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 optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

Additional information

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


  1. 1.

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

  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).

  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).

  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).

  5. 5.

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

  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).

  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).

  8. 8.

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

  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).

  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).

  11. 11.

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

  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).

  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).

  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).

  16. 16.

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

  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).

  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).

  19. 19.

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

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  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).

  29. 29.

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

  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).

  31. 31.

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

  32. 32.

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

  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).

  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).

  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).

  36. 36.

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

  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).

  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).

  39. 39.

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

  40. 40.

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

  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).

  42. 42.

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

  43. 43.

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

  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).

  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).

  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).

  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).

  48. 48.

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

  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).

  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).

  51. 51.

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

  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).

  53. 53.

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

  54. 54.

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

  55. 55.

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

  56. 56.

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

  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).

  58. 58.

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

  59. 59.

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

  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).

  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).

  62. 62.

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

  63. 63.

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

  64. 64.

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

  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).

  66. 66.

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

  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).

  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).

  69. 69.

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

  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).

  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).

  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).

  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).

Download references


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

Author notes

  1. These authors contributed equally: Jacob C. Ulirsch, Caleb A. Lareau, Erik L. Bao.


  1. Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    • Jacob C. Ulirsch
    • , Caleb A. Lareau
    • , Erik L. Bao
    • , Leif S. Ludwig
    •  & Vijay G. Sankaran
  2. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    • Jacob C. Ulirsch
    • , Caleb A. Lareau
    • , Erik L. Bao
    • , Leif S. Ludwig
    •  & Vijay G. Sankaran
  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Jacob C. Ulirsch
    • , Caleb A. Lareau
    • , Erik L. Bao
    • , Leif S. Ludwig
    • , Michael H. Guo
    • , Vinay K. Kartha
    • , Rany M. Salem
    • , Joel N. Hirschhorn
    • , Hilary K. Finucane
    • , Martin J. Aryee
    • , Jason D. Buenrostro
    •  & Vijay G. Sankaran
  4. Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA

    • Jacob C. Ulirsch
    •  & Caleb A. Lareau
  5. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    • Caleb A. Lareau
    •  & Martin J. Aryee
  6. Harvard–MIT Health Sciences and Technology, Harvard Medical School, Boston, MA, USA

    • Erik L. Bao
  7. Division of Endocrinology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    • Michael H. Guo
    • , Rany M. Salem
    •  & Joel N. Hirschhorn
  8. Department of Genetics, Harvard Medical School, Boston, MA, USA

    • Michael H. Guo
    • , Rany M. Salem
    •  & Joel N. Hirschhorn
  9. Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA, USA

    • Michael H. Guo
    • , Rany M. Salem
    •  & Joel N. Hirschhorn
  10. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland

    • Christian Benner
  11. Department of Public Health, University of Helsinki, Helsinki, Finland

    • Christian Benner
  12. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    • Ansuman T. Satpathy
  13. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    • Vinay K. Kartha
    •  & Jason D. Buenrostro
  14. Schmidt Fellows Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Hilary K. Finucane
  15. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • Martin J. Aryee
  16. Harvard Stem Cell Institute, Cambridge, MA, USA

    • Vijay G. Sankaran


  1. Search for Jacob C. Ulirsch in:

  2. Search for Caleb A. Lareau in:

  3. Search for Erik L. Bao in:

  4. Search for Leif S. Ludwig in:

  5. Search for Michael H. Guo in:

  6. Search for Christian Benner in:

  7. Search for Ansuman T. Satpathy in:

  8. Search for Vinay K. Kartha in:

  9. Search for Rany M. Salem in:

  10. Search for Joel N. Hirschhorn in:

  11. Search for Hilary K. Finucane in:

  12. Search for Martin J. Aryee in:

  13. Search for Jason D. Buenrostro in:

  14. Search for Vijay G. Sankaran in:


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.

Competing interests

The authors declare no competing interests.

Corresponding authors

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

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–17 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 1

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

  4. Supplementary Table 2

    Summary of top fine-mapped configurations in each region

  5. Supplementary Table 3

    Summary of fine-mapped coding variants

  6. Supplementary Table 4

    Summary statistics for bulk ATAC-seq libraries

  7. Supplementary Table 5

    Summary of motif-disrupting variants occupied by corresponding transcription factors

  8. Supplementary Table 6

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

  9. Supplementary Table 7

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

  10. Supplementary Table 8

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

  11. Supplementary Table 9

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

  12. Supplementary Table 10

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

  13. Supplementary Table 11

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

  14. Supplementary Table 12

    Top differentially enriched transcription factors between CMP and MEP subclusters

About this article

Publication history