Abstract
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.
Highlights
Routine genotyping is possible with the advent of low-cost, high-throughput genotyping platforms, giving rise to enormous amounts of data but presenting challenges for data management and queriability [1]
JSONb is a binary formatted JavaScript Object Notation (JSON) field, allowing for compressed data sizes and faster queries in some scenarios. Breeding methods such as genome wide association study (GWAS) and GS depend on large genotypic data and metadata, generally stored in a standardized Variant Call Format (VCF) structure
The JSON genotype storage model presented here closely follows the VCF specification and can handle any kind of variant encoded in VCF, such as different ploidy levels, multiple alleles, insertions or deletions, and structural variants
Summary
Routine genotyping is possible with the advent of low-cost, high-throughput genotyping platforms, giving rise to enormous amounts of data but presenting challenges for data management and queriability [1]. To serve these three scenarios effectively and efficiently, it is critical to store germplasm, pedigrees, experimental designs, and phenotypic and genotypic information under a unified architecture These services can either be implemented within a single database or provided by independent applications interconnected via application programming interfaces (APIs) such as, the publicly specified Plant Breeding API (BrAPI) [8]. Data can be stored via nested objects composed of heterogeneous keys and values, allowing for flexibility in the data structure and model; often non-relational data is structured using JavaScript Object Notation (JSON). JSONb is a binary formatted JSON field, allowing for compressed data sizes and faster queries in some scenarios Breeding methods such as GWAS and GS depend on large genotypic data and metadata, generally stored in a standardized Variant Call Format (VCF) structure. The preferred format for uploading and downloading genotypic data in Breedbase is VCF
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