GRETA: New Database Turns Mountains of Genome Sequencing Data into Searchable Science

Whole-genome sequencing has become one of the most powerful tools in modern medicine, but it produces a problem that few people outside the field fully appreciate: too many results. When researchers sequence the entire genomes of thousands of individuals and test millions of genetic variants for links to disease, the output is not a tidy list of discoveries. It is an avalanche of statistical associations, each with its own effect size, confidence interval, allele frequency and quality metrics. A single large study can generate billions of individual results, and until now there has been no straightforward way to store, query and interpret that flood of numbers alongside the public reference data needed to make sense of them. A team of researchers from Switzerland, Germany and South Africa believes it has built the answer, and they have described it in a new open-access paper in BMC Bioinformatics.

The system is called GRETA, short for Genetic REsults daTAbase, and it was developed by Cristian Riccio, Amra Dhabalia Ashok, Georgios Koliopanos and Andreas Ziegler at Cardio-CARE in Davos, together with colleagues at the University Medical Center Hamburg-Eppendorf, the University of Lübeck and the University of KwaZulu-Natal. At its core, GRETA is a relational database, meaning that information is organised into structured tables connected by defined keys, so that any single association result can be linked back to the exact dataset, analysis pipeline and parameter settings that produced it. That traceability is not a luxury. In genetic epidemiology, where a single spurious association can send a research group down a months-long dead end, knowing precisely how a result was generated is as important as the result itself.

What makes GRETA unusual is not merely that it stores data, but how it was designed. Rather than asking software engineers to guess what geneticists need, the team ran a formal requirements-engineering process grounded in user-centred design, a methodology borrowed from human-computer interaction in which the intended users shape the product at every stage. Through this process they defined product specifications across eight categories, covering everything from data upload and a graphical user interface to identity and access management and data protection. Only after these requirements were fixed did the team evaluate commercial database solutions and select a vendor to implement them, a deliberate choice to build on proven infrastructure rather than reinventing database technology from scratch.

The technical architecture reflects the realities of modern genomics. GRETA is built as a relational database management system, using Structured Query Language under the hood, but most users will never write a query. Instead, they interact with the system through a graphical user interface that allows interactive querying and visualisation of association results. Behind the scenes, the relational schema connects in-house association results, whether from genome-wide association studies or rare variant association studies, with public reference resources such as the Genotype-Tissue Expression project and functional annotation tools like Functional Mapping and Annotation. Every result carries links to its underlying data and analysis parameters, so a researcher can trace a finding from a summary statistic all the way back to the cohort and pipeline that produced it.

This matters because the people who most need access to genetic association results are often not the people who can get it. Traditionally, interrogating large result sets required command-line skills, familiarity with database systems and comfort manipulating enormous text files on high-performance computing clusters. Clinicians, early-career scientists and collaborators in adjacent fields were frequently locked out of the full richness of their own projects’ data. GRETA was explicitly designed to close that gap: the platform makes large-scale association results accessible to researchers without programming skills, while retaining the depth and flexibility that computational specialists expect. The result, the authors argue, is a secure, multi-user solution for exploring genetic association results and their functional annotation at scale.

The development team did not work in the abstract. GRETA was built to serve real studies, including the Hamburg City Health Study, one of Germany’s large population-based cohorts, whose participants and study staff made the underlying dataset possible. The genetic association analysis pipeline feeding the database was developed with contributions from researchers at Cardio-CARE, and the database itself was built by Mark Mallabone of Ovations Technologies Ltd, with the graphical interface implemented by Peter Wanjohi and Wisaal Behardien of iOCO GmbH. The collaboration between academic geneticists, statisticians and commercial software developers is itself a notable feature of the project, reflecting a growing recognition that scientific data infrastructure is too important and too complex to be left to ad hoc scripting.

Data protection received particular attention, and for good reason. Genomic data is among the most sensitive categories of personal information that exist, and studies like the Hamburg City Health Study operate under strict ethical oversight. The ethics committee of the Hamburg Medical Association raised no objections to the conduct of the study, and approvals were obtained from the data protection officer of the University Medical Center Hamburg-Eppendorf and the data protection commissioner of the Free and Hanseatic City of Hamburg. GRETA’s identity and access management features are designed to ensure that only authorised users can reach the results they are entitled to see, a crucial requirement for any platform intended to serve multiple research groups handling human genomic data.

Perhaps the most consequential decision the team made was to publish everything. Alongside the paper, the researchers have released the product requirements, the relational schema and the complete list of functionalities, so that other institutions can reproduce the system elsewhere. In a field where data infrastructure is often proprietary, fragmented or poorly documented, this commitment to openness could prove as influential as the software itself. The article is published open access under a Creative Commons licence, and extensive supplementary materials accompany it, giving any laboratory or consortium a detailed blueprint for building its own results database tailored to its own studies.

The implications reach well beyond cardiology, the field in which GRETA was born. As whole-genome sequencing becomes cheaper and cohort studies grow larger, the bottleneck in genetic research is shifting from data generation to data management and interpretation. Rare variant association studies, in particular, produce enormous volumes of results across vast numbers of genes, and integrating them with tissue-specific expression data and functional annotations is exactly the kind of task that overwhelms spreadsheets and home-grown scripts. A well-designed relational database that unifies results and reference data in one queryable place could accelerate everything from gene discovery to the interpretation of a patient’s genome in a clinical setting.

There are, of course, caveats. GRETA is a tool for storing and exploring association results, not a substitute for rigorous statistical analysis or biological validation, and its value depends on the quality of the results fed into it. The published paper describes an early-release version subject to further editorial revision, and the true test of any database is adoption by research groups beyond its birthplace. But the problem it addresses is real, widespread and growing, and the authors’ decision to share the full design openly means that the barrier to trying it elsewhere is unusually low. If GRETA and systems like it become standard fixtures in genomics laboratories, the era in which billions of hard-won genetic associations languished in unread files may finally be drawing to a close.

Subject of Research: A relational database for storing, integrating and querying whole-genome sequencing association results with public reference data

Article Title: GRETA: a results database for whole-genome sequencing studies

Article References: Riccio, C., Ashok, A. D., Guo, L., Koliopanos, G., Zeller, T., Twerenbold, R., & Ziegler, A. (2026). GRETA: a results database for whole-genome sequencing studies. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06688-6

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06688-6

Keywords: GRETA, whole-genome sequencing, relational database, genome-wide association study, rare variant association study, user-centred design, bioinformatics, genetic association results, functional annotation, data protection, cardiogenomics, Hamburg City Health Study