COVID-19 and Indian population: a comparative genetic analysis
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Abstract
Background
Major risk factors of COVID-19 include older age, male gender, and comorbidities. In addition, host genetic makeup is also known to play a major role in COVID-19 susceptibility and severity. To assess the genetic predisposition of the Indian population to COVID-19, a comparative analysis of the frequencies of polymorphisms directly or potentially associated with COVID-19 susceptibility, severity, immune response, and fatal outcomes was done between the Indian population and other major populations (European, African, East Asian, South Asian, and American).
Materials and methods
Polymorphisms directly or potentially associated with COVID-19 susceptibility, severity, immune response, and mortality were mined from genetic association studies, comparative genetic studies, expression quantitative trait loci studies among others. Genotype data of these polymorphisms were either sourced from the GenomegaDB™ database of Mapmygenome India Ltd. (sample size = 3054; Indian origin) or were imputed. Polymorphisms with minor allele frequency >= 0.05 and that are in Hardy-Weinberg equilibrium in the Indian population were considered for allele frequency comparison between the Indian population and 1000 Genome population groups.
Results
Allele frequencies of 421 polymorphisms were found to be significantly different in the Indian population compared to European, African, East Asian, South Asian, and American populations. 128 polymorphisms were shortlisted based on linkage disequilibrium and were analyzed in detail. Apart from well-studied genes, like ACE2, TMPRSS2, ADAM17, and FURIN, variants from AHSG, IFITM3, PTPN2, CD209, CCL5, HEATR9, SELENBP9, AGO1, HLA-G, MX1, ICAM3, MUC5B, CRP, C1GALT1, and other genes were also found to be significantly different in Indian population. These variants might be implicated in COVID-19 susceptibility and progression.
Conclusion
Our comparative study unraveled multiple genetic variants whose allele frequencies were significantly different in the Indian population and might have a potential role in COVID-19 susceptibility, its severity, and fatal outcomes. This study can be very useful for selecting candidate genes/variants for future COVID-19 related genetic association studies.
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SciScore for 10.1101/2021.12.15.21267816: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 2.1 Literature mining and polymorphism selection: Research articles related to COVID-19 genetic associations were searched in Pubmed using keywords such as ‘COVID’, ‘COVID-19’, ‘Corona virus’, ‘GWAS’, ‘genetic susceptibility’, ‘polymorphism’, ‘severity’, ‘susceptibility’ among other relevant terms. Pubmedsuggested: (PubMed, RRID:SCR_004846)For polymorphisms not present in the database, genotypes were imputed with IMPUTE2 using 1000 Genomes Phase3 data as reference [37, 38]. 1000 Genomessuggested: (1000 Genomes Project and AWS, RRID:SCR_008801)Results from OddPub: We did not detect open …
SciScore for 10.1101/2021.12.15.21267816: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 2.1 Literature mining and polymorphism selection: Research articles related to COVID-19 genetic associations were searched in Pubmed using keywords such as ‘COVID’, ‘COVID-19’, ‘Corona virus’, ‘GWAS’, ‘genetic susceptibility’, ‘polymorphism’, ‘severity’, ‘susceptibility’ among other relevant terms. Pubmedsuggested: (PubMed, RRID:SCR_004846)For polymorphisms not present in the database, genotypes were imputed with IMPUTE2 using 1000 Genomes Phase3 data as reference [37, 38]. 1000 Genomessuggested: (1000 Genomes Project and AWS, RRID:SCR_008801)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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