Evidence map›Paper›PMID 40321247›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis.

Sai Zhang, Fereshteh Jahanbani, Varuna Chander, Martin Kjellberg, Menghui Liu, Katherine A Glass, David S Iu, Faraz Ahmed, Han Li, Rajan Douglas Maynard and 9 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Sai ZhangDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Fereshteh JahanbaniDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Varuna ChanderDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Martin KjellbergDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Menghui LiuDepartment of Epidemiology, University of Florida, Gainesville, FL, USA.
Katherine A GlassDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
David S IuDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
Faraz AhmedGenomics Facility, Biotechnology Resource Center, Cornell University, Ithaca, NY, USA.
Han LiSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
Rajan Douglas MaynardDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Tristan ChouDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Johnathan Cooper-KnockSheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK.
Martin Jinye ZhangRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Durga ThotaDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Michael ZeinehDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Jennifer K GrenierGenomics Facility, Biotechnology Resource Center, Cornell University, Ithaca, NY, USA.
Andrew GrimsonDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
Maureen R HansonDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA.
Michael P SnyderDepartment of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Funding

Research CoreU54AI178855 · NIAID · CORNELL UNIVERSITY · PI ANDREW W GRIMSON, MAUREEN REBECCA HANSON · 2023 to 2026
$9.4M
Probing the Pathophysiology of ME/CFS through Proteomics and MetabolomicsU54NS105541 · NINDS · CORNELL UNIVERSITY · PI GRIMSON, ANDREW W · 2017 to 2021
$9.4M
NIAID NIH HHS U54 AI178855NINDS NIH HHS U54 NS105541
6 · The paper itself

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.

Identifiers

PMID40321247
PMCPMC12047926

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.