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Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning

Author

Listed:
  • Alex Hawkins-Hooker

    (University of Dundee
    Max-Planck Institute for Intelligent Systems
    University College London)

  • Giovanni Visonà

    (Max-Planck Institute for Intelligent Systems)

  • Tanmayee Narendra

    (University of Dundee
    University of Tübingen)

  • Mateo Rojas-Carulla

    (Max-Planck Institute for Intelligent Systems)

  • Bernhard Schölkopf

    (Max-Planck Institute for Intelligent Systems)

  • Gabriele Schweikert

    (University of Dundee
    University of Tübingen)

Abstract

Epigenetic modifications are dynamic mechanisms involved in the regulation of gene expression. Unlike the DNA sequence, epigenetic patterns vary not only between individuals, but also between different cell types within an individual. Environmental factors, somatic mutations and ageing contribute to epigenetic changes that may constitute early hallmarks or causal factors of disease. Epigenetic modifications are reversible and thus promising therapeutic targets for precision medicine. However, mapping efforts to determine an individual’s cell-type-specific epigenome are constrained by experimental costs and tissue accessibility. To address these challenges, we developed eDICE, an attention-based deep learning model that is trained to impute missing epigenomic tracks by conditioning on observed tracks. Using a recently published set of epigenomes from four individual donors, we show that transfer learning across individuals allows eDICE to successfully predict individual-specific epigenetic variation even in tissues that are unmapped in a given donor. These results highlight the potential of machine learning-based imputation methods to advance personalized epigenomics.

Suggested Citation

  • Alex Hawkins-Hooker & Giovanni Visonà & Tanmayee Narendra & Mateo Rojas-Carulla & Bernhard Schölkopf & Gabriele Schweikert, 2023. "Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40211-2
    DOI: 10.1038/s41467-023-40211-2
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    References listed on IDEAS

    as
    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Anshul Kundaje & Wouter Meuleman & Jason Ernst & Misha Bilenky & Angela Yen & Alireza Heravi-Moussavi & Pouya Kheradpour & Zhizhuo Zhang & Jianrong Wang & Michael J. Ziller & Viren Amin & John W. Whit, 2015. "Integrative analysis of 111 reference human epigenomes," Nature, Nature, vol. 518(7539), pages 317-330, February.
    3. Timothy J. Durham & Maxwell W. Libbrecht & J. Jeffry Howbert & Jeff Bilmes & William Stafford Noble, 2018. "PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
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