Long COVID Genetic Drivers Uncovered

Australian scientists have made a significant breakthrough, identifying 32 causal genes linked to an increased likelihood of developing Long COVID, as reported by Eurasia Review on December 16, 2025. This includes 13 genes not previously associated with the debilitating condition.

The discovery, spearheaded by researchers at the University of South Australia (UniSA), utilized extensive large-scale biological datasets. This innovative approach promises to accelerate the development of targeted treatments and personalized diagnostics.

The UniSA team, led by PhD candidate Sindy Pinero and Associate Professor Thuc Le, integrated genetic and molecular data from over 100 different international studies. Their methodology involved advanced bioinformatics and artificial intelligence to interpret complex "omics" data.

These crucial findings were detailed in two scientific papers, published in PLOS Computational Biology and Critical Reviews in Clinical Laboratory Sciences. The publications outline the 32 identified genes, providing a deeper understanding of the condition's biological underpinnings.

Sindy Pinero stated that these findings represent a major step towards more precise diagnosis and treatment for Long COVID. The research aims to uncover consistent molecular signatures, addressing the condition's inherent complexity and variable symptoms, according to news-medical.net.

Long COVID currently affects an estimated 400 million people globally since 2020, imposing a staggering $1 trillion annual cost to the global economy, as highlighted by the University of South Australia. This research offers a beacon of hope in mitigating this immense health and economic burden.

• The UniSA study employed a sophisticated multi-omics framework, integrating various advanced computational techniques. These included Transcriptome-Wide Mendelian Randomization (TWMR), Control Theory (CT), Expression Quantitative Trait Loci (eQTL), Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq), and Protein-Protein Interaction (PPI) networks, as detailed in PLOS Computational Biology. This comprehensive approach was crucial for identifying the causal genes and network drivers.

• Among the specific genetic discoveries, researchers identified a genetic variant in the FOX P4 gene, which is associated with immune regulation and lung function, and appears to increase susceptibility to Long COVID. The study also uncovered 71 molecular switches that can alter gene expression, persisting a year after infection, and more than 1500 altered gene expression profiles linked to immune and neurological disruption, according to Eurasia Review.

• The identification of these genetic drivers holds significant implications for developing personalized diagnostics. PrecisionLife, a computational biology company, noted that such tests could accurately assess an individual's genetic risk for Long COVID, differentiate it from other post-viral syndromes, and guide tailored prevention and treatment strategies.

• Related research by PrecisionLife has independently validated many of these genetic associations. Their studies successfully replicated 45 out of 51 previously identified genes across diverse US and UK patient cohorts, providing compelling evidence for the genetic links to Long COVID, as reported by precisionlife. This reproducibility strengthens confidence in the findings.

• The research, including findings from PrecisionLife, also highlights a genetic overlap between Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Seven out of nine genes previously found to be common to both conditions were reproduced in subsequent analyses, suggesting shared biological mechanisms and potential for combined therapeutic approaches, according to Diagnostics World News.

• Despite these breakthroughs, Long COVID remains a complex condition, affecting multiple organs with highly variable symptoms and lacking a single definitive diagnostic marker, as UniSA's Sindy Pinero emphasized. The computational models used in this study are therefore vital for uncovering consistent molecular signatures and identifying new, effective treatment targets.

• The global burden of Long COVID, impacting hundreds of millions and incurring substantial economic costs, underscores the urgency of these scientific advancements. Improved diagnostics and targeted treatments resulting from this genetic understanding could significantly reduce healthcare expenditures, enhance patient quality of life, and optimize resource allocation within healthcare systems, as noted by Health Tech World.

• Furthermore, the UniSA study identified three distinct symptom-based subtypes of Long COVID by analyzing causal gene expression profiles, according to PubMed. This understanding of the condition's heterogeneity is crucial for future research and clinical trials, enabling treatments to be precisely matched to the specific biological drivers of each subtype, rather than a one-size-fits-all approach.