Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as ME/CFS that present with similar symptoms.
We used our unique combinatorial analytics approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms.
Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis.
Other highlights include:
- 9 of the 73 genes have prior associations with acute COVID-19
- 14 were differentially expressed in a transcriptomic analysis of long COVID patients
- 42 of the genes are potentially tractable for novel drug discovery approaches
- 13 of these are already targeted by drugs in clinical development pipelines
- 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patients
This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies.
These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.
Cite this article
Taylor, K., Pearson, M., Das, S. et al. Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis. J Transl Med 21, 775 (2023). https://doi.org/10.1186/s12967-023-04588-4.