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CT-LDSC Study

I applied cross-trait linkage disequilibrium score regression3 to estimate genetic correlation between diverse phenotypes, including the ME/CFS phenotype defined in DecodeME4. The genetic correlations are plotted in the heatmap below. Asterisks denote Bonferroni-corrected statistically significant correlations.

The traits are:

  • Asthma from the GWAS of Han et al.5 Numerous authors have theorized a relationship between autonomic dysfunction, ME/CFS, and the activation of mast cells6, but the evidence supporting these theories typically comes from small studies. I added asthma as a prototypical example of a common disease involving the IgE/ mast cell immune axis2.
  • Diastolic blood pressure (DBP) from the Keaton et al.7 It has been postulated that ME/CFS is related to dysregulation of blood flow8. I included the Keaton study as a well-powered, well-measured GWAS of a blood-flow related trait.
  • Educational attainment from Lee et al9, an example of an extremely complex trait with many determinants, some which lie in the central nervous system.
  • Inflammatory bowel disease from Liu et al.10 Like asthma, IBD is an immunological disease, but it operates via a different subsystem of the immune system. I was interested in observing how this contrast is reflected in genetic correlations.
  • Levels of Epstein-Barr Viral DNA, from the GWAS of Nyeo et al.11. Many ME/CFS patients report that their illness began with a serious infection. "Post-viral fatigue" lasting several months after EBV or another viral illness is well-known, and it has been speculated that ME/CFS is a severe form of this common condition. Some researchers even theorize that ME/CFS is driven by latent viruses, though the evidence for this is inconclusive. Given this background, it makes sense to compare the genetic architecture of ME/CFS to that of various infectious phenotypes.
  • Lupus from the GWAS of Bentham et al.12 Lupus is immunological disease with a mechanism related to antibodies to nuclear proteins and immune complexes.
  • ME/CFS, from DecodeME4.
  • Multi-site pain, from Johnston et al.13 The DecodeME preprint reported colocalization between the ME/CFS signal and a multi-site chronic pain signal at a locus on chromosome 17. I added multi-site pain to investigate whether there was evidence of genome-wide matching between pain and ME/CFS, in addition to localized matching.
  • Schizophrenia from the 2022 PGC study14. Unlike certain other common complex diseases of the central nervous system, schizophrenia has a severe and relatively distinctive phenotype, which reduces the likelihood of diagnostic error. In addition to involving genes active in neurons, genetic risk for schizophrenia appears to also be driven by the immunological genes1. It may thus be reasonable to describe schizophrenia as a neuroimmune condition. ME/CFS has also been called a neuroimmune condition, though the phenotypes of the two diseases are vastly different.
  • Syncope from the GWAS of Aegisdottir et al.15 ME/CFS is often categorized as a disease of the autonomic nervous system, and grouped with Syncope and POTs.

Comment on results

  • The trait most strongly genetically correlated with ME/CFS is multi-site pain. This is consistent with the observation that the Manhattan plots for DecodeME and multi-site pain appear to match closely at several loci. This intriguing finding deserves follow-up.
  • ME/CFS has three other significant genetic correlations: with schizophrenia, with asthma, and with lupus. As is often the case in genetic correlation studies, these correlations are difficult to interpret. There are numerous possible theories. For instance, the correlation between asthma and ME/CFS could reflect an IgE-related immune etiology, while the correlation between ME/CFS and Schizophrenia could reflect neurological etiology; or all correlations could reflect some common immune pathway.

Next steps

  • Techniques like GenomicSEM16, LCV17, and MiXeR18 may elucidate the causal structure of correlations discovered so far.
  • The inclusion of additional well-chosen phenotypes in the correlation study may shed light through triangulation.

Reproducing

To reproduce these results, use this script.

  1. See "Identification of Genes for Schizophrenia Highlights the Interplay of Rare and Common Risk Variants" in Chapter 1 of Kandel et al.19 and also the S-LDSC analysis of schizophrenia in this repo. ↩

  2. See Janeway's Immunobiology,20 Chapter 14: Allergic Diseases. ↩

  3. Brendan Bulik-Sullivan, Hilary K Finucane, Verneri Anttila, Alexander Gusev, Felix R Day, Po-Ru Loh, ReproGen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Laramie Duncan, and others. An atlas of genetic correlations across human diseases and traits. Nature Genetics, 47(11):1236–1241, 2015. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC4797329/. ↩

  4. Genetics Delivery Team, Thibaud Boutin, Andrew D Bretherick, Joshua J Dibble, Esther Ewaoluwagbemiga, Emma Northwood, Gemma L Samms, Veronique Vitart, Project, Cohort Delivery Team, Øyvind Almelid, and others. Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome. medRxiv, pages 2025–08, 2025. URL: https://www.medrxiv.org/content/10.1101/2025.08.06.25333109v1. ↩↩

  5. Yi Han, Qiong Jia, Pedram Shafiei Jahani, Benjamin P Hurrell, Calvin Pan, Pin Huang, Janet Gukasyan, Nicholas C Woodward, Eleazar Eskin, Frank D Gilliland, and others. Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma. Nature Communications, 11(1):1776, 2020. URL: https://www.nature.com/articles/s41467-020-15649-3. ↩

  6. Peter Novak, Matthew P Giannetti, Emily Weller, Matthew J Hamilton, and Mariana Castells. Mast cell disorders are associated with decreased cerebral blood flow and small fiber neuropathy. Annals of Allergy, Asthma & Immunology, 128(3):299–306, 2022. URL: https://www.sciencedirect.com/science/article/abs/pii/S1081120621011558. ↩

  7. Jacob M Keaton, Zoha Kamali, Tian Xie, Ahmad Vaez, Ariel Williams, Slavina B Goleva, Alireza Ani, Evangelos Evangelou, Jacklyn N Hellwege, Loic Yengo, and others. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits. Nature Genetics, 56(5):778–791, 2024. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC11096100/. ↩

  8. C Linda MC van Campen, Freek WA Verheugt, Peter C Rowe, and Frans C Visser. Cerebral blood flow is reduced in ME/CFS during head-up tilt testing even in the absence of hypotension or tachycardia: a quantitative, controlled study using Doppler echography. Clinical Neurophysiology Practice, 5:50–58, 2020. URL: https://www.sciencedirect.com/science/article/pii/S2467981X20300044. ↩

  9. James J Lee, Robbee Wedow, Aysu Okbay, Edward Kong, Omeed Maghzian, Meghan Zacher, Tuan Anh Nguyen-Viet, Peter Bowers, Julia Sidorenko, Richard Karlsson Linnér, and others. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50(8):1112–1121, 2018. URL: https://www.nature.com/articles/s41588-018-0147-3. ↩

  10. Zhanju Liu, Ruize Liu, Han Gao, Seulgi Jung, Xiang Gao, Ruicong Sun, Xiaoming Liu, Yongjae Kim, Ho-Su Lee, Yosuke Kawai, and others. Genetic architecture of the inflammatory bowel diseases across East Asian and European ancestries. Nature Genetics, 55(5):796–806, 2023. URL: https://www.nature.com/articles/s41588-023-01384-0. ↩

  11. Sherry S Nyeo, Erin M Cumming, Oliver S Burren, Meghana S Pagadala, Jacob C Gutierrez, Thahmina A Ali, Laura C Kida, Yifan Chen, Hoyin Chu, Fengyuan Hu, and others. Population-scale sequencing resolves determinants of persistent ebv dna. Nature, pages 1–9, 2026. URL: https://www.nature.com/articles/s41586-025-10020-2. ↩

  12. James Bentham, David L Morris, Deborah S Cunninghame Graham, Christopher L Pinder, Philip Tombleson, Timothy W Behrens, Javier Martín, Benjamin P Fairfax, Julian C Knight, Lingyan Chen, and others. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nature Genetics, 47(12):1457–1464, 2015. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC4668589/. ↩

  13. Keira JA Johnston, Mark J Adams, Barbara I Nicholl, Joey Ward, Rona J Strawbridge, Amy Ferguson, Andrew M McIntosh, Mark ES Bailey, and Daniel J Smith. Genome-wide association study of multisite chronic pain in UK Biobank. PLoS Genetics, 15(6):e1008164, 2019. URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008164. ↩

  14. Vassily Trubetskoy, Antonio F Pardiñas, Ting Qi, Georgia Panagiotaropoulou, Swapnil Awasthi, Tim B Bigdeli, Julien Bryois, Chia-Yen Chen, Charlotte A Dennison, Lynsey S Hall, and others. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature, 604(7906):502–508, 2022. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC9392466/. ↩

  15. Hildur M Aegisdottir, Rosa B Thorolfsdottir, Gardar Sveinbjornsson, Olafur A Stefansson, Bjarni Gunnarsson, Vinicius Tragante, Gudmar Thorleifsson, Lilja Stefansdottir, Thorgeir E Thorgeirsson, Egil Ferkingstad, and others. Genetic variants associated with syncope implicate neural and autonomic processes. European Heart Journal, 44(12):1070–1080, 2023. URL: https://academic.oup.com/eurheartj/article/44/12/1070/7030101. ↩

  16. Andrew D Grotzinger, Mijke Rhemtulla, Ronald de Vlaming, Stuart J Ritchie, Travis T Mallard, W David Hill, Hill F Ip, Riccardo E Marioni, Andrew M McIntosh, Ian J Deary, and others. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5):513–525, 2019. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC6520146/. ↩

  17. Luke J O’Connor and Alkes L Price. Distinguishing genetic correlation from causation across 52 diseases and complex traits. Nature Genetics, 50(12):1728–1734, 2018. URL: https://www.nature.com/articles/s41588-018-0255-0. ↩

  18. Oleksandr Frei, Dominic Holland, Olav B Smeland, Alexey A Shadrin, Chun Chieh Fan, Steffen Maeland, Kevin S O’Connell, Yunpeng Wang, Srdjan Djurovic, Wesley K Thompson, and others. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation. Nature Communications, 10(1):2417, 2019. URL: https://www.nature.com/articles/s41467-019-10310-0. ↩

  19. Eric R. Kandel, John D. Koester, and Steven A. Mack, Sarah H. Siegelbaum. Principles of neural science 6th edition. Elsevier New York, 2021. URL: https://www.amazon.ca/Principles-Neural-Science-Sixth-Kandel-ebook-dp-B08LNXDCS3/dp/B08LNXDCS3. ↩

  20. Kenneth Murphy, Casey Weaver, and Leslie Berg. Janeway's Immunobiology. Norton, 2022. URL: https://wwnorton.com/books/9780393884890. ↩