DOI: 10.5281/zenodo.10140514
title: Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

description: Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation".
Filename: Description
1. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster
 
2. eqtl_permutation_scan.tar.gz: # Permutation scan eQTL results in each cell-type cluster. For significant eQTLs, filter for qvalue < 0.05. Columns:
# gene_name: gene name
# start_pheno: phenotype start (gene TSS)
# end_pheno: phenotype end (gene TSS)
# strand: gene strand
# n_variants_tested: number of variants tested for the gene
# distance_var_pheno: distance of the variant with the gene TSS
# snp: snp ID
# snp_chrom: snp chromosomze
# snp_start: snp start pos
# snp_end: snp end pos
# n_effective_tests: number of effective tests
# p_nominal: nominal p value
# slope: slope/beta of the linear regression. Keyed on the alt allele
# se: standard error of the slope
# p_beta: beta distribution adjusted p value
# qvalue: qvalue (Storey)
 
3. caqtl_permutation_scan.tar.gz: # Permutation scan caQTL results in each cell-type cluster. For significant eQTLs, filter for qvalue < 0.05 Columns:
# gene_name: peak feature coordinates
# start_pheno: phenotype start (ATAC summit, i.e. peak feature midpoint)
# end_pheno: phenotype end (ATAC summit, i.e. peak feature midpoint)
# strand: strand ("." for ATAC)
# n_variants_tested: number of variants tested for the peak feature
# distance_var_pheno: distance between the variant with the peak feature
# snp: snp ID
# snp_chrom: snp chromosome
# snp_start: snp start pos
# snp_end: snp end pos
# n_effective_tests: number of effective tests
# p_nominal: nominal p value
# slope: slope/beta of the linear regression. Keyed on the alt allele
# se: standard error of the slope
# p_beta: beta distribution adjusted p value
# qvalue: qvalue (Storey)
 
 
4. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns:
# 1: snp chromosome
# 2: snp start
# 3: snp end
# 4: snp chrom_pos_ref_alt
# 5: Bayes Factor
# 6: PIP
# 7: SNP rsid
 
5. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the egene and the signal hit id. Bed file columns:
# 1: snp chromosome
# 2: snp start
# 3: snp end
# 4: snp chrom_pos_ref_alt
# 5: Bayes Factor
# 6: PIP
# 7: SNP rsid
 
6. cicero_all.tar.gz # Cicero coaccessibility results. Columns
# Peak 1 : Macs2 narrowpeak coordinate for peak 1
# Peak 2 : Macs2 narrowpeak coordinate for peak 2
# coaccess: Cicero coaccessibility score
 
7. gene_peak_coaccessibility.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 arrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns
# Peak 1 : Macs2 narrowpeak coordinate for peak 1
# gene_name: Assigned gene
# Peak 2 : Macs2 narrowpeak coordinate for peak 2
# coaccess: Cicero coaccessibility score
 
8. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns:
# nsnps: Number of SNPs in the region
# eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set
# caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set
# PP.H0.abf: Coloc posterior probability for no signal
# PP.H1.abf: Coloc posterior probability for signal in dataset 1
# PP.H2.abf: Coloc posterior probability for signal in dataset 2
# PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2
# PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2
# idx1: Index of the SuSiE credible set for dataset 1
# idx2: Index of the SuSiE credible set for dataset 2
# cluster: cluster name
# egene: eGene name
# capeak: caPeak coordinates
 
9. cit-mrs-summary.tsv:  Summary from CIT and MR Steiger directionality tests. Columns:
# cluster: cluster name
# egene: eGene name
# capeak: caPeak coordinates
# eqhit: SNP with the highest bayes factor in the SuSiE eQTL credible set
# cahit: SNP with the highest bayes factor in the SuSiE caQTL credible set
# p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model
# q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model
# p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model
# q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model
# p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model
# q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model
# p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model
# q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model
# cit_direction: Direction inferred from CIT  
# correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct?
# correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct?
# sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model
# sensitivity_ratio--e-to-ca:  MR Steiger Sensitivity ratio for e-to-ca model
# steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model
# steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model
# steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model
# steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model
# mrs_direction: Direction inferred from MR Steiger
# direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test
 
10. coloc-gwas-eqtl.tsv and
11. coloc-gwas-caqt.tav # Summary of e/caQTL coloc with GWAS in each cluster. Columns:
# nsnps: Number of SNPs in the region
# gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set
# eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set
# caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set
# PP.H0.abf: Coloc posterior probability for no signal
# PP.H1.abf: Coloc posterior probability for signal in dataset 1
# PP.H2.abf: Coloc posterior probability for signal in dataset 2
# PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2
# PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2
# idx1: Index of the SuSiE credible set for dataset 1
# idx2: Index of the SuSiE credible set for dataset 2
# cluster: cluster name
# egene: eGene name
# capeak: caPeak coordinates
# p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization
# trait: GWAS trait name
# gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region  
# traitname: Expanded GWAS trait name
# variable_type: GWAS type
# source: Source of GWAS - either UKBB or other study
 
12. supplementary_tables.xlsx: Supplementary tables from the manuscript.
13. eqtl_full_scan.tar.gz # Full eQTL cis scan, sorted by variant chrom:pos and tabix indexed. Columns:
# snp_chrom: snp chromosome
# snp_start: snp start
# snp_end: snp end
# snp: snp id
# gene_name: gene name
# chrom: chromosome
# start_pheno: phenotype start (gene TSS)
# end_pheno: phenotype end (gene TSS)
# strand: gene strand
# n_variants_tested: number of variant tested
# distance_var_pheno: distance between the variant with the gene TSS
# p_nominal: nominal p value
# r2: the r squared of the linear regression
# slope: slope/beta of the linear regression. Keyed on the alt allele
# se: standard error of the slope
# best_hit: whether this variant was the best hit for this phenotype.