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
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