An Integrated Approach of QTL Mapping and Genome-Wide Association Analysis Identifies Candidate Genes for Phytophthora Blight Resistance in Sesame (Sesamum indicum L.)

Transcriptional Reprogramming and Allelic Variation in Pleiotropic QTL Regulates Days to Flowering and Growth Habit in Pigeonpea

The present study investigated the linkage between days to flowering (DTF) and growth habit (GH) in pigeonpea using QTL mapping, QTL-seq, and GWAS approaches. The linkage map developed here is the largest to date, spanning 1825.56 cM with 7987 SNP markers. In total, eight and four QTLs were mapped for DTF and GH, respectively, harbouring 78 pigeonpea orthologs of Arabidopsis flowering time genes. Corroboratively, QTL-seq analysis identified a single linked QTL for both traits on chromosome 3, possessing 15 genes bearing genic variants. Together, these 91 genes were clustered primarily into autonomous, photoperiod, and epigenetic pathways. Further, we identified 39 associations for DTF and 111 associations for GH through GWAS in the QTL regions. Of these, nine associations were consistent and constituted nine haplotypes (five late, two early, one each for super-early and medium duration). The involvement of multiple genes explained the range of allelic effects and the presence of multiple LD blocks. Further, the linked QTL on chromosome 3 was fine-mapped to the 0.24-Mb region with an LOD score of 8.56, explaining 36.47% of the phenotypic variance. We identified a 10-bp deletion in the first exon of TFL1 gene of the ICPL 20338 variety, which may affect its interaction with the Apetala1 and Leafy genes, resulting in determinate GH and early flowering. Further, the genic marker developed for the deletion in the TFL1 gene could be utilized as a foreground marker in marker-assisted breeding programmes to develop early-flowering pigeonpea varieties.

The role of the rhizobiome recruited by root exudates in plant disease resistance: current status and future directions

Root exudates serve as a bridge connecting plant roots and rhizosphere microbes, playing a key role in influencing the assembly and function of the rhizobiome. Recent studies have fully elucidated the role of root exudates in recruiting rhizosphere microbes to enhance plant performance, particularly in terms of plant resistance to soil-borne pathogens; however, it should be noted that the composition and amount of root exudates are primarily quantitative traits regulated by a large number of genes in plants. As a result, there are knowledge gaps in understanding the contribution of the rhizobiome to soil-borne plant disease resistance and the ternary link of plant genes, root exudates, and disease resistance-associated microbes. Advancements in technologies such as quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) offer opportunities for the identification of genes associated with quantitative traits. In the present review, we summarize recent studies on the interactions of plant and rhizosphere microbes through root exudates to enhance soil-borne plant disease resistance and also highlight methods for quantifying the contribution of the rhizobiome to plant disease resistance and identifying the genes responsible for recruiting disease resistance-associated microbes through root exudates.

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.

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.

Quantitative Trait Loci Mapping for Bacterial Wilt Resistance and Plant Height in Tomatoes

Bacterial wilt (BW) of tomatoes, caused by Ralstonia solanacearum, is a devastating disease that results in large annual yield losses worldwide. Management of BW of tomatoes is difficult due to the soil-borne nature of the pathogen. One of the best ways to mitigate the losses is through breeding for disease resistance. Moreover, plant height (PH) is a crucial element related to plant architecture, which determines nutrient management and mechanical harvesting in tomatoes. An intraspecific F2 segregating population (NC 11212) of tomatoes was developed by crossing NC 84173 (tall, BW susceptible) × CLN1466EA (short, BW resistant). We performed quantitative trait loci (QTL) mapping using single nucleotide polymorphic (SNP) markers and the NC 11212 F2 segregating population. The QTL analysis for BW resistance revealed a total of three QTLs on chromosomes 1, 2, and 3, explaining phenotypic variation (R2) ranging from 3.6% to 14.9%, whereas the QTL analysis for PH also detected three QTLs on chromosomes 1, 8, and 11, explaining R2 ranging from 7.1% to 11%. This work thus provides information to improve BW resistance and plant architecture-related traits in tomatoes.

QTL mapping in sesame (Sesamum indicum L.): A review

Sesame (Sesamum indicum L.) is an important oilseed crop used for food, feed, medicinal, and industrial applications. Inherently low genetic yield potential and susceptibility to biotic and abiotic stresses contribute to low productivity in sesame. Development of stress resistant varieties coupled with high yield is a viable option to raise the genetic potential of sesame. Conventional phenotype-based breeding methods have made an important role in the last couple of decades by developing several sesame varieties with improved quality, yield, and tolerance to biotic and abiotic stresses. However, due to adverse environmental effects, time consuming to develop new variety, and low genetic gain, conventional phenotype-based approach is not adequate to satisfy the rising population growth. In this context, advanced method of genotype selection via modern techniques of biotechnology plays essential roles in reducing the constraints and boosting sesame production to satisfy the huge demand. In line to this, quantitative trait loci (QTL) mapping is considered as a promising method to address the problems of sesame breeding. Previously, huge data have been generated in the practical use of QTL for sesame improvement. Therefore, this paper aims to review recent advances in the area of QTL mapping for yield and yield related traits in sesame for enhancing and sustaining sesame production. In this section, we present an intensive review on the identification and mapping of the most desirable potential candidate genes/QTLs associated with desirable traits. Moreover, this review focuses on the major QTL regions and/or potential candidate genes and associated molecular markers that could provide potential genetic resources for molecular marker-assisted selection and further cloning of functional genes for yield and yield-related traits as well as various biotic and abiotic stress tolerances. Finally, the summarized QTL mapping data shed light on future directions for enhanced sesame breeding programs.

Omics technologies towards sesame improvement: a review

Genetic improvement of sesame (Sesamum indicum L.), one of the most important oilseed crops providing edible oil, proteins, minerals, and vitamins, is important to ensure a balanced diet for the growing world population. Increasing yield, seed protein, oil, minerals, and vitamins is urgently needed to meet the global demand. The production and productivity of sesame is very low due to various biotic and abiotic stresses. Therefore, various efforts have been made to combat these constraints and increase the production and productivity of sesame through conventional breeding. However, less attention has been paid to the genetic improvement of the crop through modern biotechnological methods, leaving it lagging behind other oilseed crops. Recently, however, the scenario has changed as sesame research has entered the era of "omics" and has made significant progress. Therefore, the purpose of this paper is to provide an overview of the progress made by omics research in improving sesame. This review presents a number of efforts that have been made over past decade using omics technologies to improve various traits of sesame, including seed composition, yield, and biotic and abiotic resistant varieties. It summarizes the advances in genetic improvement of sesame using omics technologies, such as germplasm development (web-based functional databases and germplasm resources), gene discovery (molecular markers and genetic linkage map construction), proteomics, transcriptomics, and metabolomics that have been carried out in the last decade. In conclusion, this review highlights future directions that may be important for omics-assisted breeding in sesame genetic improvement.

Molecular mapping of quantitative trait loci for resistance to early blight in tomatoes

Early blight (EB), caused by Alternaria linariae (Neerg.) (syn. A. tomatophila) Simmons, is a disease that affects tomatoes (Solanum lycopersicum L.) throughout the world, with tremendous economic implications. The objective of the present study was to map the quantitative trait loci (QTL) associated with EB resistance in tomatoes. The F₂ and F_2:3 mapping populations consisting of 174 lines derived from NC 1CELBR (resistant) × Fla. 7775 (susceptible) were evaluated under natural conditions in the field in 2011 and in the greenhouse in 2015 by artificial inoculation. In all, 375 Kompetitive Allele Specific PCR (KASP) assays were used for genotyping parents and the F₂ population. The broad-sense heritability estimate for phenotypic data was 28.3%, and 25.3% for 2011, and 2015 disease evaluations, respectively. QTL analysis revealed six QTLs associated with EB resistance on chromosomes 2, 8, and 11 (LOD 4.0 to 9.1), explaining phenotypic variation ranging from 3.8 to 21.0%. These results demonstrate that genetic control of EB resistance in NC 1CELBR is polygenic. This study may facilitate further fine mapping of the EB-resistant QTL and marker-assisted selection (MAS) to transfer EB resistance genes into elite tomato varieties, including broadening the genetic diversity of EB resistance in tomatoes.

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.

Two decades of association mapping: Insights on disease resistance in major crops

Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

Generating better leaf traits in M2 lines of fourteen Ethiopian sesame (Sesamum indicum L.) genotypes through the treatment of their seeds with sodium azide

The present study explored the effect of sodium azide (NaN₃) on quantitative and qualitative leaf traits of M2 lines on 14 Ethiopian sesame genotypes collected from Humera Agricultural Research Center, Tigrai, Ethiopia. Qualitative data on leaf color, leaf hairiness, leaf arrangement, leaf shape, basal leaf profile, basal leaf margin, and leaf angle to main stem as well as quantitative data on length of basal leaf, length of top leaf, width of basal leaf, width of top leaf, length of marginal leaf, and width of marginal leaf were recorded and analyzed using analysis of variance, clustering analysis, Mahalanobis distance, and principal component analysis. Generally, treatment of seeds with NaN₃ has brought many distinct and statistically significant phenotypic changes on both quantitative and qualitative leaf traits of the M2 lines. The changes in the NaN₃ treated and locally adapted genotypes of Gumero and Zeri Tesfay are promising; producing the highest mean length of basal leaf (p ≤ 0.01). NaN₃ treated seeds of Baha Necho, Gumero, and Hirhir developed the highest mean width of basal leaf. Locally adapted genotypes have responded positively to NaN₃ treatment, generating better leaf traits as compared to the research improved ones. This study was the first of its kind in exploring the effects of NaN₃ seed treatment on leaf traits of sesame genotypes. The findings of this study will, therefore, serve as a steppingstone to look into the effects of the changes in sesame yield and initiate future genetic and molecular studies on the responsive genotypes.

Improved assembly and annotation of the sesame genome

Sesame (Sesamum indicum L.) is an important oilseed crop that produces abundant seed oil and has a pleasant flavor and high nutritional value. To date, several Illumina-based genome assemblies corresponding to different sesame genotypes have been published and widely used in genetic and genomic studies of sesame. However, these assemblies consistently showed low continuity with numerous gaps. Here, we reported a high-quality, reference-level sesame genome assembly by integrating PacBio high-fidelity sequencing and Hi-C technology. Our updated sesame assembly was 309.35 Mb in size with a high chromosome anchoring rate (97.54%) and contig N50 size (13.48 Mb), which were better than previously published genomes. We identified 163.38 Mb repetitive elements and 24,345 high-confidence protein-coding genes in the updated sesame assembly. Comparative genomic analysis showed that sesame shared an ancient whole-genome duplication event with two Lamiales species. A total of 2,782 genes were tandemly duplicated. We also identified several genes that were likely involved in fatty acid and triacylglycerol biosynthesis. Our improved sesame assembly and annotation will facilitate future genetic studies and genomics-assisted breeding of 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.

Evaluation of nine statistics to identify QTLs in bulk segregant analysis using next generation sequencing approaches

Background

Bulk segregant analysis (BSA) combined with next generation sequencing is a powerful tool to identify quantitative trait loci (QTL). The impact of the size of the study population and the percentage of extreme genotypes analysed have already been assessed. But a good comparison of statistical approaches designed to identify QTL regions using next generation sequencing (NGS) technologies for BSA is still lacking.

Results

We developed an R code to simulate QTLs in bulks of F2 contrasted lines. We simulated a range of recombination rates based on estimations using different crop species. The simulations were used to benchmark the ability of statistical methods identify the exact location of true QTLs. A single QTL led to a shift in allele frequency across a large fraction of the chromosome for plant species with low recombination rate. The smoothed version of all statistics performed best notably the smoothed Euclidean distance-based statistics was always found to be more accurate in identifying the location of QTLs. We propose a simulation approach to build confidence interval statistics for the detection of QTLs.

Conclusion

We highlight the statistical methods best suited for BSA studies using NGS technologies in crops even when recombination rate is low. We also provide simulation codes to build confidence intervals and to assess the impact of recombination for application to other studies. This computational study will help select NGS-based BSA statistics that are useful to the broad scientific community.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08718-y.

Prospects and potentials of underutilized leafy Amaranths as vegetable use for health-promotion

Climate change causes environmental variation worldwide, which is one of the most serious threats to global food security. In addition, more than 2 billion people in the world are reported to suffer from serious malnutrition, referred to as 'hidden hunger.' Dependence on only a few crops could lead to the loss of genetic diversity and high fragility of crop breeding in systems adapting to global scale climate change. The exploitation of underutilized species and genetic resources, referred to as orphan crops, could be a useful approach for resolving the issue of adaptability to environmental alteration, biodiversity preservation, and improvement of nutrient quality and quantity to ensure food security. Moreover, the use of these alternative crops will help to increase the human health benefits and the income of farmers in developing countries. In this review, we highlight the potential of orphan crops, especially amaranths, for use as vegetables and health-promoting nutritional components. This review highlights promising diversified sources of amaranth germplasms, their tolerance to abiotic stresses, and their nutritional, phytochemical, and antioxidant values for vegetable purposes. Betalains (betacyanins and betaxanthins), unique antioxidant components in amaranth vegetables, are also highlighted regarding their chemodiversity across amaranth germplasms and their stability and degradation. In addition, we discuss the physiological functions, antioxidant, antilipidemic, anticancer, and antimicrobial activities, as well as the biosynthesis pathway, molecular, biochemical, genetics, and genomic mechanisms of betalains in detail.

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

BACKGROUND

Unrevealing the genetic makeup of crop morpho-agronomic traits is essential for improving yield quality and sustainability. Sesame (Sesamum indicum L.) is one of the oldest oil-crops in the world. Despite its economic and agricultural importance, it is an 'orphan crop-plant' that has undergone limited modern selection, and, as a consequence preserved wide genetic diversity. Here we established a new sesame panel (SCHUJI) that contains 184 genotypes representing wide phenotypic variation and is geographically distributed. We harnessed the natural variation of this panel to perform genome-wide association studies for morpho-agronomic traits under the Mediterranean climate conditions.

RESULTS

Field-based phenotyping of the SCHUJI panel across two seasons exposed wide phenotypic variation for all traits. Using 20,294 single-nucleotide polymorphism markers, we detected 50 genomic signals associated with these traits. Major genomic region on LG2 was associated with flowering date and yield-related traits, exemplified the key role of the flowering date on productivity.

CONCLUSIONS

Our results shed light on the genetic architecture of flowering date and its interaction with yield components in sesame and may serve as a basis for future sesame breeding programs in the Mediterranean basin.

Identification of Candidate Genes Regulating the Seed Coat Color Trait in Sesame (Sesamum indicum L.) Using an Integrated Approach of QTL Mapping and Transcriptome Analysis

Seed coat color is an important seed quality trait in sesame. However, the genetic mechanism of seed coat color variation remains elusive in sesame. We conducted a QTL mapping of the seed coat color trait in sesame using an F₂ mapping population. With the aid of the newly constructed superdense genetic linkage map comprised of 22,375 bins distributed in 13 linkage groups (LGs), 17 QTLs of the three indices (i.e., L, a, and b values) of seed coat color were detected in seven intervals on four LGs, with a phenotype variance explanation rate of 4.46-41.53%. A new QTL qSCa6.1 on LG 6 and a QTL hotspot containing at least four QTLs on LG 9 were further identified. Variants screening of the target intervals showed that there were 84 genes which possessed the variants that were high-impact and co-segregating with the seed coat color trait. Meanwhile, we performed the transcriptome comparison of the developing seeds of a white- and a black-seeded variety, and found that the differentially expressed genes were significantly enriched in 37 pathways, including three pigment biosynthesis related pathways. Integration of variants screening and transcriptome comparison results suggested that 28 candidate genes probably participated in the regulation of the seed coat color in sesame; of which, 10 genes had been proved or suggested to be involved in pigments biosynthesis or accumulation during seed formation. The findings gave the basis for the mechanism of seed coat color regulation in sesame, and exhibited the effects of the integrated approach of genome resequencing and transcriptome analysis on the genetics analysis of the complex traits.

Genome-wide association study uncovers new genetic loci and candidate genes underlying seed chilling-germination in maize

As one of the major crops, maize (Zea mays L.) is mainly distributed in tropical and temperate regions. However, with the changes of the environments, chilling stress has become a significantly abiotic stress affecting seed germination and thus the reproductive and biomass accumulation of maize. Herein, we investigated five seed germination-related phenotypes among 300 inbred lines under low-temperature condition (10 °C). By combining 43,943 single nucleotide polymorphisms (SNPs), a total of 15 significant (P < 2.03 ×  10^-6) SNPs were identified to correlate with seed germination under cold stress based on the FarmCPU model in GWAS, among which three loci were repeatedly associated with multiple traits. Ten gene models were closely linked to these three variations, among which Zm00001d010454, Zm00001d010458, Zm00001d010459, and Zm00001d050021 were further verified by candidate gene association study and expression pattern analysis. Importantly, these candidate genes were previously reported to involve plant tolerance to chilling stress and other abiotic stress. Our findings contribute to the understanding of the genetic and molecular mechanisms underlying chilling germination in maize.

Genome-wide association study and its applications in the non-model crop Sesamum indicum

BACKGROUND

Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame.

RESULTS

In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD ( http://sigedid.ucad.sn/ ) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame.

CONCLUSIONS

Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop.