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Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk

Nature Genetics volume 50, pages 1171–1179 (2018)Cite this article

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Abstract

Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because of the enormous scale of the noncoding mutation space. We developed a deep learning–based framework, ExPecto, that can accurately predict, ab initio from a DNA sequence, the tissue-specific transcriptional effects of mutations, including those that are rare or that have not been observed. We prioritized causal variants within disease- or trait-associated loci from all publicly available genome-wide association studies and experimentally validated predictions for four immune-related diseases. By exploiting the scalability of ExPecto, we characterized the regulatory mutation space for human RNA polymerase II–transcribed genes by in silico saturation mutagenesis and profiled > 140 million promoter-proximal mutations. This enables probing of evolutionary constraints on gene expression and ab initio prediction of mutation disease effects, making ExPecto an end-to-end computational framework for the in silico prediction of expression and disease risk.

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Fig. 1: Deep learning–based sequence model accurately predicts cell-type-specific gene expression.
Fig. 2: Tissue-specific prediction of expression-altering variations.
Fig. 3: Prioritized putative causal variants from GWAS loci with expression effect prediction.
Fig. 4: Variation potential is predictive of gene regulatory specificity, activation status and evolutionary constraints.
Fig. 5: Ab initio prediction of allele-specific disease risk integrating predicted expression effects and inferred evolutionary constraints.

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Acknowledgements

The authors acknowledge all members of the Troyanskaya lab for helpful discussions. This work is supported by NIH grants R01HG005998, U54HL117798 and R01GM071966, HHS grant HHSN272201000054C and Simons Foundation grant 395506. The authors are pleased to acknowledge that a substantial portion of the work in this paper was performed at the TIGRESS high-performance computer center at Princeton University, which is jointly supported by the Princeton Institute for Computational Science and Engineering and the Princeton University Office of Information Technology’s Research Computing department. O.G.T. is a CIFAR fellow.

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Authors and Affiliations

  1. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Jian Zhou, Chandra L. Theesfeld & Olga G. Troyanskaya

  2. Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA

    Jian Zhou

  3. Flatiron Institute, Simons Foundation, New York, NY, USA

    Jian Zhou, Kevin Yao, Kathleen M. Chen, Aaron K. Wong & Olga G. Troyanskaya

  4. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Olga G. Troyanskaya

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Contributions

J.Z. and O.G.T. conceived and designed the study; J.Z. developed the computational methods and performed the analyses; C.L.T. designed and performed experimental studies; K.Y., K.M.C. and A.K.W. developed the ExPecto web server; J.Z., C.L.T. and O.G.T. wrote the manuscript.

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Correspondence to Olga G. Troyanskaya.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–18, Supplementary Table 3 and Supplementary Note

Reporting Summary

Supplementary Table 1

Top model-specific regulatory sequence features for tissue/cell-type-specific ExPecto expression models

Supplementary Table 2

Prioritized putative causal variants from GWAS trait/disease-associated loci

Supplementary Table 4

Synthesized DNA sequences for luciferase assays

Supplementary Data 1

Human population common and rare variants with strong predicted expression effects. The genome build version is hg19

Supplementary Data 2

Tissue/cell-type-specific gene evolutionary constraint directionality scores and the inferred directional constraint probabilities

Supplementary Data 3

GWAS disease risk allele predictions from only sequence based on inferred evolutionary constraint violations

Supplementary Data 4

List of all transcription factors, histone marks and DNase profiles used to train sequence representation models for ExPecto

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Zhou, J., Theesfeld, C.L., Yao, K. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat Genet 50, 1171–1179 (2018). https://doi.org/10.1038/s41588-018-0160-6

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