scholarly journals Strong Correlation between the Case Fatality Rate of COVID-19 and the rs6598045 Single Nucleotide Polymorphism (SNP) of the Interferon-Induced Transmembrane Protein 3 (IFITM3) Gene at the Population-Level

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 42
Author(s):  
Yong-Chan Kim ◽  
Byung-Hoon Jeong

Coronavirus disease 2019 (COVID-19) is a fatal pandemic disease that is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 13 December, 2020, over 70,000,000 cases and 1,500,000 deaths have been reported over a period of several months; however, the mechanism underlying the pathogenesis of COVID-19 has not been elucidated. To identify the novel risk genetic biomarker for COVID-19, we evaluated the correlation between the case fatality rate of COVID-19 and the genetic polymorphisms of several potential COVID-19-related genes, including interferon-induced transmembrane protein 3 (IFITM3), the angiotensin I converting enzyme 2 (ACE2) gene, transmembrane protease, serine 2 (TMPRSS2), interleukin 6 (IL6), leucine zipper transcription factor-like protein 1 (LZTFL1), and the ABO genes, in various ethnic groups. We obtained the number of COVID-19 cases and deaths from the World Health Organization (WHO) COVID-19 dashboard and calculated the case fatality rate of each ethnic group. In addition, we obtained the allele distribution of the polymorphisms of the IFITM3, ACE2, TMPRSS2, IL6, LZTFL1, and ABO genes from the 1000 Genomes Project and performed Log-linear regression analysis using SAS version 9.4. We found different COVID-19 case fatality rates in each ethnic group. Notably, we identified a strong correlation between the case fatality rate of COVID-19 and the allele frequency of the rs6598045 single nucleotide polymorphism (SNP) of the IFITM3 gene. To the best of our knowledge, this report is the first to describe a strong correlation between the COVID-19 case fatality rate and the rs6598045 SNP of the IFITM3 gene at the population-level.

2020 ◽  
Vol 22 (2) ◽  
pp. 117-128 ◽  
Author(s):  
Shivam Gupta ◽  
Kamalesh Kumar Patel ◽  
Shobana Sivaraman ◽  
Abha Mangal

As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent ( N = 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- and middle-income countries of Asia, Europe and North America. While the total number of cases and deaths are highest in USA, the prevalence, incidence and case fatality rates are higher in the European countries. The prevalence and incidence vary widely among countries included in the analysis, and the number of cases per million and the case fatality rate are correlated with the proportion of the elderly population and to a lesser extent with the proportion of the urban population.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9939
Author(s):  
Jessica F. McLaughlin ◽  
Kevin Winker

Sample size is a critical aspect of study design in population genomics research, yet few empirical studies have examined the impacts of small sample sizes. We used datasets from eight diverging bird lineages to make pairwise comparisons at different levels of taxonomic divergence (populations, subspecies, and species). Our data are from loci linked to ultraconserved elements and our analyses used one single nucleotide polymorphism per locus. All individuals were genotyped at all loci, effectively doubling sample size for coalescent analyses. We estimated population demographic parameters (effective population size, migration rate, and time since divergence) in a coalescent framework using Diffusion Approximation for Demographic Inference, an allele frequency spectrum method. Using divergence-with-gene-flow models optimized with full datasets, we subsampled at sequentially smaller sample sizes from full datasets of 6–8 diploid individuals per population (with both alleles called) down to 1:1, and then we compared estimates and their changes in accuracy. Accuracy was strongly affected by sample size, with considerable differences among estimated parameters and among lineages. Effective population size parameters (ν) tended to be underestimated at low sample sizes (fewer than three diploid individuals per population, or 6:6 haplotypes in coalescent terms). Migration (m) was fairly consistently estimated until <2 individuals per population, and no consistent trend of over-or underestimation was found in either time since divergence (T) or theta (Θ = 4Nrefμ). Lineages that were taxonomically recognized above the population level (subspecies and species pairs; that is, deeper divergences) tended to have lower variation in scaled root mean square error of parameter estimation at smaller sample sizes than population-level divergences, and many parameters were estimated accurately down to three diploid individuals per population. Shallower divergence levels (i.e., populations) often required at least five individuals per population for reliable demographic inferences using this approach. Although divergence levels might be unknown at the outset of study design, our results provide a framework for planning appropriate sampling and for interpreting results if smaller sample sizes must be used.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gabriele Sorci ◽  
Bruno Faivre ◽  
Serge Morand

Abstract While the epidemic of SARS-CoV-2 has spread worldwide, there is much concern over the mortality rate that the infection induces. Available data suggest that COVID-19 case fatality rate had varied temporally (as the epidemic has progressed) and spatially (among countries). Here, we attempted to identify key factors possibly explaining the variability in case fatality rate across countries. We used data on the temporal trajectory of case fatality rate provided by the European Center for Disease Prevention and Control, and country-specific data on different metrics describing the incidence of known comorbidity factors associated with an increased risk of COVID-19 mortality at the individual level. We also compiled data on demography, economy and political regimes for each country. We found that temporal trajectories of case fatality rate greatly vary among countries. We found several factors associated with temporal changes in case fatality rate both among variables describing comorbidity risk and demographic, economic and political variables. In particular, countries with the highest values of DALYs lost to cardiovascular, cancer and chronic respiratory diseases had the highest values of COVID-19 CFR. CFR was also positively associated with the death rate due to smoking in people over 70 years. Interestingly, CFR was negatively associated with share of death due to lower respiratory infections. Among the demographic, economic and political variables, CFR was positively associated with share of the population over 70, GDP per capita, and level of democracy, while it was negatively associated with number of hospital beds ×1000. Overall, these results emphasize the role of comorbidity and socio-economic factors as possible drivers of COVID-19 case fatality rate at the population level.


Biology ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 128 ◽  
Author(s):  
Chris Kenyon

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69–1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.


2020 ◽  
Author(s):  
Evaezi Okpokoro ◽  
Victoria Igbinomwanhia ◽  
Elima Jedy-Agba ◽  
Gbenga Kayode ◽  
Ezenwa James Onyemata ◽  
...  

AbstractBackgroundThe ongoing Coronavirus disease 2019 (COVID-19) pandemic is unprecedented in scope. High income countries (HIC) seemingly account for the majority of the mortalities considering that these countries have screened more persons. Low middle income countries (LMIC) countries may experience far worse mortalities considering the existence of a weaker health care system and the several underlying population level morbidities. As a result, it becomes imperative to understand the ecological correlation between critical underlying population level morbidities and COVID-19 case fatality rates (CFR).MethodThis is an ecological study using data on COVID-19 cases, prevalence of COPD, prevalence of tobacco use, adult HIV prevalence, quality of air and life expectancy. We plotted a histogram, performed the Shapiro-Wilk normality test and used spearman correlation to assess the degree of correlation between COVID-19 case fatality rate (CFR) and other covariates mentioned above.ResultAs at the 31st of March 2020, there were a total of 846,281 cases of COVID-19 from 204 countries and a global case fatality rate of 5% (range 0% to 29%). Angola and Sudan both had the highest CFR of 29%, while Italy had the highest number of deaths (i.e. 12,428) as at 31st of March 2020. Adult HIV prevalence has a significant but weak negative correlation with CFR (correlation coefficient = - 0.24, p value =0.01) while all the other variables have positive correlation with CFR due to COVID-19 though not statistically significant. Of the 204 countries analyzed, only 11 countries (i.e. 5%) had complete datasets across all 5 population level morbidities (i.e. prevalence of COPD, prevalence of tobacco use, life expectancy, quality of air, and adult HIV prevalence variables). Correlations of CFR from these 11 countries were similar to that from the 204 countries except for the correlation with quality of air and prevalence of tobacco use. Conclusion: While we interpret our data with caution given the fact that this is an ecological study, our findings suggest that population level factors such as prevalence of COPD, prevalence of tobacco use, life expectancy and quality of air are positively correlated with CFR from COVID-19 but, adult HIV prevalence has a weak and negative correlation with COVID-19 CFR and would require extensive research.


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