TLR3 (rs3775291) variant is not associated with SARS-CoV-2 infection and related mortality: a population-based correlation analysis

Abstract

To the Editor, With great interest, we read the recent article by Dhangadamajhi et al. [1], which identified the possible association of TLR3 exonic variant (rs3775291) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and mortality rates in global populations. The authors included TLR3 rs3775291 polymorphism data of 48,835 healthy individuals from 40 countries and detected an important correlation between the rs3775291 minor allele and susceptibility to SARS-CoV-2 infection and mortality. An earlier study [2] used minor allele frequency data from 14 different countries to show a similar correlation between the rs3775291 variant and COVID-19 susceptibility and mortality. Although the data investigation and reporting were done elegantly and scholarly, we found a few minor issues that need to be discussed further. To draw a firm conclusion in a population-scale analysis, all existing studies must be included. Genotype data from 48,835 healthy controls from 40 countries were used by the authors [1]. The minor allele frequency was obtained from 1000 genome projects and gnomAD, and other databases, such as PubMed and google scholar. After searching various databases (1000 genomes, gnomAD, dbSNP, PubMed and google scholar) for minor allele frequency, we found more reports from various populations [Tunisia (n = 5), Brazil (n = 2), China (n = 13), South Korea (n = 4), Japan (n = 4), Scotland (n = 2), Spain (n = 3), Denmark (n = 3), Germany (n = 4), Poland (n = 2), Italy (n = 2), Finland (n = 3), and India (n = 5)]. When compared to the included reports of Dhangadamajhi et al. [1], we were unable to trace allele frequency data from the Bulgarian population, and a smaller number of studies from the USA (n = 4) and Sweden cohorts (n = 2) were identified. For population-scale correlation analysis, excluding reports that do not obey Hardy–Weinberg equilibrium (HWE) is critical. In line with this, Dhangadamajhi et al., proposed that such reports be removed from the correlation analysis. However, they erroneously included genotype data from Barbados (χ 2 = 4.536, p = 0.033) and Bangladesh (χ 2 = 3.775, p = 0.052) populations, which were deviated or very close to the HWE deviation score. Despite the authors' claim that genotype data from 40 countries were used in the study, the number of countries considered for the population-scale analysis was actually 39. For the correlation study, the authors used minor allele frequency data from Finland twice. The minor allele data of Finland must be pooled before the correlation analysis. The Pearson correlation test was used to assess the relationship between the prevalence of minor allele ‘T’ and the SARS-CoV-2 infection and mortality rate per million subjects in different populations. The Spearman rank correlation coefficient would be the most suitable [3] to test the relationship between TLR3 variant and COVID-19 since the two variables were on different scales and the analysis was not conducted in SARS-CoV-2 infected cases. Using data from Dhangadamajhi et al. [1], a Spearman rank correlation study showed no significant association between SARS-CoV-2 and the TLR-3 rs3775291 polymorphism (infection: r = 0.244, p = 0.128; mortality: r = 0.247, p = 0.124). For obtaining minor allele frequency in different populations, the authors used two different search strategies: (1) genomic databases, such as 1000 Genomes Project and gnomAD, and (2) literature databases, such as PubMed and Google Scholar. Although allele frequency and the total number of healthy subjects considered for MAF calculation have been mentioned in the manuscript’s supplementary table, a piece of additional information on references and data sources would be more beneficial for the researchers. On 18 January 2020, the authors collected SARS-CoV-2 data from various countries, including infections, mortality, and recovery rates. The first cases of SARS-CoV-2 infection were identified in Wuhan, China, in December 2019, and the World Health Organization declared COVID-19 a pandemic on 11 March 2020. The date listed in the paper and the supplementary Table 1 (18th January 2020) may be a typographical error. The infection and mortality status of SARS-CoV-2 on 18 January 2021 were obtained from Dhangadamajhi et al. supplementary dataset. Data on minor allele frequency were gathered from a variety of databases, as shown in Table 1. Reports with HWE deviated genotype distributions were omitted from the present analysis [Barbados (n = 1), Bangladesh (n = 1), China (n = 2), India (n = 1), Lithuania (n = 2), Nigeria (n = 1)] and a total of 47,136 healthy subjects from 35 different populations were taken into account. Using the modified minor allele frequency data, a reanalysis of the association between rs3775291 minor allele frequency and COVID-19 showed no significant correlation between rs3775291 minor allele ‘T’ and SARS-CoV-2 infection (Spearman r = 0.181, p = 0.295, n = 35) or mortality (Spearman r = 0.146, p = 0.402). Up-to-date data of SARS-CoV-2 infection and mortality rate per million were obtained from the Worldometer website (assessed on 1st April 2021). The spearman rank correlation study of rs3775291 minor allele frequency (T) with SARS-CoV-2 infection rate (Spearman r = 0.212, p = 0.221, n = 35) and mortality rate (Spearman r = 0.143, p = 0.412, n = 35) also failed to show a potential association of rs3775291 polymorphism with COVID-19, bolstering the absence of an association between rs3775291 and related mortality. However, case–control studies in different populations are needed to confirm our findings. Table 1 TLR3 rs3775291 minor allele frequency and SARS-CoV-2 related data of different countries Population Dhangadamajhi et al. [1] Present study Data assessed on 18th January 2021 (Dhangadamajhi et al. [1]) Data assessed on 1st April 2021 Data sources/remarks Number of studies MAF Total cases Number of studies MAF Total number of Healthy controls SARS CoV-2 infection /million SARS-CoV-2 death/million SARS CoV-2 infection /million SARS-CoV-2 death/million Barbados 1 4.2 96 1 4.2 96 3603 24 12,697 146 1000 Genomes, this study was excluded for deviation of genotypes distribution from HWE Nigeria 2 0.7 207 1 0.926 108 514 7 776 10 1000 Genomes Gambia 1 1.8 113 1 1.8 113 1589 52 2213 67 1000 Genomes Luhya, Kenya 1 3.5 99 1 3.5 99 1821 32 2453 39 1000 Genomes Sierra Leone 1 1.2 85 1 1.2 85 367 10 492 10 1000 Genomes Tunisia 1 16.45 158 5 12.0 532 14,729 465 21,327 740 Moumad et al., 2013, Abida et al., 2020 Morocco 1 17.64 204 1 17.7 204 12,319 212 13,323 237 Moumad et al., 2013 USA 7 29.73 3677 4 29.4 3105 72,592 1210 93,747 1700 Dhiman et al., 2008, Edwards et al., 2008, Slattery et al., 2012, Resler et al., 2013 Colombia 2 27.88 459 2 27.9 459 36,544 935 46,920 1237 1000 Genomes, Allikmets et al., 2009 Peru 1 35.3 85 1 35.3 85 31,789 1164 46,491 1561 1000 Genomes Brazil 1 33.1 299 2 35.4 760 39,340 976 59,682 1506 Assmann et al., 2014, Sa et al., 2015 Nicaragua 1 21.21 132 1 21.2 132 923 25 999 27 Lundkvist et al., 2020 China 8 31.86 3361 13 27.3 5089 61 3 63 3 1000 Genomes, Li et al., 2017, Pang et al., 2014, Chen et al., 2015, Cheng et al., 2014., Ye et al., 2020, Rong et al., 2013,Chen et al., 2017,Wang et al., 2015, Rong et al., 2013 South Korea 2 25.85 756 4 29.9 5114 1400 24 2020 34 Korean Genome Projec, KOREAN population from KRGDB, Cho et al., 2017, Hwang et al., 2009 Taiwan 3 35.31 2281 3 34.6 1713 35 0.3 43 0.4 Yang et al., 2013,Yang et al., 2014 Japan 3 27.27 264 4 32.6 422 2449 34 3741 72 1000 Genomes, Ueta et al., 2007, Ikezoe et al., 2015, Matsuo et al., 2016 Vietnam 1 38.9 99 1 38.9 99 16 0.4 27 0.4 1000 Genomes Finland 1 33.3 99 DNA DNA DNA 7231 111 13,692 152 – Scotland, UK 1 32.4 91 2 30.3 249 30,330 990 63,766 1859 1000 Genomes, Dwyer et al., 2013, Spain 2 30.40 472 3 30.1 430 48,160 1140 70,226 1613 1000 Genomes, Sironi et al., 2012, Matas-Cobos et al., 2015 Denmark 2 28.35 1280 3 27.9 1696 32,430 301 39,708 417 Laska et al., 2014, Enevold et al., 2014, Laska et al., 2014 Germany 2 28.96 1034 4 28.8 1291 24,132 555 33,701 917 Yang et al., 2012, Gast et al., 2011,Allikmets et al., 2009,Ye et al., 2020 Poland 1 25.64 78 2 27.0 150 37,796 878 61,396 1403 Studzińska et al., 2017, Grygorczuk et al., 2017 Ireland 1 26.61 263 1 26.2 263 33,527 511 47,371 941 Cooke et al., 2018 Lithuania 1 34.0 135 61,701 894 80,231 1327 Two studies were excluded for HWE deviation Russia 1 32.6 269 1 34.6 269 24,283 446 31,135 677 Barkhash et al., 2013 Sweden 3 30.43 14,186 2 30.1 1109 51,659 1019 79,328 1327 Günaydın et al., 2014, Svensson et al., 2012 Iceland 1 27.5 169 1 27.5 169 17,393 85 18,096 85 Allikmets et al., 2009 Netherland 2 29.53 1107 2 29.5 1107 52,560 750 74,148 964 Allikmets et al., 2009 Serbia 1 33.17 104 1 33.1 104 42,420 425 68,947 609 Stanimirovic et al., 2013 Italy 1 30.8 107 2 27.1 345 38,939 1346 59,357 1811 1000 Genomes, Sironi et al., 2012 Finland 1 32.68 12,549 3 32.4 16,110 7231 111 13,962 152 1000 Genomes Estonia 1 32 2412 1 31.9 4480 27,649 241 80,187 680 Genetic variation in the Estonia population Bulgaria 1 29.94 1335 DNA DNA DNA 30,565 1222 49,591 1910 – Bangladesh 1 27.3 86 1 27.3 86 3183 48 3684 55 1000 Genomes This study was excluded for deviation of genotypes distribution from HWE India 2 24.14 205 5 12.1 766 7600 110 8791 117 1000 Genomes, Alagarasu et al., 2014, Biyani et al., 2015, Meena et al., 2015 Pakistan 1 24 96 1 23.9 96 2315 49 3003 65 1000 Genomes Sri Lanka 1 31.9 102 1 31.9 102 2420 12 4316 26 1000 Genomes Iran 1 29.66 118 1 29.6 118 15,660 671 22,237 739 Habibabadi et al., 2020 Australia 1 33.7 163 1 33.7 163 1118 35 1140 35 Allikmets et al., 2009 COVID-19 related data were obtained from article Dhangadamajhi et al and worldometer assessed on 1st April 2021. TLR3 rs3775291 polymorphism genotype or allele data were obtained from 1000 genomes, dbSNP, PubMed and Google Scholar DNA data not available