GenomeDia: a personalized medicine tool for diabetes

Project Information

GenomeDia

Grant agreement ID: 101157669

DOI

10.3030/101157669

Project closed

EC signature date 6 June 2024

Start date 1 August 2024

End date 31 January 2026

Funded under

• European Research Council (ERC)

Total cost

No data

EU contribution

€ 150 000,00

Coordinated by

FUNDACIO CENTRE DE REGULACIO GENOMICA
Spain

Periodic Reporting for period 1 - GenomeDia (GenomeDia: a personalized medicine tool for diabetes)

Reporting period: 2024-08-01 to 2026-01-31

Summary of the context and overall objectives of the project

Diabetes mellitus affects nearly 600 million people worldwide and is a leading cause of blindness, kidney failure, limb amputation, and premature death. Although it is diagnosed by a single biochemical criterion — elevated blood glucose — it encompasses a spectrum of biologically distinct diseases with different causes and very different optimal treatments. Accurate classification of diabetes subtype is therefore clinically important, yet it remains elusive in a substantial proportion of patients. This challenge is most acute in young, non-obese individuals who do not clearly fit the criteria for autoimmune type 1 diabetes (T1D) or obesity-associated type 2 diabetes (T2D). Many of these patients harbour genetic determinants of their disease that, if identified, could guide treatment decisions decisively — for example, permitting a switch from daily insulin injections to oral tablets, or enabling earlier initiation of therapies that can halt disease progression.Genetic testing is increasingly used in diabetes care, but current approaches remain fragmented and incomplete. Standard tests interrogate only a small subset of known disease-causing genes, and focus almost exclusively on the protein-coding portion of the genome — roughly 2% of the total. The remaining 98%, which contains regulatory sequences that control gene activity in pancreatic beta cells, is routinely ignored. Moreover, existing tools treat rare single-gene defects and common polygenic risk as entirely separate problems, when in reality the same patient may carry relevant variants across both categories.GenomeDia was conceived to address these gaps. The project aimed to develop an integrated computational platform that analyses whole genome sequences alongside clinical data to improve the classification of diabetes subtypes in young, non-obese patients. By drawing on extensive functional genomic maps of human pancreatic islet cells generated by our group, as well as genetic findings from the ERC-funded DecodeDiabetes project, GenomeDia sought to expand the interpretable genomic space to the entire genome and to combine rare variant analysis with polygenic risk scores within a single unified workflow.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The project developed and validated a comprehensive computational workflow for the interpretation of whole genome sequences in patients with unsolved or ambiguous diabetes. The workflow encompasses all major stages of genomic analysis: primary sequence processing and quality control, calling of diverse variant types (single nucleotide variants, small insertions and deletions, structural variants, and mitochondrial variants), functional annotation drawing on a curated knowledgebase of pancreatic islet regulatory elements and genetic findings, and integration of polygenic risk scores. A key component is the GenomeDia Knowledgebase (GenomeDiaDB), which consolidates manually curated knowledge of clinically relevant genetic variants from DecodeDiabetes and the public domain, together with rich annotations of beta cell regulatory elements generated by our team, including promoter maps, chromatin accessibility data, and three-dimensional genome contact maps. The workflow was validated in young non-obese patients with clinically suspected monogenic diabetes but negative results on standard gene panels and young non-obese patients with an early-onset T2D clinical diagnosis. Validation results were discussed with clinical genetics collaborators. In parallel, the project carried out an evaluation of intellectual property strategy and potential routes to commercial exploitation, in collaboration with CRG's Technology and Business Development Office.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

GenomeDia advances the state of the art in genetic diagnosis of diabetes in several respects. First, it extends the scope of genomic interrogation in clinical samples to the entire genome, including the noncoding regulatory sequences that control pancreatic beta cell function — a region largely ignored by existing clinical tools. Second, it integrates rare variant analysis across a broad allelic frequency spectrum with polygenic risk score evaluation, providing a unified interpretive framework rather than the fragmented approaches currently in use. Third, it incorporates clinical phenotypic data to weight the likelihood of pathogenicity of identified variants in specific patients.
The project is expected to be valorized through a collaboration with a European genomics diagnostics company, thereby enabling deployment of the platform in a clinical service context. Further development will benefit from continued expansion of the patient cohorts and functional genomic resources that underpin the knowledgebase, as well as ongoing advances in algorithms for predicting the functional impact of noncoding variants. Regulatory pathway assessment will be required prior to deployment as a clinical diagnostic tool.