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 trans-QTL 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.
Highlights
Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants
We present a multi-omics analysis that uses genomics, transcriptomics and proteomics of human atrial tissue to better understand how genetics are related to molecular changes in AF
Microarray transcriptomics and mass spectrometry-based proteomics were used to profile human atrial tissue samples collected during coronary artery bypass surgery
Summary
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. Genome-wide association studies (GWAS) have discovered thousands of disease-associated single-nucleotide polymorphisms (SNPs) and improved our understanding of genetic and phenotypic relationships[1] In this regard, GWAS have been applied to investigate atrial fibrillation (AF), which affects more than 30 million individuals worldwide[2]. The contribution of trans-effects to the genetic architecture of complex polygenic traits was theoretically assessed by the omnigenic model[11] Based on this model, it was estimated that trans-genetic effects explain at least 70% of the disease heritability by indirect propagation through gene regulatory networks[11]. AF core genes and to integrate data from multiple omics levels to improve the understanding of genotype–phenotype relationships
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