Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms.

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We thank C. de Boer, L. Dicker, J. Engreitz, T. Finucane, N. Friedman, R. Gumpert, M. Kanai, S. Kim, X. Liu, M. Mitzenmacher, J. Perry, S. Reilly, D. Reshef, S. Raychaudhuri, A. Schoech, P. Sabeti, R. Tewhey, O. Troyanskaya, P. Turley, O. Weissbrod, J. Zhou, and the CGTA discussion group for helpful discussions. This research was conducted using the UK Biobank Resource under Application No. 16549 and was supported by US National Institutes of Health grants No. U01 HG009379, R01 MH101244, R01 MH109978, and R01 MH107649. Y.A.R. was supported by award No. T32GM007753 from the National Institute of General Medical Sciences, the National Defense Science and Engineering Graduate Fellowship, and the Paul and Daisy Soros Foundation. H.K.F. was supported by the Fannie and John Hertz Foundation and by Eric and Wendy Schmidt. F.H. is supported by National Institute of Health award No. T32 DK110919. L.P. is supported by National Institutes of Health award No. R00HG008399. R.P.A. is supported by NSF grant No. IIS-1421780. Computational analyses were performed on the Orchestra High Performance Compute Cluster at Harvard Medical School, which is partially supported by grant No. NCRR 1S10RR028832-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.

Author information


  1. Department of Computer Science, Harvard University, Cambridge, MA, USA

    • Yakir A. Reshef
  2. Harvard/MIT MD/PhD Program, Boston, MA, USA

    • Yakir A. Reshef
    •  & Sharon R. Grossman
  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Yakir A. Reshef
    • , Hilary K. Finucane
    • , Dylan Kotliar
    • , Jacob C. Ulirsch
    • , Farhad Hormozdiari
    • , Joseph Nasser
    • , Po-Ru Loh
    • , Sharon R. Grossman
    • , Pier Francesco Palamara
    • , Luca Pinello
    • , Nick Patterson
    •  & Alkes L Price
  4. California Life Sciences LLC, South San Francisco, CA, USA

    • David R. Kelley
  5. Dana Farber Cancer Institute, Boston, MA, USA

    • Alexander Gusev
    •  & Jacob C. Ulirsch
  6. Boston Children’s Hospital, Boston, MA, USA

    • Jacob C. Ulirsch
  7. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • Farhad Hormozdiari
    • , Luke O’Connor
    • , Bryce van de Geijn
    • , Gaurav Bhatia
    • , Steven Gazal
    • , Pier Francesco Palamara
    •  & Alkes L Price
  8. Program in Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA

    • Luke O’Connor
  9. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    • Po-Ru Loh
  10. Department of Statistics, University of Oxford, Oxford, UK

    • Pier Francesco Palamara
  11. Massachusetts General Hospital, Charlestown, MA, USA

    • Luca Pinello
  12. Department of Pathology, Harvard Medical School, Boston, MA, USA

    • Luca Pinello
  13. Google Brain, New York, NY, USA

    • Ryan P. Adams
  14. Department of Computer Science, Princeton University, Princeton, NJ, USA

    • Ryan P. Adams
  15. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • Alkes L Price


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Y.A.R. and A.L.P. designed the study. Y.A.R., H.K.F., D.R.K., A.G., F.H., J.N., and P.-R.L. analyzed data. Y.A.R. and A.L.P. wrote the manuscript with assistance from H.K.F., D.R.K., A.G., D.K., J.C.U., F.H., J.N., L.O., B.v.d.G., P.-R.L., S.R.G., G.B., S.G., P.F.P., L.P., N.P., and R.P.A.

Competing interests

D.R.K. is employed by the Calico Life Sciences LLC. The rest of the authors declare no competing interests.

Corresponding authors

Correspondence to Yakir A. Reshef or Alkes L Price.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–14, Supplementary Tables 1, 13, 14, 16, 20 and 21, and Supplementary Note

  2. Reporting Summary

  3. Supplementary Table 2

    Numerical results for Fig. 1

  4. Supplementary Table 3

    Numerical results for Fig. 2

  5. Supplementary Table 4

    List of traits analyzed in BLUEPRINT/NTR analysis

  6. Supplementary Table 5

    Details of results of BLUEPRINT/NTR analysis

  7. Supplementary Table 6

    List of GTEx traits analyzed

  8. Supplementary Table 7

    Results of GTEx analysis

  9. Supplementary Table 8

    List of diseases and complex traits analyzed

  10. Supplementary Table 9

    Results of SLDP analysis of 46 diseases and complex traits

  11. Supplementary Table 10

    Results of enrichment analysis of signed LD profile regression disease/complex trait analysis

  12. Supplementary Table 11

    Numerical results for Fig. 6

  13. Supplementary Table 12

    Numerical results for Fig. 7

  14. Supplementary Table 15

    Numerical results for Supplementary Fig. 9

  15. Supplementary Table 17

    Results of signed LD profile regression using DeepSEA-based annotations

  16. Supplementary Table 18

    Results of signed LD profile regression using GTRD-based annotations

  17. Supplementary Table 19

    Results of signed LD profile regression using HOCOMOCO motif-based annotations

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