Construction of high-density genetic map and QTL mapping of yield-related and two quality traits in soybean RILs population by RAD-sequencing

Genotyping by sequencing for the construction of oil palm (Elaeis guineensis Jacq.) genetic linkage map and mapping of yield related quantitative trait loci

Background

Oil palm (Elaeis guineensis Jacq.) is one of the major oil-producing crops. Improving the quality and increasing the production yield of oil palm have been the primary focuses of both conventional and modern breeding approaches. However, the conventional breeding approach for oil palm is very challenging due to its longevity, which results in a long breeding cycle. Thus, the establishment of marker assisted selection (MAS) for oil palm breeding programs would speed up the breeding pipeline by generating new oil palm varieties that possess high commercial traits. With the decreasing cost of sequencing, Genotyping-by-sequencing (GBS) is currently feasible to many researchers and it provides a platform to accelerate the discovery of single nucleotide polymorphism (SNP) as well as insertion and deletion (InDel) markers for the construction of a genetic linkage map. A genetic linkage map facilitates the identification of significant DNA regions associated with the trait of interest via quantitative trait loci (QTL) analysis.

Methods

A mapping population of 112 F1 individuals from a cross of Deli dura and Serdang pisifera was used in this study. GBS libraries were constructed using the double digestion method with HindIII and TaqI enzymes. Reduced representation libraries (RRL) of 112 F1 progeny and their parents were sequenced and the reads were mapped against the E. guineensis reference genome. To construct the oil palm genetic linkage map, informative SNP and InDel markers were used to discover significant DNA regions associated with the traits of interest. The nine traits of interest in this study were fresh fruit bunch (FFB) yield, oil yield (OY), oil to bunch ratio (O/B), oil to dry mesocarp ratio (O/DM) ratio, oil to wet mesocarp ratio (O/WM), mesocarp to fruit ratio (M/F), kernel to fruit ratio (K/F), shell to fruit ratio (S/F), and fruit to bunch ratio (F/B).

Results

A total of 2.5 million SNP and 153,547 InDel markers were identified. However, only a subset of 5,278 markers comprising of 4,838 SNPs and 440 InDels were informative for the construction of a genetic linkage map. Sixteen linkage groups were produced, spanning 2,737.6 cM for the maternal map and 4,571.6 cM for the paternal map, with average marker densities of one marker per 2.9 cM and one per 2.0 cM respectively, were produced. A QTL analysis was performed on nine traits; however, only QTL regions linked to M/F, K/F and S/F were declared to be significant. Of those QTLs were detected: two for M/F, four for K/F and one for S/F. These QTLs explained 18.1-25.6% of the phenotypic variance and were located near putative genes, such as casein kinase II and the zinc finger CCCH domain, which are involved in seed germination and growth. The identified QTL regions for M/F, K/F and S/F from this study could be applied in an oil palm breeding program and used to screen palms with desired traits via marker assisted selection (MAS).

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.

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.

Construction of a high-density genetic map and localization of grazing-tolerant QTLs in Medicago falcata L

BACKGROUND

Using genomic DNA from 79 F1 plants resulted from a crossing between parents with strong and weak grazing tolerance in Medicago falcata L., we generated an EcoRI restriction site-associated DNA (RAD) sequencing library. After sequencing and assembly, a high-density genetic map with high-quality SNP markers was constructed, with a total length of 1312.238 cM and an average density of 0.844 SNP/cM.

METHODS

The phenotypic traits of 79 F1 families were observed and the QTLS of 6 traits were analyzed by interval mapping.

RESULTS

Sixty three QTLs were identified for seven traits with LOD values from 3 to 6 and the contribution rates from 15% to 30%. Among the 63 QTLs, 17 were for natural shoot height, 12 for rhizome Length, 10 for Shoot canopy diameter, 9 for Basal plant diameter, 6 for stem number, 5 for absolute shoot height, and 4 for rhizome width. These QTLs were concentrated on LG2, LG4, LG5, LG7, and LG8. LG6 had only 6 QTLs. According to the results of QTL mapping, comparison of reference genomes, and functional annotation, 10 candidate genes that may be related to grazing tolerance were screened. qRT-PCR analysis showed that two candidate genes (LOC11412291 and LOC11440209) may be the key genes related to grazing tolerance of M. falcata.

CONCLUSION

The identified trait-associated QTLs and candidate genes in this study will provide a solid foundation for future molecular breeding for enhanced grazing-tolerance in M. falcata.

QTL Mapping of Palmitic Acid Content Using Specific-Locus Amplified Fragment Sequencing (SLAF-Seq) Genotyping in Soybeans (Glycine max L.)

Soybeans are essential crops that supply protein and oil. The composition and contents of soybean fatty acids are relevant to human health and have a significant relationship with soybean oil processing and applications. Identifying quantitative trait locus (QTL) genes related to palmitic acid could facilitate the development of a range of nutritive soybean cultivars using molecular marker-assisted selection. In this study, we used a cultivar with higher palmitic acid content, ‘Dongnong42’, and a lower palmitic acid content cultivar, ‘Hobbit’, to establish F2:6 recombinant inbred lines. A high-density genetic map containing 9980 SLAF markers was constructed and distributed across 20 soybean chromosomes. The genetic map contained a total genetic distance of 2602.58 cM and an average genetic distance of 0.39 cM between adjacent markers. Two QTLs related to palmitic acid content were mapped using inclusive composite interval mapping, explaining 4.2–10.1% of the phenotypic variance in three different years and environments, including the QTL included in seed palmitic 7-3, which was validated by developing SSR markers. Based on the SNP/Indel and significant differential expression analyses of Dongnong42 and Hobbit, two genes, Glyma.15g119700 and Glyma.15g119800, were selected as candidate genes. The high-density genetic map, QTLs, and molecular markers will be helpful for the map-based cloning of palmitic acid content genes. These could be used to accelerate breeding for high nutritive value cultivars via molecular marker-assisted breeding.

Quantitative Trait Locus Mapping of Marsh Spot Disease Resistance in Cranberry Common Bean (Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) is a food crop that is an important source of dietary proteins and carbohydrates. Marsh spot is a physiological disorder that diminishes seed quality in beans. Prior research suggested that this disease is likely caused by manganese (Mn) deficiency during seed development and that marsh spot resistance is controlled by at least four genes. In this study, genetic mapping was performed to identify quantitative trait loci (QTL) and the potential candidate genes associated with marsh spot resistance. All 138 recombinant inbred lines (RILs) from a bi-parental population were evaluated for marsh spot resistance during five years from 2015 to 2019 in sandy and heavy clay soils in Morden, Manitoba, Canada. The RILs were sequenced using a genotyping by sequencing approach. A total of 52,676 single nucleotide polymorphisms (SNPs) were identified and filtered to generate a high-quality set of 2066 SNPs for QTL mapping. A genetic map based on 1273 SNP markers distributed on 11 chromosomes and covering 1599 cm was constructed. A total of 12 stable and 4 environment-specific QTL were identified using additive effect models, and an additional two epistatic QTL interacting with two of the 16 QTL were identified using an epistasis model. Genome-wide scans of the candidate genes identified 13 metal transport-related candidate genes co-locating within six QTL regions. In particular, two QTL (QTL.3.1 and QTL.3.2) with the highest R2 values (21.8% and 24.5%, respectively) harbored several metal transport genes Phvul.003G086300, Phvul.003G092500, Phvul.003G104900, Phvul.003G099700, and Phvul.003G108900 in a large genomic region of 16.8–27.5 Mb on chromosome 3. These results advance the current understanding of the genetic mechanisms of marsh spot resistance in cranberry common bean and provide new genomic resources for use in genomics-assisted breeding and for candidate gene isolation and functional characterization.

Protein Biofortification in Lentils (Lens culinaris Medik.) Toward Human Health

Lentil (Lens culinaris Medik.) is a nutritionally dense crop with significant quantities of protein, low-digestible carbohydrates, minerals, and vitamins. The amino acid composition of lentil protein can impact human health by maintaining amino acid balance for physiological functions and preventing protein-energy malnutrition and non-communicable diseases (NCDs). Thus, enhancing lentil protein quality through genetic biofortification, i.e., conventional plant breeding and molecular technologies, is vital for the nutritional improvement of lentil crops across the globe. This review highlights variation in protein concentration and quality across Lens species, genetic mechanisms controlling amino acid synthesis in plants, functions of amino acids, and the effect of antinutrients on the absorption of amino acids into the human body. Successful breeding strategies in lentils and other pulses are reviewed to demonstrate robust breeding approaches for protein biofortification. Future lentil breeding approaches will include rapid germplasm selection, phenotypic evaluation, genome-wide association studies, genetic engineering, and genome editing to select sequences that improve protein concentration and quality.

Quantitative Trait Locus Mapping of Seed Vigor in Soybean under -20 °C Storage and Accelerated Aging Conditions via RAD Sequencing

Due to its fast deterioration, soybean (Glycine max L.) has an inherently poor seed vigor. Vigor loss occurring during storage is one of the main obstacles to soybean production in the tropics. To analyze the genetic background of seed vigor, soybean seeds of a recombinant inbred line (RIL) population derived from the cross between Zhonghuang24 (ZH24, low vigor cultivar) and Huaxia3hao (HX3, vigorous cultivar) were utilized to identify the quantitative trait loci (QTLs) underlying the seed vigor under -20 °C conservation and accelerated aging conditions. According to the linkage analysis, multiple seed vigor-related QTLs were identified under both -20 °C and accelerated aging storage. Two major QTLs and eight QTL hotspots localized on chromosomes 3, 6, 9, 11, 15, 16, 17, and 19 were detected that were associated with seed vigor across two storage conditions. The indicators of seed vigor did not correlate well between the two aging treatments, and no common QTLs were detected in RIL populations stored in two conditions. These results indicated that deterioration under accelerated aging conditions was not reflective of natural aging at -20 °C. Additionally, we suggest 15 promising candidate genes that could possibly determine the seed vigor in soybeans, which would help explore the mechanisms responsible for maintaining high seed vigor.

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.

Genetic Mapping by Sequencing More Precisely Detects Loci Responsible for Anaerobic Germination Tolerance in Rice

Direct seeded rice (DSR) is a mainstay for planting rice in the Americas, and it is rapidly becoming more popular in Asia. It is essential to develop rice varieties that are suitable for this type of production system. ASD1, a landrace from India, possesses several traits desirable for direct-seeded fields, including tolerance to anaerobic germination (AG). To map the genetic basis of its tolerance, we examined a population of 200 F_2:3 families derived from a cross between IR64 and ASD1 using the restriction site-associated DNA sequencing (RAD-seq) technology. This genotyping platform enabled the identification of 1921 single nucleotide polymorphism (SNP) markers to construct a high-resolution genetic linkage map with an average interval of 0.9 cM. Two significant quantitative trait loci (QTLs) were detected on chromosomes 7 and 9, qAG7 and qAG9, with LOD scores of 7.1 and 15.0 and R² values of 15.1 and 29.4, respectively. Here, we obtained more precise locations of the QTLs than traditional simple sequence repeat and low-density SNP genotyping methods and may help further dissect the genetic factors of these QTLs.

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.

QTL mapping for soybean (Glycine max L.) leaf chlorophyll-content traits in a genotyped RIL population by using RAD-seq based high-density linkage map

BACKGROUND

Different soybean (Glycine max L.) leaf chlorophyll-content traits are considered to be significantly linked to soybean yield. To map the quantitative trait loci (QTLs) of soybean leaf chlorophyll-content traits, an advanced recombinant inbred line (RIL, ZH, Zhonghuang 24 × Huaxia 3) population was adopted to phenotypic data acquisitions for the target traits across six distinct environments (seasons and soybean growth stages). Moreover, the restriction site-associated DNA sequencing (RAD-seq) based high-density genetic linkage map of the RIL population was utilized for QTL mapping by carrying out the composite interval mapping (CIM) approach.

RESULTS

Correlation analyses showed that most traits were correlated with each other under specific chlorophyll assessing method and were regulated both by hereditary and environmental factors. In this study, 78 QTLs for soybean leaf chlorophyll-content traits were identified. Furthermore, 13 major QTLs and five important QTL hotspots were classified and highlighted from the detected QTLs. Finally, Glyma01g15506, Glyma02g08910, Glyma02g11110, Glyma07g15960, Glyma15g19670 and Glyma15g19810 were predicted from the genetic intervals of the major QTLs and important QTL hotspots.

CONCLUSIONS

The detected QTLs and candidate genes may facilitate to gain a better understanding of the hereditary basis of soybean leaf chlorophyll-content traits and may be valuable to pave the way for the marker-assisted selection (MAS) breeding of the target traits.

Quantitative Trait Locus Analysis of Protein and Oil Content in Response to Planting Density in Soybean (Glycine max [L.] Merri.) Seeds Based on SNP Linkage Mapping

Soybean varieties suitable for high planting density allow greater yields. However, the seed protein and oil contents, which determine the value of this crop, can be influenced by planting density. Thus, it is important to understand the genetic basis of the responses of different soybean genotypes to planting density. In this study, we quantified the protein and oil contents in a four-way recombinant inbred line (FW-RIL) soybean population under two planting densities and the response to density. We performed quantitative trait locus (QTL) mapping using a single nucleotide polymorphism (SNP) linkage map generated by inclusive composite interval mapping. We identified 14 QTLs for protein content and 17 for oil content at a planting density of 2.15 × 10⁵ plant/ha (D1) and 14 QTLs for protein content and 20 for oil content at a planting density 3.0 × 10⁵ plant/ha (D2). Among the QTLs detected, two oil-content QTLs was detected at both plant densities. In addition, we identified 38 QTLs for the responses of protein and oil contents to planting density. Of the QTLs detected, 70 were identified in previous studies, while 33 were newly identified. Fourty-five QTLs accounted for over 10% of the phenotypic variation of the corresponding trait, based on 23 QTLs at a marker interval distance of ~600 kb detected under different densities and with the responses to density difference. Pathway analysis revealed four candidate genes involved in protein and oil biosynthesis/metabolism. These results improve our understanding of the genetic underpinnings of protein and oil biosynthesis in soybean, laying the foundation for enhancing protein and oil contents and increasing yields in soybean.

High-resolution mapping of a major and consensus quantitative trait locus for oil content to a ~ 0.8-Mb region on chromosome A08 in peanut (Arachis hypogaea L.)

KEY MESSAGE: ddRAD-seq-based high-density genetic map comprising 2595 loci identified a major and consensus QTL with a linked marker in a 0.8-Mb physical interval for oil content in peanut. Enhancing oil content is an important breeding objective in peanut. High-resolution mapping of quantitative trait loci (QTLs) with linked markers could facilitate marker-assisted selection in breeding for target traits. In the present study, a recombined inbred line population (Xuhua 13 × Zhonghua 6) was used to construct a genetic map based on double-digest restriction-site-associated DNA sequencing (ddRAD-seq). The resulting high-density genetic map contained 2595 loci, and spanned a length of 2465.62 cM, with an average distance of 0.95 cM/locus. Seven QTLs for oil content were identified on five linkage groups, including the major and stable QTL qOCA08.1 on chromosome A08 with 10.14-27.19% phenotypic variation explained. The physical interval of qOCA08.1 was further delimited to a ~ 0.8-Mb genomic region where two genes affecting oil synthesis had been annotated. The marker SNPOCA08 was developed targeting the SNP loci associated with oil content and validated in peanut cultivars with diverse oil contents. The major and stable QTL identified in the present study could be further dissected for gene discovery. Furthermore, the tightly linked marker for oil content would be useful in marker-assisted breeding in peanut.

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.

QTL mapping for aluminum tolerance in RIL population of soybean (Glycine max L.) by RAD sequencing

Aluminum (Al3+) toxicity is a typical abiotic stress that severely limits crop production in acidic soils. In this study, an RIL (recombinant inbred line, F12) population derived from the cross of Zhonghuang 24 (ZH 24) and Huaxia 3 (HX 3) (160 lines) was tested using hydroponic cultivation. Relative root elongation (RRE) and apical Al3+ content (AAC) were evaluated for each line, and a significant negative correlation was detected between the two indicators. Based on a high-density genetic linkage map, the phenotypic data were used to identify quantitative trait loci (QTLs) associated with these traits. With composite interval mapping (CIM) of the linkage map, five QTLs that explained 39.65% of RRE and AAC variation were detected on chromosomes (Chrs) Gm04, Gm16, Gm17 and Gm19. Two new QTLs, qRRE_04 and qAAC_04, were located on the same region of bin93-bin94 on Chr Gm04, which explained 7.09% and 8.98% phenotypic variation, respectively. Furthermore, the results of the expression analysis of candidate genes in the five genetic regions of the QTLs showed that six genes (Glyma.04g218700, Glyma.04g212800, Glyma.04g213300, Glyma.04g217400, Glyma.04g216100 and Glyma.04g220600) exhibited significant differential expression between the Al3+ treatment and the control of two parents. The results of qRT-PCR analysis indicated that Glyma.04g218700 was upregulated by Al3+ treatment with the hundreds-fold increased expression level and may be a candidate gene with potential roles in the response to aluminum stress. Therefore, our efforts will enable future functional analysis of candidate genes and will contribute to the strategies for improvement of aluminum tolerance in soybean.

Whole-genome mapping identified novel "QTL hotspots regions" for seed storability in soybean (Glycine max L.)

BACKGROUND

Seed aging in soybean is a serious challenge for agronomic production and germplasm preservation. However, its genetic basis remains largely unclear in soybean. Unraveling the genetic mechanism involved in seed aging, and enhancing seed storability is an imperative goal for soybean breeding. The aim of this study is to identify quantitative trait loci (QTLs) using high-density genetic linkage maps of soybean for seed storability. In this regard, two recombinant inbred line (RIL) populations derived from Zhengyanghuangdou × Meng 8206 (ZM6) and Linhefenqingdou × Meng 8206 (LM6) crosses were evaluated for three seed-germination related traits viz., germination rate (GR), normal seedling length (SL) and normal seedling fresh weight (FW) under natural and artificial aging conditions to map QTLs for seed storability.

RESULTS

A total of 34 QTLs, including 13 QTLs for GR, 11 QTLs for SL and 10 QTLs for FW, were identified on 11 chromosomes with the phenotypic variation ranged from 7.30 to 23.16% under both aging conditions. All these QTLs were novel, and 21 of these QTLs were clustered in five QTL-rich regions on four different chromosomes viz., Chr3, Chr5, Chr17 &Chr18, among them the highest concentration of seven and six QTLs were found in "QTL hotspot A" (Chr17) and "QTL hotspot B" (Chr5), respectively. Furthermore, QTLs within all the five QTL clusters are linked to at least two studied traits, which is also supported by highly significant correlation between the three germination-related traits. QTLs for seed-germination related traits in "QTL hotspot B" were found in both RIL populations and aging conditions, and also QTLs underlying "QTL hotspot A" are identified in both RIL populations under artificial aging condition. These are the stable genomic regions governing the inheritance of seed storability in soybean, and will be the main focus for soybean breeders.

CONCLUSION

This study uncovers the genetic basis of seed storability in soybean. The newly identified QTLs provides valuable information, and will be main targets for fine mapping, candidate gene identification and marker-assisted breeding. Hence, the present study is the first report for the comprehensive and detailed investigation of genetic architecture of seed storability in soybean.

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.

High-density SNP linkage map construction and QTL mapping for flavonoid-related traits in a tea plant (Camellia sinensis) using 2b-RAD sequencing

BACKGROUND

Flavonoids are important components that confer upon tea plants a unique flavour and health functions. However, the traditional breeding method for selecting a cultivar with a high or unique flavonoid content is time consuming and labour intensive. High-density genetic map construction associated with quantitative trait locus (QTL) mapping provides an effective way to facilitate trait improvement in plant breeding. In this study, an F1 population (LJ43×BHZ) was genotyped using 2b-restriction site-associated DNA (2b-RAD) sequencing to obtain massive single nucleotide polymorphism (SNP) markers to construct a high-density genetic map for a tea plant. Furthermore, QTLs related to flavonoids were identified using our new genetic map.

RESULTS

A total of 13,446 polymorphic SNP markers were developed using 2b-RAD sequencing, and 4,463 of these markers were available for constructing the genetic linkage map. A 1,678.52-cM high-density map at an average interval of 0.40 cM with 4,217 markers, including 427 frameset simple sequence repeats (SSRs) and 3,800 novel SNPs, mapped into 15 linkage groups was successfully constructed. After QTL analysis, a total of 27 QTLs related to flavonoids or caffeine content (CAF) were mapped to 8 different linkage groups, LG01, LG03, LG06, LG08, LG10, LG11, LG12, and LG13, with an LOD from 3.14 to 39.54, constituting 7.5% to 42.8% of the phenotypic variation.

CONCLUSIONS

To our knowledge, the highest density genetic map ever reported was constructed since the largest mapping population of tea plants was adopted in present study. Moreover, novel QTLs related to flavonoids and CAF were identified based on the new high-density genetic map. In addition, two markers were located in candidate genes that may be involved in flavonoid metabolism. The present study provides valuable information for gene discovery, marker-assisted selection breeding and map-based cloning for functional genes that are related to flavonoid content in tea plants.

Chromosome level high-density integrated genetic maps improve the Pyrus bretschneideri 'DangshanSuli' v1.0 genome

Background

Chromosomal level reference genomes provide a crucial foundation for genomics research such as genome-wide association studies (GWAS) and whole genome selection. The chromosomal-level sequences of both the European (Pyrus communis) and Chinese (P. bretschneideri) pear genomes have not been published in public databases so far.

Results

To anchor the scaffolds of P. bretschneideri ‘DangshanSuli’ (DS) v1.0 genome into pseudo-chromosomes, two genetic maps (MH and YM maps) were constructed using half sibling populations of Chinese pear crosses, ‘Mantianhong’ (MTH) × ‘Hongxiangsu’ (HXS) and ‘Yuluxiang’ (YLX) × MTH, from 345 and 162 seedlings, respectively, which were prepared for SNP discovery using genotyping-by-sequencing (GBS) technology. The MH and YM maps, each with 17 linkage groups (LGs), were constructed from 2606 and 2489 SNP markers and spanned 1847 and 1668 cM, respectively, with average marker intervals of 0.7. The two maps were further merged with a previously published genetic map (BD) based on the cross ‘Bayuehong’ (BYH) × ‘Dangshansuli’ (DS) to build a new integrated MH-YM-BD map. By using 7757 markers located on the integrated MH-YM-BD map, 898 scaffolds (400.57 Mb) of the DS v1.0 assembly were successfully anchored into 17 pseudo-chromosomes, accounting for 78.8% of the assembled genome size. About 88.31% of them (793 scaffolds) were directionally anchored with two or more markers on the pseudo-chromosomes. Furthermore, the errors in each pseudo-chromosome (especially 1, 5, 7 and 11) were manually corrected and pseudo-chromosomes 1, 5 and 7 were extended by adding 19, 12 and 14 scaffolds respectively in the newly constructed DS v1.1 genome. Synteny analyses revealed that the DS v1.1 genome had high collinearity with the apple genome, and the homologous fragments between pseudo-chromosomes were similar to those found in previous studies. Moreover, the red-skin trait of Asian pear was mapped to an identical locus as identified previously.

Conclusions

The accuracy of DS v1.1 genome was improved by using larger mapping populations and merged genetic map. With more than 400 MB anchored to 17 pseudo-chromosomes, the new DS v1.1 genome provides a critical tool that is essential for studies of pear genetics, genomics and molecular breeding.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5224-6) contains supplementary material, which is available to authorized users.

Artificially designed hybrids facilitate efficient generation of high-resolution linkage maps

When sequencing eukaryotic genomes, linkage maps are indispensable for building scaffolds to assemble and/or to validate chromosomes. However, current approaches to constructing linkage maps are limited by marker density and cost-effectiveness, especially for wild organisms. We have now devised a new strategy based on artificially generated hybrid organisms to acquire ultrahigh-density genomic markers at reduced cost and build highly accurate linkage maps. We have also developed the novel analysis pipeline Scaffold Extender with Low Depth Linkage Analysis (SELDLA) for data processing to generate linkage maps and draft genomes. Using SELDLA, linkage maps and improved genomes for two species of pufferfish, Takifugu rubripes and Takifugu stictonotus, were obtained simultaneously. The strategy is applicable to a wide range of sexually reproducing organisms, and could, therefore, accelerate the whole genome analysis of various organisms including fish, mollusks, amphibians, insects, plants, and even mammals.

Construction of a high-density genetic map and mapping of QTLs for soybean (Glycine max) agronomic and seed quality traits by specific length amplified fragment sequencing

BACKGROUND

Soybean is not only an important oil crop, but also an important source of edible protein and industrial raw material. Yield-traits and quality-traits are increasingly attracting the attention of breeders. Therefore, fine mapping the QTLs associated with yield-traits and quality-traits of soybean would be helpful for soybean breeders. In the present study, a high-density linkage map was constructed to identify the QTLs for the yield-traits and quality-traits, using specific length amplified fragment sequencing (SLAF-seq).

RESULTS

SLAF-seq was performed to screen SLAF markers with 149 F8:11 individuals from a cross between a semi wild soybean, 'Huapidou', and a cultivated soybean, 'Qihuang26', which generated 400.91 M paired-end reads. In total, 53,132 polymorphic SLAF markers were obtained. The genetic linkage map was constructed by 5111 SLAF markers with segregation type of aa×bb. The final map, containing 20 linkage groups (LGs), was 2909.46 cM in length with an average distance of 0.57 cM between adjacent markers. The average coverage for each SLAF marker on the map was 81.26-fold in the male parent, 45.79-fold in the female parent, and 19.84-fold average in each F8:11 individual. According to the high-density map, 35 QTLs for plant height (PH), 100-seeds weight (SW), oil content in seeds (Oil) and protein content in seeds (Protein) were found to be distributed on 17 chromosomes, and 14 novel QTLs were identified for the first time. The physical distance of 11 QTLs was shorter than 100 Kb, suggesting a direct opportunity to find candidate genes. Furthermore, three pairs of epistatic QTLs associated with Protein involving 6 loci on 5 chromosomes were identified. Moreover, 13, 14, 7 and 9 genes, which showed tissue-specific expression patterns, might be associated with PH, SW, Oil and Protein, respectively.

CONCLUSIONS

With SLAF-sequencing, some novel QTLs and important QTLs for both yield-related and quality traits were identified based on a new, high-density linkage map. Moreover, 43 genes with tissue-specific expression patterns were regarded as potential genes in further study. Our findings might be beneficial to molecular marker-assisted breeding, and could provide detailed information for accurate QTL localization.

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.

Identification of QTLs related to the vertical distribution and seed-set of pod number in soybean [Glycine max (L.) Merri]

Pod number is an important factor that influences yield in soybean. Here, we used two associated recombinant inbred line (RIL) soybean populations, RIL3613 (containing 134 lines derived from Dongnong L13 × Heihe 36) and RIL6013 (composed of 156 individuals from Dongnong L13 × Henong 60), to identify quantitative trait loci (QTLs) regulating the vertical distribution and quantity of seeds and seed pods. The numbers of pods were quantified in the upper, middle, and lower sections of the plant, as well as in the plants as a whole, and QTLs regulating these spatial traits were mapped using an inclusive complete interval mapping method. A total of 21 and 26 QTLs controlling pod-number-related traits were detected in RIL3613 and RIL6013, respectively, which explained 1.25-11.6698% and 0.0001-7.91% of the phenotypic variation. A total of 34 QTLs were verified by comparison with previous research, were identified in both populations, or were found to regulate multiple traits, indicating their authenticity. These results enhance our understanding of the vertical distribution of pod-number-related traits and support molecular breeding for seed yield.