Will GWAS eventually allow the identification of genomic biomarkers for COVID-19 severity and mortality?

Abstract

GWAS involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have enabled the identification of numerous genomic biomarkers in various complex human diseases, including infectious ones. However, few of these studies are relevant for clinical practice or at the bedside. In this issue of the JCI, Nakanishi et al. characterized the clinical implications of a major genetic risk factor for COVID-19 severity and its age-dependent effect, using individual-level data in a large international multicenter consortium. This study indicates that a common COVID-19 genetic risk factor (rs10490770) associates with increased risks of morbidity and mortality, suggesting potential implications for future clinical risk management. How can the genomic biomarkers identified by GWAS be associated with the clinical outcomes of an infectious disease? In this Commentary, we evaluate the advantages and limitations of this approach.

Figure 1. A model for precision medicine.

In a proactive approach to precision medicine, individual health determinants are systematically complemented with GWAS, PheWAS, and functional studies. Nakanishi et al. found that the common COVID-19 genetic risk factor (rs10490770) associates with increased risks of morbidity and mortality, especially in patients under 60 years (12). Efforts to establish polygenic risk scores, validate candidate genes with mechanistic understanding, and determine additional risk factors will help clinicians stratify patients to provide targeted treatments OSAS, obstructive sleep apnea syndrome.