Genetic and functional analysis of Raynaud’s syndrome implicates loci in vasculature and immunity
Data files
Aug 21, 2024 version files 266.66 MB
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Raynaud_meta_sumstats_Tervi_Ramste_et_al_2024.txt.gz
266.65 MB
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README.md
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Abstract
Raynaud’s syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts discovering eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51 and IRX1 colocalized with eQTLs, particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNA scope clarified the specificity of ADRA2A in small vessels, and IRX1 around small capillaries in the skin. Functional contraction assay in cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3 and immune mechanisms by the HLA locus in Raynaud’s syndrome.
README: Genetic and Functional Analysis of Raynaud's Syndrome Implicates Loci in Vasculature and Immunity
https://doi.org/10.5061/dryad.1g1jwsv53
This dataset includes summary statistics from Raynaud's syndrome meta-analysis conducted from GWAS (genome-wide association study) summary statistics of four independent population cohorts: The UK Biobank, FinnGen data freeze 10, The Estonian Biobank and The Mass-General Brigham Biobank. Details of the phenotype used and the GWAS performed in the individual cohorts can be found from the corresponding publication. The meta-analysis was conducted using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation) with the standard settings and the final summary statistics are displayed in GRCh38 (for further details, see the corresponding publication https://doi.org/10.1016/j.xgen.2024.100630).
Description of the data and file structure
The summary statistics include the following columns:
- rsID - identification for the single-nucleotide variant
- CHR - Chromosome
- POS - Genomic position in GRCh38
- Alt - Alternative allele (the effect allele)
- Ref - Reference allele
- AF_Alt - Combined allele frequency of the alternative allele from all the cohorts used
- Effect - The estimate of effect size (beta)
- StdErr - Standard error of the beta
- Pval - P-value of the association
- HetPVal - Heterogeneity P-value reflecting the variation in study outcomes between studies (P < 0.05 implies heterogeneity)
- N - total number of individuals in the meta-analysis
Sharing/Access information
Data was derived from the following sources: The UK Biobank, FinnGen, The Estonian Biobank and The Mass-General Brigham Biobank.
The FinnGen individual level data may be accessed through applications to the Finnish Biobanks’ FinnBB portal, Fingenious (www.finbb.fi). For the individual level data of the UKB, applications can be made through the UKB portal at https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. For the Mass-General Brigham Biobank, individual level data are available from the Mass General Brigham Human Research Office/Institutional Review Board at Mass General Brigham (contact located at https://www.partners.org/Medical-Research/Support-Offices/Human-Research-Committee-IRB/Default.aspx) for researchers who meet the criteria for access to confidential data. Lastly, for the Estonian Biobank, preliminary inquiries to access individual level data for scientific research can be sent to releases@ut.ee.
Code/Software
GWAS in each individual cohort was conducted using REGENIE (https://rgcgithub.github.io/regenie/) and the meta-analysis was conducted using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation) (for further details, see the corresponding publication).
Methods
This dataset includes summary statistics from Raynaud's syndrome meta-analysis conducted from GWAS summary statistics of four independent population cohorts: The UK Biobank, FinnGen data freeze 10, The Estonian Biobank and The Mass-General Brigham Biobank. Details of the phenotype used and the GWAS performed in the individual cohorts can be found from the corresponding publication. The meta-analysis was conducted using METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation) with the standard settings and the final summary statistics are displayed in GRCh38 (for further details, see the corresponding publication).