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

The research team, led by Katharina Alina Riedl of the University Medical Center Hamburg-Eppendorf and collaborators at institutions including Charité Berlin and the German Centre of Cardiovascular Research, analyzed data from 2,093 participants who had undergone both advanced magnetic resonance imaging of the heart and aorta and a dedicated ultrasound examination of the abdominal aorta. The participants, drawn from the first 10,000 volunteers enrolled in the Hamburg City Health Study between February 2016 and November 2018, had a median age of 66 years, and roughly 42 percent were women. The Hamburg City Health Study is an ambitious, single-center, prospective epidemiological project aiming to follow more than 20,000 residents of Hamburg aged 45 to 74 over the long term, making it an ideal laboratory for studying how vascular aging manifests in a general population rather than in selected patient groups.

At the heart of the study are two measurements that cardiologists describe as windows into the biological age of the arteries. The first is pulse wave velocity, or PWV, which quantifies how fast the pressure wave generated by each heartbeat travels along the aortic wall. A stiffer artery propagates the wave more quickly, so higher PWV signals greater arterial stiffness, itself both a contributor to and a consequence of atherosclerosis and vascular aging. The second is aortic distensibility, or AD, which captures how much the aortic wall expands with each pulse of blood; a healthy, elastic aorta distends readily, whereas a stiffened vessel barely moves. Both parameters were derived from two-dimensional phase-contrast velocity-encoded CMR sequences acquired on a 3 Tesla scanner, with regions of interest placed manually in the ascending and descending aorta and then automatically propagated through the cardiac cycle.

The technical approach matters, because measuring aortic stiffness is notoriously method-sensitive. The team computed PWV as the distance between two measurement points divided by the transit time of the pulse wave, with the distance determined precisely by tracing an aortic centerline on a parasagittal T2-weighted HASTE image rather than estimating it from surface landmarks. The transit time was determined using the PWV 50 percent algorithm, which identifies the moment flow reaches half of its peak in each aortic segment, a method chosen because prior work showed it delivers the highest reproducibility with the fewest outliers. Distensibility, in turn, was calculated as the relative change in aortic diameter over the cardiac cycle divided by the pulse pressure measured at the brachial artery. This centerline-based CMR approach stands in contrast to the conventional carotid-femoral PWV technique, which relies on approximate surface distances and, the authors argue, may be unreliable in people with enlarged abdominal aortas because it samples a different vascular territory.

The ultrasound side of the investigation focused on the infrarenal aorta, the segment below the renal arteries where the overwhelming majority of abdominal aortic aneurysms arise. Using a convex array transducer, sonographers measured the maximum anteroposterior diameter in systole with the outer-to-outer method, as well as the peak flow velocity of blood moving through the vessel. Following current European clinical practice guidelines, a diameter between 25 and 29 millimeters was classified as ectasia and a diameter of 30 millimeters or more as an aneurysm. The median diameter in the cohort was 18.1 millimeters and the median peak flow velocity was 98.6 centimeters per second. In total, 55 participants had either ectasia or an aneurysm, including 21 with a true aneurysm, a prevalence consistent with earlier reports from the same cohort.

The statistical analysis, performed with linear and logistic regression models adjusted progressively for age, sex, and classical cardiovascular risk factors including hypertension, hyperlipidemia, diabetes, smoking, and body mass index, produced a nuanced picture. After adjustment for age and sex, the maximum infrarenal aortic diameter was significantly associated with the distensibility of the descending thoracic aorta, with a regression coefficient of −0.572 and a p-value of 0.009, meaning that people whose descending aorta distends less also tend to have wider infrarenal segments. Likewise, peak flow velocity in the infrarenal aorta was significantly associated with pulse wave velocity, with a coefficient of −0.399 and a p-value of 0.029, indicating that faster aortic pulse waves travel alongside slower peak flow in the abdominal vessel. Adding these stiffness parameters to models containing only age and sex significantly improved model fit in both cases, as confirmed by likelihood ratio tests.

Perhaps the most striking findings emerged when the researchers compared participants with ectasia or aneurysm against those with normal aortic diameters. The 55 affected individuals had significantly higher median pulse wave velocity, 8.59 versus 7.58 meters per second, alongside significantly lower distensibility in both the ascending and descending aorta and significantly lower peak flow velocity, 84.30 versus 98.70 centimeters per second. They also carried a heavier burden of cardiovascular risk factors, with significantly higher rates of hypertension, hyperlipidemia, smoking, prior myocardial infarction, and elevated body mass index. Yet when the same stiffness parameters were tested as predictors of prevalent ectasia or aneurysm in adjusted logistic regression models, none retained a statistically significant predictive value. The differences were real but modest in absolute terms, and the small number of affected participants, particularly the few women in this subgroup, limited statistical power.

This apparent paradox, significant group differences but no individual-level predictive power, carries an important message for the field. The authors conclude that CMR-based aortic stiffness parameters are associated with infrarenal aortic geometry and flow, but that their diagnostic, screening, and prognostic value remains to be established. Screening for abdominal aortic aneurysm currently rests on simple ultrasound diameter thresholds, and the new data suggest that thoracic stiffness measurements are unlikely to replace that approach. However, the findings leave open the possibility that pulse wave velocity and distensibility could help identify individuals at risk of future aneurysm development or rupture vulnerability, questions that the ongoing follow-up of the Hamburg cohort is designed to address. The biology reinforces this caution: the infrarenal aorta has a lower elastin-to-collagen ratio than the thoracic aorta, so stiffness in one segment does not simply mirror stiffness in the other.

The study is not without limitations, which the authors acknowledge candidly. It was conducted at a single center, the cohort contained far more men than women, and only 21 participants had a true aneurysm, an expected figure given the population-based design and the known prevalence of roughly 0.8 percent in this cohort. Peak flow velocity may also be confounded by the cross-sectional area of the vessel, since a larger lumen mechanically reduces peak velocity regardless of wall stiffness. The team used two-dimensional flow CMR rather than the more information-rich four-dimensional technique, a deliberate trade-off favoring fast acquisition and feasibility in large population studies, and brachial blood pressure served as a surrogate for central aortic pressure in the distensibility calculation. Nonetheless, by combining magnetic resonance and ultrasound data in the same large, unselected sample, the study represents the largest investigation to date linking CMR-derived aortic stiffness with ultrasound-based infrarenal parameters, and it sets the stage for longitudinal analyses that may finally determine whether the stiffness of the great vessels can foretell the vessels’ most dangerous failure mode.

Subject of Research: Associations between CMR-based aortic stiffness parameters and ultrasound-based infrarenal aortic parameters in a population-based cohort

Article Title: Associations of CMR-based pulse wave velocity and aortic distensibility with infrarenal ultrasound-based aortic parameters in the Hamburg City Health Study

Article References: Riedl, K. A., Di Carluccio, E., Lund, G., Huellebrand, M., Hennemuth, A., Braren, R., Kirchhof, P., Blankenberg, S., Tilemann, L., Ziegler, A., Behrendt, C. A., Muellerleile, K., & Kölbel, T. (2026). Associations of CMR-based pulse wave velocity and aortic distensibility with infrarenal ultrasound-based aortic parameters in the Hamburg City Health Study. Clinical Research in Cardiology. https://doi.org/10.1007/s00392-026-03029-8

Image Credits: AI Generated

DOI: 10.1007/s00392-026-03029-8

Keywords: pulse wave velocity, aortic distensibility, aortic stiffness, abdominal aortic aneurysm, cardiovascular magnetic resonance, infrarenal aorta, ultrasound, Hamburg City Health Study, arterial stiffening, vascular aging, aneurysm screening, cardiovascular risk factors