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Lookup NU author(s): Dr Marta Bertoli, Professor Patrick Chinnery, Dr Helen GriffinORCiD, Professor Sophie HambletonORCiD, Ruxandra Neatu, Professor Neil RajanORCiD, Professor John SayerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2025. Up to 80% of rare disease patients remain undiagnosed after genomic sequencing1, with many probably involving pathogenic variants in yet to be discovered disease–gene associations. To search for such associations, we developed a rare variant gene burden analytical framework for Mendelian diseases, and applied it to protein-coding variants from whole-genome sequencing of 34,851 cases and their family members recruited to the 100,000 Genomes Project2. A total of 141 new associations were identified, including five for which independent disease–gene evidence was recently published. Following in silico triaging and clinical expert review, 69 associations were prioritized, of which 30 could be linked to existing experimental evidence. The five associations with strongest overall genetic and experimental evidence were monogenic diabetes with the known β cell regulator3,4UNC13A, schizophrenia with GPR17, epilepsy with RBFOX3, Charcot–Marie–Tooth disease with ARPC3 and anterior segment ocular abnormalities with POMK. Further confirmation of these and other associations could lead to numerous diagnoses, highlighting the clinical impact of large-scale statistical approaches to rare disease–gene association discovery.
Author(s): Cipriani V, Vestito L, Magavern EF, Jacobsen JOB, Arno G, Behr ER, Benson KA, Bertoli M, Bockenhauer D, Bowl MR, Burley K, Chan LF, Chinnery P, Conlon PJ, Costa MA, Davidson AE, Dawson SJ, Elhassan EAE, Flanagan SE, Futema M, Gale DP, Garcia-Ruiz S, Corcia CG, Griffin HR, Hambleton S, Hicks AR, Houlden H, Houlston RS, Howles SA, Kleta R, Lekkerkerker I, Lin S, Liskova P, Mitchison HH, Morsy H, Mumford AD, Newman WG, Neatu R, O'Toole EA, Ong ACM, Pagnamenta AT, Rahman S, Rajan N, Robinson PN, Ryten M, Sadeghi-Alavijeh O, Sayer JA, Shovlin CL, Taylor JC, Teltsh O, Tomlinson I, Tucci A, Turnbull C, van Eerde AM, Ware JS, Watts LM, Webster AR, Westbury SK, Zheng SL, Caulfield M, Smedley D
Publication type: Article
Publication status: Published
Journal: Nature
Year: 2025
Pages: Epub ahead of print
Online publication date: 26/02/2025
Acceptance date: 10/01/2025
Date deposited: 10/03/2025
ISSN (print): 0028-0836
ISSN (electronic): 1476-4687
Publisher: Nature Research
URL: https://doi.org/10.1038/s41586-025-08623-w
DOI: 10.1038/s41586-025-08623-w
Data Access Statement: Access to the genetic and phenotypic data for the 100KGP participants is open to all through the Genomics England Research Environment (GeL RE) and by application at https://www.genomicsengland.co.uk/research/academic/join-gecip to become a member of the Genomics England Research Network. Multi-sample VCF files and PED files used to run Exomiser can be found under /genomes/analysis/rare_disease in the GeL RE file system. PanelApp gene panels and evidence of disease associations were obtained using the PanelApp API available at https://panelapp.genomicsengland.co.uk/api/docs/ (March 2021). Data used for UNC13A gene in Fig. 2, UniProt accession code Q9UPW8; data used for RBFOX3 gene in Fig. 2, UniProt accession code A6NFN3 and dbGaP accession code phs000424.v10.p2; data used for ARPC3 gene in Fig. 3: UniProt accession code, O15145; data used for POMK gene in Fig. 3, UniProt accession code Q9H5K3; GEO accession code GSE41616 and ENA accession code PRJEB1439.
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