Genetic Architecture of Idiopathic Inflammatory Myopathies From Meta-Analyses
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, metaanalýza
Grantová podpora
00023728
the Czech Ministry of Health
Intramural Research Program
NIEHS NIH HHS - United States
18474
Myositis UK
U19 CA203654
NCI NIH HHS - United States
Translational Precision Environmental Health Science (TPEHS)
the King Gustaf V 80 Year Foundation
MR/N003322/1
Medical Research Council - United Kingdom
The Cure JM Foundation
T32ES027801
Training Program fellowship
NIHR203308
NIHR Manchester Biomedical Research Centre
Region Stockholm Avtal om Läkarutbildning och Forskning (ALF project)
20380
Versus Arthritis - United Kingdom
2020-01378
The Swedish Research Council
the Swedish Rheumatism Association
PubMed
39679859
PubMed Central
PMC12124973
DOI
10.1002/art.43088
Knihovny.cz E-zdroje
- MeSH
- celogenomová asociační studie MeSH
- genetická predispozice k nemoci MeSH
- jednonukleotidový polymorfismus MeSH
- lidé MeSH
- lokus kvantitativního znaku MeSH
- myozitida * genetika MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- metaanalýza MeSH
OBJECTIVE: Idiopathic inflammatory myopathies (IIMs, myositis) are rare systemic autoimmune disorders that lead to muscle inflammation, weakness, and extramuscular manifestations, with a strong genetic component influencing disease development and progression. Previous genome-wide association studies identified loci associated with IIMs. In this study, we imputed data from two prior genome-wide myositis studies and analyzed the largest myositis data set to date to identify novel risk loci and susceptibility genes associated with IIMs and its clinical subtypes. METHODS: We performed association analyses on 14,903 individuals (3,206 patients and 11,697 controls) with genotypes and imputed data from the Trans-Omics for Precision Medicine reference panel. Fine-mapping and expression quantitative trait locus colocalization analyses in myositis-relevant tissues indicated potential causal variants. Functional annotation and network analyses using the random walk with restart (RWR) algorithm explored underlying genetic networks and drug repurposing opportunities. RESULTS: Our analyses identified novel risk loci and susceptibility genes, such as FCRLA, NFKB1, IRF4, DCAKD, and ATXN2 in overall IIMs; NEMP2 in polymyositis; ACBC11 in dermatomyositis; and PSD3 in myositis with anti-histidyl-transfer RNA synthetase autoantibodies (anti-Jo-1). We also characterized effects of HLA region variants and the role of C4. Colocalization analyses suggested putative causal variants in DCAKD in skin and muscle, HCP5 in lung, and IRF4 in Epstein-Barr virus (EBV)-transformed lymphocytes, lung, and whole blood. RWR further prioritized additional candidate genes, including APP, CD74, CIITA, NR1H4, and TXNIP, for future investigation. CONCLUSION: Our study uncovers novel genetic regions contributing to IIMs, advancing our understanding of myositis pathogenesis and offering new insights for future research.
Baylor College of Medicine Houston Texas
Charles University Prague Czech Republic
Duke University Durham North Carolina
Ghent University Ghent Belgium
Karolinska Institutet and Karolinska University Hospital Stockholm Sweden
Manchester Metropolitan University Manchester United Kingdom
National Institute of Environmental Health Sciences NIH Bethesda Maryland
Oslo University Hospital Oslo Norway
Sorbonne Université AP HP Myology Research Center UMR974 Pitié Salpêtrière Hospital Paris France
The Feinstein Institute Manhasset New York
The University of Manchester Manchester United Kingdom
Universitat Autonoma de Barcelona Barcelona Spain
University College London London United Kingdom
University Hospital Bern Switzerland
University Medical Center Utrecht Utrecht The Netherlands
University of Adelaide Adelaide South Australia Australia
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