WHO IS MORE LIKELY TO GET LONG COVID? NEW STUDY UNCOVERS GENETIC DRIVERS BEHIND THE DISEASE

Researchers from the University of South Australia have identified key genetic drivers behind long COVID

Why some people bounce back quickly from COVID while others remain unwell for months or even years has been one of the pandemic’s most frustrating unanswered questions.

Australian scientists now say part of the answer lies in our genes.

Shedding light on why certain people are more vulnerable to the condition and opening the door to more targeted treatments.

A global data deep dive

The research team analysed large-scale biological data from more than 100 international studies, integrating genetic and molecular information to pinpoint 32 genes that increase the likelihood of developing long COVID.

Thirteen of those genes had not previously been linked to the condition. Their findings have been published across two peer-reviewed papers in PLOS Computational Biology and Critical Reviews in Clinical Laboratory Sciences.

The scale of the work reflects the scale of the problem. An estimated 400 million people worldwide have been affected by long COVID since 2020, with the condition costing the global economy around $1 trillion each year.

Why long COVID is so hard to crack

Long COVID is defined by symptoms that persist beyond four weeks after infection and can include extreme fatigue, breathlessness, cardiovascular issues and cognitive impairment.

For many people, these symptoms last for months or even years, often without a clear diagnosis or treatment plan.

Part of the challenge is complexity. The condition affects multiple organs, presents differently from patient to patient and has no single diagnostic marker.

How genes and AI joined the dots

Lead author Sindy Pinero, a PhD candidate in bioinformatics at UniSA, says advanced computational tools are helping researchers move faster than traditional biomedical methods.

The team used artificial intelligence and bioinformatics to analyse “omics” data, including genomics, proteomics, metabolomics, transcriptomics and epigenomics.

This approach allowed them to identify consistent molecular patterns across diverse populations.

“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, exhibits highly variable symptoms, and lacks a single definitive diagnostic marker.

“However, by using computational models to integrate data from across the world, we can begin to uncover consistent molecular signatures of disease and identify biomarkers that point to new treatment targets.”

The genes that may tip the balance

Among the most significant discoveries was a genetic variant in the FOX P4 gene, which is linked to immune regulation and lung function. The variant appears to increase susceptibility to long COVID.

Researchers also identified 71 molecular switches that can turn genes on or off and persist for at least a year after infection.

More than 1,500 altered gene-expression profiles were linked to immune-system disruption and neurological changes.

Together, these markers point to immune dysfunction, ongoing inflammation and metabolic abnormalities as key drivers of the condition.

Predicting risk and future symptoms

By combining genetic, epigenetic and protein-level data using machine learning, the study demonstrates how scientists may one day predict who is most at risk of long-term complications and how their symptoms might evolve.

According to Associate Professor Thuc Le, computational science is essential for tackling conditions as complex as long COVID.

“Traditional biomedical research can’t keep pace with the complexity of this condition,” he says.

“By applying artificial intelligence to global datasets, we can identify causal relationships that are invisible in small clinical trials, for example, how specific genes interact with immune pathways to drive persistent inflammation.”

The researchers say the implications extend well beyond COVID itself

Beyond long COVID

The same computational framework could accelerate research into other post-viral and chronic conditions, including chronic fatigue syndrome and fibromyalgia.

The review also highlights the need for larger, more diverse international datasets and long-term studies that follow patients for years after infection.

“Many existing studies are small and inconsistent, which makes it hard to identify reliable biomarkers,” Associate Professor Le says.

“Global collaboration and data sharing are the key to producing results that can translate into clinical tools.”

In short, this research does not just bring scientists closer to understanding long COVID.

It offers a blueprint for how big data, artificial intelligence and molecular biology could reshape responses to future pandemics and chronic disease. No hype, just hard science finally catching up.