Genetic Susceptibility to Infectious Diseases in the Qatari Population

Background: Infectious diseases (IDs) account for 8% of deaths annually in Qatar, and therefore, represent a significant challenge for public health. Interestingly, the spread and severity of viral infections vary considerably between individuals and populations. The most recent example is SARS-CoV-2, which ranges from mild/asymptomatic to a severe respiratory syndrome. It has been previously reported that polymorphisms in genes linked to immunity can influence individuals' responses to infections as observed in tuberculosis, influenza, and HIV; however, studies exploring causal host genetic variants in IDs are still limited and dramatically skewed with regard to population inclusion. In fact, the genetic susceptibility to IDs in the Qatari population is largely unknown. Aim: To perform a comprehensive genetic screening to investigate the presence and frequency of variants previously associated with various infections in the Qatari population. Methods: Whole-genome sequencing was previously performed for 18,000 QBB participants using Illumina HiSeq X Ten1 sequencers. The initial data processing and quality assessment of the raw data has also been performed and variant calling files (VCF) were created. We were granted the access to the VCF files of 6,218 sequenced samples. The genetic variant data was then converted to PLINK file format using PLINK-1.9. Standardized quality-assurance and quality control (QA/QC) methods were followed to generate high quality and confidence on both SNPs and sample levels. The final file used for calculating allele frequency contained 6,047 subjects. Additionally, list of infections-related SNPs that were previously reported in the literature and deposited in GWAS catalog was extracted and used to calculate and compare the allelic frequency in the Qatari genomes compared to other populations. Results: The frequency of infections-related SNPs in the Qatari population was significantly lower for most infections. Most variants (78%) showed negative fold change in the Qatari genomes. Only 22% of all variants were more prevalent in Qatari population compared to others. The most significant differences were observed in genes related to TB and HIV (200-940 and 160-710 fold change, respectively). Conclusion: This study reports a lower susceptibility of the Qatari population to IDs in general. Nonetheless, this might also indicate the presence of unknown Qatari-unique variants and hence, highlights the need for further investigation in future GWAS

Host Genetic Variants Potentially Associated With SARS-CoV-2: A Multi-Population Analysis.

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. 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, = 6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). A total of 74 SNPs, located in 10 genes: , -γ, , , , , , , and promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (, , , , and ) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in , -γ, , and 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. 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.This work was supported by the Qatar University High Impact Grant (Grant Number: QUHI-BRC-20_21-1). OA was supported by a startup grant from the College of Health and Life Sciences, Hamad Bin Khalifa University. This work makes use of data generated by the Qatar Genome Programme (QGP) and Qatar Biobank (QBB), which are funded by Qatar Foundation for Education, Science and Community

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

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

Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients.

Detection of early metabolic changes in critically-ill coronavirus disease 2019 (COVID-19) patients under invasive mechanical ventilation (IMV) at the intensive care unit (ICU) could predict recovery patterns and help in disease management. Targeted metabolomics of serum samples from 39 COVID-19 patients under IMV in ICU was performed within 48 h of intubation and a week later. A generalized linear model (GLM) was used to identify, at both time points, metabolites and clinical traits that predict the length of stay (LOS) at ICU (short ≤ 14 days/long >14 days) as well as the duration under IMV. All models were initially trained on a set of randomly selected individuals and validated on the remaining individuals in the cohort. Further validation in recently published metabolomics data of COVID-19 severity was performed. A model based on hypoxanthine and betaine measured at first time point was best at predicting whether a patient is likely to experience a short or long stay at ICU [area under curve (AUC) = 0.92]. A further model based on kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, and C24.0 sphingomyelin, measured 1 week later, accurately predicted the duration of IMV (Pearson correlation = 0.94). Both predictive models outperformed Acute Physiology and Chronic Health Evaluation II (APACHE II) scores and differentiated COVID-19 severity in published data. This study has identified specific metabolites that can predict in advance LOS and IMV, which could help in the management of COVID-19 cases at ICU.This project was funded by Qatar University's internal grant number QUHI-BRC-20/21-1. This publication was made possible by GSRA grant, ID# GSRA5-1-0602-18124, from the Qatar National Research Fund (a member of Qatar Foundation)