Single-cell genomics improves the discovery of risk variants and genes of Atrial Fibrillation

Abstract

Genome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 45 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. We further leveraged our single-cell data to study genetics of gene expression. An unexpected finding from earlier studies is that expression QTLs (eQTLs) are often shared across tissues even though most regulatory elements are cell-type specific. We found that this sharing is largely driven by the limited power of eQTL studies using bulk tissues to detect cell-type-specific regulatory variants. This finding points to an important limitation of using eQTLs to interpret GWAS of complex traits. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.

Genetics of atrial fibrillation

“…Multiple single-cell sequencing studies have reinforced the central role of atrial cardiomyocytes in atrial fibrillation biology [30,31]. A subset of atrial fibrillation risk variants has also been mapped to genes expressed in fibroblasts and immune cells [30], consistent with emerging hypotheses that both cardiac fibrosis and immune dysregulation can contribute to atrial fibrillation risk. In a single-cell sequencing study of patient-derived cardiac biopsies, the serine-threonine protein kinase PLK2 in cardiac fibroblasts was linked to atrial fibrillation through mechanisms related to increased inflammation and fibrosis [32 && ].…”

“…By simultaneously quantifying gene expression and chromatin accessibility at the single-cell level, atrial fibrillation risk loci can be linked to the individual cell types which are most affected by variants in these regions. Multiple single-cell sequencing studies have reinforced the central role of atrial cardiomyocytes in atrial fibrillation biology [30,31]. A subset of atrial fibrillation risk variants has also been mapped to genes expressed in fibroblasts and immune cells [30], consistent with emerging hypotheses that both cardiac fibrosis and immune dysregulation can contribute to atrial fibrillation risk.…”

Improved multi-ancestry fine-mapping identifiescis-regulatory variants underlying molecular traits and disease risk

“…We briefly discuss potential directions for future work. First, recent studies have shown that incorporating functional annotation in the prior distribution can improve the fine-mapping precision 33,79,104 . SuShiE currently employs a uniform distribution for prior causal probability.…”

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

“…Recently, coupled with GWAS, single-cell genomics enabled prioritization of risk variants and genes in single cells obtained from human hearts and identified putative causal variants at 122 atrial fibrillation-associated loci. 21 These studies illustrate a general framework for integration of single-cell epigenomics with GWAS to reveal the relative contributions of distinct cell types to diseases and identify genes likely to be associated with complex traits.…”