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The files below contain nominal and permuted quantitative trait loci (QTL) associations between common genetic variants derived from whole genome sequencing and Leafcutter splice junction phenotypes generated from RNA-seq of post-mortem tissue sections. All QTLs were mapped with TensorQTL.

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Top association files are gzip-compressed tab-separated variable files - *cis_qtl.txt.gz

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Nominal association files are stored as Parquet files to save space. These can be converted to text files using the following code snippet:

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\n
\n
pip install pandas pyarrow\nconda install -c bioconda htslib  # provides bgzip
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\n
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python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('your_file.parquet')\ndf.to_csv('your_file.tsv.gz', sep='\\t', index=False)\n\" | bgzip > your_file.tsv.gz
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File Descriptions

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Gene annotation for each junction (GENCODE v46 Ensembl IDs and gene names).NYGC_gene_junction_annotation_GENCODE_v46.txt.gz - 

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TSV format. Junctions mapping to multiple genes have all genes assigned, split by comma.

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Results files

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Table columns are formatted as follows:

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Nominal QTL results include all SNP-gene pairs tested using a 1Mb window from each side of the transcription start site (TSS) of the gene. 

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Top association results include only the top SNP-gene association for each gene. Table columns are formatted as follows:

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Allele Information for each variant NYGC_all_common_variants_alleles.tsv.gz

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Covariates files <tisssue>_expression_eQTL_covariates.txt.gz

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Wide format for tensorQTL. Each column is a donor. The following covariates are included:

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 Sample keys <tissue>_sample_key.txt

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