Australian scientists have made a significant breakthrough in understanding the genetic factors contributing to long COVID, a condition that leaves many individuals with persistent symptoms long after their initial infection. This discovery, achieved through the analysis of extensive biological datasets, could lead to the development of targeted treatments and personalized diagnostics.
The research team, spearheaded by scientists from the University of South Australia, integrated genetic and molecular data from over 100 international studies. They identified 32 causal genes that increase the likelihood of developing long COVID, including 13 genes not previously associated with the condition. These findings have been documented in two scientific papers published in PLOS Computational Biology and Critical Reviews in Clinical Laboratory Sciences.
Understanding Long COVID’s Impact
Since 2020, an estimated 400 million people have been affected by long COVID, imposing a staggering $1 trillion annual cost on the global economy. The condition, characterized by symptoms such as prolonged fatigue, breathlessness, cardiovascular complications, and cognitive impairment, has proven difficult to diagnose and treat. Many individuals experience these symptoms for extended periods, sometimes lasting years.
Lead author and UniSA PhD candidate in Bioinformatics, Sindy Pinero, emphasized the role of large-scale datasets and advanced computational methods in identifying the causes, risk factors, and potential treatment options for long COVID. These methods utilize advanced bioinformatics and artificial intelligence to interpret vast biological datasets known as “omics” data, which include genomics, proteomics, metabolomics, transcriptomics, and epigenomics.
Key Genetic Discoveries
The research highlights several genetic, epigenetic, and protein-level biomarkers linked to immune dysfunction, persistent inflammation, and mitochondrial and metabolic abnormalities. A notable discovery is a genetic variant in the FOX P4 gene, associated with immune regulation and lung function, which appears to increase susceptibility to long COVID.
Moreover, researchers identified 71 molecular switches capable of turning genes on or off, persisting a year after infection, and more than 1500 altered gene expression profiles tied to immune and neurological disruption. By integrating these findings with machine learning, the study demonstrates how different layers of biological data can predict patients at risk of long-term complications and how their symptoms may evolve.
“These findings mark a major step towards a more precise way of diagnosing and treating the condition,” Pinero says. “Long COVID is incredibly complex. It affects multiple organs, shows highly variable symptoms, and has no single final diagnostic marker.”
The Role of Computational Science
According to UniSA Associate Professor Thuc Le, computational science is crucial in solving the long COVID puzzle. Traditional biomedical research struggles to keep pace with the complexity of this condition. By applying artificial intelligence to global datasets, researchers can identify causal relationships that are invisible in small clinical trials, such as how specific genes interact with immune pathways to drive persistent inflammation.
The review also underscores the urgent need for larger, more diverse international datasets and longitudinal studies that track patients for several years post-infection. Many existing studies are small and inconsistent, complicating the identification of reliable biomarkers. Global collaboration and data sharing are essential to producing results that can translate into clinical tools.
“This research is not only about long COVID. It represents a blueprint for how global science can use big data, AI, and molecular biology to respond to future pandemics and complex chronic diseases,” Assoc Prof Le says.
Future Implications and Research Directions
The findings from this study not only enhance our understanding of long COVID but also offer a framework for accelerating the search for treatments for other post-viral conditions such as chronic fatigue and fibromyalgia. The integration of computational models and global datasets could revolutionize the approach to diagnosing and treating complex diseases.
As the scientific community continues to unravel the mysteries of long COVID, the emphasis on international collaboration and the use of cutting-edge technology will be pivotal. The research published in PLOS Computational Biology, titled “Integrative Multi-Omics Framework for Causal Gene Discovery in long COVID,” provides a foundation for future studies and potential breakthroughs in the field.
