RESUMO
Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia faba L.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.
Assuntos
Produtos Agrícolas , Diploide , Variação Genética , Genoma de Planta , Genômica , Melhoramento Vegetal , Proteínas de Plantas , Vicia faba , Cromossomos de Plantas/genética , Produtos Agrícolas/genética , Produtos Agrícolas/metabolismo , Variações do Número de Cópias de DNA/genética , DNA Satélite/genética , Amplificação de Genes/genética , Genes de Plantas/genética , Variação Genética/genética , Genoma de Planta/genética , Estudo de Associação Genômica Ampla , Geografia , Melhoramento Vegetal/métodos , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Recombinação Genética , Retroelementos/genética , Sementes/anatomia & histologia , Sementes/genética , Vicia faba/anatomia & histologia , Vicia faba/genética , Vicia faba/metabolismoRESUMO
While the continuing decline in genotyping and sequencing costs has largely benefited plant research, some key species for meeting the challenges of agriculture remain mostly understudied. As a result, heterogeneous datasets for different traits are available for a significant number of these species. As gene structures and functions are to some extent conserved through evolution, comparative genomics can be used to transfer available knowledge from one species to another. However, such a translational research approach is complex due to the multiplicity of data sources and the non-harmonized description of the data. Here, we provide two pipelines, referred to as structural and functional pipelines, to create a framework for a NoSQL graph-database (Neo4j) to integrate and query heterogeneous data from multiple species. We call this framework Orthology-driven knowledge base framework for translational research (Ortho_KB). The structural pipeline builds bridges across species based on orthology. The functional pipeline integrates biological information, including QTL, and RNA-sequencing datasets, and uses the backbone from the structural pipeline to connect orthologs in the database. Queries can be written using the Neo4j Cypher language and can, for instance, lead to identify genes controlling a common trait across species. To explore the possibilities offered by such a framework, we populated Ortho_KB to obtain OrthoLegKB, an instance dedicated to legumes. The proposed model was evaluated by studying the conservation of a flowering-promoting gene. Through a series of queries, we have demonstrated that our knowledge graph base provides an intuitive and powerful platform to support research and development programmes.