Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application

Comparison of ddRAD derived genome-wide SSR markers in outbred and inbred Swiss albino mice

Genetic monitoring of inbred laboratory animal populations, developed at any laboratory, is one of the key elements of quality control and their colony management. The present study aimed to mine microsatellite (or SSR) markers from double digest restriction-site associated DNA (ddRAD) sequencing data of outbred foundation stock and F9 inbred generation of Swiss albino mice. Genomic DNA (12 F0 outbred and 12 F9 inbred) was isolated from tail tissue samples of F0 outbred and F9 inbred Swiss albino mice and processed for genotyping by sequencing using ddRAD platform. Double digestion of DNA was done using EcoR1 and Mse1 enzymes, and ddRAD data was subsequently analysed to identify and characterize microsatellite markers at genome-wide level. The analysis involved three key steps: pre-processing of reads, single sequence repeat (SSR) mining, and primer designing using different software i.e., PEAR, stacks and QDD. A total of 508 and 353 SSR motifs were identified in the outbred and inbred groups, respectively. Additionally, 828 and 551 primer sets were designed for the outbred and inbred groups, respectively. Furthermore, SSR loci specific to the outbred and inbred groups were also identified. Among these, eight SSR motifs (three each specific to the outbred and inbred groups, and two common) were validated using PCR amplification and gel electrophoresis. The designed primer sets successfully amplified respective SSR loci and produced reproducible bands on gel electrophoresis. The validated microsatellites were mapped to specific chromosomal locations using NCBI BLASTN with Mus musculus as the reference genome. In conclusion, the present study reports mining of SSR loci in outbred and inbred mice population. SSR loci were found to be more abundant and diverse in outbred population as compared to the inbred population. The unique SSRs identified for outbred and inbred groups will be helpful in checking the strain purity, marker assisted selection, and breeding programs without need for repeating the ddRAD sequencing in other laboratory animal population.

CropGene: a software package for the analysis of genomic and transcriptomic data of agricultural plants

Currently, the breeding of agricultural plants is increasingly based on the use of molecular biological data on genetic sequences, which makes it possible to significantly accelerate the breeding process, create new plant varieties through genomic editing. These data have a large volume, variety and require a large amount of resources, both labor and computing, to analyze the costs. Data analysis of such volume and complexity can be effective only when using modern bioinformatics methods, which include algorithms for identifying genes, predicting their function, and evaluating the effect of mutation on plant phenotype. Such an analysis has recently become impossible without the use of integrated software systems that solve problems of different levels by executing computational pipelines. The paper describes the CropGene software package developed for the comprehensive analysis of genomic and transcriptomic data of agricultural plants. CropGene includes several blocks of bioinformatic analysis, such as analysis of gene variations, assembly of genomes and transcriptomes, as well as annotation of genes and proteins. CropGene implements new methods for analyzing long non-coding RNAs, protein domains, searching and analyzing polymorphisms, and genome-wide association research. CropGene has a user-friendly interface and supports working with various types of data, which greatly simplifies its use for researchers who do not have deep knowledge in the field of bioinformatics. The paper provides examples of the use of CropGene for the analysis of agricultural organisms such as Solanum tuberosum and Zea mays. With CropGene, genetic markers have been identified that explain up to 50 % of the variability in seed color parameters; potential genes that may become promising material for producing potato varieties; more than 100 thousand new long non-coding RNAs. Orthogroups were also found, the domain structure of which shows a marked similarity with the domain architecture of characteristic secreted A2 phospholipases. Thus, CropGene is an important tool for scientists and practitioners working in the field of agrobiotechnology and plant genetics.

A Pipeline and Recommendations for Population and Individual Diagnostic SNP Selection in Non-Model Species

Despite substantial reductions in the cost of sequencing over the last decade, genetic panels remain relevant due to their cost-effectiveness and flexibility across a variety of sample types. In particular, single nucleotide polymorphism (SNP) panels are increasingly favoured for conservation applications. SNP panels are often used because of their adaptability, effectiveness with low-quality samples, and cost-efficiency for population monitoring and forensics. However, the selection of diagnostic SNPs for population assignment and individual identification can be challenging. The consequences of poor SNP selection are under-powered panels, inaccurate results, and monetary loss. Here, we develop a novel and user-friendly SNP selection pipeline (mPCRselect) that can be used to select SNPs for population assignment and/or individual identification. mPCRselect allows any researcher, who has sufficient SNP-level data, to design a successful and cost-effective SNP panel for a diploid species of conservation concern.

Adoption of Multi-omics Approaches to Address Drought Stress Tolerance in Rice and Mitigation Strategies for Sustainable Production

Drought is considered one of the major limiting factors for crop production. Drought-affected areas are consistently expanding. As rice stands as a primary grain widely consumed as a staple food by people across the globe, with a particular prominence in Asian countries. Due to its short root structure, thin cuticular wax layer and quick stomatal closure, rice is considered as drought-sensitive crop. The impact of drought on rice amplifies with plant growth and its adverse effects are more pronounced during the reproductive phase, including stages such as blooming, filling and maturity. Every year rice growers are facing a considerable deterioration of yield due to abiotic stresses specially drought. To address this undesirable consequences, multi-omics approaches are successfully being utilized as a mitigation strategy. A thorough, precise and systematic comprehension of the fundamental biological and cellular mechanisms activated by crop plants during stress is achieved through a range of omics technologies, including genomics, transcriptomics, proteomics and metabolomics. The integration of multi omics approaches offers a holistic understanding of cellular dynamics during drought or other stress conditions. These omics-based tools can identify and manipulate drought-tolerant genes. Utilizing omics approaches to stack these genes in rice contributes to the development of a drought resistant plant architecture. This review article aims to compile the latest published strategies on the application of multi omics approaches to accelerate the development of drought-tolerant rice plants.

Identification and validation of a major QTL, QFhb-6AL, for Fusarium head blight resistance on chromosome 6AL in wheat

KEY MESSAGE

A novel major QTL, QFhb-6AL, accounting for 34.6% phenotypic variation for FHB resistance, was identified in the Chinese cultivar Xunong 029, and a near-diagnostic marker was developed for marker-assisted selection. Fusarium head blight (FHB) is a destructive disease in wheat (Triticum aestivum L.) that seriously threatens global wheat production and food safety. Host resistance is the most effective strategy for reducing FHB damage. The Chinese wheat cultivar Xunong 029 possesses desirable agronomic traits and demonstrates stable FHB resistance in multiple environments. A population of 190 F6 recombinant inbred lines (RILs) was developed by crossing Xunong 029 with Xumai 35 to identify quantitative trait loci (QTLs) for FHB resistance. The RIL population was genotyped by a low-coverage whole-genome sequencing (lcWGS) technology and evaluated for FHB symptom spread within a spike (Type II resistance) in both greenhouses and field experiments. A stable major QTL, designated as QFhb-6AL, was mapped to a 3.0 cM interval between markers lcWGS613.5 and lcWGS616.5 on the long arm of chromosome 6A, and it explained up to 34.6% of the phenotypic variation for FHB Type II resistance. QFhb-6AL was validated using near-isogenic lines (NILs) and another RIL population derived from the cross Xunong 029 and Xumai 33. Four kompetitive amplicon sequence PCR (KASP) markers which tightly linked to QFhb-6AL were developed. Haplotype analysis of the target QTL region showed a low frequency distribution of QFhb-6AL in Chinese cultivars, indicating that the QTL has not been widely deployed in wheat breeding programs. The QFhb-6AL has great potential for improving wheat FHB resistance, and the tightly linked markers developed in this study will facilitate its deployment in wheat breeding programs.

Genetic diversity and population structure analysis of Forsythia ovata, a Korean endemic, based on genotyping-by-sequencing

The perennial shrub Forsythia ovata Nakai, native to the Korean Peninsula, has a highly restricted natural habitat, occurring only in a small area within the Baekdudaegan Mountain Range located in Gangwon-do Province. These characteristics give this species high conservation value, but there is a significant lack of genetic concerning about its populations for conservation purposes. In this study, we utilized genotyping-by-sequencing (GBS) to examine the genetic diversity and population structure of F. ovata. Our analysis including 5,017 single nucleotide polymorphisms (SNPs) from 72 individuals, representing nine distinct populations. The results revealed a mean expected heterozygosity (He) of 0.212, indicating a moderate level of genetic diversity within the species. Additionally, a relatively low levels of genetic differentiation (FST) and high gene flow (Nm) between populations were detected. The analysis of molecular variance (AMOVA) results indicated that most genetic variation occurred within individuals, accounting for 86.66% of the total variance. In contrast, only 6.90% and 6.44% of the molecular variance was attributed to differences among individuals and between populations, respectively. Considering the results of Bayesian structure analysis on the basis of ∆ K, principal coordinate analysis and phylogenetic analysis, we propose two management units for conservation. In addition, given the current conditions faced by F. ovata, both in situ and ex situ conservation should be considered for some populations (SG and BD).

Exploring ddRAD sequencing data of tomato genotypes evaluated for the heat stress tolerance

Climate change is a major concern for agricultural crops, and the selection of tolerant genotypes in response to abiotic stresses represents an important breeding strategy to reduce yield losses. In addition, the continuous development of new and more accurate high-throughput technologies for the analysis of DNA sequences is the key to improve biological understanding and application of biological knowledge. In the present work, 27 tomato genotypes already evaluated for their response under high temperature conditions were sequenced by using the ddRAD sequencing technology. The main goal was to provide genomic data useful for identifying candidate genes and variants to cope with current climate changes. Total genomic DNA was extracted from leaves and sequenced on the HiSeq2500 Illumina instrument. Raw reads of the dataset were processed using different bioinformatics tools to generate a Variant Calling Format (VCF) file. The availability of resources reporting polymorphisms among genomes of different genotypes provides a useful basis for studying tomato tolerance to current climate changes and can be used by researchers and breeders to investigate the molecular response mechanisms and develop new breeding programs, also aided by Marked Assisted Selection (MAS). The raw reads were deposited into SRA database (https://www.ncbi.nlm.nih.gov/sra/PRJNA1137563).

Morpho-Phenological, Chemical, and Genetic Characterization of Italian Maize Landraces from the Lazio Region

In the framework of a Collaboration Agreement between CREA and ARSIAL, a morpho-phenological, chemical, and genetic characterization of maize populations native to the Lazio region was carried out. During 2022 and 2023, a set of 50 accessions, belonging both to ARSIAL and CREA maize collections, were multiplied in Bergamo. Morpho-phenological descriptors were recorded in the field: plant height, ear height, and male and female flowering time. The grain chemical composition in terms of protein, lipid, starch, ash and fiber was evaluated by near-infrared spectroscopy (NIRS). A double-digest restriction-site-associated DNA sequencing (ddRADseq) strategy was used to genotype the landraces. The two collections were not significantly different in terms of grain chemical composition. On the other hand, the ARSIAL and CREA germplasm showed a different distribution in the three cluster-based population structure obtained by ddRADseq, which largely corresponded to the distribution map of their collection sites. The materials from the Lazio region maintained by ARSIAL and CREA were revealed to be different. The comparison between the two groups of landraces showed the importance of characterizing germplasm collections to promote the recovery and valorization of local biodiversity.

Identification of powdery mildew resistance quantitative trait loci in melon and development of resistant near-isogenic lines through marker-assisted backcrossing

BACKGROUND

Melon (Cucumis melo L.), an important cucurbit crop, faces production limitations due to powdery mildew (PM). Developing resistant varieties offers a sustainable, genetics-based alternative to chemical treatments. Therefore, identifying PM resistance quantitative trait loci (QTL) and creating trait-associated markers are essential for efficient melon PM resistance improvement through marker-assisted backcrossing (MABC).

RESULTS

Three F2 populations, A6, B2, and C4, were generated for QTL mapping of PM resistance. Major QTL were identified on chromosome 2 in A6, chromosome 5 in B2, and chromosomes 5 and 12 in C4. A series of TaqMan® assays targeting regions on chromosomes 2, 5, and 12 were developed and validated for foreground and recombinant selection, complemented by the double digest restriction-site associated DNA genotyping system to evaluate the recurrent parent genome recovery. Three MABC programs using resistant donor parents from A6 and C4 crossed with elite susceptible recurrent parents with green and orange fruit flesh were implemented. After two to three cycles of MABC, individual QTL was successfully introgressed into elite genetic backgrounds, giving six PM resistance lines in each green- and orange-fleshed background. PM inoculation on the twelve near-isogenic lines confirmed their resistance to PM.

CONCLUSIONS

We have identified major PM resistance QTL for melon on chromosomes 2, 5, and 12 and have introgressed individual QTL to elite genetic backgrounds using MABC in three and a half years. This study demonstrates the power of combining high-throughput genotyping with breeding efforts and showcases the efficiency of molecular breeding.

Increasing oil content in Brassica oilseed species

Brassica oilseed species are the third most important in the world, providing approximately 15 % of the total vegetable oils. Three species (Brassica rapa, B. juncea, B. napus) dominate with B. napus being the most common in Canada, China and Europe. Originally, B. napus was a crop producing seed with high erucic acid content, which still persists today, to some extent, and is used for industrial purposes. In contrast, cultivars which produce seed used for food and feed are low erucic acid cultivars which also have reduced glucosinolate content. Because of the limit to agricultural land, recent efforts have been made to increase productivity of oil crops, including Brassica oilseed species. In this article, we have detailed research in this regard. We have covered modern genetic, genomic and metabolic control analysis approaches to identifying potential targets for the manipulation of seed oil content. Details of work on the use of quantitative trait loci, genome-wide association and comparative functional genomics to highlight factors influencing seed oil accumulation are given and functional proteins which can affect this process are discussed. In summary, a wide variety of inputs are proving useful for the improvement of Brassica oilseed species, as major sources of global vegetable oil.

Deciphering the evolutionary development of the "Chinese lantern" within Solanaceae

MAIN CONCLUSION

The key genetic variation underlying the evo-devo of ICS in Solanaceae may be further pinpointed using an integrated strategy of forward and reverse genetics studies under the framework of phylogeny. The calyx of Physalis remains persistent throughout fruit development. Post-flowering, the fruiting calyx is inflated rapidly to encapsulate the berry, giving rise to a "Chinese lantern" structure called inflated calyx syndrome (ICS). It is unclear how this novelty arises. Over the past 2 decades, the role of MADS-box genes in the evolutionary development (evo-devo) of ICS has mainly been investigated within Solanaceae. In this review, we analyze the main achievements, challenges, and new progress. ICS acts as a source for fruit development, provides a microenvironment to protect fruit development, and assists in long-distance fruit dispersal. ICS is a typical post-floral trait, and the onset of its development is triggered by specific developmental signals that coincide with fertilization. These signals can be replaced by exogenous gibberellin and cytokinin application. MPF2-like heterotopic expression and MBP21-like loss have been proposed to be two essential evolutionary events for ICS origin, and manipulating the related MADS-box genes has been shown to affect the ICS size, sepal organ identity, and/or male fertility, but not completely disrupt ICS. Therefore, the core genes or key links in the ICS biosynthesis pathways may have undergone secondary mutations during evolution, or they have not yet been pinpointed. Recently, we have made some encouraging progress in acquiring lantern mutants in Physalis floridana. In addition to technological innovation, we propose an integrated strategy to further analyze the evo-devo mechanisms of ICS in Solanaceae using forward and reverse genetics studies under the framework of phylogeny.

Wheat improvement through advances in single nucleotide polymorphism (SNP) detection and genotyping with a special emphasis on rust resistance

KEY MESSAGE

Single nucleotide polymorphism (SNP) markers in wheat and their prospects in breeding with special reference to rust resistance. Single nucleotide polymorphism (SNP)-based markers are increasingly gaining momentum for screening and utilizing vital agronomic traits in wheat. To date, more than 260 million SNPs have been detected in modern cultivars and landraces of wheat. This rapid SNP discovery was made possible through the release of near-complete reference and pan-genome assemblies of wheat and its wild relatives, coupled with whole genome sequencing (WGS) of thousands of wheat accessions. Further, genotyping customized SNP sites were facilitated by a series of arrays (9 to 820Ks), a cost effective substitute WGS. Lately, germplasm-specific SNP arrays have been introduced to characterize novel traits and detect closely linked SNPs for marker-assisted breeding. Subsequently, the kompetitive allele-specific PCR (KASP) assay was introduced for rapid and large-scale screening of specific SNP markers. Moreover, with the advances and reduction in sequencing costs, ample opportunities arise for generating SNPs artificially through mutations and in combination with next-generation sequencing and comparative genomic analyses. In this review, we provide historical developments and prospects of SNP markers in wheat breeding with special reference to rust resistance where over 50 genetic loci have been characterized through SNP markers. Rust resistance is one of the most essential traits for wheat breeding as new strains of the Puccinia fungus, responsible for rust diseases, evolve frequently and globally.

DArTseq genotyping facilitates the transfer of "exotic" chromatin from a Secale cereale × S. strictum hybrid into wheat

Cultivated and wild species of the genus rye (Secale) are important but underexploited gene sources for increasing the genetic diversity of bread wheat. Gene transfer is possible via bridge genetic materials derived from intergeneric hybrids. During this process, it is essential to precisely identify the rye chromatin in the wheat genetic background. In the present study, backcross generation BC2F8 from a cross between Triticum aestivum (Mv9kr1) and S. cereanum ('Kriszta,' a cultivar from the artificial hybrid of S. cereale and S. strictum) was screened using in-situ hybridization (GISH and FISH) and analyzed by DArTseq genotyping in order to select potentially agronomically useful genotypes for prebreeding purposes. Of the 329,267 high-quality short sequence reads generated, 27,822 SilicoDArT and 8,842 SNP markers specific to S. cereanum 1R-7R chromosomes were identified. Heatmaps of the marker densities along the 'Lo7' rye reference pseudomolecules revealed subtle differences between the FISH- and DArTseq-based results. This study demonstrates that the "exotic" rye chromatin of S. cereanum introgressed into wheat can be reliably identified by high-throughput DArTseq genotyping. The Mv9kr1-'Kriszta' addition and translocation lines presented here may serve as valuable prebreeding genetic materials for the development of stress-tolerant or disease-resistant wheat varieties.

Milletomics: a metabolomics centered integrated omics approach toward genetic progression

Producing alternative staple foods like millet will be essential to feeding ten billion people by 2050. The increased demand for millet is driving researchers to improve its genetic variation. Millets include protein, dietary fiber, phenolic substances, and flavonoid components. Its climate resilience makes millet an appealing crop for agronomic sustainability. Integrative omics technologies could potentially identify and develop millets with desirable phenotypes that may have high agronomic value. Millets' salinity and drought tolerance have been enhanced using transcriptomics. In foxtail, finger, and pearl millet, proteomics has discovered salt-tolerant protein, phytohormone-focused protein, and drought tolerance. Metabolomics studies have revealed that certain metabolic pathways including those involving lignin, flavonoids, phenylpropanoid, and lysophospholipids are critical for many processes, including seed germination, photosynthesis, energy metabolism, and the synthesis of bioactive chemicals necessary for drought tolerance. Metabolomics integration with other omics revealed metabolome engineering and trait-specific metabolite creation. Integrated metabolomics and ionomics are still in the development stage, but they could potentially assist in comprehending the pathway of ionomers to control nutrient levels and biofortify millet. Epigenomic analysis has shown alterations in DNA methylation patterns and chromatin structure in foxtail and pearl millets in response to abiotic stress. Whole-genome sequencing utilizing next-generation sequencing is the most proficient method for finding stress-induced phytoconstituent genes. New genome sequencing enables novel biotechnological interventions including genome-wide association, mutation-based research, and other omics approaches. Millets can breed more effectively by employing next-generation sequencing and genotyping by sequencing, which may mitigate climate change. Millet marker-assisted breeding has advanced with high-throughput markers and combined genotyping technologies.

Genomic patterns of selection in morphometric traits across diverse Indian cattle breeds

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima's D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

Degenerate oligonucleotide primer MIG-seq: an effective PCR-based method for high-throughput genotyping

Next-generation sequencing (NGS) library construction often involves using restriction enzymes to decrease genome complexity, enabling versatile polymorphism detection in plants. However, plant leaves frequently contain impurities, such as polyphenols, necessitating DNA purification before enzymatic reactions. To overcome this problem, we developed a PCR-based method for expeditious NGS library preparation, offering flexibility in number of detected polymorphisms. By substituting a segment of the simple sequence repeat sequence in the MIG-seq primer set (MIG-seq being a PCR method enabling library construction with low-quality DNA) with degenerate oligonucleotides, we introduced variability in detectable polymorphisms across various crops. This innovation, named degenerate oligonucleotide primer MIG-seq (dpMIG-seq), enabled a streamlined protocol for constructing dpMIG-seq libraries from unpurified DNA, which was implemented stably in several crop species, including fruit trees. Furthermore, dpMIG-seq facilitated efficient lineage selection in wheat and enabled linkage map construction and quantitative trait loci analysis in tomato, rice, and soybean without necessitating DNA concentration adjustments. These findings underscore the potential of the dpMIG-seq protocol for advancing genetic analyses across diverse plant species.

Comparing the performances of SSR and SNP markers for population analysis in Theobroma cacao L., as alternative approach to validate a new ddRADseq protocol for cacao genotyping

Proper cacao (Theobroma cacao L.) plant genotyping is mandatory for the conservation and use of the species genetic resources. A set of 15 international standard SSR markers was assumed as universal cacao genotyping system. Recently, different SNPs and SNP genotyping techniques have been exploited in cacao. However, a consensus on which to use has not been reached yet, driving the search for new approaches. To validate a new ddRADseq protocol for cacao genotyping, we compared the performances for population analysis of a dataset with 7,880 SNPs obtained from ddRADseq and the genotypic data from the aforementioned SSR set, using 158 cacao plants from productive farms and gene bank. Four genetic groups were identified with STRUCTURE and ADMIXTURE softwares using SSR and SNP data, respectively. Similarities of cacao ancestries among these groups allowed the identification of analogous pairs of groups of individuals, referred to as: G1SSR/G1SNP, G2SSR/G2SNP, G3SSR/G3SNP, G4SSR/G4SNP, whether SSRs or SNPs were used. Both marker systems identified Amelonado and Criollo as the most abundant cacao ancestries among all samples. Genetic distance matrices from both data types were significantly similar to each other according to Mantel test (p < 0.0001). PCoA and UPGMA clustering mostly confirmed the identified genetic groups. AMOVA and FST pairwise comparison revealed a moderate to very large genetic differentiation among identified groups from SSR and SNP data. Genetic diversity parameters from SSR (Hobs = 0.616, Hexp = 0.524 and PIC = 0.544) were higher than that from SNP data (0.288, 0.264, 0.230). In both cases, genetic groups carrying the highest Amelonado proportion (G1SSR and G1SNP) had the lowest genetic diversity parameters among the identified groups. The high congruence among population analysis results using both systems validated the ddRADseq protocol employed for cacao SNP genotyping. These results could provide new ways for developing a universal SNP-based genotyping system very much needed for cacao genetic studies.

Breeding for water-use efficiency in wheat: progress, challenges and prospects

Drought poses a significant challenge to wheat production globally, leading to substantial yield losses and affecting various agronomic and physiological traits. The genetic route offers potential solutions to improve water-use efficiency (WUE) in wheat and mitigate the negative impacts of drought stress. Breeding for drought tolerance involves selecting desirable plants such as efficient water usage, deep root systems, delayed senescence, and late wilting point. Biomarkers, automated and high-throughput techniques, and QTL genes are crucial in enhancing breeding strategies and developing wheat varieties with improved resilience to water scarcity. Moreover, the role of root system architecture (RSA) in water-use efficiency is vital, as roots play a key role in nutrient and water uptake. Genetic engineering techniques offer promising avenues to introduce desirable RSA traits in wheat to enhance drought tolerance. These technologies enable targeted modifications in DNA sequences, facilitating the development of drought-tolerant wheat germplasm. The article highlighted the techniques that could play a role in mitigating drought stress in wheat.

Using ddRADseq to assess the genetic diversity of in-farm and gene bank cacao resources in the Baracoa region, eastern Cuba, for use and conservation purposes

The Baracoa region, eastern Cuba, hosts around 80 % of the country cacao (Theobroma cacao L.) plantations. Cacao plants in farms are diverse in origin and propagation, with grafted and hybrid plants being the more common ones. Less frequent are plants from cuttings, TSH progeny, and traditional Cuban cacao. A national cacao gene bank is also present in Baracoa, with 282 accessions either prospected in Cuba or introduced from other countries. A breeding program associated with the gene bank started in the 1990s based on agro-morphological descriptors. The genetic diversity of cacao resources in Baracoa has been poorly described, except for traditional Cuban cacao, affecting the proper development of the breeding program and the cacao planting policies in the region. To assess the population structure and genetic diversity of cacao resources in Baracoa region, we genotyped plants from both cacao gene bank (CG) and cacao farms (CF) applying a new ddRADseq protocol for cacao. After data processing, two SNPs datasets containing 11,425 and 6,481 high-quality SNPs were generated with 238 CG and 135 CF plants, respectively. SNPs were unevenly distributed along the 10 cacao chromosomes and laid mainly in noncoding regions of the genome. Population structure analysis with these SNP datasets identified seven and four genetic groups in CG and CF samples, respectively. Clustering using UPGMA and principal component analysis mostly agree with population structure results. Amelonado was the predominant cacao ancestry, accounting for 49.22 % (CG) and 57.73 % (CF) of the total. Criollo, Contamana, Iquitos, and Nanay ancestries were detected in both CG and CF samples, while Nacional and Marañon backgrounds were only identified in CG. Genetic differentiation among CG (FST ranging from 0.071 to 0.407) was higher than among CF genetic groups (FST: 0.093-0.282). Genetic diversity parameters showed similar values for CG and CF samples. The CG and CF genetic groups with the lowest genetic diversity parameters had the highest proportion of Amelonado ancestry. These results should contribute to reinforcing the ongoing breeding program and updating the planting policies on cacao farms, with an impact on the social and economic life of the region.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

Imputation strategies for genomic prediction using nanopore sequencing

BACKGROUND

Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies' (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance.

RESULTS

SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays.

CONCLUSIONS

Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

GBS-DP: a bioinformatics pipeline for processing data coming from genotyping by sequencing

The development of next-generation sequencing technologies has provided new opportunities for genotyping various organisms, including plants. Genotyping by sequencing (GBS) is used to identify genetic variability more rapidly, and is more cost-effective than whole-genome sequencing. GBS has demonstrated its reliability and flexibility for a number of plant species and populations. It has been applied to genetic mapping, molecular marker discovery, genomic selection, genetic diversity studies, variety identification, conservation biology and evolutionary studies. However, reduction in sequencing time and cost has led to the need to develop efficient bioinformatics analyses for an ever-expanding amount of sequenced data. Bioinformatics pipelines for GBS data analysis serve the purpose. Due to the similarity of data processing steps, existing pipelines are mainly characterised by a combination of software packages specifically selected either to process data for certain organisms or to process data from any organisms. However, despite the usage of efficient software packages, these pipelines have some disadvantages. For example, there is a lack of process automation (in some pipelines, each step must be started manually), which significantly reduces the performance of the analysis. In the majority of pipelines, there is no possibility of automatic installation of all necessary software packages; for most of them, it is also impossible to switch off unnecessary or completed steps. In the present work, we have developed a GBS-DP bioinformatics pipeline for GBS data analysis. The pipeline can be applied for various species. The pipeline is implemented using the Snakemake workflow engine. This implementation allows fully automating the process of calculation and installation of the necessary software packages. Our pipeline is able to perform analysis of large datasets (more than 400 samples).

Construction and Application of an F1-Derived Doubled-Haploid Population and High-Density Genetic Map for Ornamental Kale Breeding

Ornamental kale (Brassica oleracea var. acephala) is an attractive ornamental plant with a range of leaf colors and shapes. Breeding new varieties of ornamental kale has proven challenging due to its lengthy breeding cycle and the limited availability of genetic markers. In this study, a F1DH ornamental kale population comprising 300 DH lines was constructed using microspore culture. A high-density genetic map was developed by conducting whole-genome sequencing on 150 individuals from the F1DH population. The genetic map contained 1696 bin markers with 982,642 single-nucleotide polymorphisms (SNPs) spanning a total distance of 775.81 cM on all nine chromosomes with an average distance between markers of 0.46 cM. The ornamental kale genetic map contained substantially more SNP markers compared with published genetic maps for other B. oleracea crops. Furthermore, utilizing this high-density genetic map, we identified seven quantitative trait loci (QTLs) that significantly influence the leaf shape of ornamental kale. These findings are valuable for understanding the genetic basis of key agronomic traits in ornamental kale. The F1DH progenies provide an excellent resource for germplasm innovation and breeding new varieties of ornamental kale. Additionally, the high-density genetic map provides crucial insights for gene mapping and unraveling the molecular mechanisms behind important agronomic traits in ornamental kale.

Crop/Plant Modeling Supports Plant Breeding: I. Optimization of Environmental Factors in Accelerating Crop Growth and Development for Speed Breeding

The environmental conditions in customered speed breeding practice are, to some extent, empirical and, thus, can be further optimized. Crop and plant models have been developed as powerful tools in predicting growth and development under various environments for extensive crop species. To improve speed breeding, crop models can be used to predict the phenotypes resulted from genotype by environment by management at the population level, while plant models can be used to examine 3-dimensional plant architectural development by microenvironments at the organ level. By justifying the simulations via numerous virtual trials using models in testing genotype × environment × management, an optimized combination of environmental factors in achieving desired plant phenotypes can be quickly determined. Artificial intelligence in assisting for optimization is also discussed. We admit that the appropriate modifications on modeling algorithms or adding new modules may be necessary in optimizing speed breeding for specific uses. Overall, this review demonstrates that crop and plant models are promising tools in providing the optimized combinations of environment factors in advancing crop growth and development for speed breeding.

Breeding strategies for late blight resistance in potato crop: recent developments

Late blight (LB) is a serious disease that affects potato crop and is caused by Phytophthora infestans. Fungicides are commonly used to manage this disease, but this practice has led to the development of resistant strains and it also poses serious environmental and health risks. Therefore, breeding for resistance development can be the most effective strategies to control late blight. Various Solanum species have been utilized as a source of resistance genes to combat late blight disease. Several potential resistance genes and quantitative resistance loci (QRLs) have been identified and mapped through the application of molecular techniques. Furthermore, molecular markers closely linked to resistance genes or QRLs have been utilized to hasten the breeding process. However, the use of single-gene resistance can lead to the breakdown of resistance within a short period. To address this, breeding programs are now being focused on development of durable and broad-spectrum resistant cultivars by combining multiple resistant genes and QRLs using advanced molecular breeding tools such as marker-assisted selection (MAS) and cis-genic approaches. In addition to the strategies mentioned earlier, somatic hybridization has been utilized for the development and characterization of interspecific somatic hybrids. To further broaden the scope of late blight resistance breeding, approaches such as genomic selection, RNAi silencing, and various genome editing techniques can be employed. This study provides an overview of recent advances in various breeding strategies and their applications in improving the late blight resistance breeding program.

Genome-wide association study of maize resistance to Pythium aristosporum stalk rot

Stalk rot, a severe and widespread soil-borne disease in maize, globally reduces yield and quality. Recent documentation reveals that Pythium aristosporum has emerged as one of the dominant causal agents of maize stalk rot. However, a previous study of maize stalk rot disease resistance mechanisms and breeding had mainly focused on other pathogens, neglecting P. aristosporum. To mitigate crop loss, resistance breeding is the most economical and effective strategy against this disease. This study involved characterizing resistance in 295 inbred lines using the drilling inoculation method and genotyping them via sequencing. By combining with population structure, disease resistance phenotype, and genome-wide association study (GWAS), we identified 39 significant single-nucleotide polymorphisms (SNPs) associated with P. aristosporum stalk rot resistance by utilizing six statistical methods. Bioinformatics analysis of these SNPs revealed 69 potential resistance genes, among which Zm00001d051313 was finally evaluated for its roles in host defense response to P. aristosporum infection. Through virus-induced gene silencing (VIGS) verification and physiological index determination, we found that transient silencing of Zm00001d051313 promoted P. aristosporum infection, indicating a positive regulatory role of this gene in maize's antifungal defense mechanism. Therefore, these findings will help advance our current understanding of the underlying mechanisms of maize defense to Pythium stalk rot.

The potentialities of omics resources for millet improvement

Millets are nutrient-rich (nutri-rich) cereals with climate resilience attributes. However, its full productive potential is not realized due to the lack of a focused yield improvement approach, as evidenced by the available literature. Also, the lack of well-characterized genomic resources significantly limits millet improvement. But the recent availability of genomic data and advancement in omics tools has shown its enormous potential to enhance the efficiency and precision faced by conventional breeding in millet improvement. The development of high throughput genotyping platforms based on next-generation sequencing (NGS) has provided a low-cost method for genomic information, specifically for neglected nutri-rich cereals with the availability of a limited number of reference genome sequences. NGS has created new avenues for millet biotechnological interventions such as mutation-based study, GWAS, GS, and other omics technologies. The simultaneous discovery of high-throughput markers and multiplexed genotyping platform has aggressively aided marker-assisted breeding for millet improvement. Therefore, omics technology offers excellent opportunities to explore and combine useful variations for targeted traits that could impart high nutritional value to high-yielding cultivars under changing climatic conditions. In millet improvement, an in-depth account of NGS, integrating genomics data with different biotechnology tools, is reviewed in this context.

Vitis vinifera genotyping toolbox to highlight diversity and germplasm identification

The contribution of vine cultivation to human welfare as well as the stimulation of basic social and cultural features of civilization has been great. The wide temporal and regional distribution created a wide array of genetic variants that have been used as propagating material to promote cultivation. Information on the origin and relationships among cultivars is of great interest from a phylogenetics and biotechnology perspective. Fingerprinting and exploration of the complicated genetic background of varieties may contribute to future breeding programs. In this review, we present the most frequently used molecular markers, which have been used on Vitis germplasm. We discuss the scientific progress that led to the new strategies being implemented utilizing state-of-the-art next generation sequencing technologies. Additionally, we attempted to delimit the discussion on the algorithms used in phylogenetic analyses and differentiation of grape varieties. Lastly, the contribution of epigenetics is highlighted to tackle future roadmaps for breeding and exploitation of Vitis germplasm. The latter will remain in the top of the edge for future breeding and cultivation and the molecular tools presented herein, will serve as a reference point in the challenging years to come.

Pipeline to Design Inbred Lines and F1 Hybrids of Leaf Chicory (Radicchio) Using Male Sterility and Genotyping-by-Sequencing

Chicory, a horticultural crop cultivated worldwide, presents many botanical varieties and local biotypes. Among these, cultivars of the Italian radicchio group of the pure species Cichorium intybus L. and its interspecific hybrids with Cichorium endivia L.-as the "Red of Chioggia" biotype-includes several phenotypes. This study uses a pipeline to address the marker-assisted breeding of F1 hybrids: it presents the genotyping-by-sequencing results of four elite inbred lines using a RADseq approach and an original molecular assay based on CAPS markers for screening mutants with nuclear male sterility in the radicchio of Chioggia. A total of 2953 SNP-carrying RADtags were identified and used to compute the actual estimates of homozygosity and overall genetic similarity and uniformity of the populations, as well as to determine their genetic distinctiveness and differentiation. Molecular data were further used to investigate the genomic distribution of the RADtags among the two Cichorium species, allowing their mapping in 1131 and 1071 coding sequences in chicory and endive, respectively. Paralleling this, an assay to screen the genotype at the male sterility locus Cims-1 was developed to discriminate wild-type and mutant alleles of the causative gene myb80-like. Moreover, a RADtag mapped close to this genomic region proved the potential application of this method for future marker-assisted selection tools. Finally, after combining the genotype information of the core collection, the best 10 individuals from each inbred line were selected to compute the observed genetic similarity as a measure of uniformity as well as the expected homozygosity and heterozygosity estimates scorable by the putative progenies derived from selfing (pollen parent) and full-sibling (seed parent) or pair-wise crossing (F1 hybrids). This predictive approach was conducted as a pilot study to understand the potential application of RADseq in the fine tuning of molecular marker-assisted breeding strategies aimed at the development of inbred lines and F1 hybrids in leaf chicory.

Genomic Insights into the Origin of a Thermotolerant Tomato Line and Identification of Candidate Genes for Heat Stress

Climate change represents the main problem for agricultural crops, and the constitution of heat-tolerant genotypes is an important breeder’s strategy to reduce yield losses. The aim of the present study was to investigate the whole genome of a heat-tolerant tomato genotype (E42), in order to identify candidate genes involved in its response to high temperature. E42 presented a high variability for chromosomes 1, 4, 7 and 12, and phylogenetic analysis highlighted its relationship with the wild S. pimpinellifolium species. Variants with high (18) and moderate (139) impact on protein function were retrieved from two lists of genes related to heat tolerance and reproduction. This analysis permitted us to prioritize a subset of 35 candidate gene mapping in polymorphic regions, some colocalizing in QTLs controlling flowering in tomato. Among these genes, we identified 23 HSPs, one HSF, six involved in flowering and five in pollen activity. Interestingly, one gene coded for a flowering locus T1 and mapping on chromosome 11 resides in a QTL region controlling flowering and also showed 100% identity with an S. pimpinellifolium allele. This study provides useful information on both the E42 genetic background and heat stress response, and further studies will be conducted to validate these genes.

Double Digest Restriction-Site Associated DNA Sequencing (ddRADseq) Technology

Double digest restriction-site associated DNA sequencing (ddRADseq) technology combines genome reduced representation by digestion with two restriction enzymes and next generation sequencing (NGS) to obtain thousands of markers (SNP, SSR, and InDels) and genotype tens to hundreds of samples simultaneously. In this chapter, we describe a 96-plex derived ddRADseq protocol that can be set up to obtain different depth of coverage per locus and can be exploited to model and non-model plant species.

Innovative Advances in Plant Genotyping

Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.

Population Genomics Study and Implications for the Conservation of Zabelia tyaihyonii Based on Genotyping-By-Sequencing

Zabelia tyaihyonii (Nakai) Hisauti and H. Hara is a perennial shrub endemic to Republic of Korea that grows naturally in only a very limited region of the dolomite areas of Gangwon-do and Chungcheongbuk-do Provinces in the Republic of Korea. Given its geographical characteristics, it is more vulnerable than more widely distributed species. Despite the need for comprehensive information to support conservation, population genetic information for this species is very scarce. In this study, we analyzed the genetic diversity and population structure of 94 individuals from six populations of Z. tyaihyonii using a genotyping-by-sequencing (GBS) approach to provide important information for proper conservation and management. Our results, based on 3088 single nucleotide polymorphisms (SNPs), showed a mean expected heterozygosity (He) of 0.233, no sign of within-population inbreeding (G_IS that was close to or even below zero in all populations), and a high level of genetic differentiation (F_ST = 0.170). Analysis of molecular variance (AMOVA) indicated that the principal molecular variance existed within populations (84.5%) rather than among populations (17.0%). We suggested that six management units were proposed for conservation considering Bayesian structure analysis and phylogenetic analysis, and given the various current situations faced by Z. tyaihyonii, it is believed that not only the in situ conservation but also the ex situ conservation should be considered.

Graph-based pangenomics maximizes genotyping density and reveals structural impacts on fungal resistance in melon

The genomic sequences segregating in experimental populations are often highly divergent from the community reference and from one another. Such divergence is problematic under various short-read-based genotyping strategies. In addition, large structural differences are often invisible despite being strong candidates for causal variation. These issues are exacerbated in specialty crop breeding programs with fewer, lower-quality sequence resources. Here, we examine the benefits of complete genomic information, based on long-read assemblies, in a biparental mapping experiment segregating at numerous disease resistance loci in the non-model crop, melon (Cucumis melo). We find that a graph-based approach, which uses both parental genomes, results in 19% more variants callable across the population and raw allele calls with a 2 to 3-fold error-rate reduction, even relative to single reference approaches using a parent genome. We show that structural variation has played a substantial role in shaping two Fusarium wilt resistance loci with known causal genes. We also report on the genetics of powdery mildew resistance, where copy number variation and local recombination suppression are directly interpretable via parental genome alignments. Benefits observed, even in this low-resolution biparental experiment, will inevitably be amplified in more complex populations.

Genetic basis of maize kernel protein content revealed by high-density bin mapping using recombinant inbred lines

Maize with a high kernel protein content (PC) is desirable for human food and livestock fodder. However, improvements in its PC have been hampered by a lack of desirable molecular markers. To identify quantitative trait loci (QTL) and candidate genes for kernel PC, we employed a genotyping-by-sequencing strategy to construct a high-resolution linkage map with 6,433 bin markers for 275 recombinant inbred lines (RILs) derived from a high-PC female Ji846 and low-PC male Ye3189. The total genetic distance covered by the linkage map was 2180.93 cM, and the average distance between adjacent markers was 0.32 cM, with a physical distance of approximately 0.37 Mb. Using this linkage map, 11 QTLs affecting kernel PC were identified, including qPC7 and qPC2-2, which were identified in at least two environments. For the qPC2-2 locus, a marker named IndelPC2-2 was developed with closely linked polymorphisms in both parents, and when tested in 30 high and 30 low PC inbred lines, it showed significant differences (P = 1.9E-03). To identify the candidate genes for this locus, transcriptome sequencing data and PC best linear unbiased estimates (BLUE) for 348 inbred lines were combined, and the expression levels of the four genes were correlated with PC. Among the four genes, Zm00001d002625, which encodes an S-adenosyl-L-methionine-dependent methyltransferase superfamily protein, showed significantly different expression levels between two RIL parents in the endosperm and is speculated to be a potential candidate gene for qPC2-2. This study will contribute to further research on the mechanisms underlying the regulation of maize PC, while also providing a genetic basis for marker-assisted selection in the future.

Genetic associations with resistance to Meloidogyne enterolobii in guava (Psidium sp.) using cross-genera SNPs and comparative genomics to Eucalyptus highlight evolutionary conservation across the Myrtaceae

Tropical fruit tree species constitute a yet untapped supply of outstanding diversity of taste and nutritional value, barely developed from the genetics standpoint, with scarce or no genomic resources to tackle the challenges arising in modern breeding practice. We generated a de novo genome assembly of the Psidium guajava, the super fruit “apple of the tropics”, and successfully transferred 14,268 SNP probesets from Eucalyptus to Psidium at the nucleotide level, to detect genomic loci linked to resistance to the root knot nematode (RKN) Meloidogyne enterolobii derived from the wild relative P. guineense. Significantly associated loci with resistance across alternative analytical frameworks, were detected at two SNPs on chromosome 3 in a pseudo-assembly of Psidium guajava genome built using a syntenic path approach with the Eucalyptus grandis genome to determine the order and orientation of the contigs. The P. guineense-derived resistance response to RKN and disease onset is conceivably triggered by mineral nutrients and phytohormone homeostasis or signaling with the involvement of the miRNA pathway. Hotspots of mapped resistance quantitative trait loci and functional annotation in the same genomic region of Eucalyptus provide further indirect support to our results, highlighting the evolutionary conservation of genomes across genera of Myrtaceae in the adaptation to pathogens. Marker assisted introgression of the resistance loci mapped should accelerate the development of improved guava cultivars and hybrid rootstocks.

Genetic dissection of fruit maturity date in apricot (P. armeniaca L.) through a Single Primer Enrichment Technology (SPET) approach

Background

Single primer enrichment technology (SPET) is an emerging and increasingly popular solution for high-throughput targeted genotyping in plants. Although SPET requires a priori identification of polymorphisms for probe design, this technology has potentially higher reproducibility and transferability compared to other reduced representation sequencing (RRS) approaches, also enabling the discovery of closely linked polymorphisms surrounding the target one.

Results

The potential for SPET application in fruit trees was evaluated by developing a 25K target SNPs assay to genotype a panel of apricot accessions and progenies. A total of 32,492 polymorphic sites were genotyped in 128 accessions (including 8,188 accessory non-target SNPs) with extremely low levels of missing data and a significant correlation of allelic frequencies compared to whole-genome sequencing data used for array design. Assay performance was further validated by estimating genotyping errors in two biparental progenies, resulting in an overall 1.8% rate. SPET genotyping data were used to infer population structure and to dissect the architecture of fruit maturity date (MD), a quantitative reproductive phenological trait of great agronomical interest in apricot species. Depending on the year, GWAS revealed loci associated to MD on several chromosomes. The QTLs on chromosomes 1 and 4 (the latter explaining most of the phenotypic variability in the panel) were the most consistent over years and were further confirmed by linkage mapping in two segregating progenies.

Conclusions

Besides the utility for marker assisted selection and for paving the way to in-depth studies to clarify the molecular bases of MD trait variation in apricot, the results provide an overview of the performance and reliability of SPET for fruit tree genetics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08901-1.

Identification of QTLs and a putative candidate gene involved in rhizome enlargement of Asian lotus (Nelumbo nucifera)

QTL mapping studies identified three reliable QTLs of rhizome enlargement in lotus. NnBEL6 located within the confidence interval of the major QTL cqREI-LG2 is a key candidate gene enhancing rhizome enlargement. Lotus (Nelumbo) is perennial aquatic plant with nutritional, pharmacological, and ornamental significance. Rhizome is an underground lotus stem that acts as a storage organ and as a reproductive tissue for asexual production. The enlargement of lotus rhizome is an important adaptive strategy for surviving the cold winter. The aims of this study were to identify quantitative trait loci (QTLs) for rhizome enlargement traits including rhizome enlargement index (REI) and number of enlarged rhizome (NER), and to uncover their associated candidate genes. A high-density genetic linkage map was constructed, consisting of 2935 markers binned from 236,840 SNPs. A total of 14 significant QTLs were detected for REI and NER, which explained 6.7-22.3% of trait variance. Three QTL regions were repeatedly identified in at least 2 years, and a major QTL, designated cqREI-LG2, with a rhizome-enlargement effect and about 20% of the phenotypic contribution was identified across the 3 climatic years. A candidate NnBEL6 gene located within the confidence interval of cqREI-LG2 was considered to be putatively involved in lotus rhizome enlargement. The expression of NnBEL6 was exclusively induced by rhizome swelling. Sequence comparison of NnBEL6 among lotus cultivars revealed a functional Indel site in its promoter that likely initiates the rhizome enlargement process. Transgenic potato assay was used to confirm the role of NnBEL6 in inducing tuberization. The successful identification QTLs and functional validation of NnBEL6 gene reported in this study will enrich our knowledge on the genetic basis of rhizome enlargement in lotus.

Genotyping by Sequencing Advancements in Barley

Barley is considered an ideal crop to study cereal genetics due to its close relationship with wheat and diploid ancestral genome. It plays a crucial role in reducing risks to global food security posed by climate change. Genetic variations in the traits of interest in crops are vital for their improvement. DNA markers have been widely used to estimate these variations in populations. With the advancements in next-generation sequencing, breeders could access different types of genetic variations within different lines, with single-nucleotide polymorphisms (SNPs) being the most common type. However, genotyping barley with whole genome sequencing (WGS) is challenged by the higher cost and computational demand caused by the large genome size (5.5GB) and a high proportion of repetitive sequences (80%). Genotyping-by-sequencing (GBS) protocols based on restriction enzymes and target enrichment allow a cost-effective SNP discovery by reducing the genome complexity. In general, GBS has opened up new horizons for plant breeding and genetics. Though considered a reliable alternative to WGS, GBS also presents various computational difficulties, but GBS-specific pipelines are designed to overcome these challenges. Moreover, a robust design for GBS can facilitate the imputation to the WGS level of crops with high linkage disequilibrium. The complete exploitation of GBS advancements will pave the way to a better understanding of crop genetics and offer opportunities for the successful improvement of barley and its close relatives.

A joint learning approach for genomic prediction in polyploid grasses

Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.

Development of Breeder-Friendly KASP Markers from Genome-Wide Association Studies Results

Array-based SNP markers are commonly used in genome-wide association studies (GWAS) to identify genomic regions involved in important agronomical traits. However, conversion of these SNP markers into breeder-friendly kompetitive allele-specific PCR (KASP) markers for use in marker-assisted selection is often challenging. In this chapter we describe general considerations and successfully applied protocols for the conversion of Illumina array SNP markers into locus-specific KASP markers with a special emphasis and examples on how to overcome difficulties in polyploid wheat.

Genome-Wide Association Study of Six Forage Traits in Ramie (Boehmeria nivea L. Gaud)

Genome-wide association study (GWAS) of six forage traits using whole-genome sequencing data generated from 301 ramie accessions found that traits were continuously distributed; the maximum variant coefficient was fresh weight per clump (FWPC) (2019) and individual plant height (IPH) (2019) minimum. Correlation analysis demonstrated that 2019 and 2020 results were similar; all traits were correlated. GWAS analysis demonstrated that six traits exhibited consistent and precise association signals. Of the latter, 104 were significant and detected in 43 genomic regions. By screening forage trait-associated single nucleotide polymorphisms and combining Manhattan map with genome annotation, signals were categorized according to functional annotations. One loci associated with fresh weight per plant (FWP) (chromosome 5; Bnt05G007759), two associated with FWPC (chromosome 13; Bnt13G018582, and Bnt13G018583), and two associated with leaf dry weight per plant (LDWP) and dry weight per plant (DWP) (chromosome 4; Bnt04G005779 and Bnt04G005780), were identified. We describe forage trait candidate genes that are highly correlated with FWP and FWPC; Bnt05G007759 may be involved in nitrogen metabolism, while Bnt13G018582 and Bnt13G018583 may encode TEOSINTE branch 1/CYCLOIDEA/proliferating cytokine 1 (TCP) domains. Bnt04G005779 and Bnt04G005780, which may regulate growth and development, are highly related to LDWP and DWP. These genomic resources will provide a basis for breeding varieties.

Recent status of Genotyping by Sequencing (GBS) Technology in cucumber (Cucumis sativus L.): a review

Current and advanced breeding tools are being used to improve economically important horticultural crops to meet the consumers' needs and preferences. Genotyping-by-sequencing (GBS) is an extremely useful tool in the investigation and analysis of the genetic diversity of different cultivars. Based on a broad range of genetic backgrounds like single nucleotide polymorphism (SNPs), GBS is known as a novel technique to facilitate the detection of quantitative trait loci (QTL) regions robustly linked with interested traits compared to genome-wide association study (GWAS) and QTL. GBS has gained popularity among breeders in recent years and it is also employed in cucumber breeding programs. Cucumbers (C. sativus L.) are monoecious, gynoecious and some of them are parthenocarpic species. Cucumber is one of the most economical and essential crops in the Cucurbitaceae family. For time immemorial, cucumber has been produced and consumed all over the world like other cucurbits. To a large extent, cultivated cucurbits are beneficial to human health for providing necessary minerals and fibers.Therefore, this review portrays the current status of advances made by using GBS and its combination with other tools in various studies of cucumber such as the use of GBS and single nucleotide polymorphism (SNP) markers, GBS and GWAS, also with QTL and marker-assisted selection (MAS) are applied to display and detect explicit genetic architecture complex traits in crops and chromosome rearrangements.Cucumber breeding programs have undoubtedly benefited from genotyping-by-sequencing. Using the GBS method, research discovered lots of new candidate genes that control various traits including spine color, fruit stalk-end color, and disease resistance in cucumber lines.

Development of Novel Markers for Yield in Hevea brasiliensis Muell. Arg. Based on Candidate Genes from Biosynthetic Pathways Associated with Latex Production

Scarcity of functional genetic markers associated with candidate genes (CGs) is a serious constraint for marker-assisted selection in the natural rubber producing tree, Hevea brasiliensis. In order to develop markers associated with rubber yield, five CGs involved in latex biosynthesis were characterized from 16 popular Hevea varieties. Novel SNPs and indels were identified and developed into markers using simple genotyping techniques like allele-specific PCR, CAPS, etc. A progeny population was genotyped using these markers to validate them, to understand their segregation pattern and to map them to a genetic linkage map. Parent-specific maps were constructed using pseudo-test cross strategy with the help of additional markers. The sequence structure information generated will be useful for future studies on gene mapping, functional relevance of coding SNPs and evolution of rubber biosynthesis genes in Hevea. Concurrently, the markers developed may serve as powerful tools for yield-based selection and for genetic diversity and pedigree studies in Hevea. Above all, the marker assays designed for genotyping could be economically carried out in any laboratory having basic molecular biology infrastructure and expertise.

Genotyping-by-sequencing and genomic selection applications in hexaploid triticale

Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n = 289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) > 0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r² > 0.18 (P < 0.05). Genome-wide LD declined rapidly to r² < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement.

Limited allele-specific gene expression in highly polyploid sugarcane

Polyploidy is widespread in plants, allowing the different copies of genes to be expressed differently in a tissue-specific or developmentally specific way. This allele-specific expression (ASE) has been widely reported, but the proportion and nature of genes showing this characteristic have not been well defined. We now report an analysis of the frequency and patterns of ASE at the whole-genome level in the highly polyploid sugarcane genome. Very high depth whole-genome sequencing and RNA sequencing revealed strong correlations between allelic proportions in the genome and in expressed sequences. This level of sequencing allowed discrimination of each of the possible allele doses in this 12-ploid genome. Most genes were expressed in direct proportion to the frequency of the allele in the genome with examples of polymorphisms being found with every possible discrete level of dose from 1:11 for single-copy alleles to 12:0 for monomorphic sites. The rarer cases of ASE were more frequent in the expression of defense-response genes, as well as in some processes related to the biosynthesis of cell walls. ASE was more common in genes with variants that resulted in significant disruption of function. The low level of ASE may reflect the recent origin of polyploid hybrid sugarcane. Much of the ASE present can be attributed to strong selection for resistance to diseases in both nature and domestication.

Broadening the horizon of crop research: a decade of advancements in plant molecular genetics to divulge phenotype governing genes

Advancements in sequencing, genotyping, and computational technologies during the last decade (2011-2020) enabled new forward-genetic approaches, which subdue the impediments of precise gene mapping in varied crops. The modern crop improvement programs rely heavily on two major steps-trait-associated QTL/gene/marker's identification and molecular breeding. Thus, it is vital for basic and translational crop research to identify genomic regions that govern the phenotype of interest. Until the advent of next-generation sequencing, the forward-genetic techniques were laborious and time-consuming. Over the last 10 years, advancements in the area of genome assembly, genotyping, large-scale data analysis, and statistical algorithms have led faster identification of genomic variations regulating the complex agronomic traits and pathogen resistance. In this review, we describe the latest developments in genome sequencing and genotyping along with a comprehensive evaluation of the last 10-year headways in forward-genetic techniques that have shifted the focus of plant research from model plants to diverse crops. We have classified the available molecular genetic methods under bulk-segregant analysis-based (QTL-seq, GradedPool-Seq, QTG-Seq, Exome QTL-seq, and RapMap), target sequence enrichment-based (RenSeq, AgRenSeq, and TACCA), and mutation-based groups (MutMap, NIKS algorithm, MutRenSeq, MutChromSeq), alongside improvements in classical mapping and genome-wide association analyses. Newer methods for outcrossing, heterozygous, and polyploid plant genetics have also been discussed. The use of k-mers has enriched the nature of genetic variants which can be utilized to identify the phenotype-causing genes, independent of reference genomes. We envisage that the recent methods discussed herein will expand the repertoire of useful alleles and help in developing high-yielding and climate-resilient crops.

flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning

Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition problem, which is a more flexible graphical metric compared with the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably with state-of-the-art algorithms-up to 30 times faster with 2 times fewer switch errors on 6 × ploidy simulated data. Further, we show using real nanopore data that flopp can quickly reveal reasonable haplotype structures from the autotetraploid Solanum tuberosum (potato).

Nutritional improvement of cereal crops to combat hidden hunger during COVID-19 pandemic: Progress and prospects

COVID-19 has posed a severe challenge on food security by limiting access to food for the marginally placed population. While access to food is a challenge, access to nutritional food is a greater challenge to the population. The present-day foods are not sufficient to meet the nutritional requirements of the human body. In a pandemic condition, providing nutritious food to the population is imperative to ensure the health and well-being of humankind. Exploiting the existing biodiversity of crop species and deploying classical and modern tools to improve the nutritional potential of these species holds the key to addressing the above challenge. Breeding has been a classical tool of crop improvement that relied predominantly on genetic diversity. Collecting and conserving diverse germplasms and characterizing their diversity using molecular markers is essential to preserve diversity and use them in genetic improvement programs. These markers are also valuable for association mapping analyses to identify the genetic determinants of traits-of-interest in crop species. Association mapping identifies the quantitative trait loci (QTL) underlying the trait-of-interest by exploring marker-trait associations, and these QTLs can further be exploited for the genetic improvement of cultivated species through genomics-assisted breeding. Conventional breeding and genomics approaches are also being applied to develop biofortified cereal crops to reduce nutritional deficiencies in consumers. In this context, chapter explains the prerequisites for association mapping, population structure, genetic diversity, different approaches of performing association mapping to dissect nutritional traits, use the information for genomics-assisted breeding for nutrient-rich cereal crops, and application of genomics strategies in crop biofortification. These approaches will ensure food and nutrition security for all amidst the current COVID-19 crisis.