SGN Database: From QTLs to Genomes

Quantitative trait loci (QTL) analysis is used to dissect the genetic basis underlying polygenic traits. Several public databases have been storing and making QTL data available to research communities. To our knowledge, current QTL databases rely on manual curation where curators read literature and extract relevant QTL information to store in databases. Evidently, this approach is expensive in terms of expert manpower and time use and limits the type of data that can be curated. At the Solanaceae Genomics Network (SGN) ("http://sgn.cornell.edu":http://sgn.cornell.edu), we have developed a database to store raw phenotype and genotype data from QTL studies, perform, on the fly, QTL analysis using R/QTL statistical software ("http://www.rqtl.org":http://www.rqtl.org) and visualize QTLs on a genetic map. Users can identify peak, and flanking markers for QTLs of traits of interest. The QTL database is integrated with other SGN databases (eg. Marker, BACs, and Unigenes), and analysis tools such as the Comparative Map Viewer. Using the comparative map viewer, users can compare chromosome with QTL regions to genetic maps of interest from the same or different Solanaceae species. As the tomato genome sequencing advances, users can also identify corresponding BAC sequences or locations on the tomato physical map, which can be suggestive of candidate genes for a trait of interest.

Furthermore at SGN, images, quantitative phenotype and genotype data, publications, genetic maps generated by QTL studies are displayed and available for download. Currently, data from three F2 and two backcross population QTL studies on fruit morphology traits (18 – 46 traits per population) is available at the SGN website for viewing at population, accession, and trait levels. Traits are described using ontology terms. Phenotype data is presented in tabular and graphical formats such as frequency distributions with basic descriptive statistics. Mapping data showing location of parental alleles on individual accession genetic maps is also available.

SGN is a public database hosted at Boyce Thomson Institute, Cornell University, and funded by USDA CSREES and NSF

solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database

BACKGROUND: A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases. DESCRIPTION: The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application. CONCLUSIONS: solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode

High density genotype storage for plant breeding in the Chado schema of Breedbase.

Modern breeding programs routinely use genome-wide information for selecting individuals to advance. The large volumes of genotypic information required present a challenge for data storage and query efficiency. Major use cases require genotyping data to be linked with trait phenotyping data. In contrast to phenotyping data that are often stored in relational database schemas, next-generation genotyping data are traditionally stored in non-relational storage systems due to their extremely large scope. This study presents a novel data model implemented in Breedbase (https://breedbase.org/) for uniting relational phenotyping data and non-relational genotyping data within the open-source PostgreSQL database engine. Breedbase is an open-source, web-database designed to manage all of a breeder's informatics needs: management of field experiments, phenotypic and genotypic data collection and storage, and statistical analyses. The genotyping data is stored in a PostgreSQL data-type known as binary JavaScript Object Notation (JSONb), where the JSON structures closely follow the Variant Call Format (VCF) data model. The Breedbase genotyping data model can handle different ploidy levels, structural variants, and any genotype encoded in VCF. JSONb is both compressed and indexed, resulting in a space and time efficient system. Furthermore, file caching maximizes data retrieval performance. Integration of all breeding data within the Chado database schema retains referential integrity that may be lost when genotyping and phenotyping data are stored in separate systems. Benchmarking demonstrates that the system is fast enough for computation of a genomic relationship matrix (GRM) and genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays, 314 triploid Musa acuminata, and 924 diploid Manihot esculenta samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively

Breedbase: a digital ecosystem for modern plant breeding

Modern breeding methods integrate next-generation sequencing (NGS) and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to 1) track breeding materials, 2) store experimental evaluations, 3) record phenotypic measurements using consistent ontologies, 4) store genotypic information, and 5) implement algorithms for analysis, prediction and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/). Originally initiated as Cassavabase (https://cassavabase.org/) with the NextGen Cassava project (https://www.nextgencassava.org/), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web-based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/) and packaged in a Docker image for deployment (https://dockerhub.com/breedbase/). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem

A Snapshot of the Emerging Tomato Genome Sequence

The genome of tomato (Solanum lycopersicum L.) is being sequenced by an international consortium of 10 countries (Korea, China, the United Kingdom, India, the Netherlands, France, Japan, Spain, Italy, and the United States) as part of the larger \u201cInternational Solanaceae Genome Project (SOL): Systems Approach to Diversity and Adaptation\u201d initiative. The tomato genome sequencing project uses an ordered bacterial artificial chromosome (BAC) approach to generate a high-quality tomato euchromatic genome sequence for use as a reference genome for the Solanaceae and euasterids. Sequence is deposited at GenBank and at the SOL Genomics Network (SGN). Currently, there are around 1000 BACs finished or in progress, representing more than a third of the projected euchromatic portion of the genome. An annotation effort is also underway by the International Tomato Annotation Group. The expected number of genes in the euchromatin is 3c40,000, based on an estimate from a preliminary annotation of 11% of finished sequence. Here, we present this first snapshot of the emerging tomato genome and its annotation, a short comparison with potato (Solanum tuberosum L.) sequence data, and the tools available for the researchers to exploit this new resource are also presented. In the future, whole-genome shotgun techniques will be combined with the BAC-by-BAC approach to cover the entire tomato genome. The high-quality reference euchromatic tomato sequence is expected to be near completion by 2010

The tomato genome sequence provides insights into fleshy fruit evolution

Tomato (Solanum lycopersicum) is a major crop plant and a model system for fruit development. Solanum is one of the largest angiosperm genera1 and includes annual and perennial plants from diverse habitats. Here we present a high-quality genome sequence of domesticated tomato, a draft sequence of its closest wild relative, Solanum pimpinellifolium2, and compare them to each other and to the potato genome (Solanum tuberosum). The two tomato genomes show only 0.6% nucleotide divergence and signs of recent admixture, but show more than 8% divergence from potato, with nine large and several smaller inversions. In contrast to Arabidopsis, but similar to soybean, tomato and potato small RNAs map predominantly to gene-rich chromosomal regions, including gene promoters. The Solanum lineage has experienced two consecutive genome triplications: one that is ancient and shared with rosids, and a more recent one. These triplications set the stage for the neofunctionalization of genes controlling fruit characteristics, such as colour and fleshiness