21 Mar Mt. Sinai Geneticists Use “Rareservoir” Database to Uncover Etiologies of Three Rare Diseases

MedicalResearch.com Interview with:

Dr. Turro

Ernest Turro, PhD
Associate Professor Genetics and Genomics Sciences
The Turro group runs a research program on statistical genomics,
with a dual focus on rare diseases and blood-related traits at the Icahn School of Medicine
Mount Sinai Health System

MedicalResearch.com: What is the background for this study? Would you describe the Rareservoir database?

Response:   The main motivation for our work is that only half of the approximately 10,000 catalogued rare diseases have a resolved genetic cause (or aetiology). Patients with these diseases are unable to obtain a genetic diagnosis which could otherwise inform prognosis, treatment for themselves and affected relatives.

One route towards resolving the remaining aetiologies is to enroll large numbers of rare disease patients into research studies so that statistical analyses can be performed comparing the genetic with the clinical characteristics of the study participants. One major endeavour, the 100,000 Genomes Project (100KGP), sequenced the genomes and collected clinical phenotype data for 34,523 UK patients and 43,016 unaffected relatives across 29,741 families.

The scale of this study is unprecedented, partly thanks to the ever-decreasing cost of DNA sequencing (25 years ago, it cost $1bn to sequence a whole genome, while now it costs only a few hundred dollars). Working with such large datasets is notoriously cumbersome. To overcome this, we developed a computational approach (the Rareservoir) that distills the most important information into a relatively small database, allowing us to apply our statistical methods nimbly.

We noted that the “genetic variants” that cause rare diseases are typically kept rare in the human population by natural selection because affected individuals tend to have few children, if any. This meant that we could discard the genetic information corresponding to variants that are common in the human population without throwing away the key disease-causing variants. By focussing on these “rare variants”, we were able to perform our analyses using a small database (a `Rareservoir’), only 5.5GB in size, hastening our progress significantly.

MedicalResearch.com: What are the main findings of this analysis?

Response:  We used a statistical method to search for relationships between 20,000 genes and 269 rare diseases. In a single analysis, we found 260 genetic gene-disease relationships, of which 241 were previously known through previous work spanning publications by many research groups over many decades. Out of the 19 previously unknown gene-disease relationships, we focused on the three that seemed most plausible. By working with an outstanding group of international collaborators (at the University of Bristol, UK; KU Leuven, Belgium; the University of Tokyo; the University of Maryland; Imperial College London, and others from around the world), we were able to identify additional families in other countries and perform experiments confirming the results. We discovered that variants in a gene called ERG cause primary lymphoedema; variants in a gene called PMEPA1 cause familial thoracic aortic aneurysm disease, and variants in a gene called GPR156 cause congenital deafness. Patients with disease-causing variants in these genes can now obtain a genetic diagnosis at last. The remaining 16 gene-disease relationships merit further exploration in future work.

MedicalResearch.com: Is the Rareservoir database continually updated?  Commercially or otherwise available to other researchers?

Response:  The 100KGP data can only be accessed through the 100,000 Genomes Research Environment managed by Genomics England Ltd.

The Rareservoir schema and related tools are freely available to academic researchers. We are also open to providing commercial access through collaboration or by providing a license.

MedicalResearch.com: Is there anything else you would like to add? Any disclosures?

Response: We have been fortunate to work with an outstanding group of international collaborators from the USA, the UK, Belgium, Saudi Arabia and Japan who have contributed additional pedigrees, clinical data and experimental results with great collegiality. We also thank the participants of the study for making this research possible.

Citation:

Greene, D., Genomics England Research Consortium., Pirri, D. et al. Genetic association analysis of 77,539 genomes reveals rare disease etiologies. Nat Med (2023). https://doi.org/10.1038/s41591-023-02211-z

https://www.nature.com/articles/s41591-023-02211-z#citeas

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Last Updated on March 21, 2023 by Marie Benz