A new SNP genotyping technology Target SNP-seq and its application in genetic analysis of cucumber varieties

QTL Mapping of Adult Plant Resistance to Powdery Mildew in Chinese Wheat Landrace Baidatou

Wheat powdery mildew, caused by the biotrophic fungus Blumeria graminis f. sp. tritici (Bgt), is one of the most devastating diseases affecting wheat throughout the world. Breeding and growing resistant wheat cultivars is one of the most economic and effective methods to control the disease, and as such, identifying and mapping the new and effective resistance genes is critical. Baidatou, a Chinese wheat landrace, shows excellent field resistance to powdery mildew. To identify the resistance gene(s) in Baidatou, 170 F7:8 recombinant inbred lines (RILs) derived from the cross Mingxian 169/Baidatou were evaluated for powdery mildew response at the adult-plant stage in the experimental fields in Yangling (YL) of Shaanxi Province and Tianshui (TS) in Gansu Province in 2019, 2020, and 2021. The relative area under disease progress curve (rAUDPC) of Mingxian 169/Baidatou F7:8 RILs indicated that the resistance of Baidatou to powdery mildew was controlled by quantitative trait loci (QTLs). Based on bulk segregation analysis combined with the 660K single nucleotide polymorphism (SNP) array and genotyping by target sequencing (16K SNP) of the entire RIL population, two QTLs, QPmbdt.nwafu-2AS and QPmbdt.nwafu-3AS, were identified, and these accounted for up to 44.5% of the phenotypic variation. One of the QTLs was located on the 3.32 cM genetic interval on wheat chromosome 2AS between the kompetitive allele-specific PCR markers AX-111012288 and AX_174233809, and another was located on the 9.6 cM genetic interval on chromosome 3AS between the SNP markers 3A_684044820 and 3A_686681822. These markers could be useful for successful breeding of powdery mildew resistance in wheat.

Deciphering the population structure and genetic basis of growth traits from whole-genome resequencing of the leopard coral grouper ( Plectropomus leopardus)

The leopard coral grouper ( Plectropomus leopardus) is a species of significant economic importance. Although artificial cultivation of P. leopardus has thrived in recent decades, the advancement of selective breeding has been hindered by the lack of comprehensive population genomic data. In this study, we identified over 8.73 million single nucleotide polymorphisms (SNPs) through whole-genome resequencing of 326 individuals spanning six distinct groups. Furthermore, we categorized 226 individuals with high-coverage sequencing depth (≥14×) into eight clusters based on their genetic profiles and phylogenetic relationships. Notably, four of these clusters exhibited pronounced genetic differentiation compared with the other populations. To identify potentially advantageous loci for P. leopardus, we examined genomic regions exhibiting selective sweeps by analyzing the nucleotide diversity ( θπ) and fixation index ( F ST) in these four clusters. Using these high-coverage resequencing data, we successfully constructed the first haplotype reference panel specific to P. leopardus. This achievement holds promise for enabling high-quality, cost-effective imputation methods. Additionally, we combined low-coverage sequencing data with imputation techniques for a genome-wide association study, aiming to identify candidate SNP loci and genes associated with growth traits. A significant concentration of these genes was observed on chromosome 17, which is primarily involved in skeletal muscle and embryonic development and cell proliferation. Notably, our detailed investigation of growth-related SNPs across the eight clusters revealed that cluster 5 harbored the most promising candidate SNPs, showing potential for genetic selective breeding efforts. These findings provide a robust toolkit and valuable insights into the management of germplasm resources and genome-driven breeding initiatives targeting P. leopardus.

Construction of SNP fingerprints and genetic diversity analysis of radish (Raphanus sativus L.)

Radish (Raphanus sativus L.) is a vegetable crop with economic value and ecological significance in the genus Radish, family Brassicaceae. In recent years, developed countries have attached great importance to the collection and conservation of radish germplasm resources and their research and utilization, but the lack of population genetic information and molecular markers has hindered the development of the genetic breeding of radish. In this study, we integrated the radish genomic data published in databases for the development of single-nucleotide polymorphism (SNP) markers, and obtained a dataset of 308 high-quality SNPs under strict selection criteria. With the support of Kompetitive Allele-Specific PCR (KASP) technology, we screened a set of 32 candidate core SNP marker sets to analyse the genetic diversity of the collected 356 radish varieties. The results showed that the mean values of polymorphism information content (PIC), minor allele frequency (MAF), gene diversity and heterozygosity of the 32 candidate core SNP markers were 0.32, 0.30, 0.40 and 0.25, respectively. Population structural analysis, principal component analysis and genetic evolutionary tree analysis indicated that the 356 radish materials were best classified into two taxa, and that the two taxa of the material were closely genetically exchanged. Finally, on the basis of 32 candidate core SNP markers we calculated 15 core markers using a computer algorithm to construct a fingerprint map of 356 radish varieties. Furthermore, we constructed a core germplasm population consisting of 71 radish materials using 32 candidate core markers. In this study, we developed SNP markers for radish cultivar identification and genetic diversity analysis, and constructed DNA fingerprints, providing a basis for the identification of radish germplasm resources and molecular marker-assisted breeding as well as genetic research.

GenoBaits®WheatplusEE: a targeted capture sequencing panel for quick and accurate identification of wheat-Thinopyrum derivatives

KEY MESSAGE

A total of 90,000 capture probes derived from wheat and Thinopyrum elongatum were integrated into one chip, which served as an economical genotype for explorating Thinopyrumspecies and their derivatives. Thinopyrum species play a crucial role as a source of new genetic variations for enhancing wheat traits, including resistance to both abiotic and biotic factors. Accurate identification of exogenous chromosome(s) or chromosome segments or genes is essential following the introduction of alien genetic material into wheat, but this task remains challenging. This study aimed to develop a high-resolution wheat-Thinopyrum elongatum array, named GenoBaits®WheatplusEE, to trace alien genetic information by genotyping using a target sequencing system. This GenoBaits®WheatplusEE array included 90,000 capture probes derived from two species and integrated into one chip, with 10,000 and 80,000 originating from wheat and Th. elongatum, respectively. The capture probes were strategically positioned in genes and evenly distributed across the genome, facilitating the development of a roadmap for identifying each alien gene. The array was applied to the high-throughput identification of the alien chromosomes or segments in Thinopyrum and distantly related species and their derivatives. Our results demonstrated that the GenoBaits®WheatplusEE array could be used for direct identification of the breakpoint of alien segments, determine copy number of alien chromosomes, and reveal variations in wheat chromosomes by a single round of target sequencing of the sample. Additionally, we could efficiently and cost-effectively genotype, supporting the exploration of subgenome composition, phylogenetic relationships, and polymorphisms in essential genes (e.g., Fhb7 gene) among Thinopyrum species and their derivatives. We hope that GenoBaits®WheatplusEE will become a widely adopted tool for exporting wild germplasm for wheat improvement in the future.

A Multiplex PCR System of Novel Microsatellite Loci for Population Genetic Application in Walnuts

Multiplex polymerase chain reaction (PCR) of microsatellite loci allows for simultaneous amplification of two or more pairs of primers in a single PCR reaction; hence, it is cost and time effective. However, very few attempts have been reported in non-model species. In this study, by combining a genome-based de novo development and cross-species application approach, a multiplex PCR system comprising 5 PCR reactions of 33 microsatellites consisting of 26 novel genomic and 7 literature-sourced loci was tested for polymorphisms, cross-species transferability, and the ability to assess genetic diversity and population structure of three walnut species (Juglans spp.). We found that the genome-based approach is more efficient than other methods. An allelic ladder was developed for each locus to enhance consistent genotyping among laboratories. The population genetic analysis results showed that all 33 loci were successfully transferred across the three species, showing high polymorphism and a strong genetic structure. Hence, the multiplex PCR system is highly applicable in walnut species. Furthermore, we propose an efficient pipeline to characterize and genotype polymorphic microsatellite loci. The novel toolbox developed here will aid future ecology and evolution studies in walnut and could serve as a model for other plant species.

Construction of a strawberry breeding core collection to capture and exploit genetic variation

BACKGROUND

Genetic diversity is crucial for the success of plant breeding programs and core collections are important resources to capture this diversity. Many core collections have already been constructed by gene banks, whose main goal is to obtain a panel of a limited number of genotypes to simplify management practices and to improve shareability while retaining as much diversity as possible. However, as gene banks have a different composition and goal than plant breeding programs, constructing a core collection for a plant breeding program should consider different aspects.

RESULTS

In this study, we present a novel approach for constructing a core collection by integrating both genomic and pedigree information to maximize the representation of the breeding germplasm in a minimum subset of genotypes while accounting for future genetic variation within a strawberry breeding program. Our stepwise approach starts with selecting the most important crossing parents of advanced selections and genotypes included for specific traits, to represent also future genetic variation. We then use pedigree-genomic-based relationship coefficients combined with the 'accession to nearest entry' criterion to complement the core collection and maximize its representativeness of the current breeding program. Combined pedigree-genomic-based relationship coefficients allow for accurate relationship estimation without the need to genotype every individual in the breeding program.

CONCLUSIONS

This stepwise construction of a core collection in a strawberry breeding program can be applied in other plant breeding programs to construct core collections for various purposes.

Unraveling Extremely Damaging IRAK4 Variants and Their Potential Implications for IRAK4 Inhibitor Efficacy

Interleukin-1-receptor-associated kinase 4 (IRAK4) possesses a crucial function in the toll-like receptor (TLR) signaling pathway, and the dysfunction of this molecule could lead to various infectious and immune-related diseases in addition to cancers. IRAK4 genetic variants have been linked to various types of diseases. Therefore, we conducted a comprehensive analysis to recognize the missense variants with the most damaging impacts on IRAK4 with the employment of diverse bioinformatics tools to study single-nucleotide polymorphisms' effects on function, stability, secondary structures, and 3D structure. The residues' location on the protein domain and their conservation status were investigated as well. Moreover, docking tools along with structural biology were engaged in analyzing the SNPs' effects on one of the developed IRAK4 inhibitors. By analyzing IRAK4 gene SNPs, the analysis distinguished ten variants as the most detrimental missense variants. All variants were situated in highly conserved positions on an important protein domain. L318S and L318F mutations were linked to changes in IRAK4 secondary structures. Eight SNPs were revealed to have a decreasing effect on the stability of IRAK4 via both I-Mutant 2.0 and Mu-Pro tools, while Mu-Pro tool identified a decreasing effect for the G198E SNP. In addition, detrimental effects on the 3D structure of IRAK4 were also discovered for the selected variants. Molecular modeling studies highlighted the detrimental impact of these identified SNP mutant residues on the druggability of the IRAK4 ATP-binding site towards the known target inhibitor, HG-12-6, as compared to the native protein. The loss of important ligand residue-wise contacts, altered protein global flexibility, increased steric clashes, and even electronic penalties at the ligand-binding site interfaces were all suggested to be associated with SNP models for hampering the HG-12-6 affinity towards IRAK4 target protein. This given model lays the foundation for the better prediction of various disorders relevant to IRAK4 malfunction and sheds light on the impact of deleterious IRAK4 variants on IRAK4 inhibitor efficacy.

CoreSNP: an efficient pipeline for core marker profile selection from genome-wide SNP datasets in crops

BACKGROUND

DNA marker profiles play a crucial role in the identification and registration of germplasm, as well as in the distinctness, uniformity, and stability (DUS) testing of new plant variety protection. However, selecting minimal marker sets from large-scale SNP dataset can be challenging to distinguish a maximum number of samples.

RESULTS

Here, we developed the CoreSNP pipeline using a "divide and conquer" strategy and a "greedy" algorithm. The pipeline offers adjustable parameters to guarantee the distinction of each sample pair with at least two markers. Additionally, it allows datasets with missing loci as input. The pipeline was tested in barley, soybean, wheat, rice and maize. A few dozen of core SNPs were efficiently selected in different crops with SNP array, GBS, and WGS dataset, which can differentiate thousands of individual samples. The core SNPs were distributed across all chromosomes, exhibiting lower pairwise linkage disequilibrium (LD) and higher polymorphism information content (PIC) and minor allele frequencies (MAF). It was shown that both the genetic diversity of the population and the characteristics of the original dataset can significantly influence the number of core markers. In addition, the core SNPs capture a certain level of the original population structure.

CONCLUSIONS

CoreSNP is an efficiency way of core marker sets selection based on Genome-wide SNP datasets of crops. Combined with low-density SNP chip or genotyping technologies, it can be a cost-effective way to simplify and expedite the evaluation of genetic resources and differentiate different crop varieties. This tool is expected to have great application prospects in the rapid comparison of germplasm and intellectual property protection of new varieties.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Generic Platform for the Multiplexed Targeted Electrochemical Detection of Osteoporosis-Associated Single Nucleotide Polymorphisms Using Recombinase Polymerase Solid-Phase Primer Elongation and Ferrocene-Modified Nucleoside Triphosphates

Osteoporosis is a multifactorial disease influenced by genetic and environmental factors, which contributes to an increased risk of bone fracture, but early diagnosis of this disease cannot be achieved using current techniques. We describe a generic platform for the targeted electrochemical genotyping of SNPs identified by genome-wide association studies to be associated with a genetic predisposition to osteoporosis. The platform exploits isothermal solid-phase primer elongation with ferrocene-labeled nucleoside triphosphates. Thiolated reverse primers designed for each SNP were immobilized on individual gold electrodes of an array. These primers are designed to hybridize to the SNP site at their 3'OH terminal, and primer elongation occurs only where there is 100% complementarity, facilitating the identification and heterozygosity of each SNP under interrogation. The platform was applied to real blood samples, which were thermally lysed and directly used without the need for DNA extraction or purification. The results were validated using Taqman SNP genotyping assays and Sanger sequencing. The assay is complete in just 15 min with a total cost of 0.3€ per electrode. The platform is completely generic and has immense potential for deployment at the point of need in an automated device for targeted SNP genotyping with the only required end-user intervention being sample addition.

A New SNP Genotyping Technology by Target SNP-Seq

In order to promote the widespread application of single-nucleotide polymorphism (SNP)-based genotyping, a new method was developed and called target SNP-seq which combined the advantages of multiplex polymerase chain reaction (PCR) amplification and high throughput sequencing on Illumina X Ten platform. Compared with kompetitive allele-specific PCR (KASP), microchips, and genotyping by sequencing (GBS), target SNP-seq uses perfect SNPs based on the analysis of variome (whole-genome sequence data of different accessions) and is flexible, cost-effective, and highly accurate for genotyping middle-scale SNPs. It could genotype hundreds of SNPs in massive DNA samples within 3 days at the cost of $7 for each DNA sample. The high efficiency and low cost of target SNP-seq make it more competitive than current SNP genotyping methods, and it has excellent potential for application in genetic research, as well as in promoting plant-breeding processes in the near future.

Development of Nuclear DNA Markers for Applications in Genetic Diversity Study of Oil Palm-Pollinating Weevil Populations

The oil palm-pollinating weevil (Elaeidobius kamerunicus Faust) was introduced from Cameroon, West Africa, to Malaysia in 1981, and subsequently, to other oil palm-growing countries as well. This study aims to develop a set of robust E. kamerunicus-specific nuclear DNA markers to directly assess the genetic diversity of the weevil populations. A total of 19,148 SNP and 223,200 SSR were discovered from 48 weevils representing three origins (Peninsular Malaysia, Sabah, and Riau) using RAD tag sequencing. Subsequent filtering steps further reduced these to 1000 SNP and 120 SSR. The selected 220 SNP exhibited a polymorphism information content (PIC) of 0.2387 (±0.1280), and 8 SSR had the PIC of 0.5084 (±0.1928). These markers were found to show sufficient polymorphism, making it possible to assign 180 weevils into three major clusters from Ghana, Cameroon, and Southeast Asia (mainly in Malaysia and Indonesia). These DNA markers successfully confirmed the Cameroon origin of the Southeast Asian cluster. However, the presence of null alleles in the SSR markers, due to limited flexibility of the probe design on the short RAD tags, led to an underestimation of heterozygosity within the populations. Hence, the developed SNP markers turned out to be more efficient than the SSR markers in the genetic diversity assessment of the E. kamerunicus populations. The genetic information provides useful insight into developing guidelines for the genetic monitoring and conservation planning of E. kamerunicus.

Genetic diversity analysis and variety identification using SSR and SNP markers in melon

Melon is an important horticultural crop with a pleasant aromatic flavor and abundance of health-promoting substances. Numerous melon varieties have been cultivated worldwide in recent years, but the high number of varieties and the high similarity between them poses a major challenge for variety evaluation, discrimination, as well as innovation in breeding. Recently, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), two robust molecular markers, have been utilized as a rapid and reliable method for variety identification. To elucidate the genetic structure and diversity of melon varieties, we screened out 136 perfect SSRs and 164 perfect SNPs from the resequencing data of 149 accessions, including the most representative lines worldwide. This study established the DNA fingerprint of 259 widely-cultivated melon varieties in China using Target-seq technology. All melon varieties were classified into five subgruops, including ssp. agrestis, ssp. melo, muskmelon and two subgroups of foreign individuals. Compared with ssp. melo, the ssp. agrestis varieties might be exposed to a high risk of genetic erosion due to their extremely narrow genetic background. Increasing the gene exchange between ssp. melo and ssp. agrestis is therefore necessary in the breeding procedure. In addition, analysis of the DNA fingerprints of the 259 melon varieties showed a good linear correlation (R² = 0.9722) between the SSR genotyping and SNP genotyping methods in variety identification. The pedigree analysis based on the DNA fingerprint of 'Jingyu' and 'Jingmi' series melon varieties was consistent with their breeding history. Based on the SNP index analysis, ssp. agrestis had low gene exchange with ssp. melo in chromosome 4, 7, 10, 11and 12, two specific SNP loci were verified to distinguish ssp. agrestis and ssp. melon varieties. Finally, 23 SSRs and 40 SNPs were selected as the core sets of markers for application in variety identification, which could be efficiently applied to variety authentication, variety monitoring, as well as the protection of intellectual property rights in melon.

Analysis of Copy Number Variation in the Whole Genome of Normal-Haired and Long-Haired Tianzhu White Yaks

Long-haired individuals in the Tianzhu white yak population are a unique genetic resource, and have important landscape value. Copy number variation (CNV) is an important source of phenotypic variation in mammals. In this study, we used resequencing technology to detect the whole genome of 10 long-haired Tianzhu white yaks (LTWY) and 10 normal-haired Tianzhu white yaks (NTWY), and analyzed the differences of CNV in the genome of LTWYs and NTWYs. A total of 110268 CNVs were identified, 2006 CNVRs were defined, and the distribution map of these CNVRs on chromosomes was constructed. The comparison of LTWYs and NTWYs identified 80 differential CNVR-harbored genes, which were enriched in lipid metabolism, cell migration and other functions. Notably, some differential genes were identified as associated with hair growth and hair-follicle development (e.g., ASTN2, ATM, COL22A1, GK5, SLIT3, PM20D1, and SGCZ). In general, we present the first genome-wide analysis of CNV in LTWYs and NTWYs. Our results can provide new insights into the phenotypic variation of different hair lengths in Tianzhu white yaks.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Development of the maize 5.5K loci panel for genomic prediction through genotyping by target sequencing

Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement.

Novel candidate loci for morpho-agronomic and seed quality traits detected by targeted genotyping-by-sequencing in common bean

Phaseolus vulgaris L., known as common bean, is one of the most important grain legumes cultivated around the world for its immature pods and dry seeds, which are rich in protein and micronutrients. Common bean offers a cheap food and protein sources to ameliorate food shortage and malnutrition around the world. However, the genetic basis of most important traits in common bean remains unknown. This study aimed at identifying QTL and candidate gene models underlying twenty-six agronomically important traits in common bean. For this, we assembled and phenotyped a diversity panel of 200 P. vulgaris genotypes in the greenhouse, comprising determinate bushy, determinate climbing and indeterminate climbing beans. The panel included dry beans and snap beans from different breeding programmes, elite lines and landraces from around the world with a major focus on accessions of African, European and South American origin. The panel was genotyped using a cost-conscious targeted genotyping-by-sequencing (GBS) platform to take advantage of highly polymorphic SNPs detected in previous studies and in diverse germplasm. The detected single nucleotide polymorphisms (SNPs) were applied in marker-trait analysis and revealed sixty-two quantitative trait loci (QTL) significantly associated with sixteen traits. Gene model identification via a similarity-based approach implicated major candidate gene models underlying the QTL associated with ten traits including, flowering, yield, seed quality, pod and seed characteristics. Our study revealed six QTL for pod shattering including three new QTL potentially useful for breeding. However, the panel was evaluated in a single greenhouse environment and the findings should be corroborated by evaluations across different field environments. Some of the detected QTL and a number of candidate gene models only elucidate the understanding of the genetic nature of these traits and provide the basis for further studies. Finally, the study showed the possibility of using a limited number of SNPs in performing marker-trait association in common bean by applying a highly scalable targeted GBS approach. This targeted GBS approach is a cost-efficient strategy for assessment of the genetic basis of complex traits and can enable geneticists and breeders to identify novel loci and targets for marker-assisted breeding more efficiently.

Construction of an SNP fingerprinting database and population genetic analysis of 329 cauliflower cultivars

Cauliflower is one of the most important vegetable crops grown worldwide. However, the lack of genetic diversity information and efficient molecular markers hinders efforts to improve cauliflower. This study aims to construct DNA fingerprints for 329 cauliflower cultivars based on SNP markers and the KASP system. After rigorous filtering, a total of 1662 candidate SNPs were obtained from nearly 17.9 million SNP loci. The mean values of PIC, MAF, heterozygosity and gene diversity of these SNPs were 0.389, 0.419, 0.075, and 0.506, respectively. We developed a program for in silico simulations on 153 core germplasm samples to generate ideal SNP marker sets from the candidates. Finally, 41 highly polymorphic KASP markers were selected and applied to identify 329 cauliflower cultivars, mainly collected from the public market. Furthermore, based on the KASP genotyping data, we performed phylogenetic analysis and population structure analysis of the 329 cultivars. As a result, these cultivars could be classified into three major clusters, and the classification patterns were significantly related to their curd solidity and geographical origin. Finally, fingerprints of the 329 cultivars and 2D barcodes with the genetic information of each sample were generated. The fingerprinting database developed in this study provides a practical tool for identifying the authenticity and purity of cauliflower seeds and valuable genetic information about the current cauliflower cultivars.

Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis

The NRAS gene is a well-known oncogene that acts as a major player in carcinogenesis. Mutations in the NRAS gene have been linked to multiple types of human tumors. Therefore, the identification of the most deleterious single nucleotide polymorphisms (SNPs) in the NRAS gene is necessary to understand the key factors of tumor pathogenesis and therapy. We aimed to retrieve NRAS missense SNPs and analyze them comprehensively using sequence and structure approaches to determine the most deleterious SNPs that could increase the risk of carcinogenesis. We also adopted structural biology methods and docking tools to investigate the behavior of the filtered SNPs. After retrieving missense SNPs and analyzing them using six in silico tools, 17 mutations were found to be the most deleterious mutations in NRAS. All SNPs except S145L were found to decrease NRAS stability, and all SNPs were found on highly conserved residues and important functional domains, except R164C. In addition, all mutations except G60E and S145L showed a higher binding affinity to GTP, implicating an increase in malignancy tendency. As a consequence, all other 14 mutations were expected to increase the risk of carcinogenesis, with 5 mutations (G13R, G13C, G13V, P34R, and V152F) expected to have the highest risk. Thermodynamic stability was ensured for these SNP models through molecular dynamics simulation based on trajectory analysis. Free binding affinity toward the natural substrate, GTP, was higher for these models as compared to the native NRAS protein. The Gly13 SNP proteins depict a differential conformational state that could favor nucleotide exchange and catalytic potentiality. A further application of experimental methods with all these 14 mutations could reveal new insights into the pathogenesis and management of different types of tumors.

Serial-Omics and Molecular Function Study Provide Novel Insight into Cucumber Variety Improvement

Cucumbers are rich in vitamins and minerals. The cucumber has recently become one of China’s main vegetable crops. More specifically, the adjustment of the Chinese agricultural industry’s structure and rapid economic development have resulted in increases in the planting area allocated to Chinese cucumber varieties and in the number of Chinese cucumber varieties. After complete sequencing of the “Chinese long” genome, the transcriptome, proteome, and metabolome were obtained. Cucumber has a small genome and short growing cycle, and these traits are conducive to the application of molecular breeding techniques for improving fruit quality. Here, we review the developments and applications of molecular markers and genetic maps for cucumber breeding and introduce the functions of gene families from the perspective of genomics, including fruit development and quality, hormone response, resistance to abiotic stress, epitomizing the development of other omics, and relationships among functions.

The vegetable SNP database: An integrated resource for plant breeders and scientists

Single nucleotide polymorphisms (SNPs) are widely used in genetic research and molecular breeding. To date, the genomes of many vegetable crops have been assembled, and hundreds of core germplasms for each vegetable have been sequenced. However, these data are not currently easily accessible because they are stored on different public databases. Therefore, a vegetable crop SNP database should be developed that hosts SNPs demonstrated to have a high success rate in genotyping for genetic research (herein, "alpha SNPs"). We constructed a database (VegSNPDB, http://www.vegsnpdb.cn/) containing the sequence data of 2032 germplasms from 16 vegetable crop species. VegSNPDB hosts 118,725,944 SNPs of which 4,877,305 were alpha SNPs. SNPs can be searched by chromosome number, position, SNP type, genetic population, or specific individuals, as well as the values of MAF, PIC, and heterozygosity. We hope that VegSNPDB will become an important SNP database for the vegetable research community.

Advances in Understanding and Harnessing the Molecular Regulatory Mechanisms of Vegetable Quality

The quality of vegetables is facing new demands in terms of diversity and nutritional health. Given the improvements in living standards and the quality of consumed products, consumers are looking for vegetable products that maintain their nutrition, taste, and visual qualities. These requirements are directing scientists to focus on vegetable quality in breeding research. Thus, in recent years, research on vegetable quality has been widely carried out, and many applications have been developed via gene manipulation. In general, vegetable quality traits can be divided into three parts. First, commodity quality, which is most related to the commerciality of plants, refers to the appearance of the product. The second is flavor quality, which usually represents the texture and flavor of vegetables. Third, nutritional quality mainly refers to the contents of nutrients and health ingredients such as soluble solids (sugar), vitamin C, and minerals needed by humans. With biotechnological development, researchers can use gene manipulation technologies, such as molecular markers, transgenes and gene editing to improve the quality of vegetables. This review attempts to summarize recent studies on major vegetable crops species, with Brassicaceae, Solanaceae, and Cucurbitaceae as examples, to analyze the present situation of vegetable quality with the development of modern agriculture.

Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP

Modeling environmental spatial heterogeneity can improve the efficiency of forest tree genomic evaluation. Furthermore, genotyping costs can be lowered by reducing the number of markers needed. We investigated the impact on variance components, breeding value accuracy, and bias of two phenotypic data adjustments (experimental design and autoregressive spatial models), and a relationship matrix calculated from a subset of markers selected for their ability to infer ancestry. Using a multiple-trait multiple-site single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, four scenarios (2 phenotype adjustments × 2 marker sets) were applied to diameter at breast height (DBH), height (HT), and resistance to western gall rust (WGR) in four open-pollinated progeny trials of lodgepole pine, with 1490 (out of 11,188) trees genotyped with 25,099 SNPs. As a control, we fitted the conventional ABLUP model using pedigree information. The highest heritability estimates were achieved for the ABLUP followed closely by the ssGBLUP with the full marker set and using the spatial phenotype adjustments. The highest predictive ability was obtained by using a reduced marker subset (8000 SNPs) when either the spatial (DBH: 0.429, and WGR: 0.513) or design (HT: 0.467) phenotype corrections were used. No significant difference was detected in prediction bias among the six fitted models, and all values were close to 1 (0.918-1.014). Results demonstrated that selecting informative markers, such as those capturing ancestry, can improve the predictive ability. The use of spatial correlation structure increased traits' heritability and reduced prediction bias, while increases in predictive ability were trait-dependent.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

Revealing the Genetic Components Responsible for the Unique Photosynthetic Stem Capability of the Wild Almond Prunus arabica (Olivier) Meikle

Almond [Prunus dulcis (Mill.) D. A. Webb] is a major deciduous fruit tree crop worldwide. During dormancy, under warmer temperatures and inadequate chilling hours, the plant metabolic activity increases and may lead to carbohydrate deficiency. Prunus arabica (Olivier) Meikle is a bushy wild almond species known for its green, unbarked stem, which stays green even during the dormancy period. Our study revealed that P. arabica green stems assimilate significantly high rates of CO₂ during the winter as compared to P. dulcis cv. Um el Fahem (U.E.F.) and may improve carbohydrate status throughout dormancy. To uncover the genetic inheritance and mechanism behind the P. arabica stem photosynthetic capability (SPC), a segregated F1 population was generated by crossing P. arabica to U.E.F. Both parent's whole genome was sequenced, and SNP calling identified 4,887 informative SNPs for genotyping. A robust genetic map for U.E.F. and P. arabica was constructed (971 and 571 markers, respectively). QTL mapping and association study for the SPC phenotype revealed major QTL [log of odd (LOD) = 20.8] on chromosome 7 and another minor but significant QTL on chromosome 1 (LOD = 3.9). As expected, the P. arabica allele in the current loci significantly increased the SPC phenotype. Finally, a list of 64 candidate genes was generated. This work sets the stage for future research to investigate the mechanism regulating the SPC trait, how it affects the tree's physiology, and its importance for breeding new cultivars better adapted to high winter temperatures.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

Synergistic Effects of Aldehyde Dehydrogenase 2 Polymorphisms and Alcohol Consumption on Cognitive Impairment after Ischemic Stroke in Han Chinese

Aldehyde dehydrogenase 2 (ALDH2) polymorphisms are related to both stroke risk and alcohol consumption. However, the influence of ALDH2 polymorphisms and alcohol consumption on cognitive impairment after ischemic stroke remains unknown, as do the possible mechanisms. We enrolled 180 Han Chinese ischemic stroke patients from four community health centers in Bengbu, China. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), and two different MoCA cutoff scores were used to define cognitive impairment in ischemic stroke patients. The ALDH2 genotypes were determined using polymerase chain reaction and direct sequencing. To assess the associations of ALDH2 polymorphisms and alcohol consumption with cognitive impairment after ischemic stroke, we performed binary logistic regression analysis with odds ratios. We revealed that individuals with the ALDH2 wild-type genotype were more likely to have high MoCA scores than those with the mutant and heterozygous types (p = 0.034). In addition, using two MoCA cutoff scores, the percentage of moderate to excessive alcohol consumption in the cognitive impairment group was higher than that in the nonimpairment group (p = 0.001). The levels of 4-hydroxy-2-nonenal (p = 0.001) and swallowing function (p = 0.001) were also higher in the cognitive impairment group than in the nonimpairment group. Moreover, after adjusting for other potential risk factors, ALDH2 polymorphisms and alcohol consumption had a significant synergistic effect on cognitive impairment (p = 0.022). Specifically, the ALDH2∗2 mutant allele and higher alcohol consumption were associated with cognitive impairment and swallowing ability after ischemic stroke. Targeting ALDH2 may be a useful biomarker for cognitive rehabilitation following ischemic stroke.

Development of GBTS and KASP Panels for Genetic Diversity, Population Structure, and Fingerprinting of a Large Collection of Broccoli (Brassica oleracea L. var. italica) in China

Broccoli (Brassica oleracea var. italica) is one of the most important and nutritious vegetables widely cultivated in China. In the recent four decades, several improved varieties were bred and developed by Chinese breeders. However, the efforts for improvement of broccoli are hindered by limited information of genetic diversity and genetic relatedness contained within the available germplasms. This study evaluated the genetic diversity, genetic relationship, population structure, and fingerprinting of 372 accessions of broccoli representing most of the variability of broccoli in China. Millions of SNPs were identified by whole-genome sequencing of 23 representative broccoli genotypes. Through several stringent selection criteria, a total of 1,167 SNPs were selected to characterize genetic diversity and population structure. Of these markers, 1,067 SNPs were genotyped by target sequencing (GBTS), and 100 SNPs were genotyped by kompetitive allele specific PCR (KASP) assay. The average polymorphism information content (PIC) and expected heterozygosity (gene diversity) values were 0.33 and 0.42, respectively. Diversity analysis revealed the prevalence of low to moderate genetic diversity in the broccoli accessions indicating a narrow genetic base. Phylogenetic and principal component analyses revealed that the 372 accessions could be clustered into two main groups but with weak groupings. STRUCTURE analysis also suggested the presence of two subpopulations with weak genetic structure. Analysis of molecular variance (AMOVA) identified 13% variance among populations and 87% within populations revealing very low population differentiation, which could be attributed to massive gene flow and the reproductive biology of the crop. Based on high resolving power, a set of 28 KASP markers was chosen for DNA fingerprinting of the broccoli accessions for seed authentication and varietal identification. To the best of our knowledge, this is the first comprehensive study to measure diversity and population structure of a large collection of broccoli in China and also the first application of GBTS and KASP techniques in genetic characterization of broccoli. This work broadens the understanding of diversity, phylogeny, and population structure of a large collection of broccoli, which may enhance future breeding efforts to achieve higher productivity.

Increased accuracy of genomic predictions for growth under chronic thermal stress in rainbow trout by prioritizing variants from GWAS using imputed sequence data

Through imputation of genotypes, genome‐wide association study (GWAS) and genomic prediction (GP) using whole‐genome sequencing (WGS) data are cost‐efficient and feasible in aquaculture breeding schemes. The objective was to dissect the genetic architecture of growth traits under chronic heat stress in rainbow trout (Oncorhynchus mykiss) and to assess the accuracy of GP based on imputed WGS and different preselected single nucleotide polymorphism (SNP) arrays. A total of 192 and 764 fish challenged to a heat stress experiment for 62 days were genotyped using a customized 1 K and 26 K SNP panels, respectively, and then, genotype imputation was performed from a low‐density chip to WGS using 102 parents (36 males and 66 females) as the reference population. Imputed WGS data were used to perform GWAS and test GP accuracy under different preselected SNP scenarios. Heritability was estimated for body weight (BW), body length (BL) and average daily gain (ADG). Estimates using imputed WGS data ranged from 0.33 ± 0.05 to 0.55 ± 0.05 for growth traits under chronic heat stress. GWAS revealed that the top five cumulatively SNPs explained a maximum of 0.94%, 0.86% and 0.51% of genetic variance for BW, BL and ADG, respectively. Some important functional candidate genes associated with growth‐related traits were found among the most important SNPs, including signal transducer and activator of transcription 5B and 3 (STAT5B and STAT3, respectively) and cytokine‐inducible SH2‐containing protein (CISH). WGS data resulted in a slight increase in prediction accuracy compared with pedigree‐based method, whereas preselected SNPs based on the top GWAS hits improved prediction accuracies, with values ranging from 1.2 to 13.3%. Our results support the evidence of the polygenic nature of growth traits when measured under heat stress. The accuracies of GP can be improved using preselected variants from GWAS, and the use of WGS marginally increases prediction accuracy.

A rapid and cost-effective multiplex ARMS-PCR method for the simultaneous genotyping of the circulating SARS-CoV-2 phylogenetic clades

Tracing the globally circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) phylogenetic clades by high-throughput sequencing is costly, time-consuming, and labor-intensive. We here propose a rapid, simple, and cost-effective amplification refractory mutation system (ARMS)-based multiplex reverse-transcription polymerase chain reaction (PCR) assay to identify six distinct phylogenetic clades: S, L, V, G, GH, and GR. Our multiplex PCR is designed in a mutually exclusive way to identify V-S and G-GH-GR clade variants separately. The pentaplex assay included all five variants and the quadruplex comprised of the triplex variants alongside either V or S clade mutations that created two separate subsets. The procedure was optimized with 0.2-0.6 µM primer concentration, 56-60°C annealing temperature, and 3-5 ng/µl complementary DNA to validate on 24 COVID-19-positive samples. Targeted Sanger sequencing further confirmed the presence of the clade-featured mutations with another set of primers. This multiplex ARMS-PCR assay is a fast, low-cost alternative and convenient to discriminate the circulating phylogenetic clades of SARS-CoV-2.

Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

K-seq, an affordable, reliable, and open Klenow NGS-based genotyping technology

BACKGROUND

K-seq, a new genotyping methodology based on the amplification of genomic regions using two steps of Klenow amplification with short oligonucleotides, followed by standard PCR and Illumina sequencing, is presented. The protocol was accompanied by software developed to aid with primer set design.

RESULTS

As the first examples, K-seq in species as diverse as tomato, dog and wheat was developed. K-seq provided genetic distances similar to those based on WGS in dogs. Experiments comparing K-seq and GBS in tomato showed similar genetic results, although K-seq had the advantage of finding more SNPs for the same number of Illumina reads. The technology reproducibility was tested with two independent runs of the tomato samples, and the correlation coefficient of the SNP coverages between samples was 0.8 and the genotype match was above 94%. K-seq also proved to be useful in polyploid species. The wheat samples generated specific markers for all subgenomes, and the SNPs generated from the diploid ancestors were located in the expected subgenome with accuracies greater than 80%.

CONCLUSION

K-seq is an open, patent-unencumbered, easy-to-set-up, cost-effective and reliable technology ready to be used by any molecular biology laboratory without special equipment in many genetic studies.

Genetic Diversity Assessment and Cultivar Identification of Cucumber (Cucumis sativus L.) Using the Fluidigm Single Nucleotide Polymorphism Assay

Genetic diversity analysis and cultivar identification were performed using a core set of single nucleotide polymorphisms (SNPs) in cucumber (Cucumis sativus L.). For the genetic diversity study, 280 cucumber accessions collected from four continents (Asia, Europe, America, and Africa) by the National Agrobiodiversity Center of the Rural Development Administration in South Korea and 20 Korean commercial F1 hybrids were genotyped using 151 Fluidigm SNP assay sets. The heterozygosity of the SNP loci per accession ranged from 4.76 to 82.76%, with an average of 32.1%. Population genetics analysis was performed using population structure analysis and hierarchical clustering (HC), which indicated that these accessions were classified mainly into four subpopulations or clusters according to their geographical origins. The subpopulations for Asian and European accessions were clearly distinguished from each other (F_ST value = 0.47), while the subpopulations for Korean F1 hybrids and Asian accessions were closely related (F_ST = 0.34). The highest differentiation was observed between American and European accessions (F_ST = 0.41). Nei's genetic distance among the 280 accessions was 0.414 on average. In addition, 95 commercial F1 hybrids of three cultivar groups (Baekdadagi-, Gasi-, and Nakhap-types) were genotyped using 82 Fluidigm SNP assay sets for cultivar identification. These 82 SNPs differentiated all cultivars, except seven. The heterozygosity of the SNP loci per cultivar ranged from 12.20 to 69.14%, with an average of 34.2%. Principal component analysis and HC demonstrated that most cultivars were clustered based on their cultivar groups. The Baekdadagi- and Gasi-types were clearly distinguished, while the Nakhap-type was closely related to the Baekdadagi-type. Our results obtained using core Fluidigm SNP assay sets provide useful information for germplasm assessment and cultivar identification, which are essential for breeding and intellectual right protection in cucumber.

Agarose-resolvable InDel markers based on whole genome re-sequencing in cucumber

Insertion and Deletion (InDel) are common features in genomes and are associated with genetic variation. The whole-genome re-sequencing data from two parents (X1 and X2) of the elite cucumber (Cucumis sativus) hybrid variety Lvmei No.1 was used for genome-wide InDel polymorphisms analysis. Obtained sequence reads were mapped to the genome reference sequence of Chinese fresh market type inbred line ‘9930’ and gaps conforming to InDel were pinpointed. Further, the level of cross-parents polymorphism among five pairs of cucumber breeding parents and their corresponding hybrid varieties were used for evaluating hybrid seeds purity test efficiency of InDel markers. A panel of 48 cucumber breeding lines was utilized for PCR amplification versatility and phylogenetic analysis of these markers. In total, 10,470 candidate InDel markers were identified for X1 and X2. Among these, 385 markers with more than 30 nucleotide difference were arbitrary chosen. These markers were selected for experimental resolvability through electrophoresis on an Agarose gel. Two hundred and eleven (211) accounting for 54.81% of markers could be validated as single and clear polymorphic pattern while 174 (45.19%) showed unclear or monomorphic genetic bands between X1 and X2. Cross-parents polymorphism evaluation recorded 68 (32.23%) of these markers, which were designated as cross-parents transferable (CPT) InDel markers. Interestingly, the marker InDel114 presented experimental transferability between cucumber and melon. A panel of 48 cucumber breeding lines including parents of Lvmei No. 1 subjected to PCR amplification versatility using CPT InDel markers successfully clustered them into fruit and common cucumber varieties based on phylogenetic analysis. It is worth noting that 16 of these markers were predominately associated to enzymatic activities in cucumber. These agarose-based InDel markers could constitute a valuable resource for hybrid seeds purity testing, germplasm classification and marker-assisted breeding in cucumber.

Construction of a SNP Fingerprinting Database and Population Genetic Analysis of Cigar Tobacco Germplasm Resources in China

Cigar tobacco is an important economic crop that is widely grown around the world. In recent years, varietal identification has become a frequent problem in germplasm preservation collections, which causes considerable inconvenience and uncertainty in the cataloging and preservation of cigar germplasm resources, in the selection of parental lines for breeding, and in the promotion and use of high quality varieties. Therefore, the use of DNA fingerprints to achieve rapid and accurate identification of varieties can play an important role in germplasm identification and property rights disputes. In this study, we used genotyping-by-sequencing (GBS) on 113 cigar tobacco accessions to develop SNP markers. After filtering, 580,942 high-quality SNPs were obtained. We used the 580,942 SNPs to perform principal component analysis (PCA), population structure analysis, and neighbor joining (NJ) cluster analysis on the 113 cigar tobacco accessions. The results showed that the accessions were not completely classified based on their geographical origins, and the genetic backgrounds of these cigar resources are complex and diverse. We further selected from these high-quality SNPs to obtained 163 SNP sites, 133 of which were successfully converted into KASP markers. Finally, 47 core KASP markers and 24 candidate core markers were developed. Using the core markers, we performed variety identification and fingerprinting in 216 cigar germplasm accessions. The results of SNP fingerprinting, 2D barcoding, and genetic analysis of cigar tobacco germplasm in this study provide a scientific basis for screening and identifying high-quality cigar tobacco germplasm, mining important genes, and broadening the basis of cigar tobacco genetics and subsequent breeding work at the molecular level.

Genetic Diversity, Pedigree Relationships, and A Haplotype-Based DNA Fingerprinting System of Red Bayberry Cultivars

High throughput sequencing was used to reveal the distribution of whole-genome variations in cultivated Morella rubra (Sieb. et Zucc.). A total of 3,151,123 SNPs, 371,757 small indels, and 15,904 SVs were detected in 52 accessions. Verification by Sanger sequencing demonstrated that the positive rate of the SNPs was approximately 97.3%. Search for more genetic variations was expanded to 141 red bayberry accessions, most of which were cultivars, by sequencing 19 selected genomic segments (SEG1-19). The results showed that each segment harbored, on average, 7.8 alleles (haplotypes), a haplotype diversity of 0.42, and a polymorphic information content (PIC) of 0.40. Seventy-two different genotypes were identified from the 141 accessions, and statistical analysis showed that the accessions with duplicated genotypes were either somatic mutants or simply synonyms. Core set selection results showed that a minimum of 34 genotypes could already have covered all the alleles on the segments. A DNA fingerprinting system was developed for red bayberry, which used the diversity information of only 8 DNA segments yet still achieved a very high efficiency without losing robustness. No large clade was robustly supported by hierarchical clustering, and well-supported small clusters mainly included close relatives. These results should lead to an improved understanding of the genetic diversity of red bayberry and be valuable for future molecular breeding and variety protection.