Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

Journal: Proceedings of the National Academy of Sciences of the United States of America
PMID: 35969790

Abstract

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Authors

  • Jiyun Zhou
  • Qiang Chen
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
  • Patricia R Braun
    Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD 21287.
  • Kira A Perzel Mandell
    Lieber Institute for Brain Development, The Johns Hopkins Medical Campus, Baltimore, MD 21287.
  • Andrew E Jaffe
    Lieber Institute for Brain Development, The Johns Hopkins Medical Campus, Baltimore, MD 21287.
  • Hao Yang Tan
    Lieber Institute for Brain Development, The Johns Hopkins Medical Campus, Baltimore, MD 21287.
  • Thomas M Hyde
    Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.
  • Joel E Kleinman
    Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA.
  • James B Potash
    From the Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City.
  • Gen Shinozaki
    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA 94305.
  • Daniel R Weinberger
    Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA; The Lieber Institute for Brain DevelopmentBaltimore, MD, USA; Departments of Psychiatry, Neurology and Neuroscience, Johns Hopkins University School of MedicineBaltimore, MD, USA; The Institute of Genetic Medicine, Johns Hopkins University School of MedicineBaltimore, MD, USA.
  • Shizhong Han
    12 Sigma Technologies, San Diego, CA 92130, USA.

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