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

• Published in: Nature communications 2023-08, Vol.14 (1), p.4750-4750, Article 4750
• Main Authors: Hawkins-Hooker, Alex, Visonà, Giovanni, Narendra, Tanmayee, Rojas-Carulla, Mateo, Schölkopf, Bernhard, Schweikert, Gabriele
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/337/100/2285; 631/80/458; Deep learning; DNA Methylation - genetics; Environmental factors; Epigenesis, Genetic; Epigenetics; Epigenome; Epigenomics - methods; Gene expression; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; multidisciplinary; Nucleotide sequence; Precision medicine; Precision Medicine - methods; Science; Science (multidisciplinary); Therapeutic targets; Transfer learning
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-40211-2

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. The authors present eDICE, an attention-based model that enables accurate imputation of missing portions of the observed epigenetic landscape, and show that eDICE can be used to predict individualspecific epigenomic variation in the EN-TEx dataset.