Host genetic variants potentially associated with SARS-CoV-2: A multi-population analysis

Abstract

Background: Clinical outcomes of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) showed enormous inter-individual and inter-population differences, possibly due to host genetics differences. Earlier studies identified single nucleotide polymorphisms (SNPs) associated with SARS-CoV-1 in Eastern Asian (EAS) populations. In this report, we aimed at exploring the frequency of a set of genetic polymorphisms that could affect SARS-CoV-2 susceptibility or severity, including those that were previously associated with SARS-CoV-1. Methods: We extracted the list of SNPs that could potentially modulate SARS-CoV-2 from the genome wide association studies (GWAS) on SARS-CoV-1 and other viruses. We also collected the expression data of these SNPs from the expression quantitative trait loci (eQTLs) databases. Sequences from Qatar Genome Programme (QGP, n=6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). Results: A total of 74 SNPs, located in 10 genes: ICAM3, IFN-?, CCL2, CCL5, AHSG, MBL, Furin, TMPRSS2, IL4, and CD209 promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (CCL2, MBL, CCL5, AHSG, and IL4) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in TMPRSS2, IFN-?, ICAM3, and Furin were more common among Qataris (average 2-fold change). Inter-population analysis showed that the distribution of risk alleles among Europeans differs substantially from Africans and EASs. Remarkably, Africans seem to carry extremely lower frequencies of SARS-CoV-1 susceptibility alleles, reaching to 32-fold decrease compared to other populations. Conclusion: Multiple genetic variants, which could potentially modulate SARS-CoV-2 infection, are significantly variable between populations, with the lowest frequency observed among Africans. Our results highlight the importance of exploring population genetics to understand and predict COVID-19 outcomes. Indeed, further studies are needed to validate these findings as well as to identify new genetic determinants linked to SARS-CoV-2.