GREGoR: accelerating genomics for rare diseases
Article Date: 12 November 2025
Article URL: https://www.nature.com/articles/s41586-025-09613-8
Article Image: (none provided)
Summary
GREGoR (Genomics Research to Elucidate the Genetics of Rare Diseases) is a large, NIH NHGRI–supported consortium that brings together multiple centres, methods and datasets to speed gene discovery and molecular diagnosis for rare diseases. The consortium integrates genome, exome, long-read sequencing, transcriptomics, methylation and advanced computational pipelines across many institutions to increase diagnostic yield, enable novel gene discovery and promote systematic reanalysis and data sharing.
Author style: Punchy — this paper is a major, coordinated push to close the diagnostic gap in rare disease genomics; read the detail if you care about real-world impact and new methods.
Key Points
- GREGoR is a multi-institutional consortium funded by NHGRI (multiple U01s/U24) designed to accelerate rare-disease genomics at scale.
- The initiative combines short- and long-read genome sequencing, RNA-seq, methylation profiling and improved structural-variant detection to solve previously unsolved cases.
- GREGoR has contributed to or led 83 papers reporting molecular diagnoses across 365 genes, with >1/3 representing novel disease genes or phenotypic expansions.
- Systematic reanalysis and new computational tools are central — reanalysis driven by novel gene discovery substantially increases diagnostic yield.
- The consortium emphasises equity, data sharing (FAIR principles), and the development of scalable pipelines and resources for clinical translation.
Content summary
GREGoR unites sequencing centres, clinical teams and computational groups to address persistent diagnostic gaps in Mendelian and rare disorders. By applying a layered diagnostic strategy — from exome to genome to long-read and multi-omic assays — the consortium resolves complex variants (structural variants, tandem repeats, paralogous regions), multilocus diagnoses and regulatory/non-coding mechanisms that short-read exomes often miss.
The paper summarises consortium organisation, methods, key outputs (gene discoveries and diagnostic statistics) and resources: benchmark datasets, pipelines for CNV and SV discovery, and tools for phased and methylation-aware analyses. It highlights the practical gains of regular reanalysis, leveraging population resources (gnomAD, pangenomes) and new variant-prioritisation strategies, and discusses barriers — reference genome issues, variant interpretation inequities, and the need for broader population representation.
Context and relevance
Rare-disease diagnosis increasingly depends on integrating diverse genomic technologies and large collaborative networks. GREGoR represents a coordinated, well-funded example of that approach, demonstrating measurable improvements in solved cases and novel gene discovery. For clinicians, genetic counsellors and researchers, the paper provides a roadmap: when to escalate testing (genome, long-read, RNA, methylation), how to implement systematic reanalysis, and where new tools can reduce missed diagnoses.
It also ties into current trends: movement toward long-read and multi-omic assays, pangenome references, improved SV and repeat detection, and attention to equity and data sharing to reduce variant-classification disparities across ancestries.
Why should I read this?
If you work in rare-disease genomics — or just want to stop missing diagnoses — this is the sort of consortium paper that tells you what actually works. It’s full of practical lessons (which tests to run, why reanalyse, where short reads fail) and points to tools and datasets you can adopt. Short version: it saves you time and points you straight to the parts that will boost diagnostic yield.