EURISCO update 2023: the European Search Catalogue for Plant Genetic Resources, a pillar for documentation of genebank material.

The European Search Catalogue for Plant Genetic Resources (EURISCO) is a central entry point for information on crop plant germplasm accessions from institutions in Europe and beyond. In total, it provides data on more than two million accessions, making an important contribution to unlocking the vast genetic diversity that lies deposited in >400 germplasm collections in 43 countries. EURISCO serves as the reference system for the Plant Genetic Resources Strategy for Europe and represents a significant approach for documenting and making available the world's agrobiological diversity. EURISCO is well established as a resource in this field and forms the basis for a wide range of research projects. In this paper, we present current developments of EURISCO, which is accessible at http://eurisco.ecpgr.org.

Reliable genomic strategies for species classification of plant genetic resources.

BACKGROUND: To address the need for easy and reliable species classification in plant genetic resources collections, we assessed the potential of five classifiers (Random Forest, Neighbour-Joining, 1-Nearest Neighbour, a conservative variety of 3-Nearest Neighbours and Naive Bayes) We investigated the effects of the number of accessions per species and misclassification rate on classification success, and validated theirs generic value results with three complete datasets. RESULTS: We found the conservative variety of 3-Nearest Neighbours to be the most reliable classifier when varying species representation and misclassification rate. Through the analysis of the three complete datasets, this finding showed generic value. Additionally, we present various options for marker selection for classification taks such as these. CONCLUSIONS: Large-scale genomic data are increasingly being produced for genetic resources collections. These data are useful to address species classification issues regarding crop wild relatives, and improve genebank documentation. Implementation of a classification method that can improve the quality of bad datasets without gold standard training data is considered an innovative and efficient method to improve gene bank documentation.

EURISCO: The European search catalogue for plant genetic resources.

The European Search Catalogue for Plant Genetic Resources, EURISCO, provides information about 1.8 million crop plant accessions preserved by almost 400 institutes in Europe and beyond. EURISCO is being maintained on behalf of the European Cooperative Programme for Plant Genetic Resources. It is based on a network of National Inventories of 43 member countries and represents an important effort for the preservation of world's agrobiological diversity by providing information about the large genetic diversity kept by the collaborating collections. Moreover, EURISCO also assists its member countries in fulfilling legal obligations and commitments, e.g. with respect to the International Treaty on Plant Genetic Resources, the Second Global Plan of Action for Plant Genetic Resources for Food and Agriculture of the United Nation's Food and Agriculture Organization, or the Convention on Biological Diversity. EURISCO is accessible at http://eurisco.ecpgr.org.

Collaboration between Private and Public Genebanks in Conserving and Using Plant Genetic Resources.

Among the most important users of plant genetic resources, conserved predominantly in public genebanks around the world, are public and private plant breeders. Through their breeding efforts, they contribute significantly to global, regional, and local food and nutrition security. Plant breeders need genetic diversity to be able to develop competitive new varieties that are adapted to the changing environmental conditions and suit the needs of consumers. To ensure continued and timely access to the genetic resources that contain the required characteristics and traits, plant breeders established working collections with breeding materials and germplasm for the crops they were breeding. However, with the changing and increasingly more restrictive access conditions, triggered by new global legal instruments like the Convention on Biological Diversity/Nagoya Protocol and the International Treaty, plant breeders started to establish their own genebanks at the turn of the 21st century. This paper analyses the conditions that contributed to this situation as well as the historical ways that plant breeders used to acquire the germplasm they needed. Public genebanks played and continue to play a conducive role in providing genetic resources to users, including private-sector plant breeders. However, also the practices of the germplasm curators to collect and distribute germplasm were affected by the new legal framework that had been developed in global fora. It is against this background that the complementarity and collaboration between public and private sector genebanks have been assessed. Whenever possible, vegetable genetic resources and vegetable private breeding companies have been used to analyze and illustrate such collaboration. The authors look at reported successful examples of collaborative efforts and consider opportunities and approaches under which such collaboration can be established and strengthened to ensure the continued availability of the building blocks for food and nutrition security.

LettuceDB: an integrated multi-omics database for cultivated lettuce.

Crop genomics has advanced rapidly during the past decade, which generated a great abundance of omics data from multi-omics studies. How to utilize the accumulating data becomes a critical and urgent demand in crop science. As an attempt to integrate multi-omics data, we developed a database, LettuceDB (https://db.cngb.org/lettuce/), aiming to assemble multidimensional data for cultivated and wild lettuce germplasm. The database includes genome, variome, phenome, microbiome and spatial transcriptome. By integrating user-friendly bioinformatics tools, LettuceDB will serve as a one-stop platform for lettuce research and breeding in the future. Database URL: https://db.cngb.org/lettuce/.

Whole-genome resequencing of 445 Lactuca accessions reveals the domestication history of cultivated lettuce.

Lettuce (Lactuca sativa) is an important vegetable crop worldwide. Cultivated lettuce is believed to be domesticated from L. serriola; however, its origins and domestication history remain to be elucidated. Here, we sequenced a total of 445 Lactuca accessions, including major lettuce crop types and wild relative species, and generated a comprehensive map of lettuce genome variations. In-depth analyses of population structure and demography revealed that lettuce was first domesticated near the Caucasus, which was marked by loss of seed shattering. We also identified the genetic architecture of other domestication traits and wild introgressions in major resistance clusters in the lettuce genome. This study provides valuable genomic resources for crop breeding and sheds light on the domestication history of cultivated lettuce.

Genetic diversity trends in twentieth century crop cultivars: a meta analysis.

In recent years, an increasing number of papers has been published on the genetic diversity trends in crop cultivars released in the last century using a variety of molecular techniques. No clear general trends in diversity have emerged from these studies. Meta analytical techniques, using a study weight adapted for use with diversity indices, were applied to analyze these studies. In the meta analysis, 44 published papers were used, addressing diversity trends in released crop varieties in the twentieth century for eight different field crops, wheat being the most represented. The meta analysis demonstrated that overall in the long run no substantial reduction in the regional diversity of crop varieties released by plant breeders has taken place. A significant reduction of 6% in diversity in the 1960s as compared with the diversity in the 1950s was observed. Indications are that after the 1960s and 1970s breeders have been able to again increase the diversity in released varieties. Thus, a gradual narrowing of the genetic base of the varieties released by breeders could not be observed. Separate analyses for wheat and the group of other field crops and separate analyses on the basis of regions all showed similar trends in diversity.

The Generation Challenge Programme comparative plant stress-responsive gene catalogue.

The Generation Challenge Programme (GCP; www.generationcp.org) has developed an online resource documenting stress-responsive genes comparatively across plant species. This public resource is a compendium of protein families, phylogenetic trees, multiple sequence alignments (MSA) and associated experimental evidence. The central objective of this resource is to elucidate orthologous and paralogous relationships between plant genes that may be involved in response to environmental stress, mainly abiotic stresses such as water deficit ('drought'). The web-based graphical user interface (GUI) of the resource includes query and visualization tools that allow diverse searches and browsing of the underlying project database. The web interface can be accessed at http://dayhoff.generationcp.org.

Homoplasy corrected estimation of genetic similarity from AFLP bands, and the effect of the number of bands on the precision of estimation.

AFLP is a DNA fingerprinting technique, resulting in binary band presence-absence patterns, called profiles, with known or unknown band positions. We model AFLP as a sampling procedure of fragments, with lengths sampled from a distribution. Bands represent fragments of specific lengths. We focus on estimation of pairwise genetic similarity, defined as average fraction of common fragments, by AFLP. Usual estimators are Dice (D) or Jaccard coefficients. D overestimates genetic similarity, since identical bands in profile pairs may correspond to different fragments (homoplasy). Another complicating factor is the occurrence of different fragments of equal length within a profile, appearing as a single band, which we call collision. The bias of D increases with larger numbers of bands, and lower genetic similarity. We propose two homoplasy- and collision-corrected estimators of genetic similarity. The first is a modification of D, replacing band counts by estimated fragment counts. The second is a maximum likelihood estimator, only applicable if band positions are available. Properties of the estimators are studied by simulation. Standard errors and confidence intervals for the first are obtained by bootstrapping, and for the second by likelihood theory. The estimators are nearly unbiased, and have for most practical cases smaller standard error than D. The likelihood-based estimator generally gives the highest precision. The relationship between fragment counts and precision is studied using simulation. The usual range of band counts (50-100) appears nearly optimal. The methodology is illustrated using data from a phylogenetic study on lettuce.

Genebank Operation in the Arena of Access and Benefit-Sharing Policies.

Since the 1990s, the exchange of genetic resources has been increasingly regulated. The Convention on Biological Diversity (CBD), the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA) and the Nagoya Protocol recognize that countries have sovereign rights over their genetic resources and provide a framework for domestic legislations on Access and Benefit-Sharing (ABS). However, within the rules of these international agreements, countries can follow their own interpretations and establish their own rules and regulations, resulting in restricted access to genetic resources and limited benefit-sharing, effects that are contrary to the objectives of these agreements. Although the ITPGRFA's Multilateral System of Access and Benefit-Sharing provides opportunities for easier access to plant genetic resources for food and agriculture (PGRFA), plant genebanks face increasing complexity in their operation. Adding material to genebank collections has become more difficult, not only because collecting missions need to be negotiated with national and local authorities, but also because acquiring material from other collections is only possible if the origin of the material is properly documented and is done in compliance with regulations. Genebanks may only provide access to their own collections if the material that is to be released is distributed in compliance with a) the conditions under which the material was received and b) the national laws of the country where the genebank is located. The only way genebanks can deal with this new complexity, apart from ceasing to add or distribute material, is by setting up proper procedures to document the origin of every accession and the conditions for their use and further distribution. To prevent a further decrease in access to PGRFA, complexity must be fought. Applying the ITPGRFA's Standard Material Transfer Agreement (SMTA) only, even for material that does not fall under the ITPGRFA, would simplify matters. The scope of the ITPGRFA could be expanded to include all crops. Furthermore, certain ambiguities (e.g. regarding in situ material and wild species) could be resolved. Finally, compliance with the ITPGRFA should be improved and better monitored.

Generation Challenge Programme (GCP): standards for crop data.

The Generation Challenge Programme (GCP) is an international research consortium striving to apply molecular biological advances to crop improvement for developing countries. Central to its activities is the creation of a next generation global crop information platform and network to share genetic resources, genomics, and crop improvement information. This system is being designed based on a comprehensive scientific domain object model and associated shared ontology. This model covers germplasm, genotype, phenotype, functional genomics, and geographical information data types needed in GCP research. This paper provides an overview of this modeling effort.

Empirical evaluation of DArT, SNP, and SSR marker-systems for genotyping, clustering, and assigning sugar beet hybrid varieties into populations.

Dominant and co-dominant molecular markers are routinely used in plant genetic research. In the present study we assessed the success-rate of three marker-systems for estimating genotypic diversity, clustering varieties into populations, and assigning a single variety into the expected population. A set of 54 diploid sugar beet (Beta vulgaris L. ssp. vulgaris) hybrid varieties from five seed companies was genotyped with 702 Diversity Array-Technology (DArT), 34 Single Nucleotide Polymorphisms (SNP), and 30 Simple Sequence Repeats (SSR) markers. Analysis of the population structure revealed three well-defined populations and clustering of varieties that generally correlates with their seed company origin. Two populations each contained varieties from two different seed companies indicating genetic similarity of this material. The third population was comprised only of varieties from a single seed company. Analysis of the SSR and SNP datasets indicates that some of the hybrid varieties likely have a common (or very closely related) parent. Comparison of the three marker-systems revealed substantial differences in the number of loci needed for analyses. Varietal clustering required approximately 1.8-2×more SSR, 3-4.5×more SNP, and 4.8×more DArT markers than were required for detection of genotypic diversity. When marker-systems were compared across different types of analyses per locus success-rate was the highest for the SSR and the lowest for the DArT markers. Generally, about 1.4-3×more SNPs, and 4.9-13.3×more DArTs then SSRs were needed to achieve the 100% success-rate. However, using only DArT markers with a high level of polymorphism decreased the number of DArT loci needed for analyses by 38-61%. Results from the present work provide a premise to selecting the type(s) and number of markers that are needed for genetic diversity analysis of sugar beet hybrid varieties.

GERMINATE. a generic database for integrating genotypic and phenotypic information for plant genetic resource collections.

The extensive germplasm resource collections that are now available for major crop plants and their wild relatives will increasingly provide valuable biological and bioinformatics resources for plant physiologists and geneticists to dissect the molecular basis of key traits and to develop highly adapted plant material to sustain future breeding programs. A key to the efficient deployment of these resources is the development of information systems that will enable the collection and storage of biological information for these plant lines to be integrated with the molecular information that is now becoming available through the use of high-throughput genomics and post-genomics technologies. The GERMINATE database has been designed to hold a diverse variety of data types, ranging from molecular to phenotypic, and to allow querying between such data for any plant species. Data are stored in GERMINATE in a technology-independent manner, such that new technologies can be accommodated in the database as they emerge, without modification of the underlying schema. Users can access data in GERMINATE databases either via a lightweight Perl-CGI Web interface or by the more complex Genomic Diversity and Phenotype Connection software. GERMINATE is released under the GNU General Public License and is available at http://germinate.scri.sari.ac.uk/germinate/.

Molecular markers: tools to improve genebank efficiency.

Possibilities for using molecular markers to improve genebank efficiency are increasingly present thanks to developments in genebanks and developments in molecular genetics. These possibilities relate to all aspects of genebank management: acquisition, maintenance, characterisation and utilisation. However, two pitfalls should be avoided. The first lies in the neutrality of the most generally used markers, making them less suitable for optimising genetic diversity. The second is related to the considerable costs involved in using molecular markers. In many cases an economical analysis will have to decide if the markers can routinely be used in genebank operations. Some examples of model studies and applications of molecular markers in genebank operations will be presented, in which both genetic and economic aspects will be illustrated briefly. These examples involved existing genebank collections of wild lettuce, cabbage and wild potato.