Reproducibility of Genetic Risk Factors for Long COVID Across Diverse Populations

Reproducibility of Genetic Risk Factors for Long COVID Across Diverse Populations

New research published in the Journal of Translational Medicine demonstrates the reproducibility of genetic associations with long COVID across ancestrally diverse patient cohorts in the United States and United Kingdom.

This marks a significant advance in understanding the biological basis of long COVID and the development of precision diagnostics and treatments for this debilitating condition.

Key Highlights:

• High Reproducibility: 77–83% of long COVID genetic “disease signatures” identified in the original UK-based Sano GOLD study were also positively associated with long COVID in the US-based All of Us (AoU) cohort.
• Cross-Ancestry Validation: Genetic associations were replicated across self-identified White, Black/African-American, and Hispanic/Latino populations, supporting the clinical applicability of findings across diverse ancestry groups.
• Mechanistic Insights: 92% of the originally identified genes showed consistent associations in both cohorts, highlighting potential pathways for drug repurposing and targeted treatment strategies.
• Drug Repurposing Support: 11 out of 13 previously identified repurposing candidates were linked to reproducible disease signatures, reinforcing their relevance for clinical trials.
• Combinatorial Advantage: PrecisionLife’s combinatorial analytics identified complex, multi-SNP signatures that traditional GWAS missed, offering deeper insights into disease biology.

This study showcases the power of combinatorial analytics to identify a broader and more reliable set of genetic risk factors than traditional genome-wide association studies (GWAS) in complex diseases. By validating these findings in a larger and more ancestrally diverse population, this research paves the way for equitable, precision medicine approaches to diagnosing and treating long COVID.

The reproducibility of disease signatures across populations, especially in smaller cohorts, demonstrates the robustness of the PrecisionLife platform and its potential to inform drug development, patient stratification, and personalized care.

In the paper, we show how this can be used to identify mechanisms in the background of normal cellular biology that work to slow or stop progression of complex, chronic diseases.

These protective signatures can potentially be used to identify novel drug targets, pharmacogenomic and/or therapeutic mRNA opportunities and to better stratify patients by overall disease risk and mechanistic subtype.

Read the Full Study

🔗 Reproducibility of genetic risk factors identified for long COVID using combinatorial analysis across US and UK patient cohorts with diverse ancestries.

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