Genetics, genomics and breeding of cucurbits

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Collectively, the information obtained from the GBS data enabled deep insight into the diversity present and genetic relationships among accessions within the collection, and will provide a valuable resource for genetic analyses, gene discovery, crop improvement, and germplasm preservation. Improvements in crop yield, ability to withstand abiotic and biotic stresses, and superior product quality all depend on genetic variation for key agronomic and horticultural traits. In search of such variation, breeders frequently turn to germplasm collections to find new sources of valuable characteristics, especially resistances to diseases, insects, and environmental stresses such as heat, drought, salt, or cold.

To facilitate these breeding efforts and maintain critical diversity for future generations, many national and international institutions have developed extensive germplasm collections to provide repositories of genetic variation. More than gene banks have been established worldwide 1. Collections are typically made from locations throughout the globe, with particular emphasis on centers of crop diversity. The importance of such collections as a critical first step to conserve biological variation, especially in light of genetic erosion resulting from habitat loss, adoption of modern varieties, and climate change, is increasingly recognized as a critical global good, both in scientific and broader public spheres 2 , 3.

While creation and maintenance of these valuable collections is essential, questions arise as to how to catalog, unlock, manage, and preserve the valuable diversity they contain.

How do we evaluate the extent and nature of variation that exists within the collection? How can we access that variation for crop improvement? Fortunately, the past decade has ushered in powerful genomic tools that allow for high throughput, high resolution, genetic characterization, while also providing breeders more efficient access to, and use of, the diversity available within collections.

Collections for the Cucurbitaceae family, which includes many high-value crops consumed as vegetables and fruits throughout the world, face the above-mentioned challenges for germplasm preservation and utilization 4. Cucumber Cucumis sativus L. The primary and secondary centers of diversity for the species are located in India and Southeast Asia, respectively 7 , 8. Genomic analysis of cultivated cucumber C. The Indian group, which is thought to form the basal group, maintains a large proportion of the genetic diversity and also includes the wild cucumber, C.


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Deep resequencing of a core collection of cucumber lines, sampled from accessions worldwide, suggests that the domestication process led to a severe genetic bottleneck, resulting in reduction in diversity relative to wild accessions More than putative selective sweeps appear to be associated with domestication, including extended linkage disequilibrium in regions surrounding loci associated with key fruit traits such as size and bitterness. Results of the genomic analyses, including assignment of a basal role of the Indian group and separation of the orange-endocarp Xishuangbanna group, complement prior genetic and morphological assessments 12 , 13 , 14 , 15 , These analyses have allowed for evolutionary insight into the relationships and domestication trajectories among cucumber accessions.

The NPGS collection comprises cucumber accessions representing the primary cucumber gene pool C. This collection, which is primarily composed of cultivars, land races, and varieties collected from around the world, has been extensively utilized by breeders searching for a variety of traits, including resistance to downy mildew 17 causal agent: Pseudoperonospora cubensis , powdery mildew 18 causal agent: Podosphaera xanthii , Phytophthora fruit rot 19 , 20 causal agent: Phytophthora capsici , belly rot 21 , 22 causal agent: Rhizoctonia solani , and root knot nematodes Meloidogyne spp.

However, to date, there have been very limited efforts to genetically characterize the US cucumber collection.

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Meglic et al. Lv et al. Current genomic technologies allow for much higher throughput and full genome analyses. The dramatically reduced cost of sequencing, high-throughput sample preparation, and efficient bioinformatics now make it feasible to perform genomic analysis on increasingly large numbers of samples for plant germplasm research 29 , The resultant high-throughput single-nucleotide polymorphism SNP markers provided high-definition genetic characterization of the US cucumber germplasm collection, allowing for assessment of genetic diversity and population structure, identification of markers that are highly associated with important agronomic traits through genome-wide association studies GWAS , and development of a molecularly informed publicly accessible core population to facilitate breeding and preservation efforts.

Genotyping of the cucumber accessions was performed following the GBS protocol 30 , using ApeK I as the restriction enzyme. The resulting plex or plex libraries were sequenced on a HiSeq system Illumina Inc. Principal component analysis PCA was performed using Plink To visualize the pairwise F ST values among different groups, multidimensional scaling MDS was conducted using the cmdscale function in R to transform F ST values into two-dimensional values, which were used for plotting.

Genetics, Genomics, and Breeding of Tomato

The phenotypic data of 13 important traits for cucumber, including three related to disease resistance anthracnose, downy mildew, and gummy stem blight GSB resistance , three related to root knot nematode resistance resistance to Meloidogyne hapla race 1, M. The phenotypic data were collected over the last 30 years by the Cucurbit Breeding program of North Carolina State University.

Phenotypic data from accessions genotyped in the present study were used for GWAS. We used a total of 72, biallelic SNPs without any filtering to construct the kinship K matrix, which was used to correct for population structure and kinship in the GWAS analyses. GenoCore 42 was used to select a subset of accessions that captured the majority of the allelic diversity of the cucumber accessions, with the following parameters: -d 0.

Combined with phenotypic analysis, we obtained the final core collection containing cucumber accessions, of which were genotyped in the current study. The percentage of the allelic diversity captured by the accessions in this core collection was determined using GenoCore.

The core collection was further evaluated by PCA, using the same methods described above for the entire collection. The accessions from India along with 32 plant introductions PIs from the surrounding regions of Bhutan, Malaysia, Nepal, Myanmar, Pakistan, Sri Lanka, and Thailand were classified separately from accessions from other Asian countries, since India and the surrounding regions are considered as the center of origin of cultivated cucumber 6 , In addition, since Turkey is a country straddling Asia and Europe, we put accessions from Turkey as an independent group.

We genotyped these cucumber accessions, as well as two non-cucumber but closely related accessions PI C. The 1. Approximately 3. The average MAF was 0. Three major clades were identified. The remaining accessions were separated into two major clades. PI , C. Each accession is represented by a vertical bar. Each color represents one ancestral population, and the length of each colored segment in each vertical bar represents the proportion contributed by ancestral populations. PCA of these cucumber accessions illustrated a similar pattern of their phylogenetic relationships Fig.

Our results are consistent with those reported in Qi et al. To investigate the population structure of cucumber, the Bayesian clustering algorithm implemented in the STUCTURE program 37 was first used to estimate ancestry proportions for each cucumber accession. As shown in Fig.

The population structure result at this optimal K was consistent with the phylogenetic tree and PCA results; all suggested three primary clusters in the cucumber accessions collected from NPGS. The Indian accessions within the U. NPGS were collected in two time periods: a first set of materials was entered into the system prior to , and a second set collected in The accessions collected in were primarily from the states of Rajasthan, Uttar Pradesh, and Madhya Pradesh, representing regions in North and Central India that were largely missed in the prior collection The Indian accessions were differentially distributed among the different subclades, especially those from Rajasthan that were primarily associated with subclade 2, suggesting that the subclades, in part, reflect geographic distribution within India.

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Accessions from prior collections from South or Southwest India Maharashtra, Karnataka, and Kerala clustered in subclade 3. Subclade 1 primarily contained accessions from Madhya Pradesh in central India. The great majority of the East Asian accessions were collected from China. Those from Japan and South Korea largely clustered with each other; the remaining subclades were almost exclusively composed of accessions from China Supplementary Fig. The North American accessions, also showed division into two distinct subclades.

One group was largely comprised of pickling processing cultigens and the other of slicing fresh market cultigens Supplementary Fig.

Cucurbit / Instytut ogrodnictwa

S1 , reflecting the two predominant market classes produced in the US. Variable LD decays were detected in different groups. We then evaluated the genetic diversity within different groups. We further investigated population divergence among different groups by calculating pairwise fixation index F ST values. F ST between East Asia vs. Multidimensional scaling of pairwise F ST values between different cucumber groups.

We collected historical phenotypic data of cucumber accessions from the NPGS for 13 agronomic traits, which included three traits related to disease resistance anthracnose, downy mildew, and GSB resistance , three related to root knot nematode resistance resistance to Meloidogyne hapla race 1, M. For each trait, data were available for around — accessions that were genotyped using GBS in this study Supplementary Table S 3. The phenotypic data largely followed normal distribution without significant skewness except for resistance to M.

Significantly associated SNPs could be identified except for resistance to M. For anthracnose resistance, two regions on chromosome 7 were identified Fig. A total of 11 SNPs spanning one region from 1. Other significantly associated SNPs were identified at For downy mildew resistance, a region on chromosome 5 spanning from Eight other SNPs significantly associated with downy mildew resistance were identified, with one on chromosome 3 For GSB resistance, three regions, one on chromosome 2, one on chromosome 5 and one on chromosome 7 were identified Fig.

The region on chromosome 2 spanned from Another two SNPs, on chromosome 3 For root knot nematode resistance, no regions were identified to be significantly associated with resistance to M. Six SNPs, one on chromosome 3 Fruit yield trait in the cucumber accessions was investigated at two locations, Iowa and North Carolina. GWAS for fruit yield using data from each of the two locations as well as combined identified a total of nine significantly associated SNPs, one on chromosome 2 S4a and Supplementary Table S 5.

Breadcrumb

GWAS were performed for three traits related to fruit shelf life, weight loss, loss of firmness, and shriveling. Five SNPs, three on chromosome 4 2. S4b and Supplementary Table S 5.


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  8. For chilling tolerance, eighteen SNPs were identified, with ten on chromosome 1, one on chromosome 2, two on chromosome 4 and five on chromosome 7.