{"doi":"10.5281/zenodo.21382723","title":"Integration of dilated cardiomyopathy genomics with transcriptomics from the human heart implicates regulatory molecular mechanisms","abstract":"TensorQTL Results for TOPCHeF This repository contains the complete set of quantitative trait locus (QTL) summary statistics generated using TensorQTL. The dataset includes cis- and trans- eQTL and sQTL analyses, permutation-based significance results, and fine-mapping results using SuSiE. All large result directories are provided as compressed tar.gz archives. Directory and File Overview cis-eQTL Results cis_eQTL_nominal.tar.gzNominal cis-eQTL association results testing genetic variants within a predefined cis-window around each gene. Typical contents (per-chromosome files): Variant–gene pairs Effect size (beta) Standard error Nominal p-value These results are intended for downstream filtering, visualization, and coloc analyses. cis_eQTL_permutation.tar.gzPermutation-based cis-eQTL results used to estimate gene-level empirical p-values and false discovery rates (FDR). Typical contents: Gene-level permutation p-values Empirical significance estimates These files are typically used to identify significantly regulated genes (eGenes). cis_eQTL_SuSiE.tar.gzFine-mapping results for significant cis-eQTLs using SuSiE (Sum of Single Effects). Typical contents: Credible sets Posterior inclusion probabilities (PIPs) Lead variants per credible set cis-sQTL Results cis_sQTL_nominal.tar.gzNominal cis-sQTL association results testing genetic variants for associations with splicing phenotypes. Typical contents: Variant–splicing event pairs Effect size (beta) Standard error Nominal p-value cis_sQTL_permutation.tar.gzPermutation-based cis-sQTL results providing event-level empirical p-values and FDR estimates. Used to identify significantly regulated splicing events (sQTLs). cis_sQTL_SuSiE.tar.gzFine-mapping results for significant cis-sQTLs generated using SuSiE. Includes credible sets and posterior probabilities for putatively causal variants. trans-QTL Results trans_eQTL.tar.gzGenome-wide trans-eQTL association results testing variants and genes located on different chromosomes or beyond the cis-window.. trans_sQTL.tar.gzGenome-wide trans-sQTL association results for splicing phenotypes. File Formats All result files are parquet files (.parquet). Each tar.gz archive contains the per-chromosome result files. Software and Methods QTL mapping was performed using TensorQTL. Fine-mapping was conducted using SuSiE as implemented in TensorQTL workflows. Analyses were performed on normalized gene expression and splicing phenotypes with appropriate covariate adjustment (e.g., genotype PCs, expression PCs, and other technical covariates). Summary statistic coordinates are reported accoring to the HG38 human reference genome. Human reference genome used for mapping: https://www.gencodegenes.org/human/release_34.html A1 (effect allele or minor allele) and A2 (non-effect allele or reference allele) are standardized across the summary statistics. Intended Use These data are intended for: Reproducibility of published QTL analyses Secondary analyses and meta-analyses Colocalization and fine-mapping studies Integration with GWAS and other functional genomics datasets Contact For questions regarding the dataset or analysis details, please contact the corresponding author listed in the associated manuscript. Or, Connor Murray, PhD (csm6hg@virginia.edu) Methods/Code Gene and variant mappability calculation: A concern for cis and trans-QTL mapping is that stretches of similar sequence across distinct regions of the genome will result in alignment errors from short read experiments. Alignment errors can result in the inflation of false positive signals and increase the burden for multiple testing correction, especially for trans-QTL which typically have lower effect sizes (Saha & Battle, 2019). We used the nextflow implementation of crossmapp (https://github.com/porchard/crossmap-nextflow) and the GENCODE v34 GTF, with exon kmer length set to 100 bps, UTR kmer length set to 36 bps, and allowing two mismatches to calculate a gene-level bed file for mappability s","journal":"Zenodo (CERN European Organization for Nuclear Research)","year":2025,"id":588114,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10062,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9486,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":820816,"name":"Connor S. Murray","orcid":"0000-0002-8302-6585","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:59:43.096742Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}