Coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) in March 2020 due to its high spread. The epidemic came on abruptly and left the Public Health System under severe pressure and not knowing how to manage the cases that arose, inducing infected people into a critical state and consequently many deaths (Chen et al., 2020; Choudhury and Mukherjee, 2020). As it is a highly transmissible pathogen, it was recommended that people should be restricted from circulating to reduce contamination rates (Bastos et al., 2021). However, health professionals did exactly the opposite of what was recommended for most of the population, which made them more susceptible to acquiring the disease (Coelho et al., 2022).

There are different clinical manifestations, depending on the host’s immune status, the genetic variability of the virus circulating at the time, and other genetic and even sociodemographic aspects; in this sense, the symptomatology of COVID-19 ranges from asymptomatic or with mild manifestations similar to other severe acute respiratory syndromes (SARS)—even acute respiratory distress, sepsis, septic shock, acute thrombosis, and/or multiple organ dysfunction. These complications often require hospitalizations for several days in ICUs, with many patients not surviving, and those who recovered would have to live with the sequelae left behind (post-COVID-19) (Cham et al., 2021; Fronteira et al., 2024).

The COVID-19 pandemic has affected the world in a big way, with the first pandemic wave characterized by rapid spread and high mortality. According to studies, from 1 January 2020 to 31 December 2021, there were approximately 5.94 million deaths worldwide. In Brazil, the first wave hit the country hard, showing the weaknesses of the health system and placing health professionals on the front line. Professionals faced long working hours, a lack of personal and collective protective equipment (PPE and CPE), and the anguish of dealing with a high number of critically ill patients. The emotional and physical impacts were significant, with many professionals suffering from anxiety and other mental health problems (Agrawal et al., 2021). According to the Brazilian Institute of Geography and Statistics (IBGE), approximately 1.5 million deaths occurred in 2020 in Brazil because of COVID-19. The front line of the pandemic in Brazil was marked by the dedication of health professionals, who fought to save lives in the midst of a troubled scenario (Almeida et al., 2020; COVID-19 Excess Mortality Collaborators, 2022).

Immunology explores the complex defense system of the body against pathogens. Innate immunity, the first line of defense, uses Toll-like receptors (TLRs) to recognize molecular patterns associated with pathogens (PAMPs). In particular, TLR2 detects lipopeptides and bacterial peptidoglycans, whereas TLR3 recognizes viral double-stranded RNA. Both TLRs play a crucial role in the detection of viruses, including SARS-CoV-2. Studies indicate that SARS-CoV-2 can be recognized using TLRs, including TLR2 and TLR3, through different mechanisms, with the interaction of viral proteins with these receptors, leading to the activation of the innate immune response, thus preventing excessive inflammatory response (Dhangadamajhi and Rout, 2021; Choudhary et al., 2022; Machado et al., 2023).

Haplotypes are combinations of alleles at different markers along the same chromosome that are inherited as a unit. In research, their main purpose is to identify the causes of diseases, marking recombination events in family and population-based studies, and clinically, they apply to the greater or lesser correlation in predicting the severity of diseases. Single-nucleotide polymorphisms (SNPs) can originate from point mutations in DNA, which are intragenic and cause important biological effects such as association with complex diseases and reactions in responses to treatments in humans (Bafica et al., 2005; Chen et al., 2017; Bortolotti et al., 2021).

This study of the TLR2–TLR3 haplotype, which includes two SNPs (rs3804100 in TLR2 and rs3775290 and rs3775291 in TLR3), may aid in the identification of genetic differences that influence COVID-19 susceptibility and clinical progression, hence facilitating the development of individualized disease containment and treatment methods. These SNPs (rs3804100 in TLR2 and rs3775290 and rs3775291 in TLR3) were selected because they are in nearby functional regions, previously associated in the literature with viral immune response and respiratory diseases (such as tuberculosis, influenza, and other viral infections) (Bucciol et al., 2022; Córdova-Dávalos et al., 2022; Chen et al., 2023; Silva et al., 2023a). Thus, investigating the haplotypes of SNP rs3804100 in the TLR2 gene and SNPs rs3775290 and rs3775291 in the TLR3 gene is a viable approach to understanding the immunogenetic mechanisms driving COVID-19 and its clinical symptoms (Chen et al., 2011; Vega et al., 2017; Sharif et al., 2020; Djordjevic et al., 2022).

Considering the impact of the pandemic on the workforce of healthcare professionals and the need to identify the association of haplotypes for the prognosis of COVID-19 in these professionals (Zangoue et al., 2021), along with the relevance of immunogenetics in this context, in this study, we aimed to analyze the haplotypes of the TLR2 and TLR3 genes on the human chromosome, in the genomic area near SNP rs3804100 of TLR2 and SNPs rs3775290 and rs3775291 of TLR3 in the labor cohort of Belém-PA, Brazil, from the first pandemic wave.

COVID-19 continues to be a public health problem with numerous endemic foci around the world, and variants of concern (VOCs) continue to emerge in the face of parasite–host interactions and high morbidity in the elderly and people with comorbidities (Cui et al., 2025). Immunogenetic studies with haplotypes, such as this study, are important for revealing genetic patterns of ethnicities (populations), along with epigenetic and/or environmental aspects, and associating them with phenotypes/clinical–epidemiological conditions such as COVID-19 (Contes and Liu, 2025). Candidate genes, especially those encoding immune receptors that are essential in host defense, are preponderant in determining associative genetic biomarkers. In this sense, TLR genes (such as TLR2 and TLR3) are potential typical elements for consideration in immunogenetic analyses (Bochud et al., 2007; CHEN et al., 2021).

Previous studies have demonstrated associations between TLR2 and TLR3 polymorphisms and COVID-19 severity and susceptibility, underscoring the importance of these receptors in viral recognition and immune activation (Alseoudy et al., 2022; Alhabibi et al., 2023). Considering that both tuberculosis and COVID-19 affect the respiratory system and that TLR2 is crucial in IFN-γ-mediated cellular immunity, the analysis of SNP rs3804100 is relevant to understanding the heterogeneity of the host’s immune response to SARS-CoV-2 infection in terms of more frequent symptoms typical of lung involvement (Bafica et al., 2005).

Our work extends these insights by focusing on haplotype analyses within a Brazilian cohort, providing valuable data relevant to the genetic diversity and epidemiology of COVID-19 in this Amazon region (Secolin et al., 2021). The investigation of the TLR2–TLR3 haplotype that includes these three SNPs may help identify other genetic variations that influence susceptibility and clinical progression of COVID-19, aiding in the development of personalized containment and treatment strategies for the disease. Thus, the study of the haplotype of SNP rs3804100 in the TLR2 gene and SNP rs3775291 in the TLR3 gene represents a promising approach to unravel the immunogenetic mechanisms underlying COVID-19 and its clinical manifestations.

Given TLR2’s critical role in the innate immune response to viral infections, especially SARS-CoV-2, the haplotype including SNP rs3804100 in the TLR2 gene is a key topic of investigation in COVID-19 immunogenetics. The TLR2 gene activates the release of pro-inflammatory cytokines, including TNF-α and interferon-gamma, to defend against respiratory infections (Bafica et al., 2005; Bochud et al., 2007; Khanmohammadi and Rezaei, 2021).

SNP rs3804100, located in the exon 3 of the TLR2 gene, characterized by the substitution of thymine (T) with cytosine (C), may alter the stability of the TLR2 protein; however, studies conducted among healthcare professionals during the first wave of the pandemic indicated that this variant is benign and showed no significant association with the severity or symptoms of COVID-19 in that population (Cheng et al., 2015; Silva et al., 2023b). In a systematic review study by Schurz et al. (2015), more severe tuberculosis cases were associated in all genetic predictive models, with a higher risk of susceptibility due to the presence of the mutant allele (C) of the TLR2 rs3804100 SNP in Asian populations (Schurz et al., 2015).

TLR3 recognizes double-stranded viral RNA (dsRNA), activating signaling pathways that lead to the production of interferons and other antiviral cytokines, which are essential for controlling viral replication and modulating the inflammatory response (Bortolotti et al., 2021). SNP rs3775290, located in the exon 4 of the TLR3 gene, is a genetic variant that has been studied for its influence on susceptibility to COVID-19 due to the crucial role of TLR3 in the innate immune response against viruses, including SARS-CoV-2. TLR3 recognizes viral RNA and activates signaling pathways that promote the production of interferons and other antiviral cytokines, which are essential for controlling the infection. Studies have investigated the association of rs3775290 with the severity and susceptibility to COVID-19, seeking to understand how this variant may alter the efficiency of the host’s antiviral response. In particular, Alseoudy et al. (2022) found that the T allele of rs3775290 was associated with a higher risk of severe COVID-19 pneumonia (Alseoudy et al., 2022; Silva et al., 2023c).

The rs3775291 variant (located on exon 4 of the TLR3 gene) is a nucleotide substitution (C>T) that can cause structural and functional changes in the TLR3 protein, potentially impacting the effectiveness of the antiviral immune response. It results in a proline-to-serine change (P554S) and has also been associated with diminished TLR3 signaling and IFN-β production (Bucciol et al., 2022). Studies have investigated the association of this variation with COVID-19 susceptibility and severity; however, the results are inconsistent. Some studies found no significant association between SNP rs3775291 and COVID-19 severity or symptoms in specific populations (Dhangadamajhi and Rout, 2021; Silva et al., 2023c). However, research by Silva et al. (2023b) indicated that this variant may be associated with an increased risk of viral infections in Asian and American populations, suggesting a potential high-risk genetic pattern for COVID-19 in these groups (Xie and Zhu, 2020; Dhangadamajhi and Rout, 2021; Silva et al., 2023c; Silva et al., 2023c).

SNP rs3804100 was chosen because it (T > C) has already been widely studied in other respiratory diseases, such as tuberculosis, which, like COVID-19, affects the lungs (Flores-González et al., 2024). This suggests that variations in this SNP may influence the immune response in lung infections, causing instability in the formation of the TLR2 protein. More directly, Flores-González et al. (2024) demonstrated that the rs3804100 variant induces a distinct profile of TLR2 expression and cytokine release (e.g., IL-6 and TNF-α) in response to the SARS-CoV-2 spike protein, providing a direct functional link between this SNP and the COVID-19 immune response (Flores-González et al., 2024). The rs3775291 SNP has already been associated with susceptibility to various viral infections, and variations in this SNP in a non-synonymous way, that is, that alter the structure of the TLR3 protein, can impact its immune function and, consequently, the host’s response to COVID-19 infection (Andrade et al., 2024). In the case of SNP rs3775290, this biomarker was chosen for analysis in this study because of its location in the TLR3 gene, which encodes a key receptor for detecting RNA viruses and activating the innate immune response (Wang et al., 2015).

The TLR2 and TLR3 genes encode receptors essential for recognizing pathogen molecular patterns and activating essential innate immune responses. The rs3804100 variant in TLR2 has been linked to alterations in protein stability and modulation of inflammatory activation, whereas the rs3775290 and rs3775291 variants in TLR3 may influence viral RNA detection and subsequent interferon production (Dos Santos et al., 2025). The lack of a significant association suggests that in this group of professionals exposed and infected during the first wave of the pandemic in the Amazon region, these TLR2 and TLR3 variants do not play a determining role in modulating susceptibility or clinical severity of COVID-19. This may indicate that other genetic, epigenetic, environmental, or immunological factors have a greater influence on the host response, or that the genetic heterogeneity of the Amazonian population confers different risk patterns (Evdokimov et al., 2021; Minashkin et al., 2022).

The low LD observed between the SNPs evaluated (r² < 0.08) indicates that these variants segregate virtually independently, which may explain the absence of strong haplotypic blocks in the study population. This genetic independence confirms that the combined effect of SNPs on the same chromosome 4 is limited, reinforcing the lack of a clearly defined risk haplotype associated with COVID-19 in the group evaluated (Salamaikina et al., 2023; Teräsjärvi et al., 2024).

From a clinical perspective, the lack of association between the tested SNPs and the severity or symptoms of COVID-19 in healthcare workers may reflect local genetic and environmental particularities. Previous studies have indicated that the immune response to viral infections is multifactorial, involving multiple genes, cellular pathways, and external influences beyond SNPs (Feng et al., 2022).

The comparison of the genetic diversity in our present study provides crucial context, showing that the allele frequencies in our Brazilian Amazonian cohort are significantly different from those observed in European populations but are more closely aligned, although still distinct, with those of other Latino/admixed American populations. This genetic distinctiveness strongly supports our conclusion that population-specific genetic architecture is a key factor in explaining the lack of association found in our study, which might be present in other, more well-studied populations. In addition, this comparative analysis underscores the unique genetic background of the Amazonian population studied and reinforces that the lack of association observed may be attributable to population-specific genetic patterns, limiting the generalizability of findings from other ethnic groups (Rodrigues de Moura et al., 2015; Silva et al., 2023c; Silva et al., 2023b).

This disparity has two critical implications, which provide a possible fundamental explanation for this study’s negative findings. First, it validates the unique genetic background of the Amazonian population studied, which is not adequately represented even in large databases that include Latinos (Lins et al., 2010). Second, and more crucial for the interpretation of our null results, it demonstrates that potentially risky or protective alleles, previously associated with COVID-19 in European and Asian populations where they are more common, are simply very rare or absent in this specific cohort. Consequently, the statistical power to detect any significant effect of these specific variants was inevitably low. Therefore, the lack of association does not necessarily invalidate findings from other studies but rather highlights that the genetic determinants of COVID-19 are highly dependent on the population context. Our negative findings are, in fact, a positive finding that highlights the importance of conducting genetic studies in diverse and underrepresented populations, as risk factors are not universal (Pena et al., 2020).

Despite the lack of statistical association, the present study contributes to mapping genetic variations and limitations of the role of these SNPs in the analyzed population. The findings reinforce the complexity of the COVID-19 genetic profile and the importance of studying diverse populations, especially those in the Amazon, whose genetic variability and environmental factors are poorly represented in large genomic databases. The lack of association between the analyzed SNPs highlights the need to address a broader panel of immunological variants and molecular mechanisms to understand how clinical response is modulated in COVID-19. These data are useful for directing future research, indicating that studies with larger sample sizes and integrative analyses involving genomics, transcriptomics, and function are necessary to elucidate relevant immunogenetic mechanisms in the Amazon population (Secolin et al., 2021).

This present study has some important limitations that should be considered when interpreting its results. A central limitation is the relatively small sample size (N = 212) and the use of a convenience sampling methodology, focused on healthcare professionals from a specific region (Belém, Pará, in the Brazilian Amazon Region). Although the calculated sample power (p = 0.94) is adequate for more common variants, this limited sample substantially reduces the statistical power to detect significant associations with low allele frequency variants (MAF <5%), such as SNPs rs3775291 and rs3804100. Consequently, the results for these rarer polymorphisms should be interpreted with extreme caution as the absence of a statistical association may reflect an inability to detect a true effect (type II error) rather than definitive evidence of its absence.

Another important consideration relates to the definition of COVID-19 cases during the early stages of the pandemic in Brazil. At that time, the criteria established by the Ministry of Health were still evolving and relied predominantly on clinical–epidemiological suspicion, with limited access to confirmatory laboratory testing (Hakim et al., 2021; Rodrigues et al., 2024). This context increased the probability of misclassification bias, with some true cases being missed (false negatives) and other respiratory infections being incorrectly categorized as COVID-19 (false positives). Additionally, the evolving understanding of COVID-19 symptomatology—ranging from asymptomatic to atypical systemic manifestations—further complicated the consistency of case definition. The lack of detailed longitudinal data also limits the analysis of the genetic influence on post-COVID-19 syndrome. Therefore, the findings should be considered hypothesis-generating for this unique population, highlighting the need for future studies with larger sample sizes, more comprehensive genetic panels, and a design that allows for more robust control of environmental and epigenetic variables (Sharpe, 2015).

The understanding of individual variations and associated genetic patterns in genomic blocks can aid in characterizing the immune response to SARS-CoV-2 and may, in the future, help in developing personalized treatment and prevention strategies for the disease (Caratachea, 2007; Aguiar et al., 2021). However, further investigations are necessary to clarify their exact role in the pathogenesis and clinical evolution of COVID-19.

Therefore, the TLR2–TLR3 haplotype (SNPs rs3804100, rs3775290, and rs3775291) showed little determination in the clinic of individuals with COVID-19 in Belém (Northern Brazil), which may indicate differences in collective genetic patterns and/or epigenetic influences compared to those in other more affected populations that have the same haplotype pattern worldwide. Moreover, the results obtained in this epidemiological study provide important insights into the Brazilian Amazon population genomics about COVID-19 involvement; however, the complexity of the factors involved and the identified methodological limitations indicate the need for additional studies to strengthen the prediction and confirmation of the data presented here.

Then, it is recommended to conduct complementary research, preferably with more robust analytical designs, greater control of confounding variables, and representative samples, to validate the raised hypotheses and improve the understanding of the causal mechanisms involved. In this way, it will be possible to increase the precision of the estimates and the validity of the inferences, contributing to the development of more effective preventive strategies and interventions in the studied population.