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.

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What follows is the protocol for performing single-cell Nucleosome Occupancy and Methylome sequencing on single nuclei (scNOMe-seq). This plate-based method, which can be used to simultaneously assess nucleosome occupancy and DNA methylation in single nuclei, draws upon the approach laid out in Luo et al., 2018. Everything from the initial nuclei sorting step to the final library preparation that directly precedes sequencing is included in this protocol. This protocol is optimized for non-neuronal nuclei and can be applied to a range of different cell types (e.g., intestinal organoid, fibroblasts). This is the method we are using and it is working. Adapted from "Robust single-cell DNA methylome profiling with snmC-seq2," by C. Luo et al., 2018, Nature Communications 9(1), pp. 1-6. Copyright 2018 by the authors.

What follows is the protocol for performing single-cell Nucleosome Occupancy and Methylome sequencing on single nuclei (scNOMe-seq). This plate-based method, which can be used to simultaneously assess nucleosome occupancy and DNA methylation in single nuclei, draws upon the approach laid out in Luo et al., 2018. Everything from the initial nuclei sorting step to the final library preparation that directly precedes sequencing is included in this protocol. This protocol is optimized for non-neuronal nuclei and can be applied to a range of different cell types (e.g., intestinal organoid, fibroblasts). This is the method we are using and it is working.

What follows is the protocol for performing single-cell Nucleosome Occupancy and Methylome sequencing on single nuclei (scNOMe-seq). This plate-based method, which can be used to simultaneously assess nucleosome occupancy and DNA methylation in single nuclei, draws upon the approach laid out in Luo et al., 2018. Everything from the initial nuclei sorting step to the final library preparation that directly precedes sequencing is included in this protocol. This protocol is optimized for non-neuronal nuclei and can be applied to a range of different cell types (e.g., intestinal organoid, fibroblasts). This is the method we are using and it is working.