Journal Article Preprint Epublish
PubMed: 40321247 DOI: 10.1101/2025.04.15.25325899
Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis.
medRxiv
Authors: Sai Zhang, Fereshteh Jahanbani, Varuna Chander, Martin Kjellberg, Menghui Liu, Katherine A Glass, David S Iu, Faraz Ahmed, Han Li, Rajan Douglas Maynard, Tristan Chou, Johnathan Cooper-Knock, Martin Jinye Zhang, Durga Thota, Michael Zeineh, Jennifer K Grenier, Andrew Grimson, Maureen R Hanson, Michael P Snyder
Affiliations
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA. Department of Epidemiology, University of Florida, Gainesville, FL, USA. Department of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Epidemiology, University of Florida, Gainesville, FL, USA.
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
- Genomics Facility, Biotechnology Resource Center, Cornell University, Ithaca, NY, USA.
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK.
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, including unexplained persistent fatigue, post-exertional malaise (PEM), cognitive impairment, myalgia, orthostatic intolerance, and unrefreshing sleep. The disease mechanism of ME/CFS is unknown, with no effective curative treatments. In this study, we present a multi-site ME/CFS whole-genome analysis, which is powered by a novel deep learning framework, HEAL2. We show that HEAL2 not only has predictive value for ME/CFS based on personal rare variants, but also links genetic risk to various ME/CFS-associated symptoms. Model interpretation of HEAL2 identifies 115 ME/CFS-risk genes that exhibit significant intolerance to loss-of-function (LoF) mutations. Transcriptome and network analyses highlight the functional importance of these genes across a wide range of tissues and cell types, including the central nervous system (CNS) and immune cells. Patient-derived multi-omics data implicate reduced expression of ME/CFS risk genes within ME/CFS patients, including in the plasma proteome, and the transcriptomes of B and T cells, especially cytotoxic CD4 T cells, supporting their disease relevance. Pan-phenotype analysis of ME/CFS genes further reveals the genetic correlation between ME/CFS and other complex diseases and traits, including depression and long COVID-19. Overall, HEAL2 provides a candidate genetic-based diagnostic tool for ME/CFS, and our findings contribute to a comprehensive understanding of the genetic, molecular, and cellular basis of ME/CFS, yielding novel insights into therapeutic targets. Our deep learning model also offers a potent, broadly applicable framework for parallel rare variant analysis and genetic prediction for other complex diseases and traits.
MeSH
Cells, Genes, Encephalomyelitis, Transcriptome, Multiple Chronic Conditions, Fatigue Syndrome, Chronic, Genome, Signs And Symptoms, Deep Learning, Fatigue, Therapeutics, Learning