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INTRODUCTION Genome-wide association studies (GWASs) have identified thousands of human genetic variants associated with diverse diseases and traits, and most of these variants map to noncoding loci with unknown target genes and function. Current approaches to understand which GWAS loci harbor causal variants and to map these noncoding regulators to target genes suffer from low throughput. With newer multiancestry GWASs from individuals of diverse ancestries, there is a pressing and growing need to scale experimental assays to connect GWAS variants with molecular mechanisms. Here, we combined biobank-scale GWASs, massively parallel CRISPR screens, and single-cell sequencing to discover target genes of noncoding variants for blood trait loci with systematic targeting and inhibition of noncoding GWAS loci with single-cell sequencing (STING-seq). RATIONALE Blood traits are highly polygenic, and GWASs have identified thousands of noncoding loci that map to candidate cis -regulatory elements (CREs). By combining CRE-silencing CRISPR perturbations and single-cell readouts, we targeted hundreds of GWAS loci in a single assay, revealing target genes in cis and in trans . For select CREs that regulate target genes, we performed direct variant insertion. Although silencing the CRE can identify the target gene, direct variant insertion can identify magnitude and direction of effect on gene expression for the GWAS variant. In select cases in which the target gene was a transcription factor or microRNA, we also investigated the gene-regulatory networks altered upon CRE perturbation and how these networks differ across blood cell types. RESULTS We inhibited candidate CREs from fine-mapped blood trait GWAS variants (from ~750,000 individual of diverse ancestries) in human erythroid progenitors. In total, we targeted 543 variants (254 loci) mapping to candidate CREs, generating multimodal single-cell data including transcriptome, direct CRISPR gRNA capture, and cell surface proteins. We identified target genes in cis (within 500 kb) for 134 CREs. In most cases, we found that the target gene was the closest gene and that specific enhancer-associated biochemical hallmarks (H3K27ac and accessible chromatin) are essential for CRE function. Using multiple perturbations at the same locus, we were able to distinguished between causal variants from noncausal variants in linkage disequilibrium. For a subset of validated CREs, we also inserted specific GWAS variants using base-editing STING-seq (beeSTING-seq) and quantified the effect size and direction of GWAS variants on gene expression. Given our transcriptome-wide data, we examined dosage effects in cis and trans in cases in which the cis target is a transcription factor or microRNA. We found that trans target genes are also enriched for GWAS loci, and identified gene clusters within trans gene networks with distinct biological functions and expression patterns in primary human blood cells. CONCLUSION In this work, we investigated noncoding GWAS variants at scale, identifying target genes in single cells. These methods can help to address the variant-to-function challenges that are a barrier for translation of GWAS findings (e.g., drug targets for diseases with a genetic basis) and greatly expand our ability to understand mechanisms underlying GWAS loci. Identifying causal variants and their target genes with STING-seq. Uncovering causal variants and their target genes or function are a major challenge for GWASs. STING-seq combines perturbation of noncoding loci with multimodal single-cell sequencing to profile hundreds of GWAS loci in parallel. This approach can identify target genes in cis and trans , measure dosage effects, and decipher gene-regulatory networks.
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Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity
BackgroundUnderstanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible. MethodsWe present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools’ resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. ResultsWe first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5’ scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn’s disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.
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- Award ID(s):
- 2022138
- PAR ID:
- 10630681
- Publisher / Repository:
- Pubmed
- Date Published:
- Journal Name:
- Frontiers in Immunology
- Volume:
- 15
- ISSN:
- 1664-3224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3389/fimmu.2024.1454263
