic basis regulating seed-weight for the development of improved cultivars. In this regard, the present study used high-density interspecific linkage map of NJIR4P recombinant inbred population evaluated in four different environments to detect stable Quantitative trait loci (QTLs) as well as mine candidate genes for 100-seed weight. In total, 19 QTLs distributed on 12 chromosomes were identified in all individual environments plus combined environment, out of which seven were novel and eight are stable identified in more than one environment. However, all the novel QTLs were minor (R 2 < 10%). The remaining 12 QTLs detected in this study were co-localized with the earlier reported QTLs with narrow genomic regions, and out of these only 2 QTLs were major (R 2 > 10%) viz., qSW-17-1 and qSW-17-4. Beneficial alleles of all identified QTLs were derived from cultivated soybean parent (Nannong493-1). Based on Protein ANalysis THrough Evolutionary Relationships, gene annotation information, and literature search, 29 genes within 5 stable QTLs were predicted to be possible candidate genes that might regulate seed-weight/size in soybean. However, it needs further validation to confirm their role in seed development. In conclusion, the present study provides better understanding of trait genetics and candidate gene information through the use high-density inter-specific bin map, and also revealed considerable scope for genetic improvement of 100-seed weight in soybean using marker-assisted breeding.

Genome wide association mapping and candidate gene analysis for hundred seed weight in soybean [Glycine max (L.) Merrill]

BACKGROUND

The hundred seed weight (HSW) is one of the yield components of soybean [Glycine max (L.) Merrill] and is especially critical for various soybean food types. In this study, a representative sample consisting of 185 accessions was selected from Northeast China and analysed in three tested environments to determine the quantitative trait nucleotide (QTN) of HSW through a genome-wide association study (GWAS).

RESULT

A total of 24,180 single nucleotide polymorphisms (SNPs) with minor allele frequencies greater than 0.2 and missing data less than 3% were utilized to estimate linkage disequilibrium (LD) levels in the tested association panel. Thirty-four association signals were identified as associated with HSW via GWAS. Among them, nineteen QTNs were novel, and another fifteen QTNs were overlapped or located near the genomic regions of known HSW QTL. A total of 237 genes, derived from 31 QTNs and located near peak SNPs from the three tested environments in 2015 and 2016, were considered candidate genes, were related to plant growth regulation, hormone metabolism, cell, RNA, protein metabolism, development, starch accumulation, secondary metabolism, signalling, and the TCA cycle, some of which have been found to participate in the regulation of HSW. A total of 106 SNPs from 16 candidate genes were significantly associated with HSW in soybean.

CONCLUSIONS

The identified loci with beneficial alleles and candidate genes might be valuable for the molecular network and MAS of HSW.

Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean

BACKGROUND

Breeding programs benefit from information about marker-trait associations for many traits, whether the goal is to place those traits under active selection or to maintain them through background selection. Association studies are also important for identifying accessions bearing potentially useful alleles by characterizing marker-trait associations and allelic states across germplasm collections. This study reports the results of a genome-wide association study and evaluation of epistatic interactions for four agronomic and seed-related traits in soybean.

RESULTS

Using 419 diverse soybean accessions, together with genotyping data from the SoySNP50K Illumina Infinium BeadChip, we identified marker-trait associations for internode number (IN), plant height (PH), seed weight (SW), and seed yield per plant (SYP). We conducted a genome-wide epistatic study (GWES), identifying candidate genes that show evidence of SNP-SNP interactions. Although these candidate genes will require further experimental validation, several appear to be involved in developmental processes related to the respective traits. For IN and PH, these include the Dt1 determinacy locus (a soybean meristematic transcription factor), as well as a pectinesterase gene and a squamosa promoter binding gene that in other plants are involved in cell elongation and the vegetative-to-reproductive transition, respectively. For SW, candidate genes include an ortholog of the AP2 gene, which in other species is involved in maintaining seed size, embryo size, seed weight and seed yield. Another SW candidate gene is a histidine phosphotransfer protein - orthologs of which are involved in cytokinin-mediated seed weight regulating pathways. The SYP association loci overlap with regions reported in previous QTL studies to be involved in seed yield.

CONCLUSIONS

This study further confirms the utility of GWAS and GWES approaches for identifying marker-trait associations and interactions within a diverse germplasm collection.

QTL fine-mapping of soybean (Glycine max L.) leaf type associated traits in two RILs populations

BACKGROUND

The different leaf type associated traits of soybean (Glycine max L.) including leaf area, leaf length, leaf width, leaf shape and petiole length are considered to be associated with seed yield. In order to identify quantitative trait loci (QTLs) affecting leaf type traits, two advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3; GB, Guizao 1 × Brazil 13) populations were introduced to score phenotypic values in plants across nine different environments (years, seasons, locations and soybean growth stages). Two restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage maps with an average distance of 1.00 centimorgan (cM) between adjacent bin markers were utilized for QTL fine mapping.

RESULTS

Correlation analysis showed that most of the traits were correlated with each other and regulated both by hereditary and environmental factors. A total of 190 QTLs were identified for leaf type associated traits in the two populations, of which 14 loci were found to be environmentally stable. Moreover, these detected QTLs were categorized into 34 QTL hotspots, and four important QTL hotspots with phenotypic variance ranging from 3.89-23.13% were highlighted. Furthermore, Glyma04g05840, Glyma19g37820, Glyma14g07140 and Glyma19g39340 were predicted in the intervals of the stable loci and important QTL hotspots for leaf type traits by adopting Gene Ontology (GO) enrichment analysis.

CONCLUSIONS

Our findings of the QTLs and the putative genes will be beneficial to gain new insights into the genetic basis for soybean leaf type traits and may further accelerate the breeding process for reasonable leaf type soybean.

GmBRC1 is a Candidate Gene for Branching in Soybean (Glycine max (L.) Merrill)

Branch number is one of the main factors affecting the yield of soybean (Glycine max (L.)). In this study, we conducted a genome-wide association study combined with linkage analysis for the identification of a candidate gene controlling soybean branching. Five quantitative trait nucleotides (QTNs) were associated with branch numbers in a soybean core collection. Among these QTNs, a linkage disequilibrium (LD) block qtnBR6-1 spanning 20 genes was found to overlap a previously identified major quantitative trait locus qBR6-1. To validate and narrow down qtnBR6-1, we developed a set of near-isogenic lines (NILs) harboring high-branching (HB) and low-branching (LB) alleles of qBR6-1, with 99.96% isogenicity and different branch numbers. A cluster of single nucleotide polymorphisms (SNPs) segregating between NIL-HB and NIL-LB was located within the qtnBR6-1 LD block. Among the five genes showing differential expression between NIL-HB and NIL-LB, BRANCHED1 (BRC1; Glyma.06G210600) was down-regulated in the shoot apex of NIL-HB, and one missense mutation and two SNPs upstream of BRC1 were associated with branch numbers in 59 additional soybean accessions. BRC1 encodes TEOSINTE-BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTORS 1 and 2 transcription factor and functions as a regulatory repressor of branching. On the basis of these results, we propose BRC1 as a candidate gene for branching in soybean.

GmBRC1 is a Candidate Gene for Branching in Soybean (Glycine max (L.) Merrill)

Branch number is one of the main factors affecting the yield of soybean (Glycine max (L.)). In this study, we conducted a genome-wide association study combined with linkage analysis for the identification of a candidate gene controlling soybean branching. Five quantitative trait nucleotides (QTNs) were associated with branch numbers in a soybean core collection. Among these QTNs, a linkage disequilibrium (LD) block qtnBR6-1 spanning 20 genes was found to overlap a previously identified major quantitative trait locus qBR6-1. To validate and narrow down qtnBR6-1, we developed a set of near-isogenic lines (NILs) harboring high-branching (HB) and low-branching (LB) alleles of qBR6-1, with 99.96% isogenicity and different branch numbers. A cluster of single nucleotide polymorphisms (SNPs) segregating between NIL-HB and NIL-LB was located within the qtnBR6-1 LD block. Among the five genes showing differential expression between NIL-HB and NIL-LB, BRANCHED1 (BRC1; Glyma.06G210600) was down-regulated in the shoot apex of NIL-HB, and one missense mutation and two SNPs upstream of BRC1 were associated with branch numbers in 59 additional soybean accessions. BRC1 encodes TEOSINTE-BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTORS 1 and 2 transcription factor and functions as a regulatory repressor of branching. On the basis of these results, we propose BRC1 as a candidate gene for branching in soybean.

Identification of the Genomic Region Underlying Seed Weight per Plant in Soybean (Glycine max L. Merr.) via High-Throughput Single-Nucleotide Polymorphisms and a Genome-Wide Association Study

Seed weight per plant (SWPP) of soybean (Glycine max (L.) Merr.), a complicated quantitative trait controlled by multiple genes, was positively associated with soybean seed yields. In the present study, a natural soybean population containing 185 diverse accessions primarily from China was used to analyze the genetic basis of SWPP via genome-wide association analysis (GWAS) based on high-throughput single-nucleotide polymorphisms (SNPs) generated by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) method. A total of 33,149 SNPs were finally identified with minor allele frequencies (MAF) > 5% which were present in 97% of all the genotypes. Twenty association signals associated with SWPP were detected via GWAS. Among these signals, eight SNPs were novel loci, and the other twelve SNPs were overlapped or located in the linked genomic regions of the reported QTL from SoyBase database. Several genes belonging to the categories of hormone pathways, RNA regulation of transcription in plant development, ubiquitin, transporting systems, and other metabolisms were considered as candidate genes associated with SWPP. Furthermore, nine genes from the flanking region of Gm07:19488264, Gm08:15768591, Gm08:15768603, or Gm18:23052511 were significantly associated with SWPP and were stable among multiple environments. Nine out of 18 haplotypes from nine genes showed the effect of increasing SWPP. The identified loci along with the beneficial alleles and candidate genes could be of great value for studying the molecular mechanisms underlying SWPP and for improving the potential seed yield of soybean in the future.

Genome-Wide Association Studies for Dynamic Plant Height and Number of Nodes on the Main Stem in Summer Sowing Soybeans

Plant height (PH) and the number of nodes on the main stem (NN) serve as major plant architecture traits affecting soybean seed yield. Although many quantitative trait loci for the two traits have been reported, their genetic controls at different developmental stages in soybeans remain unclear. Here, 368 soybean breeding lines were genotyped using 62,423 single nucleotide polymorphism (SNP) markers and phenotyped for the two traits at three different developmental stages over two locations in order to identify their quantitative trait nucleotides (QTNs) using compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) approaches. As a result, 11 and 13 QTNs were found by CMLM to be associated with PH and NN, respectively. Among these QTNs, 8, 3, and 4 for PH and 6, 6, and 8 for NN were found at the three stages, and 3 and 6 were repeatedly detected for PH and NN. In addition, 34 and 30 QTNs were found by mrMLM to be associated with PH and NN, respectively. Among these QTNs, 11, 13, and 16 for PH and 11, 15, and 8 for NN were found at the three stages. A majority of these QTNs overlapped with the previously reported loci. Moreover, one QTN within the known E2 locus for flowering time was detected for the two traits at all three stages, and another that overlapped with the Dt1 locus for stem growth habit was also identified for the two traits at the mature stage. This may explain the highly significant correlation between the two traits. Our findings provide evidence for mixed major plus polygenes inheritance for dynamic traits and an extended understanding of their genetic architecture for molecular dissection and breeding utilization in soybeans.

Genotyping of Soybean Cultivars With Medium-Density Array Reveals the Population Structure and QTNs Underlying Maturity and Seed Traits

Soybean was domesticated about 5,000 to 6,000 years ago in China. Although genotyping technologies such as genotyping by sequencing (GBS) and high-density array are available, it is convenient and economical to genotype cultivars or populations using medium-density SNP array in genetic study as well as in molecular breeding. In this study, 235 cultivars, collected from China, Japan, USA, Canada and some other countries, were genotyped using SoySNP8k iSelect BeadChip with 7,189 single nucleotide polymorphisms (SNPs). In total, 4,471 polymorphic SNP markers were used to analyze population structure and perform genome-wide association study (GWAS). The most likely K value was 7, indicating this population can be divided into 7 subpopulations, which is well in accordance with the geographic origins of cultivars or accession studied. The LD decay rate was estimated at 184 kb, where r2 dropped to half of its maximum value (0.205). GWAS using FarmCPU detected a stable quantitative trait nucleotide (QTN) for hilum color and seed color, which is consistent with the known loci or genes. Although no universal QTNs for flowering time and maturity were identified across all environments, a total of 30 consistent QTNs were detected for flowering time (R1) or maturity (R7 and R8) on 16 chromosomes, most of them were corresponding to known E1 to E4 genes or QTL region reported in SoyBase (soybase.org). Of 16 consistent QTNs for protein and oil contents, 11 QTNs were detected having antagonistic effects on protein and oil content, while 4 QTNs soly for oil content, and one QTN soly for protein content. The information gained in this study demonstrated that the usefulness of the medium-density SNP array in genotyping for genetic study and molecular breeding.

Combining QTL-seq and linkage mapping to fine map a wild soybean allele characteristic of greater plant height

BACKGROUND

Plant height (PH) is an important agronomic trait and is closely related to yield in soybean [Glycine max (L.) Merr.]. Previous studies have identified many QTLs for PH. Due to the complex genetic background of PH in soybean, there are few reports on its fine mapping.

RESULTS

In this study, we used a mapping population derived from a cross between a chromosome segment substitution line CSSL3228 (donor N24852 (G. Soja), a receptor NN1138-2 (G. max)) and NN1138-2 to fine map a wild soybean allele of greater PH by QTL-seq and linkage mapping. We identified a QTL for PH in a 1.73 Mb region on soybean chromosome 13 through QTL-seq, which was confirmed by SSR marker-based classical QTL mapping in the mapping population. The linkage analysis showed that the QTLs of PH were located between the SSR markers BARCSOYSSR_13_1417 and BARCSOYSSR_13_1421 on chromosome 13, and the physical distance was 69.3 kb. RT-PCR and sequence analysis of possible candidate genes showed that Glyma.13 g249400 revealed significantly higher expression in higher PH genotypes, and the gene existed 6 differences in the amino acids encoding between the two parents.

CONCLUSIONS

Data presented here provide support for Glyma.13 g249400 as a possible candidate genes for higher PH in wild soybean line N24852.