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Interpreting non-coding disease-associated human variants using single-cell epigenomics

Nature Reviews Genetics volume 24, pages 516–534 (2023)Cite this article

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Abstract

Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.

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Fig. 1: Identifying common trait-enriched and disease-enriched cell types and subtypes.
Fig. 2: Prioritizing candidate causal variants at disease-associated loci.
Fig. 3: Identifying disease variants that affect cell-type regulatory activity.
Fig. 4: Linking disease variants to putative target genes.

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Acknowledgements

The authors apologize to those authors whose work they fail to include in this Review owing to space constraints. Research in the Ren lab is funded by the Ludwig Institute and the NIH grants 1UM1HG009402, 1U19MH114831, 1U01MH121282, 1R01AG066018, R01AG067153, U01DA052769, 1UM1HG011585, RF1MH124612, 1R56AG069107, R01EY031663, 1U01HG012059, R24AG073198, RF1MH128838, R41MH128993, UM1 MH130994 and 1U54AG079758. The Center for Epigenomics was supported, in part, by the UC San Diego School of Medicine. The Gaulton lab is funded by NIH grants DK114650, DK120429, DK122607, DK105554 and HG012059.

Author information

Authors and Affiliations

  1. Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA

    Kyle J. Gaulton

  2. Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA

    Sebastian Preissl & Bing Ren

  3. Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Sebastian Preissl

  4. Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA

    Bing Ren

  5. Ludwig Institute for Cancer Research, La Jolla, CA, USA

    Bing Ren

Authors
  1. Kyle J. Gaulton
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  2. Sebastian Preissl
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  3. Bing Ren
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Contributions

The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Kyle J. Gaulton, Sebastian Preissl or Bing Ren.

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Competing interests

B.R. is a shareholder and consultant of Arima Genomics, Inc., and a co-founder of Epigenome Technologies, Inc. K.J.G. is a consultant of Genentech and a shareholder in Vertex Pharmaceuticals and Neurocrine Biosciences. These relationships have been disclosed to and approved by the UCSD Independent Review Committee. S.P. declares no competing interests.

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Glossary

Activity-by-contact

(ABC). A method for predicting the target genes of a distal candidate cis-regulatory element (cCRE) based on cCRE activity and chromatin contacts between the cCRE and gene promoter cCREs.

Allelic imbalance (AI) mapping

A statistical technique to identify genetic variants with allelic differences in a molecular phenotype (such as chromatin accessibility, transcription factor binding or epigenetic marks) by comparing the number of reads directly covering each allele of a sample heterozygous for the variant.

Candidate cis-regulatory elements

(cCREs). Genomic DNA sequences with molecular hallmarks of regulatory elements such as chromatin accessibility, transcription factor binding, DNA methylation and histone modifications that have not yet been shown to regulate gene transcription.

Chromatin conformation

The nuclear organization of chromatin that enables physical proximity of genomic regions such as distal enhancers and promoters in 3D space. Chromatin conformation can be mapped using proximity ligation-based assays such as Hi-C.

Cis-regulatory elements

(CREs). Genomic DNA sequences that regulate transcription of a gene, including enhancers, promoters and insulators. CREs can be identified using molecular markers such as chromatin accessibility, transcription factor binding, DNA methylation and histone modifications.

Co-accessible

Describes pairs of cis-regulatory elements (CREs) that have correlated accessible chromatin profiles either across samples or cell types if using bulk profiles, or across cells if using single-cell profiles. Co-accessibility can be used to predict the target genes of candidate CRE activity.

Co-activity

Describes cis-regulatory elements (CREs) with accessible chromatin profiles that are correlated with the expression level of a gene across samples or single cells and that can be used to predict the target genes of candidate CRE activity.

Credible sets

Minimum sets of variants obtained from statistical or functionally informed fine-mapping that cumulatively explain a high percentage of the total posterior probability of association or posterior inclusion probability at a disease signal (usually 99% or 95%). These variants are considered candidates for being causal for the signal.

Fluorescence-activated cell sorting

(FACS). A technique for separating cell populations using flow cytometry based on cells labelled with fluorescent markers.

Functionally informed fine-mapping

(FIFM). Statistical methods that integrate genetic data with functional annotations to identify independent association signals at a disease-associated locus, determine the enrichment of functional annotations for disease association and resolve variants causal for each association signal using functional enrichments as weights or priors on variants.

Genome-wide association study

(GWAS). Systematic testing of directly assayed or imputed genotypes of variants genome wide for association to a binary (for example, case or control) or quantitative phenotype.

Index variant

The variant at a disease-associated locus that shows the strongest association P value, which is often used to designate the locus, but may not necessarily be causal for disease. In some cases, it is also called the ‘sentinel’ variant.

LD variants

Variants at disease-associated loci that have strong association P values due to linkage disequilibrium (LD) with the index variant and which may or may not be causal for disease.

Linkage disequilibrium

(LD). The non-random inheritance of variant alleles at a locus owing to limited recombination events between variant positions leading to only a subset of observed haplotypes and highly correlated variant genotypes. At disease loci, many variants will show significant association owing to being in LD with the true causal variant but are not directly causal themselves.

Posterior inclusion probability

(PIP). Probability obtained from Bayesian fine-mapping analyses that a variant is included in any causal model that relates to the evidence that it is causal for a trait or disease.

Posterior probability of association

(PPA). Probability obtained from Bayesian fine-mapping analyses that a variant is causal for a trait or disease association signal.

Quantitative trait locus (QTL) mapping

A statistical technique to identify genetic variants that have genotypes correlated with the levels of a molecular, cellular, tissue or physiological phenotype such as chromatin accessibility, transcription factor binding or epigenetic marks across different samples.

Sequence-based machine learning models

Methods that use machine learning to learn the sequence grammar underlying sets of active genomic regions (such as candidate cis-regulatory elements active in a cell type) compared with non-active regions. These machine learning models can then be used to predict whether a sequence is likely to have regulatory activity, which can be further leveraged to predict variant alleles with differences in predicted regulatory activity.

Statistical fine-mapping

Statistical methods that use only genetic data to identify independent association signals at a disease-associated locus as well as resolve variants causal for each association signal.

Stratified linkage disequilibrium score regression

Statistical technique to identify genomic annotations with enriched heritability for a common complex trait or disease based on linkage disequilibrium with associated variants genome wide.

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Gaulton, K.J., Preissl, S. & Ren, B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 24, 516–534 (2023). https://doi.org/10.1038/s41576-023-00598-6

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