Identification and validation of QTLs for 100-seed weight using chromosome segment substitution lines in soybean

Identification of the domestication gene GmCYP82C4 underlying the major quantitative trait locus for the seed weight in soybean

KEY MESSAGE

A major quantitative trait locus (QTL) for the hundred-seed weight (HSW) was identified and confirmed in the two distinct soybean populations, and the target gene GmCYP82C4 underlying this locus was identified that significantly associated with soybean seed weight, and it was selected during the soybean domestication and improvement process. Soybean is a major oil crop for human beings and the seed weight is a crucial goal of soybean breeding. However, only a limited number of target genes underlying the quantitative trait loci (QTLs) controlling seed weight in soybean are known so far. In the present study, six loci associated with hundred-seed weight (HSW) were detected in the first population of 573 soybean breeding lines by genome-wide association study (GWAS), and 64 gene models were predicted in these candidate QTL regions. The QTL qHSW_1 exhibits continuous association signals on chromosome four and was also validated by region association study (RAS) in the second soybean population (409 accessions) with wild, landrace, and cultivar soybean accessions. There were seven genes in qHSW_1 candidate region by linkage disequilibrium (LD) block analysis, and only Glyma.04G035500 (GmCYP82C4) showed specifically higher expression in flowers, pods, and seeds, indicating its crucial role in the soybean seed development. Significant differences in HSW trait were detected when the association panels are genotyped by single-nucleotide polymorphisms (SNPs) in putative GmCYP82C4 promoter region. Eight haplotypes were generated by six SNPs in GmCYP82C4 in the second soybean population, and two superior haplotypes (Hap2 and Hap4) of GmCYP82C4 were detected with average HSW of 18.27 g and 18.38 g, respectively. The genetic diversity of GmCYP82C4 was analyzed in the second soybean population, and GmCYP82C4 was most likely selected during the soybean domestication and improvement process, leading to the highest proportion of Hap2 of GmCYP82C4 both in landrace and cultivar subpopulations. The QTLs and GmCYP82C4 identified in this study provide novel genetic resources for soybean seed weight trait, and the GmCYP82C4 could be used for soybean molecular breeding to develop desirable seed weight in the future.

Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean

Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.

Genome-wide analysis of KIX gene family for organ size regulation in soybean (Glycine max L.)

The KIX domain, conserved among various nuclear and co-activator factors, acts as a binding site that interacts with other transcriptional activators and co-activators, playing a crucial role in gene expression regulation. In plants, the KIX domain is involved in plant hormone signaling, stress response regulation, cell cycle control, and differentiation, indicating its potential relevance to crop productivity. This study aims to identify and characterize KIX domains within the soybean (Glycine max L.) genome to predict their potential role in improving crop productivity. The conservation and evolutionary history of the KIX domains were explored in 59 plant species, confirming the presence of the KIX domains in diverse plants. Specifically, 13 KIX domains were identified within the soybean genome and classified into four main groups, namely GmKIX8/9, GmMED15, GmHAC, and GmRECQL, through sequence alignment, structural analysis, and phylogenetic tree construction. Association analysis was performed between KIX domain haplotypes and soybean seed-related agronomic traits using re-sequencing data from a core collection of 422 accessions. The results revealed correlations between SNP variations observed in GmKIX8-3 and GmMED15-4 and soybean seed phenotypic traits. Additionally, transcriptome analysis confirmed significant expression of the KIX domains during the early stages of soybean seed development. This study provides the first characterization of the structural, expression, genomic haplotype, and molecular features of the KIX domain in soybean, offering a foundation for functional analysis of the KIX domain in soybean and other plants.

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.

Major genetic locus with pleiotropism determined seed-related traits in cultivated and wild soybeans

KEY MESSAGE

Here, a novel pleiotropic QTL qSS14 simultaneously regulating four seed size traits and two consistently detected QTLs qSW17 and qSLW02 were identified across multiple years. Seed-related traits were the key agronomic traits that have been artificially selected during the domestication of wild soybean. Identifying the genetic loci and genes that regulate seed size could clarify the genetic variations in seed-related traits and provide novel insights into high-yield soybean breeding. In this study, we used a high-density genetic map constructed by F₁₀ RIL populations from a cross between Glycine max and Glycine soja to detect additive QTLs for seven seed-related traits over the last three years. As a result, we identified one novel pleiotropic QTL, qSS14, that simultaneously controlled four seed size traits (100-seed weight, seed length, seed width, and seed thickness) and two consistently detected QTLs, qSW17, and qSLW02, in multiple years of phenotypic data. Furthermore, we predicted two, two and three candidate genes within these three critical loci based on the parental resequencing data and gene function annotations. And the relative expression of four candidate genes GLYMA_14G155100, GLYMA_17G061000, GLYMA_02G273100, and GLYMA_02G273300 showed significant differences among parents and the extreme materials through qRT-PCR analysis. These findings could facilitate the determination of beneficial genes in wild soybean and contribute to our understanding of the soybean domestication process.

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.

Identification of quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight in soybean [Glycine max (L.) Merr.]

Seed size and shape are important traits determining yield and quality in soybean. Seed size and shape are also desirable for specialty soy foods like tofu, natto, miso, and edamame. In order to find stable quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight, the current study used vegetable type and seed soybean-derived F₂ and F_2:3 mapping populations. A total of 42 QTLs were mapped, which were dispersed across 13 chromosomes. Of these, seven were determined to be stable QTLs and five of them were major QTLs, namely qSL-10-1, qSW-4-1, qSV-4-1, qSLW-10-1, and qSLH-10-1. Thirteen of the 42 QTLs detected in the current study were found at known loci, while the remaining 29 were discovered for the first time. Out of these 29 novel QTLs, 17 were major QTLs. Based on Protein Analysis Through Evolutionary Relationships (PANTHER), gene annotation information, and literature search, 66 genes within seven stable QTLs were predicted to be possible candidate genes that might regulate seed shape and seed weight in soybean. The current study identified the key candidate genes and quantitative trait loci (QTLs) controlling soybean seed shape and weight, and these results will be very helpful in marker-assisted breeding for developing soybean varieties with improved seed weight and desired seed shape.

Identification, pyramid, and candidate gene of QTL for yield-related traits based on rice CSSLs in indica Xihui18 background

Chromosome segment substitution line (CSSL) is important for functional analysis and design breeding of target genes. Here, a novel rice CSSL-Z431 was identified from indica restorer line Xihui18 as recipient and japonica Huhan3 as donor. Z431 contained six segments from Huhan3, with average substitution length of 2.12 Mb. Compared with Xihui18, Z431 increased panicle number per plant (PN) and displayed short-wide grains. The short-wide grain of Z431 was caused by decreased length and increased width of glume cell. Then, thirteen QTLs were identified in secondary F2 population from Xihui18/Z431. Again, eleven QTLs (qPL3, qPN3, qGPP12, qSPP12, qGL3, qGW5, qRLW2, qRLW3, qRLW5, qGWT3, qGWT5-2) were validated by six single-segment substitution lines (SSSLs, S1-S6) developed in F3. In addition, fifteen QTLs (qPN5, qGL1, qGL2, qGL5, qGW1, qGW5-1, qRLW1, qRLW5-2, qGWT1, qGWT2, qYD1, qYD2, qYD3, qYD5, qYD12) were detected by these SSSLs, while not be identified in the F2 population. Multiple panicles of Z431 were controlled by qPN3 and qPN5. OsIAGLU should be the candidate gene for qPN3. The short-wide grain of Z431 was controlled by qGL3, qGW5, etc. By DNA sequencing and qRT-PCR analysis, two best candidate genes for qGL3 and qGW5 were identified, respectively. In addition, pyramid of different QTLs in D1-D3 and T1-T2 showed independent inheritance or various epistatic effects. So, it is necessary to understand all genetic effects of target QTLs for designing breeding. Furthermore, these secondary substitution lines improved the deficiencies of Xihui18 to some extent, especially increasing yield per plant in S1, S3, S5, D1-D3, T1, and T2.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01284-x.

GIS-Based Analysis for UAV-Supported Field Experiments Reveals Soybean Traits Associated With Rotational Benefit

Recent advances in unmanned aerial vehicle (UAV) remote sensing and image analysis provide large amounts of plant canopy data, but there is no method to integrate the large imagery datasets with the much smaller manually collected datasets. A simple geographic information system (GIS)-based analysis for a UAV-supported field study (GAUSS) analytical framework was developed to integrate these datasets. It has three steps: developing a model for predicting sample values from UAV imagery, field gridding and trait value prediction, and statistical testing of predicted values. A field cultivation experiment was conducted to examine the effectiveness of the GAUSS framework, using a soybean-wheat crop rotation as the model system Fourteen soybean cultivars and subsequently a single wheat cultivar were grown in the same field. The crop rotation benefits of the soybeans for wheat yield were examined using GAUSS. Combining manually sampled data (n = 143) and pixel-based UAV imagery indices produced a large amount of high-spatial-resolution predicted wheat yields (n = 8,756). Significant differences were detected among soybean cultivars in their effects on wheat yield, and soybean plant traits were associated with the increases. This is the first reported study that links traits of legume plants with rotational benefits to the subsequent crop. Although some limitations and challenges remain, the GAUSS approach can be applied to many types of field-based plant experimentation, and has potential for extensive use in future studies.

Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

A Major and Stable Quantitative Trait Locus qSS2 for Seed Size and Shape Traits in a Soybean RIL Population

Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80-14.0 and R ² of 2.36%-39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.

GmKIX8-1 regulates organ size in soybean and is the causative gene for the major seed weight QTL qSw17-1

Seed weight is one of the most important agronomic traits in soybean for yield improvement and food production. Several quantitative trait loci (QTLs) associated with the trait have been identified in soybean. However, the genes underlying the QTLs and their functions remain largely unknown. Using forward genetic methods and CRISPR/Cas9 gene editing, we identified and characterized the role of GmKIX8-1 in the control of organ size in soybean. GmKIX8-1 belongs to a family of KIX domain-containing proteins that negatively regulate cell proliferation in plants. Consistent with this predicted function, we found that loss-of-function GmKIX8-1 mutants showed a significant increase in the size of aerial plant organs, such as seeds and leaves. Likewise, the increase in organ size is due to increased cell proliferation, rather than cell expansion, and increased expression of CYCLIN D3;1-10. Lastly, molecular analysis of soybean germplasms harboring the qSw17-1 QTL for the big-seeded phenotype indicated that reduced expression of GmKIX8-1 is the genetic basis of the qSw17-1 phenotype.

High-Resolution Mapping in Two RIL Populations Refines Major "QTL Hotspot" Regions for Seed Size and Shape in Soybean (Glycine max L.)

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., "QTL Hotspot A", "QTL Hotspot B", "QTL Hotspot C", and "QTL Hotspot D" located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four "QTL Hotspots" were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

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.

Dissection of soybean populations according to selection signatures based on whole-genome sequences

BACKGROUND

Domestication and improvement processes, accompanied by selections and adaptations, have generated genome-wide divergence and stratification in soybean populations. Simultaneously, soybean populations, which comprise diverse subpopulations, have developed their own adaptive characteristics enhancing fitness, resistance, agronomic traits, and morphological features. The genetic traits underlying these characteristics play a fundamental role in improving other soybean populations.

RESULTS

This study focused on identifying the selection signatures and adaptive characteristics in soybean populations. A core set of 245 accessions (112 wild-type, 79 landrace, and 54 improvement soybeans) selected from 4,234 soybean accessions was re-sequenced. Their genomic architectures were examined according to the domestication and improvement, and accessions were then classified into 3 wild-type, 2 landrace, and 2 improvement subgroups based on various population analyses. Selection and gene set enrichment analyses revealed that the landrace subgroups have selection signals for soybean-cyst nematode HG type 0 and seed development with germination, and that the improvement subgroups have selection signals for plant development with viability and seed development with embryo development, respectively. The adaptive characteristic for soybean-cyst nematode was partially underpinned by multiple resistance accessions, and the characteristics related to seed development were supported by our phenotypic findings for seed weights. Furthermore, their adaptive characteristics were also confirmed as genome-based evidence, and unique genomic regions that exhibit distinct selection and selective sweep patterns were revealed for 13 candidate genes.

CONCLUSIONS

Although our findings require further biological validation, they provide valuable information about soybean breeding strategies and present new options for breeders seeking donor lines to improve soybean populations.

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 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.

Neglected treasures in the wild - legume wild relatives in food security and human health

The legume family (Fabaceae) is the third-largest flowering family with over 18 000 species worldwide that are rich in proteins, oils, and nutrients. However, the production potential of legume-derived food cannot meet increasing global demand. Wild legumes represent a large group of wild species adaptive to diverse habitats and harbor rich genetic diversity for the improvement of the agronomic, nutritional, and medicinal values of the domesticated legumes. Accumulating evidence suggests that the genetic variation retained in these under-exploited leguminous wild relatives can be used to improve crop yield, nutrient contents, and resistance/tolerance to environmental stresses via the integration of omics, genetics, and genome-editing technologies.

Development and use of chromosome segment substitution lines as a genetic resource for crop improvement

CSSLs are a complete library of introgression lines with chromosomal segments of usually a distant genotype in an adapted background and are valuable genetic resources for basic and applied research on improvement of complex traits. Chromosome segment substitution lines (CSSLs) are genetic stocks representing the complete genome of any genotype in the background of a cultivar as overlapping segments. Ideally, each CSSL has a single chromosome segment from the donor with a maximum recurrent parent genome recovered in the background. CSSL development program requires population-wide backcross breeding and genome-wide marker-assisted selection followed by selfing. Each line in a CSSL library has a specific marker-defined large donor segment. CSSLs are evaluated for any target phenotype to identify lines significantly different from the parental line. These CSSLs are then used to map quantitative trait loci (QTLs) or causal genes. CSSLs are valuable prebreeding tools for broadening the genetic base of existing cultivars and harnessing the genetic diversity from the wild- and distant-related species. These are resources for genetic map construction, mapping QTLs, genes or gene interactions and their functional analysis for crop improvement. In the last two decades, the utility of CSSLs in identification of novel genomic regions and QTL hot spots influencing a wide range of traits has been well demonstrated in food and commercial crops. This review presents an overview of how CSSLs are developed, their status in major crops and their use in genomic studies and gene discovery.