The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases.
cystic fibrosis
digenic inheritance
gene expression
modifier gene
spinal muscular atrophy
Journal
Molecular systems biology
ISSN: 1744-4292
Titre abrégé: Mol Syst Biol
Pays: England
ID NLM: 101235389
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
11
05
2020
revised:
20
10
2020
accepted:
03
11
2020
entrez:
13
1
2021
pubmed:
14
1
2021
medline:
27
8
2021
Statut:
ppublish
Résumé
Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre-mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient-derived cells leads to increased full-length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.
Identifiants
pubmed: 33438800
doi: 10.15252/msb.20209701
pmc: PMC7754056
doi:
Banques de données
GEO
['GSE159642']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
e9701Subventions
Organisme : NINDS NIH HHS
ID : R01 NS106875
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS096770
Pays : United States
Organisme : NINDS NIH HHS
ID : F31 NS105376
Pays : United States
Informations de copyright
© 2020 The Authors. Published under the terms of the CC BY 4.0 license.
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