Identifying Putative Causal Links between MicroRNAs and Severe COVID-19 Using Mendelian Randomization

Area of Science:

• Genetics and Molecular Biology
• Infectious Diseases
• Biomarker Discovery

Background:

• The COVID-19 pandemic necessitates improved early risk assessment for severe cases.
• Circulating microRNAs (miRNAs) are underexplored as potential COVID-19 biomarkers.
• Establishing causal relationships between biomarkers and disease severity is crucial.

Purpose of the Study:

• To investigate the causal effects of circulating miRNAs on COVID-19 severity using Mendelian randomization.
• To identify novel miRNA biomarkers for predicting severe COVID-19 outcomes.

Main Methods:

• Employed two-sample Mendelian randomization analysis.
• Utilized genome-wide association study (GWAS) summary statistics for circulating miRNAs (exposure) and severe COVID-19 cases (outcome).
• Validated findings using independent miRNA expression data.

Main Results:

• Identified ten unique miRNAs with a causal link to COVID-19 severity across three phenotype groups.
• Validated two high-confidence miRNAs, hsa-miR-30a-3p and hsa-miR-139-5p, causally associated with severe COVID-19.
• Explored potential causative roles of these miRNAs through literature and database analysis.

Conclusions:

• Hsa-miR-30a-3p and hsa-miR-139-5p show potential as causal biomarkers for severe COVID-19.
• Utilizing miRNA expression quantitative trait loci (eQTL) data offers a novel approach for biomarker identification.
• These findings can aid in early diagnosis and risk stratification of severe COVID-19 cases.