Dissection Of Traits Research Articles

Genomic-Wide Association Studies For Resistance to Anthracnose in a Pseudo-F2 Mango Population

Goal: This study aimed to apply genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with resistance to mango anthracnose. Theoretical Framework: The statistical methods of GWAS aim to identify associations among genotypes with phenotypes for marker-assisted selection (MAS) or genome dissection of important traits. Method: Ninety-four plants of a ‘Haden’ × ‘Tommy Atkins’ pseudo-F2 population were genotyped with 705 SNPs and phenotyped for resistance to Colletotrichum siamense, anthracnose causal agent, by spraying young leaves, three times at different periods, with 103 conidia/mL. Qualitative GWAS, with Plink Fisher’s exact test and Cochran-Armitage trend genotypic test, and quantitative GWAS, with Tassel general (GLM) and mixed linear models (MLM) were applied. F2 plants with symptoms mean absence >75% were considered moderately resistant to the fungus. Results and discussion: Qualitative and quantitative GWAS methods combined enable the identification of two consensus SNP loci controlling 24.76% of total variance to mango anthracnose resistance. Other four SNPs, identified, exclusively in quantitative or qualitative analysis, explained 28.55% of total variance to mango anthracnose. These SNPs loci are promising candidate for further PCR primers design in order to apply MAS in mango. Research implications: This study facilitates the identification of consensus chromosomal regions associated with mango anthracnose resistance. The application of MAS in mango breeding will accelerate the development of new cultivars. Originality/Value: To our knowledge, this a pioneering GWAS study applied to anthracnose, which is the most significant and widespread fungal disease affecting mango fruit globally.

Genetic Dissection of Flowering and Plant Architectural Traits to Develop Early Maturing Compact Upland Cotton Genotypes for High-Density Planting

Genetic Dissection of Flowering and Plant Architectural Traits to Develop Early Maturing Compact Upland Cotton Genotypes for High-Density Planting

The Duck 1000 Genomes Project: Achievements and perspectives

AbstractThe duck (Anas platyrhynchos) is not only a vital farm animal but also an excellent model for genetic dissection of economic traits. The integration of multiomics data provides a powerful approach to elucidate the genetic basis of domestication and phenotype variation. Since its inception in 2014, the Duck 1000 Genomes Project has aimed to uncover the genetic foundation of key economic traits in ducks by combining multiomics data including genomic, transcriptomic, and metabolomic from various natural and segregating populations. This paper summarizes the strategies and achievements of the Duck 1000 Genomes Project, highlighting the reference genome assembly, genome evolution analysis, and the identification of genes and causative mutations responsible for key economic traits in ducks. We also discuss perspectives and potential challenges in functional genomic studies that could further accelerate duck molecular breeding.

Genetic dissection of value-added quality traits and agronomic parameters through genome-wide association mapping in bread wheat (T. aestivum L.).

Bread wheat (T. aestivum) is one of the world's most widely consumed cereals. Since micronutrient deficiencies are becoming more common among people who primarily depend upon cereal-based diets, a need for better-quality wheat varieties has been felt. An association panel of 154 T. aestivum lines was evaluated for the following quality traits: grain appearance (GA) score, grain hardness (GH), phenol reaction (PR) score, protein percent, sodium dodecyl sulfate (SDS) sedimentation value, and test weight (TWt). In addition, the panel was also phenotyped for grain yield and related traits such as days to heading, days to maturity, plant height, and thousand kernel weight for the year 2017-18 at the Borlaug Institute for South Asia (BISA) Ludhiana and Jabalpur sites. We performed a genome-wide association analysis on this panel using 18,351 genotyping-by-sequencing (GBS) markers to find marker-trait associations for quality and grain yield-related traits. We detected 55 single nucleotide polymorphism (SNP) marker trait associations (MTAs) for quality-related traits on chromosomes 7B (10), 1A (9), 2A (8), 3B (6), 2B (5), 7A (4), and 1B (3), with 3A, 4A, and 6D, having two and the rest, 4B, 5A, 5B, and 1D, having one each. Additionally, 20 SNP MTAs were detected for yield-related traits based on a field experiment conducted in Ludhiana on 7D (4) and 4D (3) chromosomes, while 44 SNP MTAs were reported for Jabalpur on chromosomes 2D (6), 7A (5), 2A (4), and 4A (4). Utilizing these loci in marker-assisted selection will benefit from further validation studies for these loci to improve hexaploid wheat for better yield and grain quality.

Comprehensive identification of genomic and environmental determinants of phenotypic plasticity in maize.

Maize phenotypes are plastic, determined by the complex interplay of genetics and environmental variables. Uncovering the genes responsible and understanding how their effects change across a large geographic region are challenging. In this study, we conducted systematic analysis to identify environmental indices that strongly influence 19 traits (including flowering time, plant architecture, and yield component traits) measured in the maize nested association mapping (NAM) population grown in 11 environments. Identified environmental indices based on day length, temperature, moisture, and combinations of these are biologically meaningful. Next, we leveraged a total of more than 20 million SNP and SV markers derived from recent de novo sequencing of the NAM founders for trait prediction and dissection. When combined with identified environmental indices, genomic prediction enables accurate performance predictions. Genome-wide association studies (GWASs) detected genetic loci associated with the plastic response to the identified environmental indices for all examined traits. By systematically uncovering the major environmental and genomic factors underlying phenotypic plasticity in a wide variety of traits and depositing our results as a track on the MaizeGDB genome browser, we provide a community resource as well as a comprehensive analytical framework to facilitate continuing complex trait dissection and prediction in maize and other crops. Our findings also provide a conceptual framework for the genetic architecture of phenotypic plasticity by accommodating two alternative models, regulatory gene model and allelic sensitivity model, as special cases of a continuum.

Unravelling the genetic basis and regulation networks related to fibre quality improvement using chromosome segment substitution lines in cotton.

The elucidation of genetic architecture and molecular regulatory networks underlying complex traits remains a significant challenge in life science, largely due to the substantial background effects that arise from epistasis and gene-environment interactions. The chromosome segment substitution line (CSSL) is an ideal material for genetic and molecular dissection of complex traits due to its near-isogenic properties; yet a comprehensive analysis, from the basic identification of substitution segments to advanced regulatory network, is still insufficient. Here, we developed two cotton CSSL populations on the Gossypium hirsutum background, representing wide adaptation and high lint yield, with introgression from G. barbadense, representing superior fibre quality. We sequenced 99 CSSLs that demonstrated significant differences from G. hirsutum in fibre, and characterized 836 dynamic fibre transcriptomes in three crucial developmental stages. We developed a workflow for precise resolution of chromosomal substitution segments; the genome sequencing revealed substitutions collectively representing 87.25% of the G.barbadense genome. Together, the genomic and transcriptomic survey identified 18 novel fibre-quality-related quantitative trait loci with high genetic contributions and the comprehensive landscape of fibre development regulation. Furthermore, analysis determined unique cis-expression patterns in CSSLs to be the driving force for fibre quality alteration; building upon this, the co-expression regulatory network revealed biological relationships among the noted pathways and accurately described the molecular interactions of GhHOX3, GhRDL1 and GhEXPA1 during fibre elongation, along with reliable predictions for their interactions with GhTBA8A5. Our study will enhance more strategic employment of CSSL in crop molecular biology and breeding programmes.

Empowering cotton breeding programs through the strategic exploration and exploitation of phenotypic diversity of genetic resources under climate change conditions

Plant phenotype assessment approaches have facilitated crop improvement in recent years by providing opportunities for the dissection of complex nature quantitative traits. Fiber quality and yield are significant economic traits with a complex nature mainly due to interactions between its genetic architecture and the surrounding environment. These economic traits showed stagnant performance in recent decades due to eroded genetic diversity via the selection process. It necessitates exploiting conserved germplasm resources to rebuild genetic diversity within cotton cultivars. Compared to the conventional univariate selection methods using a single trait at a time, the multivariate analysis extends its selection to multiple variables for the selection of genotypes. In evaluating a core collection of cotton over two years using various phenotyping methods, multivariate analysis, particularly principal component analysis (PCA), was employed to assess genetic diversity. The PCA analysis demonstrated substantial variability of 39.83% and 45.37% across two years, highlighting a significant impact on yield and fiber quality attributes. The cumulative eigenvalues of principal components exceeding 80% supported the classification of accessions into six distinct groups based on their trait variability. Both PCA and cluster analysis successfully categorized accessions into six groups, indicating high diversity and suggesting their potential utility in breeding programs. The diverse genotypes identified in this study would be potential resources for variety development and accelerated breeding programs, particularly focusing on stress resilience under climate change scenarios. By utilizing these diverse genotypes, there is an opportunity to enhance breeding strategies and accelerate the development of improved cotton varieties with enhanced yield and fiber quality attributes. The significance of our study lies in its demonstration of the importance of genetic diversity in improving yield and fiber quality attributes, thus underscoring its relevance to cotton breeding programs.

TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression–phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

Genetic dissection of yield and quality-related traits in a Colombian Andigenum potato collection, revealed by genome-wide association and genomic prediction analyses

Genetic dissection of yield and quality-related traits in a Colombian Andigenum potato collection, revealed by genome-wide association and genomic prediction analyses

Computational tools for plant genomics and breeding.

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein–protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Genome assembly of autotetraploid Actinidia arguta highlights adaptive evolution and enables dissection of important economic traits

Actinidia arguta, the most widely distributed Actinidia species and the second cultivated species in the genus, can be distinguished from the currently cultivated Actinidia chinensis on the basis of its small and smooth fruit, rapid softening, and excellent cold tolerance. Adaptive evolution of tetraploid Actinidia species and the genetic basis of their important agronomic traits are still unclear. Here, we generated a chromosome-scale genome assembly of an autotetraploid male A. arguta accession. The genome assembly was 2.77 Gb in length with a contig N50 of 9.97 Mb and was anchored onto 116 pseudo-chromosomes. Resequencing and clustering of 101 geographically representative accessions showed that they could be divided into two geographic groups, Southern and Northern, which first diverged 12.9 million years ago. A. arguta underwent two prominent expansions and one demographic bottleneck from the mid-Pleistocene climate transition to the late Pleistocene. Population genomics studies using paleoclimate data enabled us to discern the evolution of the species’ adaptation to different historical environments. Three genes (AaCEL1, AaPME1, and AaDOF1) related to flesh softening were identified by multi-omics analysis, and their ability to accelerate flesh softening was verified through transient expression assays. A set of genes that characteristically regulate sexual dimorphism located on the sex chromosome (Chr3) or autosomal chromosomes showed biased expression during stamen or carpel development. This chromosome-level assembly of the autotetraploid A. arguta genome and the genes related to important agronomic traits will facilitate future functional genomics research and improvement of A. arguta.

Multi-omic dissection of the cold resistance traits of white water lily.

The white water lily (Nymphaea candida), exemplifying nature's resilience, thrives in the high-altitude terrains of Xinjiang, China, serving as an ideal model for investigating cold adaptation mechanisms in aquatic plants. This study meticulously elucidates the complex cold adaptation mechanisms of the white water lily through a comprehensive and integrated methodological approach. We discovered that the water lily undergoes ecodormancy in winter, retaining high cellular viability and growth potential. During overwintering, the white water lily demonstrates effective resource reallocation, a process facilitated by morphological adjustments, thereby strengthening its resistance to cold temperatures. This enhancement is achieved particularly through the compartmentalization of large vacuoles, the accumulation of osmoregulatory substances, and an increased antioxidant capacity. We established the first exhaustive full-length transcriptome for the white water lily. A subsequent comprehensive analysis of the transcriptome, phytohormones, and metabolome uncovered a multifaceted regulatory network orchestrating cold adaptation. Our research spotlights phytohormone signaling, amino acid metabolism, and circadian rhythms as key elements in the water lily's defense against cold. The results emphasize the critical role of nitrogen metabolism, especially amino acid-related pathways, during cold stress. Metabolite profiling revealed the importance of compounds like myo-inositol and L-proline in enhancing cold tolerance. Remarkably, our study demonstrates that the white water lily notably diminishes the utilization of unsaturated fatty acids in its temperature regulation strategies. In conclusion, this research substantially enriches our understanding of the white water lily's intricate cold adaptation mechanisms, offering new perspectives on the adaptive strategies of aquatic plants and potential applications in agricultural advancement.

Uncovering the Genomic Regions Associated with Yield Maintenance in Rice Under Drought Stress Using an Integrated Meta-Analysis Approach.

The complex trait of yield is controlled by several quantitative trait loci (QTLs). Given the global water deficit issue, the development of rice varieties suitable for non-flooded cultivation holds significant importance in breeding programs. The powerful approach of Meta-QTL (MQTL) analysis can be used for the genetic dissection of complicated quantitative traits. In the current study, a comprehensive MQTL analysis was conducted to identify consistent QTL regions associated with drought tolerance and yield-related traits under water deficit conditions in rice. In total, 1087 QTLs from 134 rice populations, published between 2000 to 2021, were utilized in the analysis. Distinct MQTL analysis of the relevant traits resulted in the identification of 213 stable MQTLs. The confidence interval (CI) for the detected MQTLs was between 0.12 and 19.7cM. The average CI of the identified MQTLs (4.68cM) was 2.74 times narrower compared to the average CI of the initial QTLs. Interestingly, 63 MQTLs coincided with SNP peak positions detected by genome-wide association studies for yield and drought tolerance-associated traits under water deficit conditions in rice. Considering the genes located both in the QTL-overview peaks and the SNP peak positions, 19 novel candidate genes were introduced, which are associated with drought response index, plant height, panicle number, biomass, and grain yield. Moreover, an inclusive MQTL analysis was performed on all the traits to obtain "Breeding MQTLs". This analysis resulted in the identification of 96 MQTLs with a CI ranging from 0.01 to 9.0cM. The mean CI of the obtained MQTLs (2.33cM) was 4.66 times less than the mean CI of the original QTLs. Thirteen MQTLs fulfilling the criteria of having more than 10 initial QTLs, CI < 1cM, and an average phenotypic variance explained greater than 10%, were designated as "Breeding MQTLs". These findings hold promise for assisting breeders in enhancing rice yield under drought stress conditions.

The Genetic Dissection of Nitrogen Use-Related Traits in Flax (Linum usitatissimum L.) at the Seedling Stage through the Integration of Multi-Locus GWAS, RNA-seq and Genomic Selection.

Nitrogen (N), the most important macro-nutrient for plant growth and development, is a key factor that determines crop yield. Yet its excessive applications pollute the environment and are expensive. Hence, studying nitrogen use efficiency (NUE) in crops is fundamental for sustainable agriculture. Here, an association panel consisting of 123 flax accessions was evaluated for 21 NUE-related traits at the seedling stage under optimum N (N+) and N deficiency (N-) treatments to dissect the genetic architecture of NUE-related traits using a multi-omics approach integrating genome-wide association studies (GWAS), transcriptome analysis and genomic selection (GS). Root traits exhibited significant and positive correlations with NUE under N- conditions (r = 0.33 to 0.43, p < 0.05). A total of 359 QTLs were identified, accounting for 0.11% to 23.1% of the phenotypic variation in NUE-related traits. Transcriptomic analysis identified 1034 differentially expressed genes (DEGs) under contrasting N conditions. DEGs involved in N metabolism, root development, amino acid transport and catabolism and others, were found near the QTLs. GS models to predict NUE stress tolerance index (NUE_STI) trait were tested using a random genome-wide SNP dataset and a GWAS-derived QTLs dataset. The latter produced superior prediction accuracy (r = 0.62 to 0.79) compared to the genome-wide SNP marker dataset (r = 0.11) for NUE_STI. Our results provide insights into the QTL architecture of NUE-related traits, identify candidate genes for further studies, and propose genomic breeding tools to achieve superior NUE in flax under low N input.

Recombinant inbred lines derived from wide crosses in Pisum

Genomic resources are becoming available for Pisum but to link these to phenotypic diversity requires well marked populations segregating for relevant traits. Here we describe two such resources. Two recombinant inbred populations, derived from wide crosses in Pisum are described. One high resolution mapping population involves cv Caméor, for which the first pea whole genome assembly was obtained, crossed to JI0281, a basally divergent P. sativum sativum landrace from Ethiopia. The other is an inter sub-specific cross between P. s. sativum and the independently domesticated P. s. abyssinicum. The corresponding genetic maps provide information on chromosome level sequence assemblies and identify structural differences between the genomes of these two Pisum subspecies. In order to visualise chromosomal translocations that distinguish the mapping parents, we created a simplified version of Threadmapper to optimise it for interactive 3-dimensional display of multiple linkage groups. The genetic mapping of traits affecting seed coat roughness and colour, plant height, axil ring pigmentation, leaflet number and leaflet indentation enabled the definition of their corresponding genomic regions. The consequence of structural rearrangement for trait analysis is illustrated by leaf serration. These analyses pave the way for identification of the underlying genes and illustrate the utility of these publicly available resources. Segregating inbred populations derived from wide crosses in Pisum, together with the associated marker data, are made publicly available for trait dissection. Genetic analysis of these populations is informative about chromosome scale assemblies, structural diversity in the pea genome and has been useful for the fine mapping of several discrete and quantitative traits.

Genetic dissection of domestication traits in interspecific chickpea populations.

Chickpea (Cicer arietinum) is a pulse crop that provides an integral source of nutrition for human consumption. The close wild relatives Cicer reticulatum and Cicer echinospermum harbor untapped genetic diversity that can be exploited by chickpea breeders to improve domestic varieties. Knowledge of genomic loci that control important chickpea domestication traits will expedite the development of improved chickpea varieties derived from interspecific crosses. Therefore, we set out to identify genomic loci underlying key chickpea domestication traits by both association and quantitative trait locus (QTL) mapping using interspecific F2 populations. Diverse phenotypes were recorded for various agronomic traits. A total of 11 high-confidence markers were detected on chromosomes 1, 3, and 7 by both association and QTL mapping; these were associated with growth habit, flowering time, and seed traits. Furthermore, we identified candidate genes linked to these markers, which advanced our understanding of the genetic basis of domestication traits and validated known genes such as the FLOWERING LOCUS gene cluster that regulates flowering time. Collectively, this study has elucidated the genetic basis of chickpea domestication traits, which can facilitate the development of superior chickpea varieties.

QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize

Genetic dissection of agronomic traits is important for crop improvement and global food security. Phenotypic variation of tassel branch number (TBN), a major breeding target, is controlled by many quantitative trait loci (QTLs). The lack of large-scale QTL cloning methodology constrains the systematic dissection of TBN, which hinders modern maize breeding. Here, we devise QTG-Miner, a multi-omics data-based technique for large-scale and rapid cloning of quantitative trait genes (QTGs) in maize. Using QTG-Miner, we clone and verify seven genes underlying seven TBN QTLs. Compared to conventional methods, QTG-Miner performs well for both major- and minor-effect TBN QTLs. Selection analysis indicates that a substantial number of genes and network modules have been subjected to selection during maize improvement. Selection signatures are significantly enriched in multiple biological pathways between female heterotic groups and male heterotic groups. In summary, QTG-Miner provides a large-scale approach for rapid cloning of QTGs in crops and dissects the genetic base of TBN for further maize breeding.

Metabolic-GWAS provides insights into genetic architecture of seed metabolome in buckwheat

BackgroundBuckwheat (Fagopyrum spp.), belonging to the Polygonaceae family, is an ancient pseudo-cereal with high nutritional and nutraceutical properties. Buckwheat proteins are gluten-free and show balanced amino acid and micronutrient profiles, with higher content of health-promoting bioactive flavonoids that make it a golden crop of the future. Plant metabolome is increasingly gaining importance as a crucial component to understand the connection between plant physiology and environment and as a potential link between the genome and phenome. However, the genetic architecture governing the metabolome and thus, the phenome is not well understood. Here, we aim to obtain a deeper insight into the genetic architecture of seed metabolome in buckwheat by integrating high throughput metabolomics and genotyping-by-sequencing applying an array of bioinformatics tools for data analysis.ResultsHigh throughput metabolomic analysis identified 24 metabolites in seed endosperm of 130 diverse buckwheat genotypes. The genotyping-by-sequencing (GBS) of these genotypes revealed 3,728,028 SNPs. The Genome Association and Prediction Integrated Tool (GAPIT) assisted in the identification of 27 SNPs/QTLs linked to 18 metabolites. Candidate genes were identified near 100 Kb of QTLs, providing insights into several metabolic and biosynthetic pathways.ConclusionsWe established the metabolome inventory of 130 germplasm lines of buckwheat, identified QTLs through marker trait association and positions of potential candidate genes. This will pave the way for future dissection of complex economic traits in buckwheat.

Pan-genome analysis sheds light on structural variation-based dissection of agronomic traits in melon crops.

Sweetness and appearance of fresh fruits are key palatable and preference attributes for consumers and are often controlled by multiple genes. However, fine-mapping the key loci or genes of interest by single genome-based genetic analysis is challenging. Herein, we present the chromosome-level genome assembly of one landrace melon accession (Cucumis melo ssp. agrestis) with wild morphologic features and thus construct a melon pan-genome atlas via integrating sequenced melon genome datasets. Our comparative genomic analysis reveals a total of 3.4 million genetic variations, of which the presence/absence variations (PAVs) are mainly involved in regulating the function of genes for sucrose metabolism during melon domestication and improvement. We further resolved several loci that are accountable for sucrose contents, flesh color, rind stripe and suture using a structural variation (SV)-based genome-wide association study (GWAS). Furthermore, via BSA-seq and map-based cloning, we uncovered that a single gene, (CmPIRL6), determines the edible or inedible characteristics of melon fruit exocarp. These findings provide important melon pan-genome information and provide a powerful toolkit for future pan-genome-informed cultivar breeding of melon.