HLA Haplotypes and Differential Regional Incidence of COVID-19 in Brazil: A Population Study Based in a Large Bone Marrow Donors Bank Dataset

doi:10.21203/rs.3.rs-325153/v1

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HLA Haplotypes and Differential Regional Incidence of COVID-19 in Brazil: A Population Study Based in a Large Bone Marrow Donors Bank Dataset

https://doi.org/10.21203/rs.3.rs-325153/v1

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Background

Coronavirus disease 2019 (COVID-19) rapidly spread all over the world causing high morbidity and mortality. Brazil is currently the third country in the world in the number of COVID-19 cases. Even though all Brazilian regions and states have reported a high number of cases, mortality rates varies among them. Environmental and genetic factors may influence the immune response towards SARS-CoV-2. The Brazilian population is highly heterogeneous, with different colonization and immigration histories in each region resulting in different genetic backgrounds. Here, we test if specific HLA haplotypes are associated with COVID-19 incidence and mortality in Brazil.

Methods

HLA data was obtained from The Brazilian Voluntary Bone Marrow Donors Registry (REDOME) which harbors data from more than four million individual donors, and COVID-19 data was retrieved from epidemiological bulletins issued by State Health Secretariats via the Ministry of Health of Brazil. We tested the association between the most frequent HLA haplotypes in Brazil and COVID-19 incidence and mortality using Spearman's correlation analysis.

Results

No correlation between HLA haplotypes and COVID-19 rates was found when we analyzed data from the 26 states and Federal District, as well as when we analyzed data from the 90 cities with at least 50 deaths registered in the São Paulo state. Significant negative correlation (suggestive of protection) between COVID-19 mortality and haplotypes HLA-A*01~B*08~DRB1*03, HLA-A*29~B*44~DRB1*07 and HLA-A*02~B*44~DRB1*04 was found when analyzing data from cities with at least 50 deaths registered in the entire country.

Conclusions

Our results do not support an association of specific HLA haplotypes with an increased risk of contracting SARS-CoV2 or dying from COVID-19 in Brazil. Nevertheless, using bone marrow donor registries for testing for associations between HLA variation and COVID-19 outcomes may represent an additional tool for health policymakers in the fight against COVID-19.

Translational Medicine

COVID-19 variability

COVID-19 mortality

Genetic susceptibility

SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, rapidly spread to various countries causing high morbidity and mortality, and the World Health Organization (WHO) declared it a pandemic on March 11, 2020 [1]. Brazil is the third country in the world in the number of COVID-19 cases, with 8,933,356 confirmed cases and 218,878 deaths as of January 26th, 2021, accounting for 1,026 deaths per million inhabitants [2]. The first COVID-19 case in Brazil occurred in February 2020 in São Paulo state (Southeast region). A few days later, suspected cases were reported from all remaining 26 Brazilian states. By the end of April 2020, the North region had high community transmission and the highest mortality rates in the country. In June and July, the number of daily confirmed cases continued to increase in all regions, reaching peaks in July 29th, 2020 and, again, in January 7th, 2021. The actual number of cases could be underestimated due to the low number of tests performed [3]. Even though all Brazilian regions and states have reported a high number of cases, mortality rates varied between them. Indeed, the symptomatology and mortality rates vary among different geographical regions and depend on the patient clinical profile, presence of comorbidities, and access to the health system [4–6]. Individual environmental and genetic factors may also influence susceptibility and immune response to the SARS-CoV-2.

Immune response variability may involve variants of the innate immune system and variants in the specific adaptive immune system [7–11]. Antigen presentation to T lymphocytes is one of the most important steps in determining immune response. The genetic complex of the classic human leukocyte antigen (HLA) encodes the proteins of the main histocompatibility complex (MHC), which mediates intracellular antigen presentation (HLA-A,-B,-C - MHC Class-I) and extracellular antigen presentation (HLA-DP, -DM, -DO, -DQ, and -DR - MHC class II) to the T cell receptors (TCR) on the surface of the CD8 + and CD4 + T lymphocytes, respectively [12]. Thus, by affecting the ability of T cells to respond to a specific antigen, HLA is one of the most important molecules of the immune system. The HLA alleles are highly polymorphic and their frequency varies widely in human populations, including among Brazilian regions [13]. In fact, Brazilian population is highly heterogeneous, with different colonization and immigration histories in each region resulting in different genetic backgrounds [14].

Previous case-control and bioinformatics studies have associated a few HLA genotypes with higher susceptibility or protection against the development of severe disease in those infected with SARS-CoV [15–18]. SARS-CoV-2 is very similar to SARS-CoV regarding aminoacid sequences, and, therefore, it is likely that HLA alleles associated with susceptibility to SARS-CoV may also play a role in COVID-19. Recently, HLA-A*02 and HLA-B*15 alleles were found to be the main presenters of SARS-CoV-2 antigens and HLA-A*25 and HLA-B*46 alleles were shown to have fewer predicted binding SARS-CoV-2 peptides in an in-silico analysis [19]. Another study analyzed the regional frequencies for the most common Italian haplotypes from the Italian Bone Marrow Donor Registry and found that the two most frequent HLA haplotypes in the Italian population were correlated with COVID-19 incidence and mortality [20]. The Brazilian Voluntary Bone Marrow Donors Registry (REDOME, in Portuguese Registro de Doadores de Medula Óssea) is the third largest bank of bone marrow donors in the world, with more than 5.2 million individuals registered to date. REDOME keeps information such as HLA-A, -B, and -DRB1 genotypes in low and/or medium resolution and city of residence of the donors.

In this study we evaluate if the most frequent HLA haplotypes in Brazil correlated to COVID-19 incidence and mortality. We also describe the frequencies of HLA genotypes previously associated with susceptibility or protection to SARS-CoV or SARS-CoV-2 infection in Brazil, which may help in establishing public control policies against COVID-19.

Data sources

For HLA data, we used a dataset composed by 4,148,713 individuals who volunteered as potential hematopoietic stem cell donors registered at REDOME until September, 2017. This registry includes information such as city of residence and HLA-A, -B, and -DRB1 genotypes. Donors come from recruitment centers distributed throughout the country and their DNA was genotyped in Health Ministry accredited Brazilian laboratories. Only data genotyped by molecular methods were included in the analyses. The volunteers are genotyped for the allelic group at the time of registration, and high-resolution genotyping is performed only on those potential donors selected after an initial screening. Thus, only low resolution (allelic group) genotypes were used in this study. The dataset was subdivided according to both state and city of residence. The Brazilian territory is divided into 26 states and one Federal District and counts with 5,570 municipalities.

Data about rates of COVID-19 cases and deaths were obtained from epidemiological bulletins issued by State Health Secretariats via the Ministry of Health of Brazil, available at https://bigdata-covid19a.icict.fiocruz.br/ [21]. COVID-19 rates for states were obtained on November 16th, 2020 and the rates for cities were obtained on October 24th, 2020.

This study was approved by the Ethics Committee in Research of Hospital de Clínicas de Porto Alegre, Brazil (CAAE 34701720300005327, GPPG 2020 − 0361), and all methods were carried out in accordance with local guidelines and regulations.

Statistical analysis

The incidence and mortality rates of COVID-19 were obtained based on the confirmed cases and deaths issued by each of the State Health Secretariats. To calculate the coefficient of incidence and mortality of each municipality, the number of confirmed cases or deaths, respectively, was divided by the resident population and multiplied by the population base of 100 thousand inhabitants.

Allele and haplotype frequency estimations and Hardy-Weinberg equilibrium (HWE) test were performed using the GENE[RATE] tools as described elsewhere [22–24]. Boxplots of allele and haplotype frequency were generated at RStudio Version 1.3.1093. Maps of rates of COVID-19 cases and deaths were performed in ArcGis v10.3. Spearman’s correlation test between HLA alleles and the five most frequent haplotypes in Brazil [25] versus rates of COVID-19 cases and deaths was performed using IBM SPSS software, Version 20.0 (IBM Corp., Armonk, NY). We evaluated the HLA x COVID-19 cases and deaths correlations in the following scenarios: (1) the 26 states and Federal District of Brazil (data obtained until November 16th ); (2) only cities with at least 50 deaths due to COVID-19 registered in the entire Brazilian territory (data obtained until October 24th, 2020) and; (3) only cities with at least 50 deaths registered in São Paulo state (data obtained until October 24th, 2020). In scenario #1 we replicated the approach previously performed by Pisanti et al. (2000) [20]; in scenario #2 we used municipalities with a defined minimum number of deaths to increase the number of observations and, therefore, gain statistical power; finally, in scenario #3 we used data from a single state (São Paulo) in order to control population heterogeneity. São Paulo state was chosen as a study model since it can be considered as similar to a European country in terms of population and territorial dimensions. P-values below 0.05 were considered statistically significant. We applied the FDR (false discovery rate) correction for multiple tests to avoid having too many false-positives in haplotype and allele correlations.

Geographical distribution of COVID-19 epidemic in Brazil

The states with the highest incidence of COVID-19 cases registered until November 16h, 2020 were Roraima, the Federal District and Amazonas, respectively; while the states with the lowest incidence were Pernambuco, Minas Gerais and Rio de Janeiro. Regarding COVID-19 deaths, the Federal District, Rio de Janeiro and Mato Grosso had the highest rates, respectively, while the lowest death rates were observed in Minas Gerais, Santa Catarina and Paraná, respectively (Table 1 and Fig. 1). The cities with the highest incidence of COVID-19 cases are Parauapebas (Pará), Boa Vista (Roraima) and Araguaína (Tocantins), all of them located in the North region of the country, while Santa Helena de Goiás (Goiás), Guajará-Mirim (Rodônia) and Rio de Janeiro (Rio de Janeiro), located in Central-West, North and Southeast regions of Brazil, respectively, had the highest rates of COVID-19 deaths.

Table 1

Regional (state) data relative to the impact of COVID-19 on the Brazilian population until November 16th, 2020.
State	Region	Total cases	Cases/100,000 inhabitants	Total deaths	Deaths/100,000 inhabitants
Distrito Federal	CW	220,403	7220.30	3,825	125.31
Goiás	CW	268,997	3780.10	6,060	85.16
Mato Grosso	CW	152,847	4334.58	4,053	114.94
Mato Grosso do Sul	CW	88,111	3136.30	1,693	60.26
Alagoas	NE	92,642	2764.53	2,298	68.57
Bahia	NE	374,721	2509.78	7,967	53.36
Ceará	NE	286,674	3120.13	9,443	102.78
Maranhão	NE	189,705	2666.42	4,187	58.85
Paraíba	NE	139,168	3445.37	3,211	79.49
Pernambuco	NE	171,216	1780.33	8,838	91.9
Piauí	NE	120,466	3671.96	2,541	77.45
Rio Grande do Norte	NE	84,021	2377.39	2,652	75.04
Sergipe	NE	86,146	3714.74	2,260	97.45
Acre	NO	33,295	3722.32	708	79.15
Amapá	NO	169,552	4029.55	4,699	111.68
Amazonas	NO	55,156	6400.29	780	90.51
Pará	NO	262,345	3018.67	6,838	78.68
Rondônia	NO	75,468	4200.93	1,503	83.66
Roraima	NO	60,579	9597.72	706	111.85
Tocantins	NO	78,169	4915.52	1,137	71.5
Espírito Santo	SE	171,237	4213.45	4,030	99.16
Minas Gerais	SE	383,473	1800.96	9,517	44.7
Rio de Janeiro	SE	327,455	1885.59	21,301	122.66
São Paulo	SE	1,169,377	2526.23	40,576	87.66
Paraná	SO	240,783	2090.70	5,728	49.74
Rio Grande do Sul	SO	279,872	2450.08	6,243	54.65
Santa Catarina	SO	297,400	4100.65	3,330	45.92
CW: Central-West; NE: Northeast; NO: North; SE: Southeast; SO: South.

Regional Distribution Of Most Frequent Hla Haplotypes And Alleles

The five most frequent HLA-A ~ B ~ DRB1 haplotypes in Brazil are presented in Table 2. The most frequent haplotype was HLA-A*01 ~ B*08 ~ DRB1*03, with a frequency ranging from 1.2% in Pará, Maranhão and Amapá, states located in the North (Pará, Amapá) and Northeast (Maranhão) of the country, to 3.1% in Rio Grande do Sul and Santa Catarina (both states located in the South region). Rio Grande do Sul and Santa Catarina had a high outlier frequency for haplotypes #1 (HLA-A*01 ~ B*08 ~ DRB1*03) and #3 (HLA-A*03 ~ B*07 ~ DRB1*15), while Santa Catarina and Rio Grande do Norte were high outliers for haplotype #4 (HLA-A*02 ~ B*44 ~ DRB1*04), and Amapá had a low outlier frequency for haplotype #5 (HLA-A*33 ~ B*14 ~ DRB1*01) (Fig. 2).

Considering HLA alleles, frequencies of COVID-19 deaths per state and per cities with at least 50 deaths are presented in Supplementary file 1. Allele frequencies did not deviate from HWE expectations. The most frequent alleles in Brazil were HLA-A*02, HLA-B*35 and HLA-DRB1*13. Boxplots with allele frequencies per state are presented in Supplementary file 2. It is possible to observe high and low outliers, mostly in states from the South of Brazil, such as Santa Catarina and Rio Grande do Sul, or by states from the North of Brazil, such as Amapá and Amazonas.

Table 2

Frequencies (%) of the five most common HLA haplotypes observed in the Brazilian population.
State	Region	#1 HLA-A01 ~ B08 ~DRB1*03	#2 HLA-A29 ~ B44 ~DRB1*07	#3 HLA-A03 ~ B07 ~DRB1*15	#4 HLA-A02 ~ B44 ~DRB1*04	#5 HLA-A33 ~ B14 ~DRB1*01
Distrito Federal	CW	1,97	1,53	0,97	0,83	0,92
Goiás	CW	2,16	1,69	0,81	0,90	0,76
Mato Grosso	CW	2,13	1,38	0,95	0,87	0,80
Mato Grosso do Sul	CW	2,03	1,39	0,97	0,86	0,73
Alagoas	NE	1,71	1,29	0,98	0,95	0,93
Bahia	NE	1,50	1,19	0,77	0,87	0,82
Ceará	NE	1,73	1,55	0,86	0,75	0,85
Maranhão	NE	1,27	1,25	0,75	0,79	0,74
Paraíba	NE	1,53	1,49	1,21	1,01	0,80
Pernambuco	NE	1,39	1,28	0,96	0,82	0,91
Piauí	NE	1,52	1,38	0,90	0,77	0,85
Rio Grande do Norte	NE	1,71	1,42	0,90	1,17	0,62
Sergipe	NE	1,65	1,22	0,61	0,87	0,59
Acre	NO	1,67	1,41	0,81	0,76	0,72
Amazonas	NO	1,30	1,26	0,61	0,65	0,79
Amapá	NO	1,29	1,31	0,63	0,77	0,46
Pará	NO	1,26	1,29	0,68	0,76	0,60
Rondônia	NO	2,05	1,39	1,01	0,84	0,85
Rorâima	NO	1,52	1,25	0,77	0,67	0,80
Tocantins	NO	1,61	1,58	0,87	0,82	0,75
Espírito Santo	SE	2,12	1,39	1,02	0,79	0,80
Minas Gerais	SE	2,11	1,65	0,89	0,96	0,85
Rio de Janeiro	SE	1,91	1,54	1,03	0,93	0,83
São Paulo	SE	2,18	1,46	0,93	0,74	0,83
Paraná	SO	2,69	1,39	1,27	0,75	0,79
Rio Grande do Sul	SO	3,16	1,64	1,48	0,98	0,61
Santa Catarina	SO	3,13	1,72	1,66	1,18	0,70
CW: Central-West; NE: Northeast; NO: North; SE: Southeast; SO: South.

Correlation between HLA haplotype frequency and COVID-19 incidence and mortality

Spearman correlation coefficient was calculated to test if the regional COVID-19 incidence and mortality correlated with any of the five most frequent haplotypes in Brazilian population. Table 3 present the results for the first scenario, where no significant correlation was found between COVID-19 rates and haplotype frequency among the 26 states and the Federal District. COVID-19 rates and correlations with HLA haplotype frequencies among cities with at least 50 deaths are presented in Table 4. In total, 377 cities were included in this analysis, where haplotypes #1 (HLA-A*01 ~ B*08 ~ DRB1*03), #2 (HLA-A*29 ~ B*44 ~ DRB1*07) and #4 (HLA-A*02 ~ B*44 ~ DRB1*04) presented negative correlation between haplotype frequency and rates of COVID-19 deaths (-0.322, -0.193, -0.134, respectively). Finally, in the third scenario, we analyzed cities with at least 50 COVID-19 deaths in the São Paulo state. No significant correlation was observed in the 90 cities included in the analysis (Table 5).

Table 3

Bivariate correlation analysis among regional haplotypes estimated frequency in the population and COVID-19 incidence and mortality in Brazilian states.
N° cases/100,000 inhabitants (until November 16th, 2020)
Haplotypes	Correlation	P
A01 ~ B08 ~ DRB1*03	-0.064	0.751
A29 ~ B44 ~ DRB1*07	-0.06	0.765
A03 ~ B07 ~ DRB1*15	-0.203	0.311
A02 ~ B44 ~ DRB1*04	-0.274	0.167
A33 ~ B14 ~ DRB1*01	-0.103	0.611
N° deaths/100,000 inhabitants (until November 16th, 2020)
Haplotypes	Correlation	P
A01 ~ B08 ~ DRB1*03	-0.153	0.445
A29 ~ B44 ~ DRB1*07	-0.153	0.447
A03 ~ B07 ~ DRB1*15	-0.188	0.348
A02 ~ B44 ~ DRB1*04	-0.345	0.078
A33 ~ B14 ~ DRB1*01	0.194	0.332
Correlation = Spearman Correlation Coefficient (rho).

Table 4

Bivariate correlation analysis among regional haplotypes estimated frequency in the population and COVID-19 incidence and mortality in Brazilian cities with at least 50 deaths.
N° cases/100,000 inhabitants (until October 24th, 2020)
Haplotypes	Correlation	P
A01 ~ B08 ~ DRB1*03	-0.027	0.603
A29 ~ B44 ~ DRB1*07	0.111	0.031
A03 ~ B07 ~ DRB1*15	0.009	0.859
A02 ~ B44 ~ DRB1*04	0.084	0.103
A33 ~ B14 ~ DRB1*01	-0.041	0.425
N° deaths/100,000 inhabitants (until October 24th, 2020)
Haplotypes	Correlation	P
A01 ~ B08 ~ DRB1*03	-0.322	< 0.001*
A29 ~ B44 ~ DRB1*07	-0.193	< 0.001*
A03 ~ B07 ~ DRB1*15	-0.110	0.033
A02 ~ B44 ~ DRB1*04	-0.134	0.009*
A33 ~ B14 ~ DRB1*01	0.015	0.772
Correlation = Spearman Correlation Coefficient (rho). *Remained significant after FDR correction

Table 5

Bivariate correlation analysis among regional haplotypes estimated frequency in the population and COVID-19 incidence and mortality in cities with at least 50 deaths in São Paulo.
N° cases/100,000 inhabitants (until October 24th, 2020)
Haplotypes	Correlation	P
A01 ~ B08 ~ DRB1*03	0.011	0.917
A29 ~ B44 ~ DRB1*07	0.009	0.935
A03 ~ B07 ~ DRB1*15	-0.056	0.603
A02 ~ B44 ~ DRB1*04	0.044	0.68
A33 ~ B14 ~ DRB1*01	0.011	0.918
N° deaths/100,000 inhabitants (until October 24th, 2020)
Haplotypes	Correlation	P
A01 ~ B08 ~ DRB1*03	-0.184	0.083
A29 ~ B44 ~ DRB1*07	-0.149	0.16
A03 ~ B07 ~ DRB1*15	0.118	0.268
A02 ~ B44 ~ DRB1*04	0.074	0.488
A33 ~ B14 ~ DRB1*01	-0.025	0.817
Correlation = Spearman Correlation Coefficient (rho).

Correlation between HLA allele frequency and COVID-19 incidence and mortality

Considering HLA allele frequency (at low resolution level or allelic group), there was no significant correlation with COVID-19 rates considering the 26 states and the Federal District. When the analysis included only Brazilian cities with at least 50 COVID-19 deaths, considering both positive and negative correlations, we found six HLA-A, four HLA-B and two HLA-DRB1 alleles significantly correlated with COVID-19 incidence, and 15 HLA-A, 20 HLA-B and seven HLA-DRB1 alleles significantly correlated with COVID-19 mortality. Finally, considering cities from São Paulo state with at least 50 COVID-19 deaths, we found three HLA-A, seven HLA-B and four HLA-DRB1 alleles that were significantly associated with COVID-19 incidence, while five HLA-A alleles were associated with COVID-19 mortality. Correlation values for all alleles are presented in Supplementary file 3.

In the present study, we investigated the potential correlation between HLA allele and haplotype frequencies and the different regional distribution of COVID-19 incidence and mortality in Brazil. We included HLA data at low resolution of 4,148,713 donors from REDOME and COVID-19 data registered until November 16th, 2020. No correlation between haplotype frequencies and COVID-19 rates was found when we analyzed data from the 26 states and Federal District, or when we analyzed data from the 90 cities with at least 50 deaths registered in São Paulo state (Tables 3 and 5). When analyzing data from cities with at least 50 deaths registered in the entire country (377), a significant negative correlation (suggestive of protection) was observed between COVID-19 mortality and haplotypes #1 (HLA-A*01 ~ B*08 ~ DRB1*03), #2 (HLA-A*29 ~ B*44 ~ DRB1*07) and #4 (HLA-A*02 ~ B*44 ~ DRB1*04), with haplotype #1 showing a larger effect. Recently, Pisanti et al. (2020) found significant correlation between the two most frequent haplotypes in the Italian population with both COVID-19 incidence and mortality [20]. In their study, HLA data at high resolution of 104,135 donors from Italian Bone Marrow Donors Registry (IBMDR) and COVID-19 mortality and incidence registered until May 24th, 2020 were included. The haplotype HLA-A*01:01g ~ B*08:01g ~ C*07:01g ~ DRB1*03:01g showed a positive correlation (suggestive of susceptibility) and HLA-A*02.01g ~ B*18.01g ~ C*07.01g ~ DRB1*11.04g showed negative correlation (suggestive of protection) with both COVID-19 incidence and mortality [20]. At low resolution, HLA-A*01 ~ B*08 ~ DRB1*03 is the most frequent HLA haplotype in both Brazil and Italy. However, while this haplotype has been positively correlated with COVID-19 incidence and mortality in Italy, suggesting increased susceptibility, we found, depending on the analyzed dataset, no correlation or a negative correlation with mortality in Brazil, which might suggest protection against COVID-19. One possibility is that HLA versus environment interaction is different in Italy and Brazil resulting in opposite associations between HLA variation and COVID-19 risk, in a context-dependent manner. Another possibility is that the positive association in the Italian population reflects regional genetic structure, as both COVID-19 rates [20] and genetic population structure in Italy [26, 27] show a North-South gradient. Another important point to highlight is that none of the studies took into consideration the SARS-CoV-2 variants, and it becomes increasingly important to consider this point in future studies.

In general, we observed that the four most common HLA haplotypes had higher frequencies in the South region and lower frequencies in the North of Brazil (Table 2 and Fig. 2). For example, the most frequent haplotype (HLA-A*-01 ~ B*08 ~ DRB1*03) ranged from 1.26% in Pará (North) to 3.16% in Rio Grande do Sul (South) (Fig. 2). These regional differences reflect the different contributions of Native American, European, and African populations across the country after a long history of colonization and immigration. From a broad perspective, the South region has more European influence; the Northeast region is characterized by more African influence, while in the North region the Native American influence is comparatively more pronounced [28].

In addition to the study that evaluated HLA haplotypes in the Italian population to the differential regional incidence of COVID-19, two bioinformatic studies predicted the binding affinity between HLA alleles and SARS-CoV-2 antigenic peptides [19, 29]. For HLA-A, it has been predicted that -A*02 had the best binding affinity, resulting in a more favorable immune response, while -A*25 had the weakest binding affinity, resulting in a less favorable response. Recently, Shkurnikov et al. (2021) [30] performed HLA class 1 genotyping through next-generation sequencing in deceased patients with COVID-19. In their analysis, HLA-A*01:01 allele (especially in homozygosis) was associated with high risk to present SARS-CoV-2, while HLA-A*02:01 and HLA-A*03:01 mainly contributed to low risk. In our study, considering the scenario #2, for which we have more statistical power, we found significant negative correlations for HLA-A*01 (-0.276), HLA-A*02 (-0.175), HLA-A*03 (-0.302) and HLA-A*25 (-0.164) (Supplementary file 3). As for HLA-B alleles, both bioinformatics studies suggested that alleles -B*46:01 and -B*15:03 behaved as low and high affinity ligands against COVID-19 peptides, and Barquera et al. (2000) further characterized alleles -B*44:06, -B*51:07, -B*08:03, and B*52:01 as weak binders, and -B*35:10, as well as other molecules of the -B*15 lineage, as strong binders [29]. We found a significant negative correlation for -B*35 (-0.160) with COVID-19 mortality, but, unexpectedly, a significant positive correlation between -B*15 and COVID-19 mortality (0.280) (Supplementary file 3). Finally, for HLA-DRB1, Barquera et al. (2020) suggested that the alleles -DRB1*01:01 and -DRB1*10:01 were the strongest binders, while - DRB1*03:02 and -DRB1*03:03 were the weakest binders [29]. In line with these results, we found a significant negative correlation for allele -DRB1*01 (-0.246) and mortality (Supplementary file 3). Overall, our analysis provided mixed support for the inferences made by bioinformatics models (Supplementary file 3). This may be related to the complexity of the immune response against SARS-CoV-2, which probably depends on many genetic and environmental factors. For example, variants in other genes related to the immune response, like genes that code for inflammatory factors and interleukin 6 (IL-6), and genes related to SARS-CoV-2 entry into the cell may also influence the disease course. Recent studies have already correlated and reviewed the role of variants in IL6 [31], ACE2 [32], and TMPRSS2 [33] genes in COVID-19 disease.

Together with genetic and immune system variation, environmental and social disparities among Brazilian regions may contribute to the differential burden of COVID-19, affecting disproportionally individuals carrying genetic factors of susceptibility and/or the most vulnerable people regarding social assistance. For instance, at the time we were collecting our data, Rio de Janeiro and Minas Gerais, both in the Southeast region, had about 1800 cases/100,000 inhabitants. However, the mortality rate was almost 3 times higher in Rio de Janeiro (122.66/100,000 inhabitants) than in Minas Gerais (44.7/100,000 inhabitants) (Table 1). Besides Minas Gerais, only Paraná and Santa Catarina, both in the South region, maintained mortality rates below 50/100,000 inhabitants, while higher mortality rates were found in states located in the Central-West and North regions (Table 1). These numbers should be taken with caution. The number of reported cases depends on the number and type of diagnostic tests performed by each city and state, and with limited testing it is unlikely that asymptomatic subjects would have been diagnosed for SARS-CoV-2 infection. The number of COVID-19 related deaths could be more reliable since, during the period analyzed in this study, the Brazilian health system was able to provide medical assistance for most people showing COVID-19 symptoms. Our study included only confirmed deaths by COVID-19 in our analysis, however, when considering deaths due to SARS reported in DATASUS (Department of Informatics of the Unified Healthcare System of Brazil), it was expected an average underreporting of 40% [34]. Therefore, it is possible that the number of cases have been underestimated in our analysis. The mortality rate may also vary according to the coverage of local health systems, number of hospital beds available, and measures of social isolation, for example. It is important to highlight that in Brazil there was no unified policy regarding COVID-19 protocols, resulting in huge differences in how different state governors and city mayors managed COVID-19 locally. Distinguishing among genetic, social and environmental variability is essential for building an efficient way to prevent, control and understand disease outbreaks [35, 36]. Our study focused on genetic variation of the HLA system and is an important initial step in the understanding of COVID-19 dispersion and behavior in Brazil.

An important limitation of this study is that we used HLA data from bone marrow donors instead of directly genotyping individuals affected by COVID-19. However, this approach has some advantages. Bone marrow donor registries usually include very large sample sizes and a wide geographic coverage, as illustrated by the REDOME registry. In addition, because several countries maintain such large banks, statistically significant associations could reveal regions or populations in higher genetic risk for COVID-19, thus representing an additional tool for health policymakers in the fight against COVID-19.

In summary, we investigated if there was a correlation between HLA allele and haplotype frequencies and the different regional distribution of COVID-19 incidence and mortality in the country. No correlation between HLA haplotypes and COVID-19 rates was found when we analyzed data from the 26 states and Federal District, or when we analyzed data from the 90 cities with at least 50 deaths registered in São Paulo (Tables 3 and 5). However, when analyzing data from cities with at least 50 deaths registered in the entire country (n = 377), we found a significant negative correlation (suggestive of protection) between COVID-19 mortality and haplotypes #1 (HLA-A*01 ~ B*08 ~ DRB1*03), #2 (HLA-A*29 ~ B*44 ~ DRB1*07) and #4 (HLA-A*02 ~ B*44 ~ DRB1*04), with haplotype #1 showing a larger effect.

COVID-19

Coronavirus Disease 2019

DATASUS

Department of Informatics of the Unified Healthcare System of Brazil

HWE

Hardy-Weinberg Equilibrium

HLA

Human Leukocyte Antigen

IBMDR

Italian Bone Marrow Donors Registry

MHC

Main Histocompatibility Complex

REDOME

Registro de Doadores de Medula Óssea

TCR

T Cell Receptors

WHO

World Health Organization

Ethics approval and consent to participate

This study was approved by the Ethics Committee in Research of Hospital de Clínicas de Porto Alegre, Brazil (CAAE 34701720300005327, GPPG 2020-0361), and all methods were carried out in accordance with local guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

COVID-19 data used in this study are freely available from the source cited. HLA data are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundo de Incentivo a Pesquisa do Hospital de Clínicas de Porto Alegre (FIPE-HCPA), number 2020-0361.

Authors' contributions

JAB participated on study design, obtained HLA data, analyzed and interpreted HLA and COVID-19 data and was a major contributor in writing the manuscript; FSLV participated on study design, analyzed and interpreted HLA and COVID-19 data and participated in writing and reviewing the manuscript; NJRF was a major contributor on statistical analysis and participated in manuscript writing and revision; LS was responsible for graph and map design and contributed to data interpretation; MB participated on study design and statistical analysis; MZO participated on COVID-19 data collection and map design; TFA participated on COVID-19 data collection and writing and reviewing the manuscript; LCMSP participated on study design and availability of HLA data; JABC participated on study design, writing and reviewing the manuscript; LS-F participated in writing and reviewing the manuscript; PA-P participated in writing and reviewing the manuscript, and CR was responsible for study design and approval, participated in analysis and interpretation of HLA and COVID-19 data, and was a major contributor in writing and reviewing the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We would like to thank FIOCRUZ and REDOME for maintenance and availability of COVID-19 and HLA data, respectively.

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HLA Haplotypes and Differential Regional Incidence of COVID-19 in Brazil: A Population Study Based in a Large Bone Marrow Donors Bank Dataset

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Abstract

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Background

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Statistical analysis

Results

Geographical distribution of COVID-19 epidemic in Brazil

Discussion

Conclusions

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