Expression QTLs for NYGC ALS Consortium Paper

The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and gene expression phenotypes generated from RNA-seq of post-mortem tissue sections. All QTLs were mapped with TensorQTL. Top association files are gzip-compressed tab-separated variable files - *cis_qtl.txt.gz Nominal association files are stored as Parquet files to save space. These can be converted to text files using the following code snippet: pip install pandas pyarrow conda install -c bioconda htslib # provides bgzip python3 -c " import pandas as pd df = pd.read_parquet('your_file.parquet') df.to_csv('your_file.tsv.gz', sep='\t', index=False) " | bgzip > your_file.tsv.gz File Descriptions Table columns are formatted as follows: Nominal QTL results include all SNP-gene pairs tested using either a 1Mb window from each side of the transcription start site (TSS) of the gene. phenotype_id - ensembl ID of the gene tested (GENCODE v30) variant_id - SNP tested for association (rsid or chr:position:ref:alt) tss_distance - distance of the SNP to the gene transcription start site (TSS) maf - minor allele frequency in cohort ma_samples - number of samples carrying the minor allele ma_count - total number of minor alleles across individuals pval_nominal - nominal P-value from linear regression slope - slope of the linear regression slope_se - standard error of the slope Top association results include only the top SNP-gene association for each gene. Table columns are formatted as follows: phenotype_id - ensembl ID of the gene tested (GENCODE v30 num_var - total number of variants tested in cis beta_shape1 - first parameter value of the fitted beta distribution beta_shape2 - second parameter value of the fitted beta distribution true_df - effective degrees of freedom the beta distribution approximation pval_true_df - empirical P-value for the beta distribution approximation variant_id - ID of the top variant (rsid or chr:position:ref:alt) tss_distance - distance of the SNP to the gene transcription start site (TSS) ma_samples - number of samples carrying the minor allele ma_count - total number of minor alleles across individuals maf -minor allele frequency in MiGA cohort ref_factor - flag indicating if the alternative allele is the minor allele in the cohort (1 if AF <= 0.5, -1 if not) pval_nominal - nominal P-value from linear regression slope - slope of the linear regression slope_se - standard error of the slope pval_perm - first permutation P-value directly obtained from the permutations with the direct method pval_beta - second permutation P-value obtained via beta approximation. This is the one to use for downstream analysis qval - Storey q-value derived from pval_beta (FDR adjusted) pval_nominal_threshold - nominal P-value threshold for calling a variant-gene pair significant for the gene Allele Information for each variant NYGC_all_common_variants_alleles.tsv.gz CHROM - chromosome position of the variant POS - position of the variant in the chromosome REF - reference allele (GRCh38) ALT - alternative allele (this is the effect allele in the eQTL analysis) ID - variant id (rsid or chr:position:ref:alt) Covariates files <tisssue>_expression_eQTL_covariates.txt.gz Wide format for tensorQTL. Each column is a donor. The following covariates are included: PC1-5 - principal components of the genotypes wgs_prep - library preparation method for WGS age_at_death - age of death rna_prep - library preparation method for RNA-seq biological_sex - the biological sex of each donor InferredCov1-15 - PEER factors generated on the expression matrix. Larger tissues have 15, smaller have 10. Sample keys <tissue>_sample_key.txt sample_id - the RNA-seq sample participant_id - the WGS donor