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

Alex Hawkins-Hooker (Lead / Corresponding author), Giovanni Visonà, Tanmayee Narendra, Mateo Rojas-Carulla, Bernhard Schölkopf, Gabriele Schweikert (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

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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.

Original languageEnglish
Article number4750
Number of pages14
JournalNature Communications
Volume14
DOIs
Publication statusPublished - 7 Aug 2023

Keywords

  • Humans
  • Epigenomics/methods
  • Epigenesis, Genetic
  • Machine Learning
  • Epigenome
  • Precision Medicine/methods
  • DNA Methylation/genetics

ASJC Scopus subject areas

  • General Physics and Astronomy
  • General Chemistry
  • General Biochemistry,Genetics and Molecular Biology

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