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

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.)

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding 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.

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.

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

Identification of candidate genes for soybean seed coat-related traits using QTL mapping and GWAS

Seed coat color is a typical morphological trait that can be used to reveal the evolution of soybean. The study of seed coat color-related traits in soybeans is of great significance for both evolutionary theory and breeding practices. In this study, 180 F₁₀ recombinant inbred lines (RILs) derived from the cross between the yellow-seed coat cultivar Jidou12 (ZDD23040, JD12) and the wild black-seed coat accession Y9 (ZYD02739) were used as materials. Three methods, single-marker analysis (SMA), interval mapping (IM), and inclusive composite interval mapping (ICIM), were used to identify quantitative trait loci (QTLs) controlling seed coat color and seed hilum color. Simultaneously, two genome-wide association study (GWAS) models, the generalized linear model (GLM) and mixed linear model (MLM), were used to jointly identify seed coat color and seed hilum color QTLs in 250 natural populations. By integrating the results from QTL mapping and GWAS analysis, we identified two stable QTLs (qSCC02 and qSCC08) associated with seed coat color and one stable QTL (qSHC08) related to seed hilum color. By combining the results of linkage analysis and association analysis, two stable QTLs (qSCC02, qSCC08) for seed coat color and one stable QTL (qSHC08) for seed hilum color were identified. Upon further investigation using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we validated the previous findings that two candidate genes (CHS3C and CHS4A) reside within the qSCC08 region and identified a new QTL, qSCC02. There were a total of 28 candidate genes in the interval, among which Glyma.02G024600, Glyma.02G024700, and Glyma.02G024800 were mapped to the glutathione metabolic pathway, which is related to the transport or accumulation of anthocyanin. We considered the three genes as potential candidate genes for soybean seed coat-related traits. The QTLs and candidate genes detected in this study provide a foundation for further understanding the genetic mechanisms underlying soybean seed coat color and seed hilum color and are of significant value in marker-assisted breeding.

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.

Domestication of azuki bean and soybean in Japan: From the insight of archeological and molecular evidence

Domestication of azuki bean and soybean has enabled them to acquire non-dormant seeds, non-shattering pods, and larger seed size. Seed remains of the Jomon period recently discovered at archeological sites in the Central Highlands of Japan (6,000-4,000 BP) suggest that the use of azuki bean and soybean and their increase in seed size began earlier in Japan than in China and Korea; molecular phylogenetic studies indicate that azuki bean and soybean originated in Japan. Recent identification of domestication genes indicate that the domestication traits of azuki bean and soybean were established by different mechanisms. Analyses of domestication related genes using DNA extracted from the seed remains would reveal further details about their domestication processes.

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 of major quantitative trait loci and candidate genes for seed weight in soybean

Four major quantitative trait loci for 100-seed weight were identified in a soybean RIL population under five environments, and the most likely candidate genes underlying these loci were identified. Seed weight is an important target of soybean breeding. However, the genes underlying the major quantitative trait loci (QTL) controlling seed weight remain largely unknown. In this study, a soybean population of 300 recombinant inbred lines (RILs) derived from a cross between PI595843 (PI) and WH was used to map the QTL and identify candidate genes for seed weight. The RIL population was genotyped through whole genome resequencing, and phenotyped for 100-seed weight under five environments. A total of 38 QTL were detected, and four major QTL, each explained at least 10% of the variation in 100-seed weight, were identified. Six candidate genes within these four major QTL regions were identified by analyses of their tissue expression patterns, gene annotations, and differential gene expression levels in soybean seeds during four developmental stages between two parental lines. Further sequence variation analyses revealed a C to T substitution in the first exon of the Glyma.19G143300, resulting in an amino acid change between PI and WH, and thus leading to a different predicted kinase domain, which might affect its protein function. Glyma.19G143300 is highly expressed in soybean seeds and encodes a leucine-rich repeat receptor-like protein kinase (LRR-RLK). Its predicted protein has typical domains of LRR-RLK family, and phylogenetic analyses reveled its similarity with the known LRR-RLK protein XIAO (LOC_Os04g48760), which is involved in controlling seed size. The major QTL and candidate genes identified in this study provide useful information for molecular breeding of new soybean cultivars with desirable seed weight.

Genome-wide association study reveals the genetic basis of brace root angle and diameter in maize

Brace roots are the main organ to support the above-ground part of maize plant. It involves in plant growth and development by water absorption and lodging resistance. The bracing root angle (BRA) and diameter (BRD) are important components of brace root traits. Illuminating the genetic basis of BRA and BRD will contribute the improvement for mechanized harvest and increasing production. A GWAS of BRA and BRD was conducted using an associated panel composed of 508 inbred lines of maize. The broad-sense heritability of BRA and BRD was estimated to be respectively 71% ± 0.19 and 52% ± 0.14. The phenotypic variation of BRA and BRD in the non-stiff stalk subgroup (NSS) and the stiff stalk subgroup (SS) subgroups are significantly higher than that in the tropical/subtropical subgroup (TST) subgroups. In addition, BRA and BRD are significantly positive with plant height (PH), ear length (EL), and kernel number per row (KNPR). GWAS revealed 27 candidate genes within the threshold of p < 1.84 × 10⁻⁶ by both MLM and BLINK models. Among them, three genes, GRMZM2G174736, GRMZM2G445169 and GRMZM2G479243 were involved in cell wall function, and GRMZM2G038073 encoded the NAC transcription factor family proteins. These results provide theoretical support for clarifying the genetic basis of brace roots traits.

Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.

Identification of major genomic regions for soybean seed weight by genome-wide association study

The hundred-seed weight (HSW) is an important yield component and one of the principal breeding traits in soybean. More than 250 quantitative trait loci (QTL) for soybean HSW have been identified. However, most of them have a large genomic region or are environmentally sensitive, which provide limited information for improving the phenotype in marker-assisted selection (MAS) and identifying the candidate genes. Here, we utilized 281 soybean accessions with 58,112 single nucleotide polymorphisms (SNPs) to dissect the genetic basis of HSW in across years in the northern Shaanxi province of China through one single-locus (SL) and three multi-locus (ML) genome-wide association study (GWAS) models. As a result, one hundred and fifty-four SNPs were detected to be significantly associated with HSW in at least one environment via SL-GWAS model, and 27 of these 154 SNPs were detected in all (three) environments and located within 7 linkage disequilibrium (LD) block regions with the distance of each block ranged from 40 to 610 Kb. A total of 15 quantitative trait nucleotides (QTNs) were identified by three ML-GWAS models. Combined with the results of different GWAS models, the 7 LD block regions associated with HSW detected by SL-GWAS model could be verified directly or indirectly by the results of ML-GWAS models. Eleven candidate genes underlying the stable loci that may regulate seed weight in soybean were predicted. The significantly associated SNPs and the stable loci as well as predicted candidate genes may be of great importance for marker-assisted breeding, polymerization breeding, and gene discovery for HSW in soybean.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01310-y.

Detection of a major QTL and development of KASP markers for seed weight by combining QTL-seq, QTL-mapping and RNA-seq in peanut

Combining QTL-seq, QTL-mapping and RNA-seq identified a major QTL and candidate genes, which contributed to the development of KASP markers and understanding of molecular mechanisms associated with seed weight in peanut. Seed weight, as an important component of seed yield, is a significant target of peanut breeding. However, relatively little is known about the quantitative trait loci (QTLs) and candidate genes associated with seed weight in peanut. In this study, three major QTLs on chromosomes A05, B02, and B06 were determined by applying the QTL-seq approach in a recombinant inbred line (RIL) population. Based on conventional QTL-mapping, these three QTL regions were successfully narrowed down through newly developed single nucleotide polymorphism (SNP) and simple sequence repeat markers. Among these three QTL regions, qSWB06.3 exhibited stable expression, contributing mainly to phenotypic variance across environments. Furthermore, differentially expressed genes (DEGs) were identified at the three seed developmental stages between the two parents of the RIL population. It was found that the DEGs were widely distributed in the ubiquitin-proteasome pathway, the serine/threonine-protein pathway, signal transduction of hormones and transcription factors. Notably, DEGs at the early stage were mostly involved in regulating cell division, whereas DEGs at the middle and late stages were primarily involved in cell expansion during seed development. The expression patterns of candidate genes related to seed weight in qSWB06.3 were investigated using quantitative real-time PCR. In addition, the allelic diversity of qSWB06.3 was investigated in peanut germplasm accessions. The marker Ah011475 has higher efficiency for discriminating accessions with different seed weights, and it would be useful as a diagnostic marker in marker-assisted breeding. This study provided insights into the genetic and molecular mechanisms of seed weight in peanut.

Genome-Wide Association Study Reveals Genetic Architecture and Candidate Genes for Yield and Related Traits under Terminal Drought, Combined Heat and Drought in Tropical Maize Germplasm

Maize (Zea mays L.) production is constrained by drought and heat stresses. The combination of these two stresses is likely to be more detrimental. To breed for maize cultivars tolerant of these stresses, 162 tropical maize inbred lines were evaluated under combined heat and drought (CHD) and terminal drought (TD) conditions. The mixed linear model was employed for the genome-wide association study using 7834 SNP markers and several phenotypic data including, days to 50% anthesis (AD) and silking (SD), husk cover (HUSKC), and grain yield (GY). In total, 66, 27, and 24 SNPs were associated with the traits evaluated under CHD, TD, and their combined effects, respectively. Of these, four single nucleotide polymorphism (SNP) markers (SNP_161703060 on Chr01, SNP_196800695 on Chr02, SNP_195454836 on Chr05, and SNP_51772182 on Chr07) had pleiotropic effects on both AD and SD under CHD conditions. Four SNPs (SNP_138825271 (Chr03), SNP_244895453 (Chr04), SNP_168561609 (Chr05), and SNP_62970998 (Chr06)) were associated with AD, SD, and HUSKC under TD. Twelve candidate genes containing phytohormone cis-acting regulating elements were implicated in the regulation of plant responses to multiple stress conditions including heat and drought. The SNPs and candidate genes identified in the study will provide invaluable information for breeding climate smart maize varieties under tropical conditions following validation of the SNP markers.

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.

Genome-Wide Association Study of Root and Shoot Related Traits in Spring Soybean (Glycine max L.) at Seedling Stages Using SLAF-Seq

Root systems can display variable genetic architectures leading to nutrient foraging or improving abiotic stress tolerance. Breeding for new soybean varieties with efficient root systems has tremendous potential in enhancing resource use efficiency and plant adaptation for challenging climates. In this study, root related traits were analyzed in a panel of 260 spring soybean with genome-wide association study (GWAS). Genotyping was done with specific locus amplified fragment sequencing (SLAF-seq), and five GWAS models (GLM, MLM, CMLM, FaST-LMM, and EMMAX) were used for analysis. A total of 179,960 highly consistent SNP markers distributed over the entire genome with an inter-marker distance of 2.36 kb was used for GWAS analysis. Overall, 27 significant SNPs with a phenotypic contribution ranging from 20 to 72% and distributed on chromosomes 2, 6, 8, 9, 13, 16 and 18 were identified and two of them were found to be associated with multiple root-related traits. Based on the linkage disequilibrium (LD) distance of 9.5 kb for the different chromosomes, 11 root and shoot regulating genes were detected based on LD region of a maximum 55-bp and phenotypic contribution greater than 22%. Expression analysis revealed an association between expression levels of those genes and the degree of root branching number. The current study provides new insights into the genetic architecture of soybean roots, and the underlying SNPs/genes could be critical for future breeding of high-efficient root system in soybean.

Identification of Additive-Epistatic QTLs Conferring Seed Traits in Soybean Using Recombinant Inbred Lines

Seed weight and shape are important agronomic traits that affect soybean quality and yield. In the present study, we used image analysis software to evaluate 100-seed weight and seed shape traits (length, width, perimeter, projection area, length/width, and weight/projection area) of 155 novel recombinant inbred soybean lines (NJRISX) generated by crossing "Su88-M21" and "XYXHD". We examined quantitative trait loci (QTLs) associated with the six traits (except seed weight per projection area), and identified 42 additive QTLs (5-8 QTLs per trait) accounting for 24.9-37.5% of the phenotypic variation (PV). Meanwhile, 2-4 epistatic QTL pairs per trait out of a total of 18 accounted for 2.5-7.2% of the PV; and unmapped minor QTLs accounted for the remaining 35.0-56.7% of the PV. A total of 28 additive and 11 epistatic QTL pairs were concentrated in nine joint QTL segments (JQSs), indicating that QTLs associated with seed weight and shape are closely related and interacted. An interaction was also detected between additive and epistatic QTL pairs and environment, which made significant contributions of 1.4-9.5% and 0.4-0.8% to the PV, respectively. We annotated 18 candidate genes in the nine JQSs, which were important for interpreting the close relationships among the six traits. These findings indicate that examining the interactions between closely related traits rather than only analyzing individual trait provides more useful insight into the genetic system of the interrelated traits for which there has been limited QTL information.