ABSTRACT
Background/Objectives: COVID-19 can affect anyone with the disease's symptoms ranging from mild to very severe. Although environmental, clinical, and social factors play an important role in the disease process, host genetic factors are not negligible either. In the present article, we attempted to elaborate on the spectrum of risk variants and genes identified in different ways and their possible relationship to COVID-19 severity and/or mortality. Method(s): We present three different approaches to search host genetic risk factors that influence the development of COVID-19 disease. First, we analyzed the exome sequencing data obtained from Slovak patients who died of COVID-19. Second, we selected risk factors/genes that were associated with COVID-19. Finally, we compared each group of found risk variants with data from dead patients and two control groups, worldwide public data of the Non-Finnish European population from the gnomAD database, and genetic data from Non-invasive prenatal testing in the Slovak population. Result(s): We illustrate the utility of genomic data showed strong association in meta-analyses conducted by the COVID-19 HGI Browser. Conclusion(s): To our knowledge, the present study is the first population analysis of COVID-19 variants worldwide and also in the Slovak population that provides different approaches to the analysis of genetic variants in whole-exome sequencing data from patients who have died of COVID-19.
ABSTRACT
The ongoing SARS-CoV-2 pandemic, which emerged in December 2019, revolutionized genomic surveillance, leading to new means of tracking viral spread and monitoring genetic changes in their genomes over time. One of the key sequencing methods used during the pandemic is based on massively parallel short read sequencing based on Illumina technology. In this work, we present a highly scalable and easily deployable computational pipeline for the analysis of Illumina sequencing data, which is used in Slovak SARS-CoV-2 genomic surveillance efforts. We discuss several issues that arose during the pipeline design, and which could both provide useful insight into the analysis processes and serve as a guideline for optimized future outbreak surveillance projects. Copyright © 2021 for this paper by its authors.
ABSTRACT
Since December 2019, coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world and caused a large global pandemic which drastically changed our everyday lives. As the COVID-19 pandemic progressed, a number of its characteristics showed enormous inter-individual and inter-population differences. Earlier genome-wide association studies (GWAS) have identified potential key genes and genetic variants associated with the risk and prognosis of COVID-19, but the underlying biological interpretation is largely unclear. Our previous work described genomic data generated through non-invasive prenatal testing (NIPT) based on low-coverage massively parallel whole-genome sequencing of total plasma DNA of pregnant women in Slovakia as a valuable source of population specific data. In the present study, we have performed a literature search of studies and used NIPT data to determine the population allele frequency of risk COVID-19 variants that have been reported in GWAS studies to date. We also focused on variants located in the ACE2 gene, encoding angiotensin-converting enzyme 2 (ACE2), which is hypothesized to be a possible genetic risk factor for SARS-CoV-2 infection. Allele frequencies of identified variants were compared with six world populations from the gnomAD database to detect significant differences between populations. We interpreted variants and searched for functional consequences and clinical significance of variants using publicly available databases. Finally, 2 COVID-19 risk variants were found that showed statistically significant differences in population allele frequencies - rs383510 and rs1801274. Copyright © 2021 for this paper by its authors.