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

Alex Hawkins-Hooker (), Giovanni Visonà, Tanmayee Narendra, Mateo Rojas-Carulla, Bernhard Schölkopf and Gabriele Schweikert ()
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Alex Hawkins-Hooker: University of Dundee
Giovanni Visonà: Max-Planck Institute for Intelligent Systems
Tanmayee Narendra: University of Dundee
Mateo Rojas-Carulla: Max-Planck Institute for Intelligent Systems
Bernhard Schölkopf: Max-Planck Institute for Intelligent Systems
Gabriele Schweikert: University of Dundee

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40211-2

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