Variant to function mapping at single-cell resolution through network propagation

Yu, Fulong; Cato, Liam D; Weng, Chen; Liggett, L. Alexander; Jeon, Soyoung; Xu, Keren; Chiang, Charleston W.K.; Weissman, Jonathan S.; Smith, Adam J. de; Sankaran, Vijay G.

doi:10.1038/s41587-022-01341-y

Cited by 46 publications

(53 citation statements)

References 69 publications

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“…4a). We also checked the enrichment of genetic risk of AF at open chromatin regions at individual cells, using the method SCAVENGE 36 . This analysis confirms that the vast majority of cells enriched with AF risk are CMs (Extended Data Figure 8).…”

Section: Resultsmentioning

confidence: 99%

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

Selewa

Luo

Wasney

et al. 2022

Preprint

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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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Section: Resultsmentioning

confidence: 99%

“…SCAVENGE 36 was used to calculate for each cell a trait relevance score (TRS) for Atrial fibrillation. SCAVENGE was run under default settings, with ATAC-seq peak matrix and fine-mapping results (under the uniform prior) as inputs.…”

Section: Methodsmentioning

confidence: 99%

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

Selewa

Luo

Wasney

et al. 2022

Preprint

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“…Network propagation has been widely applied to the analysis of gene-gene networks for candidate gene prioritization 51 and recently cell-cell networks to find phenotype relevant cells 52 . Here network propagation is applied to the calculation of gene set activity scores for each cell.…”

Section: Calculating the Gene Set Activity At Single Cell Levelmentioning

confidence: 99%

Pathway Centric Analysis for single-cell RNA-seq and Spatial Transcriptomics Data with GSDensity

Liang

Chen

2022

Preprint

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Advances in single-cell technology have enabled molecular cellular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Although cluster-centric approaches followed by gene-set analysis can reveal distinct cell types and states, they have limited power in dissecting and interpretating highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. We show that GSDensity can not only accurately detect biologically distinct gene sets but also reveal novel cell-pathway associations that are ignored by existing methods. This is particularly evident in characterizing cancer cell states that are transcriptomically distinct but are driven by shared tumor-immune interaction mechanisms. Moreover, we show that GSDensity, combined with trajectory analysis can identify pathways that are active at various stages of mouse brain development. Finally, we show that GSDensity can identify spatially relevant pathways in mouse brains including those following a high-order organizational patterns in the ST data. We also created a pan-cancer pathway activity ST map, which revealed pathways spatially relevant and recurrently active across six different tumor types. GSDensity is available as an open-source R package and can be widely applied to single-cell and ST data generated by various technologies.

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“…Fine-mapping seeks to prioritize the causal SNPs underlying complex traits and diseases. Recent progress shows that, by integrating fine-mapping results and single-cell data, it becomes feasible to identify disease/trait-relevant cell types and cell states [5,6]. Therefore, fine-mapping is a critical step to interpret GWAS findings by elucidating their biological mechanisms of identified risk variants, and fine-mapping results will offer an invaluable resource for precision medicine [7].…”

Section: Introductionmentioning

confidence: 99%

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

Cai

Wang

Xiao

et al. 2023

Preprint

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Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.

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Variant to function mapping at single-cell resolution through network propagation

Cited by 46 publications

References 69 publications

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

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

Pathway Centric Analysis for single-cell RNA-seq and Spatial Transcriptomics Data with GSDensity

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

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