Extended data: Tissue-specific multi-omics analysis of atrial fibrillation

Summary statistics and result repository for the publication Tissue-specific multi-omics analysis of atrial fibrillation:

Assum, I., Krause, J., Scheinhardt, M.O. et al. Tissue-specific multi-omics analysis of atrial fibrillation. Nat Commun 13, 441 (2022). https://doi.org/10.1038/s41467-022-27953-1

For the related source code, see https://doi.org/https://doi.org/10.5281/zenodo.5094276 or https://github.com/heiniglab/symatrial.

Ines Assum1,2,†, Julia Krause3,4,†, Markus O. Scheinhardt5, Christian Müller3,4, Elke Hammer6,7, Christin S. Börschel4,8, Uwe Vöker6,7, Lenard Conradi9, Bastiaan Geelhoed4,8,10, Tanja Zeller3,4,*, Renate B. Schnabel4,8,*, Matthias Heinig1,2,11,*

† ,* These authors contributed equally.

1 Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.
2 Department of Informatics, Technical University Munich, München, Germany.
3 University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany.
4 Partner site Hamburg/Kiel/Lübeck, DZHK (German Center for Cardiovascular Research), Hamburg, Germany.
5 Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.
6 Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.
7 Partner site Greifswald, DZHK (German Center for Cardiovascular Research), Greifswald, Germany.
8 Department of Cardiology, University Heart and Vascular Center Hamburg, Hamburg, Germany.
9 Department of Cardiovascular Surgery, University Heart and Vascular Center Hamburg, Hamburg, Germany.
10 Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
11Partner site Munich, DZHK (German Center for Cardiovascular Research), Munich, Germany.



ABSTRACT:

Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted transQTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.

This version adds a reference file identifying effect alleles for all QTL results.

TABLE OF CONTENTS:


Reference for effect alleles
map_AFHRI_B_effect_alleles.txt
Single-omic cis-QTL results

cis-eQTLs (all pairs, incl. LD clump info)
eQTL_right_atrial_appendage_allpairs_clump.txt
cis-pQTLs (all pairs, incl. LD clump info)
pQTL_right_atrial_appendage_allpairs_clump.txt
cis-res eQTLs (all pairs, incl. LD clump info)
res_eQTL_right_atrial_appendage_allpairs_clump.txt
cis-res pQTLs (all pairs, incl. LD clump info)
res_pQTL_right_atrial_appendage_allpairs_clump.txt
cis-ratioQTLs (all pairs, incl. LD clump info)
ratioQTL_right_atrial_appendage_allpairs_clump.txt


Functional cis-QTL categories and eQTL/pQTL overlap:

All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL and pQTL (FDR<0.05)
Fig2a_source_data_Shared_eQTL_pQTL_clump.txt
All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with a significant eQTL but no pQTL (FDR<0.05)
Fig2b_source_data_Independent_eQTL_clump.txt
All eQTLs, pQTLs, res eQTLs, res pQTLs and ratioQTLs for all SNP-gene pairs with no eQTL but a significant pQTL (FDR<0.05)
Fig2c_source_data_Independent_pQTL_clump.txt

QTS rankings and enrichment results


eQTS rankings and enrichments
TableS6_source_data_eQTS_ranking.txt
TableS7_source_data_eQTS_GSEA_results.txt
pQTS rankings and enrichments
TableS8_source_data_pQTS_ranking.txt
TableS9_source_data_pQTS_GSEA_results.txt


Trans-QTLs
all tested pairs including trans-pQTLs for trans-eQTLs and trans-eQTLs for trans-pQTLs
Table2_source_data_Trans-QTL_results.txt