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='', 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 paramet
  

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