Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
21 Dec 2023
Historique:
medline: 10 1 2024
pubmed: 10 1 2024
entrez: 10 1 2024
Statut: epublish

Résumé

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following

Identifiants

pubmed: 38196618
doi: 10.1101/2023.12.20.23300294
pmc: PMC10775325
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : Medical Research Council
ID : MR/X004597/1
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 25514
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 207556/Z/17/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_EX_MR/M009203/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UP_1102/20
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S031820/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_14089
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/M009203/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S021329/1
Pays : United Kingdom

Auteurs

Classifications MeSH