Interrogation of human hematopoiesis at single-cell and single-variant resolution

Abstract

Widespread linkage disequilibrium and incomplete annotation of cell-to-cell state variation represent substantial challenges to elucidating mechanisms of trait-associated genetic variation. Here we perform genetic fine-mapping for blood cell traits in the UK Biobank to identify putative causal variants. These variants are enriched in genes encoding proteins in trait-relevant biological pathways and in accessible chromatin of hematopoietic progenitors. For regulatory variants, we explore patterns of developmental enhancer activity, predict molecular mechanisms, and identify likely target genes. In several instances, we localize multiple independent variants to the same regulatory element or gene. We further observe that variants with pleiotropic effects preferentially act in common progenitor populations to direct the production of distinct lineages. Finally, we leverage fine-mapped variants in conjunction with continuous epigenomic annotations to identify trait–cell type enrichments within closely related populations and in single cells. Our study provides a comprehensive framework for single-variant and single-cell analyses of genetic associations.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Overview of hematopoiesis, UKB GWAS, and fine-mapping.
Fig. 2: Mechanisms of core gene regulation in blood production.
Fig. 3: Characterization and validation of the CCND3 and AK3 regions with multiple causal variants.
Fig. 4: Dissecting the mechanisms of pleiotropic variants across multiple blood cell lineages.
Fig. 5: Overview of g-chromVAR and application to hematopoietic cell types.
Fig. 6: Application of g-chromVAR to single-cell chromatin accessibility data.

Code availability

g-chromVAR is available as an open-source R package distributed freely at http://caleblareau.github.io/gchromVAR. All code required to reproduce the results discussed herein has been made available at http://github.com/caleblareau/singlecell_bloodtraits.

Data availability

All processed data are available on GitHub (https://github.com/caleblareau/singlecell_bloodtraits/). ATAC-seq profiles are available from the Gene Expression Omnibus (GEO) under accession GSE119453 and from the Sequence Read Archive (SRA) under accession PRJNA491478.

References

  1. 1.

    Doulatov, S., Notta, F., Laurenti, E. & Dick, J. E. Hematopoiesis: a human perspective. Cell Stem Cell 10, 120–136 (2012).

    CAS  Article  Google Scholar 

  2. 2.

    Sankaran, V. G. & Orkin, S. H. Genome-wide association studies of hematologic phenotypes: a window into human hematopoiesis. Curr. Opin. Genet. Dev. 23, 339–344 (2013).

    CAS  Article  Google Scholar 

  3. 3.

    Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 (2016).

    CAS  Article  Google Scholar 

  4. 4.

    Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    CAS  Article  Google Scholar 

  6. 6.

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

    Article  Google Scholar 

  7. 7.

    Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    CAS  Article  Google Scholar 

  8. 8.

    Flister, M. J. et al. Identifying multiple causative genes at a single GWAS locus. Genome Res. 23, 1996–2002 (2013).

    CAS  Article  Google Scholar 

  9. 9.

    Galarneau, G. et al. Fine-mapping at three loci known to affect fetal hemoglobin levels explains additional genetic variation. Nat Genet. 42, 1049–1051 (2010).

    CAS  Article  Google Scholar 

  10. 10.

    Chung, C. C. et al. Fine mapping of a region of chromosome 11q13 reveals multiple independent loci associated with risk of prostate cancer. Hum. Mol. Genet. 20, 2869–2878 (2011).

    CAS  Article  Google Scholar 

  11. 11.

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

    CAS  Article  Google Scholar 

  12. 12.

    Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    CAS  Article  Google Scholar 

  13. 13.

    Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at type 2 diabetes susceptibility loci. eLife 7, e31977 (2018).

  14. 14.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  15. 15.

    Benner, C. et al. Prospects of fine-mapping trait-associated genomic regions by using summary statistics from genome-wide association studies. Am. J. Hum. Genet. 101, 539–551 (2017).

    CAS  Article  Google Scholar 

  16. 16.

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

    CAS  Article  Google Scholar 

  17. 17.

    Trynka, G. et al. Disentangling the effects of colocalizing genomic annotations to functionally prioritize non-coding variants within complex-trait loci. Am. J. Hum. Genet. 97, 139–152 (2015).

    CAS  Article  Google Scholar 

  18. 18.

    Giani, F. C. et al. Targeted application of human genetic variation can improve red blood cell production from stem cells. Cell Stem Cell 18, 73–78 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Thom, C. S. et al. Trim58 degrades dynein and regulates terminal erythropoiesis. Dev. Cell 30, 688–700 (2014).

    CAS  Article  Google Scholar 

  20. 20.

    Wakabayashi, A. et al. Insight into GATA1 transcriptional activity through interrogation of cis elements disrupted in human erythroid disorders. Proc. Natl Acad. Sci. USA 113, 4434–4439 (2016).

    CAS  Article  Google Scholar 

  21. 21.

    Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47, 1272–1281 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Kulakovskiy, I. V. et al. HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models. Nucleic Acids Res. 44, D116–D125 (2016).

    CAS  Article  Google Scholar 

  23. 23.

    Oki, S. et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 19, e46255 (2018).

  24. 24.

    Arinobu, Y. et al. Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell 1, 416–427 (2007).

    CAS  Article  Google Scholar 

  25. 25.

    Hoppe, P. S. et al. Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature 535, 299–302 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Loughran, S. J. et al. The transcription factor Erg is essential for definitive hematopoiesis and the function of adult hematopoietic stem cells. Nat. Immunol. 9, 810–819 (2008).

    CAS  Article  Google Scholar 

  27. 27.

    Carmichael, C. L. et al. Hematopoietic overexpression of the transcription factor Erg induces lymphoid and erythro-megakaryocytic leukemia. Proc. Natl Acad. Sci. USA 109, 15437–15442 (2012).

    CAS  Article  Google Scholar 

  28. 28.

    Kruse, E. A. et al. Dual requirement for the ETS transcription factors Fli-1 and Erg in hematopoietic stem cells and the megakaryocyte lineage. Proc. Natl Acad. Sci. USA 106, 13814–13819 (2009).

    CAS  Article  Google Scholar 

  29. 29.

    Vo, K. K. et al. FLI1 level during megakaryopoiesis affects thrombopoiesis and platelet biology. Blood 129, 3486–3494 (2017).

    CAS  Article  Google Scholar 

  30. 30.

    Wang, S., He, Q., Ma, D., Xue, Y. & Liu, F. Irf4 regulates the choice between T lymphoid–primed progenitor and myeloid lineage fates during embryogenesis. Dev. Cell 34, 621–631 (2015).

    Article  Google Scholar 

  31. 31.

    Elagib, K. E. et al. RUNX1 and GATA-1 coexpression and cooperation in megakaryocytic differentiation. Blood 101, 4333–4341 (2003).

    CAS  Article  Google Scholar 

  32. 32.

    Blyth, K. et al. Runx1 promotes B-cell survival and lymphoma development. Blood Cells Mol. Dis. 43, 12–19 (2009).

    CAS  Article  Google Scholar 

  33. 33.

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

    CAS  Article  Google Scholar 

  34. 34.

    Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548 (2018).

    CAS  Article  Google Scholar 

  35. 35.

    Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  Article  Google Scholar 

  36. 36.

    Li, P. et al. IRF8 and IRF3 cooperatively regulate rapid interferon-β induction in human blood monocytes. Blood 117, 2847–2854 (2011).

    CAS  Article  Google Scholar 

  37. 37.

    Hohaus, S. et al. PU.1 (Spi-1) and C/EBPα regulate expression of the granulocyte-macrophage colony-stimulating factor receptor α gene. Mol. Cell. Biol. 15, 5830–5845 (1995).

    CAS  Article  Google Scholar 

  38. 38.

    Dufner, A. et al. The ubiquitin-specific protease USP8 is critical for the development and homeostasis of T cells. Nat. Immunol. 16, 950–960 (2015).

    CAS  Article  Google Scholar 

  39. 39.

    Reincke, M. et al. Mutations in the deubiquitinase gene USP8 cause Cushing’s disease. Nat. Genet. 47, 31–38 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Burley, K., Westbury, S. K. & Mumford, A. D. TUBB1 variants and human platelet traits. Platelet 29, 209–211 (2018).

    CAS  Article  Google Scholar 

  41. 41.

    Sankaran, V. G. et al. Cyclin D3 coordinates the cell cycle during differentiation to regulate erythrocyte size and number. Genes Dev. 26, 2075–2087 (2012).

    CAS  Article  Google Scholar 

  42. 42.

    Gieger, C. et al. New gene functions in megakaryopoiesis and platelet formation. Nature 480, 201–208 (2011).

    CAS  Article  Google Scholar 

  43. 43.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  Article  Google Scholar 

  44. 44.

    Giladi, A. et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat. Cell Biol. 20, 836–846 (2018).

    CAS  Article  Google Scholar 

  45. 45.

    Guo, M. H. et al. Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms. Proc. Natl Acad. Sci. USA 114, E327–E336 (2017).

    CAS  Article  Google Scholar 

  46. 46.

    Zhang, D.-E. et al. Absence of granulocyte colony-stimulating factor signaling and neutrophil development in CCAAT enhancer binding protein α–deficient mice. Proc. Natl Acad. Sci. USA 94, 569 (1997).

    CAS  Article  Google Scholar 

  47. 47.

    Edling, C. E. & Hallberg, B. c-Kit: a hematopoietic cell essential receptor tyrosine kinase. Int. J. Biochem. Cell Biol. 39, 1995–1998 (2007).

    CAS  Article  Google Scholar 

  48. 48.

    Opferman, J. T. & Kothari, A. Anti-apoptotic BCL-2 family members in development. Cell Death Differ. 25, 37 (2017).

    Article  Google Scholar 

  49. 49.

    Paul, S. P., Taylor, L. S., Stansbury, E. K. & McVicar, D. W. Myeloid specific human CD33 is an inhibitory receptor with differential ITIM function in recruiting the phosphatases SHP-1 and SHP-2. Blood 96, 483 (2000).

    CAS  PubMed  Google Scholar 

  50. 50.

    Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    CAS  Article  Google Scholar 

  51. 51.

    Drissen, R. et al. Distinct myeloid progenitor–differentiation pathways identified through single-cell RNA sequencing. Nat. Immunol. 17, 666–676 (2016).

    CAS  Article  Google Scholar 

  52. 52.

    Lee, J. et al. Lineage specification of human dendritic cells is marked by IRF8 expression in hematopoietic stem cells and multipotent progenitors. Nat. Immunol. 18, 877–888 (2017).

    CAS  Article  Google Scholar 

  53. 53.

    Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016).

    Article  Google Scholar 

  54. 54.

    Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    CAS  Article  Google Scholar 

  55. 55.

    Khajuria, R. K. et al. Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis. Cell 173, 90–103.e19 (2018).

    CAS  Article  Google Scholar 

  56. 56.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  Article  Google Scholar 

  57. 57.

    Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

    CAS  Article  Google Scholar 

  58. 58.

    Yu, A. et al. Comparison of human genetic and sequence-based physical maps. Nature 409, 951–953 (2001).

    CAS  Article  Google Scholar 

  59. 59.

    McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article  Google Scholar 

  60. 60.

    Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  Google Scholar 

  61. 61.

    Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 29.1–29.9 (2015).

    Google Scholar 

  62. 62.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  63. 63.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  64. 64.

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

    CAS  Article  Google Scholar 

  65. 65.

    Shin, J. et al. Single-Cell RNA-seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015).

    CAS  Article  Google Scholar 

  66. 66.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  Google Scholar 

  67. 67.

    Mifsud, B. et al. Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C. Nat. Genet. 47, 598–606 (2015).

    CAS  Article  Google Scholar 

  68. 68.

    Coetzee, S. G., Coetzee, G. A. & Hazelett, D. J. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 31, 3847–3849 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  Article  Google Scholar 

  70. 70.

    Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    CAS  Article  Google Scholar 

  71. 71.

    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).

    CAS  Article  Google Scholar 

  72. 72.

    Chung, D., Yang, C., Li, C., Gelernter, J. & Zhao, H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 10, e1004787 (2014).

    Article  Google Scholar 

  73. 73.

    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).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank members of the Sankaran, Buenrostro, and Finucane laboratories for their helpful discussions. This work was supported by National Institutes of Health (NIH) grants R01 DK103794 and R33 HL120791 (to V.G.S.), by the New York Stem Cell Foundation (NYSCF; to V.G.S.), and by the Harvard Society and Broad Institute Fellows programs (to J.D.B.). J.C.U. is supported by an NIH training grant (5T32 GM007226-43). C.A.L. is supported by an NIH predoctoral fellowship (F31 CA232670). E.L.B. is supported by the Howard Hughes Medical Institute Medical Research Fellows Program. V.G.S. is supported as an NYSCF-Robertson Investigator. This research was conducted by using the UK Biobank resource under projects 11898 and 31063.

Author information

Affiliations

Authors

Contributions

J.C.U., C.A.L., E.L.B., M.J.A., J.D.B., and V.G.S. designed the study. J.C.U., C.A.L., E.L.B., and M.H.G. analyzed data. L.S.L. performed experiments. C.B., A.T.S., V.K.K., R.M.S., and J.N.H. contributed ideas and insights. H.K.F., M.J.A., J.D.B., and V.G.S. supervised this work. J.D.B. and V.G.S. obtained funding. J.C.U., C.A.L., E.L.B., and V.G.S. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Jason D. Buenrostro or Vijay G. Sankaran.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17 and Supplementary Note

Reporting Summary

Supplementary Table 1

Summary statistics and information for all fine-mapped variants with PP > 0.001

Supplementary Table 2

Summary of top fine-mapped configurations in each region

Supplementary Table 3

Summary of fine-mapped coding variants

Supplementary Table 4

Summary statistics for bulk ATAC-seq libraries

Supplementary Table 5

Summary of motif-disrupting variants occupied by corresponding transcription factors

Supplementary Table 6

Summary of putative gene targets for variants mapping to PCHi-C interactions

Supplementary Table 7

Summary of putative gene targets for variants mapping to ATAC–RNA correlations

Supplementary Table 8

Fine-mapped variants with PP > 0.05 identified in the same 3-Mb region

Supplementary Table 9

Pleiotropic variants (PP > 0.01) for blood cell count traits

Supplementary Table 10

g-chromVAR results for 39 predominantly immune-related disorders previously fine-mapped with PICS to 18 chromatin accessibility profiles

Supplementary Table 11

Application of g-chromVAR to DNase I hypersensitivity data for 53 tissues from Roadmap Epigenomics

Supplementary Table 12

Top differentially enriched transcription factors between CMP and MEP subclusters

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ulirsch, J.C., Lareau, C.A., Bao, E.L. et al. Interrogation of human hematopoiesis at single-cell and single-variant resolution. Nat Genet 51, 683–693 (2019). https://doi.org/10.1038/s41588-019-0362-6

Download citation

Further reading