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Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Nature Genetics volume 50, pages 1483–1493 (2018)Cite this article

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Abstract

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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Fig. 1: Simulations assessing null calibration.
Fig. 2: Simulations assessing power, bias, and variance.
Fig. 3: Analysis of blood molecular traits using signed LD profile regression.
Fig. 4: Analysis of GTEx eQTL using signed LD profile regression.
Fig. 5: Analysis of diseases and complex traits using signed LD profile regression.
Fig. 6: Highlighted transcription factor binding-complex trait associations that refine emerging theories of disease.
Fig. 7: Highlighted previously unknown transcription factor binding-complex trait associations.

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Acknowledgements

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.

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Authors and Affiliations

  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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Contributions

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.

Corresponding authors

Correspondence to Yakir A. Reshef or Alkes L Price.

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Competing interests

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

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Supplementary information

Supplementary Text and Figures

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

Reporting Summary

Supplementary Table 2

Numerical results for Fig. 1

Supplementary Table 3

Numerical results for Fig. 2

Supplementary Table 4

List of traits analyzed in BLUEPRINT/NTR analysis

Supplementary Table 5

Details of results of BLUEPRINT/NTR analysis

Supplementary Table 6

List of GTEx traits analyzed

Supplementary Table 7

Results of GTEx analysis

Supplementary Table 8

List of diseases and complex traits analyzed

Supplementary Table 9

Results of SLDP analysis of 46 diseases and complex traits

Supplementary Table 10

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

Supplementary Table 11

Numerical results for Fig. 6

Supplementary Table 12

Numerical results for Fig. 7

Supplementary Table 15

Numerical results for Supplementary Fig. 9

Supplementary Table 17

Results of signed LD profile regression using DeepSEA-based annotations

Supplementary Table 18

Results of signed LD profile regression using GTRD-based annotations

Supplementary Table 19

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

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Reshef, Y.A., Finucane, H.K., Kelley, D.R. et al. Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat Genet 50, 1483–1493 (2018). https://doi.org/10.1038/s41588-018-0196-7

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