Genome-wide association analysis uncovers the genetic architecture of tradeoff between flowering date and yield components in sesame

The candidate gene SibHLHA regulates anthocyanin-driven purple pigmentation in Sesamum indicum flowers

Anthocyanins not only serve as critical pigments determining floral hues but also play essential roles in attracting insects for pollination, feeding animals and mitigating abiotic stress. However, the molecular mechanisms underlying the regulation of flower color in sesame has not yet been reported. In this study, an F2 population was constructed by crossing 'Ganzhi 9' (purple-flowered) with 'BS377' (white-flowered). Genetic analysis revealed that purple flower is controlled by a single locus named as SiFC (Sesamum indicum flower color). Using the BSA-seq approach, SiFC was preliminarily identified on chromosome 6, which was further mapped to a 473 kb interval using Kompetitive Allele Specific PCR (KASP) marker analysis. Moreover, functional annotation, expression profiling, and sequence analyses confirmed that the SibHLHA (Sesame10992) was the most likely candidate gene for SiFC. In addition, SibHLHA, highly homologous to AtTT8 (a key regulator in the anthocyanin synthesis pathway), was found to interact with WER-like or TTG1 proteins, enhancing anthocyanin accumulation in tobacco leaves. Furthermore, an SNP in the second exon of Sibhlha (BS377 variant) was found to alter the encoding amino acids, which affected Sibhlha binding to MYB protein and showed low anthocyanin in tobacco leaves compared with SibHLHA binding with WER-like or TTG1 proteins. These findings not only deepen our understanding of the molecular mechanisms controlling sesame corolla color, but also provide valuable insights for developing ornamental and consumable sesame varieties.

Genome-wide association scan and candidate gene analysis for seed coat color in sesame (Sesamum indicum L.)

Introduction

Seed coat color in sesame is a crucial trait for breeding programs as it is closely associated with important characteristics such as oil content, protein levels, and disease resistance, which directly influence seed quality and market value.

Methods

This study investigates the genetic basis of seed coat color in 200 Sudanese sesame genotypes grown for two consecutive years through comprehensive phenotyping, genomic diversity analysis, genome-wide association studies (GWAS), and candidate gene discovery.

Results and discussion

Phenotypic analysis across two growing seasons revealed high heritability and significant correlations among color parameters (L*, a*, and b*), indicating strong genetic control over seed coat color. The genomic analysis identified distinct clusters among sesame accessions, with rapid linkage disequilibrium decay suggesting a high level of recombination. GWAS identified significant SNPs associated with seed coat color traits, revealing key genomic regions on chromosomes 3, 6, 9, 12, and 13. Candidate gene analysis highlighted several genes, including DOF zinc finger proteins and WRKY transcription factors, which may play essential roles in pigment biosynthesis pathways. These findings provide valuable insights for breeding programs to enhance desirable seed coat color traits in sesame.

Exploring the wild almond, Prunus arabica (Olivier), as a genetic source for almond breeding

During the process of almond (Prunus dulcis) domestication, essential traits, which gave plants the plasticity for facing unstable environmental conditions, were lost. In general, the domestication process often narrows the natural genetic diversity. Modern selections (i.e., breeding programs) dramatically accelerated this genetic bottleneck trend to a few successful almond cultivars, which are presently the founders of most commercial cultivars worldwide. The concept of utilizing wild species as a source for important traits and for the enrichment of the gene pool was deeply discussed in previous studies. However, in almonds and other Prunus species, deliberate utilization of wild species as a genetic resource for breeding programs is quite rare. To address these significant challenges, we generated an interspecific F1 population between the Israeli almond cultivar Um el Fahem (UEF) and a specimen of a local wild almond species, Prunus arabica (P. arabica), originating from the Judea desert. This interspecific F1 population possesses high phenotypic variability, and sixteen segregating traits were phenotyped. Among the segregating traits, we were able to genetically associate six agriculturally important traits, such as leaf chlorophyll content (LCC), flower size, and fruit size. The alleles for Self-Compatibility (SC) and kernel bitterness were previously mapped in almond and were reexamined on the background of the distinctive wild genetic material of P. arabica. Finally, phenotypic interactions between traits were suggested, such as rootstock perimeter and canopy area that were positively correlated with total yield in the F1 population. This study is a first step towards developing a well-characterized almond interspecies genetic population. The availability of such a genetic tool with detailed phenotypic analysis is crucial to address and explore the profound influence of almond wild species in Prunus genetic research and breeding. By using the interspecific population as the infrastructure, we show the advantages and importance of utilizing wild relatives.

Supplementary information

The online version contains supplementary material available at 10.1007/s11295-024-01668-4.

Sesame, an Underutilized Oil Seed Crop: Breeding Achievements and Future Challenges

Sesame seeds and their edible oil are highly nutritious and rich in mono- and polyunsaturated fatty acids. Bioactive compounds such as sterols, tocopherols, and sesamol provide significant medicinal benefits. The high oil content (50%) and favorable mono- and polyunsaturated fatty acid balance, as well as resilience to water stress, make sesame a promising candidate crop for global agricultural expansion. However, sesame production faces challenges such as low yields, poor response to agricultural inputs, and losses due to capsule dehiscence. To enhance yield, traits like determinate growth, dwarfism, a high harvest index, non-shattering capsules, disease resistance, and photoperiod sensitivity are needed. These traits can be achieved through variation or induced mutation breeding. Crossbreeding methods often result in unwanted genetic changes. The gene editing CRISPR/Cas9 technology has the potential to suppress detrimental alleles and improve the fatty acid profile by inhibiting polyunsaturated fatty acid biosynthesis. Even though sesame is an orphan crop, it has entered the genomic era, with available sequences assisting molecular breeding efforts. This progress aids in associating single-nucleotide polymorphisms (SNPs) and simple sequence repeats (SSR) with key economic traits, as well as identifying genes related to adaptability, oil production, fatty acid synthesis, and photosynthesis. Additionally, transcriptomic research can reveal genes involved in abiotic stress responses and adaptation to diverse climates. The mapping of quantitative trait loci (QTL) can identify loci linked to key traits such as capsule size, seed count per capsule, and capsule number per plant. This article reviews recent advances in sesame breeding, discusses ongoing challenges, and explores potential strategies for future improvement. Hence, integrating advanced genomic tools and breeding strategies provides promising ways to enhance sesame production to meet global demands.

Evaluation of Agronomic Traits, Total Phenolic Content, and Antioxidant Properties of Sesame Seeds of Different Colors and Origin

Rising health concerns regarding chronic diseases call for exploring natural sources of antioxidants and factors that influence their activity. This study evaluated the diversity of 112 sesame germplasms from Africa and Asia based on ten agronomic traits (seven quantitative and three qualitative), two antioxidant activities (ABTS and DPPH radical scavenging activities), and the content of one metabolite (TPC). TPC, DPPH, and ABTS were in the ranges of 4.98-87.88 µg GAE/mg DE, 3.97-46.23 µg AAE/mg DE, and 3.42-176.01 µg TE/mg DE, respectively. Statistical analyses revealed significant variations in agronomic traits, TPC, and antioxidant activities among the sesame germplasms (p < 0.05). Furthermore, the individual and interaction effects of seed color and the continent of origin on the levels of the quantitative traits, TPC, ABTS, and DPPH were analyzed, and the correlation among the traits was further evaluated. Diversity in TPC, ABTS, and DPPH was significantly associated with seed color and most of the quantitative agronomic traits (p < 0.05) but not with continent of origin. Principal component analysis revealed TPC, ABTS, DPPH, and five quantitative traits as the most discriminant traits. In general, six sesame accessions with high TPC and antioxidant activities (IT194356, IT170094, IT29971, IT185998, IT104246, and IT169623) as well as important agronomic traits were identified and, hence, could be used for developing improved sesame varieties.

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Induced mutation in the SiALS gene offers new weed management opportunities for sesame crop

Weeds are the primary biotic constraint affecting sesame growth and production. Here, we applied EMS mutagenesis to an elite sesame cultivar and discovered a novel point mutation in the sesame SiALS gene conferring resistance to imidazolinone, a group of acetolactate-synthase (ALS)-inhibitors. The mutant line exhibited high resistance to imazamox, an ALS-inhibitor, with hybrid plants displaying an intermediate response. Field-based validation confirmed the mutant line's substantial resistance, leading to a significantly higher yield under imazamox treatment. Under pre-emergence application of imazapic, the mutant plants sustained growth, whereas wild-type and weed were effectively controlled. Field trials using s-metolachlor and imazapic combined resulted in weed-free plots compared to untreated controls. Consequently, this treatment showed a significantly greater yield (2280 vs. 880 Kg ha-1) than the commercial practice (s-metolachlor). Overall, our study unveils the potential of utilizing this point mutation in sesame breeding programs, offering new opportunities for integrated weed management strategies for sesame cultivation. Developing herbicide-resistant crop plants holds promise for supporting sustainable production and addressing the challenges of weed infestations in sesame farming.

Refining flowering date enhances sesame yield independently of day-length

BACKGROUND

The transition from vegetative to reproductive growth is a key factor in yield maximization. Sesame (Sesamum indicum), an indeterminate short-day oilseed crop, is rapidly being introduced into new cultivation areas. Thus, decoding its flowering mechanism is necessary to facilitate adaptation to environmental conditions. In the current study, we uncover the effect of day-length on flowering and yield components using F2 populations segregating for previously identified quantitative trait loci (Si_DTF QTL) confirming these traits.

RESULTS

Generally, day-length affected all phenotypic traits, with short-day preceding days to flowering and reducing yield components. Interestingly, the average days to flowering required for yield maximization was 50 to 55 days, regardless of day-length. In addition, we found that Si_DTF QTL is more associated with seed-yield and yield components than with days to flowering. A bulk-segregation analysis was applied to identify additional QTL differing in allele frequencies between early and late flowering under both day-length conditions. Candidate genes mining within the identified major QTL intervals revealed two flowering-related genes with different expression levels between the parental lines, indicating their contribution to sesame flowering regulation.

CONCLUSIONS

Our findings demonstrate the essential role of flowering date on yield components and will serve as a basis for future sesame breeding.

Spectral-genomic chain-model approach enhances the wheat yield component prediction under the Mediterranean climate

In light of the changing climate that jeopardizes future food security, genomic selection is emerging as a valuable tool for breeders to enhance genetic gains and introduce high-yielding varieties. However, predicting grain yield is challenging due to the genetic and physiological complexities involved and the effect of genetic-by-environment interactions on prediction accuracy. We utilized a chained model approach to address these challenges, breaking down the complex prediction task into simpler steps. A diversity panel with a narrow phenological range was phenotyped across three Mediterranean environments for various morpho-physiological and yield-related traits. The results indicated that a multi-environment model outperformed a single-environment model in prediction accuracy for most traits. However, prediction accuracy for grain yield was not improved. Thus, in an attempt to ameliorate the grain yield prediction accuracy, we integrated a spectral estimation of spike number, being a major wheat yield component, with genomic data. A machine learning approach was used for spike number estimation from canopy hyperspectral reflectance captured by an unmanned aerial vehicle. The spectral-based estimated spike number was utilized as a secondary trait in a multi-trait genomic selection, significantly improving grain yield prediction accuracy. Moreover, the ability to predict the spike number based on data from previous seasons implies that it could be applied to new trials at various scales, even in small plot sizes. Overall, we demonstrate here that incorporating a novel spectral-genomic chain-model workflow, which utilizes spectral-based phenotypes as a secondary trait, improves the predictive accuracy of wheat grain yield.

Identification of Genomic Regions for Traits Associated with Flowering in Cassava (Manihot esculenta Crantz)

Flowering in cassava (Manihot esculenta Crantz) is crucial for the generation of botanical seed for breeding. However, genotypes preferred by most farmers are erect and poor at flowering or never flower. To elucidate the genetic basis of flowering, 293 diverse cassava accessions were evaluated for flowering-associated traits at two locations and seasons in Uganda. Genotyping using the Diversity Array Technology Pty Ltd. (DArTseq) platform identified 24,040 single-nucleotide polymorphisms (SNPs) distributed on the 18 cassava chromosomes. Population structure analysis using principal components (PCs) and kinships showed three clusters; the first five PCs accounted for 49.2% of the observed genetic variation. Linkage disequilibrium (LD) estimation averaged 0.32 at a distance of ~2850 kb (kilo base pairs). Polymorphism information content (PIC) and minor allele frequency (MAF) were 0.25 and 0.23, respectively. A genome-wide association study (GWAS) analysis uncovered 53 significant marker-trait associations (MTAs) with flowering-associated traits involving 27 loci. Two loci, SNPs S5_29309724 and S15_11747301, were associated with all the traits. Using five of the 27 SNPs with a Phenotype_Variance_Explained (PVE) ≥ 5%, 44 candidate genes were identified in the peak SNP sites located within 50 kb upstream or downstream, with most associated with branching traits. Eight of the genes, orthologous to Arabidopsis and other plant species, had known functional annotations related to flowering, e.g., eukaryotic translation initiation factor and myb family transcription factor. This study identified genomic regions associated with flowering-associated traits in cassava, and the identified SNPs can be useful in marker-assisted selection to overcome hybridization challenges, like unsynchronized flowering, and candidate gene validation.

QTL analysis of traits related to seed size and shape in sesame (Sesamum indicum L.)

Seed size and shape are important traits that determine seed yield in sesame. Understanding the genetic basis of seed size and shape is essential for improving the yield of sesame. In this study, F2 and BC1 populations were developed by crossing the Yuzhi 4 and Bengal small-seed (BS) lines for detecting the quantitative trait loci (QTLs) of traits related to seed size and shape. A total of 52 QTLs, including 13 in F2 and 39 in BC1 populations, for seed length (SL), seed width (SW), and length to width ratio (L/W) were identified, explaining phenotypic variations from 3.68 to 21.64%. Of these QTLs, nine stable major QTLs were identified in the two populations. Notably, three major QTLs qSL-LG3-2, qSW-LG3-2, and qSW-LG3-F2 that accounted for 4.94-16.34% of the phenotypic variations were co-localized in a 2.08 Mb interval on chromosome 1 (chr1) with 279 candidate genes. Three stable major QTLs qSL-LG6-2, qLW-LG6, and qLW-LG6-F2 that explained 8.14-33.74% of the phenotypic variations were co-localized in a 3.27 Mb region on chr9 with 398 candidate genes. In addition, the stable major QTL qSL-LG5 was co-localized with minor QTLs qLW-LG5-3 and qSW-LG5 to a 1.82 Mb region on chr3 with 195 candidate genes. Gene annotation, orthologous gene analysis, and sequence analysis indicated that three genes are likely involved in sesame seed development. These results obtained herein provide valuable in-formation for functional gene cloning and improving the seed yield of sesame.

Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame

Introduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted.

Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses.

Results: In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%–58% compared to the single-environment model, particularly when borrowing information from other environments was made possible.

Discussion: Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.

Current Progress, Applications and Challenges of Multi-Omics Approaches in Sesame Genetic Improvement

Sesame is one of the important traditional oil crops in the world, and has high economic and nutritional value. Recently, due to the novel high throughput sequencing techniques and bioinformatical methods, the study of the genomics, methylomics, transcriptomics, proteomics and metabonomics of sesame has developed rapidly. Thus far, the genomes of five sesame accessions have been released, including white and black seed sesame. The genome studies reveal the function and structure of the sesame genome, and facilitate the exploitation of molecular markers, the construction of genetic maps and the study of pan-genomes. Methylomics focus on the study of the molecular level changes under different environmental conditions. Transcriptomics provide a powerful tool to study abiotic/biotic stress, organ development, and noncoding RNAs, and proteomics and metabonomics also provide some support in studying abiotic stress and important traits. In addition, the opportunities and challenges of multi-omics in sesame genetics breeding were also described. This review summarizes the current research status of sesame from the perspectives of multi-omics and hopes to provide help for further in-depth research on sesame.

Discovering favorable genes, QTLs, and genotypes as a genetic resource for sesame (Sesamum indicum L.) improvement

Sesame (Sesamum indicum L.) is an ancient diploid oilseed crop with high oil content, quality protein, and antioxidant characteristics that is produced in many countries worldwide. The genes, QTLs, and genetic resources of sesame are utilized by sesame researchers and growers. Researchers have identified the many useful traits of this crop, which are available on different platforms. The genes, genotypes, QTLs, and other genetic diversity data of sesame have been collected and stored in more than nine genomic resources, and five sesame crop marker databases are available online. However, data on phenotypic and genotypic variability, which would contribute to sesame improvements, are limited and not yet accessible. The present study comprehensively reviewed more than 110 original published research papers and scientifically incorporated the results. The candidate genes, genotypes, and QTLs of significantly important traits of sesame were identified. Genetic resources related to grain yield and yield component traits, oil content and quality, drought tolerance, salt tolerance, waterlogging resistance, disease resistance, mineral nutrient, capsule shattering resistance, and other agronomic important traits of sesame were studied. Numerous candidate genotypes, genes, QTLs, and alleles associated with those traits were summarized and discovered. The chromosome regions and linkage groups, maps associated with the best traits, and candidate genes were also included. The variability presented in this paper combined with sesame genetic information will help inform further sesame improvement.

Elucidating the pleiotropic effects of sesame KANADI1 locus on leaf and capsule development

Autonomous seed dispersal is a critical trait for wild plants in natural ecosystems; however, for domesticated crop-plants it can lead to significant yield losses. While seed shattering was a major selection target during the initial domestication of many crops, this trait is still targeted in breeding programs, especially in 'orphan crops' such as sesame, whose capsules dehisce upon ripening. Here we used a mapping population derived from a cross between wild-type (dehiscent) × indehiscent lines to test the hypothesis that the selection against indehiscent alleles in sesame is a consequence of complex genetic interactions associated with yield reduction. We identified a major pleiotropic locus, SiKANADI1, associated with abnormal hyponastic leaf and indehiscent capsule, and genetically dissected its underlying mechanism using a set of near-isogenic lines. Transcriptional, anatomical and physiological information shed light, for the first time, on the polar regulatory gene network in sesame. The pleiotropic effect of SiKANADI1 on leaf and capsule structure and its influence on photosynthetic capacity and final yield are thoroughly characterized. Overall, our results provide new insights on the genetic and morphological mechanisms regulating capsule indehiscence in sesame, and discuss their evolutionary consequences and potential for future sesame breeding.