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

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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References

  1. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Boix, C. A., James, B. T., Park, Y. P., Meuleman, W. & Kellis, M. Regulatory genomic circuitry of human disease loci by integrative epigenomics. Nature 590, 300–307 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  6. Roadmap Epigenomics Consortium Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  PubMed Central  Google Scholar 

  7. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Consortium, E. P. et al. Perspectives on ENCODE. Nature 583, 693–698 (2020).

    Article  Google Scholar 

  9. Gorkin, D. U. et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature 583, 744–751 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. He, Y. et al. Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature 583, 752–759 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Cavalli, G. & Heard, E. Advances in epigenetics link genetics to the environment and disease. Nature 571, 489–499 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Preissl, S., Gaulton, K. J. & Ren, B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. 24, 21–43 (2023).

    Article  CAS  PubMed  Google Scholar 

  14. Consortium, E. P. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  Google Scholar 

  15. Stunnenberg, H. G. et al. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell 167, 1145–1149 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Javierre, B. M. et al. Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell 167, 1369–1384 e1319 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Harada, A. et al. A chromatin integration labelling method enables epigenomic profiling with lower input. Nat. Cell Biol. 21, 287–296 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Wu, S. J. et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 39, 819–824 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ai, S. et al. Profiling chromatin states using single-cell itChIP-seq. Nat. Cell Biol. 21, 1164–1172 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Carter, B. et al. Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT-seq). Nat. Commun. 10, 3747 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Grosselin, K. et al. High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nat. Genet. 51, 1060–1066 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bartosovic, M. & Castelo-Branco, G. Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01535-4 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Stuart, T. et al. Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01588-5 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hainer, S. J., Boskovic, A., McCannell, K. N., Rando, O. J. & Fazzio, T. G. Profiling of pluripotency factors in single cells and early embryos. Cell 177, 1319–1329 e1311 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    Article  CAS  PubMed  Google Scholar 

  37. Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mooijman, D., Dey, S. S., Boisset, J. C., Crosetto, N. & van Oudenaarden, A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat. Biotechnol. 34, 852–856 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Zhu, C. et al. Single-cell 5-formylcytosine landscapes of mammalian early embryos and ESCs at single-base resolution. Cell Stem Cell 20, 720–731 e725 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Wu, X., Inoue, A., Suzuki, T. & Zhang, Y. Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells. Genes Dev. 31, 511–523 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  46. Luo, C. et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Chiou, J. et al. Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk. Nat. Genet. 53, 455–466 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hocker, J. D. et al. Cardiac cell type-specific gene regulatory programs and disease risk association. Sci. Adv. 7, eabf1444 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ord, T. et al. Single-cell epigenomics and functional fine-mapping of atherosclerosis GWAS loci. Circ. Res. 129, 240–258 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Morabito, S. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat. Genet. 53, 1143–1155 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang, A. et al. Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. eLife 9, e62522 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang, K. et al. A single-cell atlas of chromatin accessibility in the human genome. Cell 184, 5985–6001 e5919 (2021). This study generated a large, comprehensive atlas of cCREs in hundreds of adult and fetal cell types that identified many trait–cell-type links and annotated fine-mapped risk variants.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Orchard, P. et al. Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. Genome Res. 31, 2258–2275 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Rai, V. et al. Single-cell ATAC-seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020). This study generated a comprehensive atlas of cCREs in fetal cell types using high-content combinatorial indexing and identified traits enriched for fetal cell types.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Sheng, X. et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat. Genet. 53, 1322–1333 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Trevino, A. E. et al. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 184, 5053–5069 e5023 (2021). This article describes an example of a study that leveraged deep learning of single-cell epigenome profies to predict functional rare variants affecting autism risk.

    Article  CAS  PubMed  Google Scholar 

  61. Becker, W. R. et al. Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer. Nat. Genet. 54, 985–995 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Briel, N. et al. Single-nucleus chromatin accessibility profiling highlights distinct astrocyte signatures in progressive supranuclear palsy and corticobasal degeneration. Acta Neuropathol. 144, 615–635 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Duong, T. E. et al. A single-cell regulatory map of postnatal lung alveologenesis in humans and mice. Cell Genom. 2, 100108 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Finkbeiner, C. et al. Single-cell ATAC-seq of fetal human retina and stem-cell-derived retinal organoids shows changing chromatin landscapes during cell fate acquisition. Cell Rep. 38, 110294 (2022).

    Article  CAS  PubMed  Google Scholar 

  65. Herring, C. A. et al. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell 185, 4428–4447 e4428 (2022).

    Article  CAS  PubMed  Google Scholar 

  66. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Liu, H. et al. Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease. Nat. Genet. 54, 950–962 (2022).

    Article  CAS  PubMed  Google Scholar 

  68. Luo, C. et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom. 2, 100107 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Benaglio, P. et al. Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines. Cell Genomics 2, 100214 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Su, C. et al. 3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk. Cell Metab. 34, 1394–1409 e1394 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Turner, A. W. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat. Genet. 54, 804–816 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Uzquiano, A. et al. Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex. Cell 185, 3770–3788 e3727 (2022).

    Article  CAS  PubMed  Google Scholar 

  73. Wang, Q. et al. Single-cell chromatin accessibility landscape in kidney identifies additional cell-of-origin in heterogenous papillary renal cell carcinoma. Nat. Commun. 13, 31 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Wilson, P. C. et al. Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression. Nat. Commun. 13, 5253 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lee, D. S. et al. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Muto, Y. et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat. Commun. 12, 2190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Muto, Y. et al. Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis. Nat. Commun. 13, 6497 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    Article  CAS  PubMed  Google Scholar 

  82. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Ulirsch, J. C. et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat. Genet. 51, 683–693 (2019). This study reports the method gchromVAR, which specifically leveraged single-cell epigenome profiles in genetic enrichment analyses and which was used to understand the role of blood cell trait variants in haematopoiesis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Hansen, D. V., Hanson, J. E. & Sheng, M. Microglia in Alzheimer’s disease. J. Cell Biol. 217, 459–472 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 e727 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Wellcome Trust Case Control, C. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

    Article  Google Scholar 

  92. Gaulton, K. J. et al. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat. Genet. 47, 1415–1425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B 82, 1273–1300 (2020).

    Article  Google Scholar 

  95. Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Chen, W., McDonnell, S. K., Thibodeau, S. N., Tillmans, L. S. & Schaid, D. J. Incorporating functional annotations for fine-mapping causal variants in a Bayesian framework using summary statistics. Genetics 204, 933–958 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Li, Y. & Kellis, M. Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases. Nucleic Acids Res. 44, e144 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. van de Bunt, M. et al. Evaluating the performance of fine-mapping strategies at common variant GWAS loci. PLoS Genet. 11, e1005535 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Tabula Sapiens, C. et al. The Tabula sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).

    Article  Google Scholar 

  106. Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. & Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Calderon, D. et al. Inferring relevant cell types for complex traits by using single-cell gene expression. Am. J. Hum. Genet. 101, 686–699 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Jagadeesh, K. A. et al. Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat. Genet. 54, 1479–1492 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Watanabe, K., Umicevic Mirkov, M., de Leeuw, C. A., van den Heuvel, M. P. & Posthuma, D. Genetic mapping of cell type specificity for complex traits. Nat. Commun. 10, 3222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Bossini-Castillo, L. et al. Immune disease variants modulate gene expression in regulatory CD4+ T cells. Cell Genom. 2, 100117 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Liang, D. et al. Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation. Nat. Neurosci. 24, 941–953 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Robertson, C. C. et al. Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes. Nat. Genet. 53, 962–971 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Consortium, G. T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  119. Neavin, D. et al. Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. Genome Biol. 22, 76 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Nathan, A. et al. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 606, 120–128 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Benaglio, P. et al. Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex trait variants using single nucleus ATAC-seq. bioRxiv https://doi.org/10.1101/2020.12.03.387894 (2020).

    Article  Google Scholar 

  122. Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48, 206–213 (2016).

    Article  CAS  PubMed  Google Scholar 

  123. Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 e1324 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376, eabf3041 (2022).

    Article  CAS  PubMed  Google Scholar 

  125. Perez, R. K. et al. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376, eabf1970 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Kim-Hellmuth, S. et al. Cell type-specific genetic regulation of gene expression across human tissues. Science 369, eaaz8528 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Aguirre-Gamboa, R. et al. Deconvolution of bulk blood eQTL effects into immune cell subpopulations. BMC Bioinforma. 21, 243 (2020).

    Article  CAS  Google Scholar 

  128. Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990–999 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Lee, D. et al. A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50, 1171–1179 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Shrikumar, A., Prakash, E. & Kundaje, A. GkmExplain: fast and accurate interpretation of nonlinear gapped k-mer SVMs. Bioinformatics 35, i173–i182 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat Genet. 53, 354–366 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Yuan, H. & Kelley, D. R. scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nat. Methods 19, 1088–1096 (2022).

    Article  CAS  PubMed  Google Scholar 

  135. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 e858 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Zhang, L., Zhang, J. & Nie, Q. DIRECT-NET: an efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data. Sci. Adv. 8, eabl7393 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Dey, K. K. et al. SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease. Cell Genom. 2, 100145 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Zhang, R., Zhou, T. & Ma, J. Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Syst. 13, 798–807 e796 (2022).

    Article  CAS  PubMed  Google Scholar 

  140. Zhang, S. et al. DeepLoop robustly maps chromatin interactions from sparse allele-resolved or single-cell Hi-C data at kilobase resolution. Nat. Genet. 54, 1013–1025 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Li, X. et al. SnapHiC2: a computationally efficient loop caller for single cell Hi-C data. Comput. Struct. Biotechnol. J. 20, 2778–2783 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Yu, M. et al. SnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data. Nat. Methods 18, 1056–1059 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Mulqueen, R. M. et al. High-content single-cell combinatorial indexing. Nat. Biotechnol. 39, 1574–1580 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  146. Gazal, S. et al. Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity. Nat. Genet. 54, 827–836 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Yao, C. et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat. Commun. 9, 3268 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Liu, Y. et al. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. eLife 10, e65554 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Giambartolomei, C. et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34, 2538–2545 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Doni Jayavelu, N., Jajodia, A., Mishra, A. & Hawkins, R. D. Candidate silencer elements for the human and mouse genomes. Nat. Commun. 11, 1061 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Inoue, F. & Ahituv, N. Decoding enhancers using massively parallel reporter assays. Genomics 106, 159–164 (2015).

    Article  CAS  PubMed  Google Scholar 

  153. Patwardhan, R. P. et al. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat. Biotechnol. 27, 1173–1175 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Cheung, R. et al. A multiplexed assay for exon recognition reveals that an unappreciated fraction of rare genetic variants cause large-effect splicing disruptions. Mol. Cell 73, 183–194 e188 (2019).

    Article  CAS  PubMed  Google Scholar 

  155. Li, H. et al. Applications of genome editing technology in the targeted therapy of human diseases: mechanisms, advances and prospects. Signal Transduct. Target. Ther. 5, 1 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  156. Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464 e4417 (2022).

    Article  CAS  PubMed  Google Scholar 

  161. Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 e1817 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Gasperini, M. et al. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176, 377–390 e319 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376 e317 (2019).

    Article  CAS  PubMed  Google Scholar 

  164. Chavez, M., Chen, X., Finn, P. B. & Qi, L. S. Advances in CRISPR therapeutics. Nat. Rev. Nephrol. 19, 9–22 (2023).

    Article  CAS  PubMed  Google Scholar 

  165. Sankaran, V. G., Xu, J. & Orkin, S. H. Transcriptional silencing of fetal hemoglobin by BCL11A. Ann. N. Y. Acad. Sci. 1202, 64–68 (2010).

    Article  CAS  PubMed  Google Scholar 

  166. Frangoul, H. et al. CRISPR-Cas9 gene editing for sickle cell disease and beta-thalassemia. N. Engl. J. Med. 384, 252–260 (2021).

    Article  CAS  PubMed  Google Scholar 

  167. Bauer, D. E. et al. An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science 342, 253–257 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. de Almeida, B. P., Reiter, F., Pagani, M. & Stark, A. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers. Nat. Genet. 54, 613–624 (2022).

    Article  PubMed  Google Scholar 

  169. Nichols, R. V. et al. High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nat. Commun. 13, 7627 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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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.

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The authors contributed equally to all aspects of the article.

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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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