Introduction

Ancestry Informative Markers (AIMs) are polymorphisms that present an allele frequency differential of > 30% between geographically distinct populations1,2. Investigating this ancestral genetic profile is relevant, as it can assist in determining the interethnic mixture, detecting population substructures, and providing insights into population history, migrations, and socio-cultural interactions3,4.

Although studies have evaluated these ancestral genetic profiles in mixed populations, such as the Brazilian population5,6,7,8,9, only a few have described the ancestral genetics of Brazilian Amerindian populations5. Indigenous ethnic groups are still distributed throughout Brazil. According to the Brazilian Institute of Geography and Statistics (IBGE)10, 1,693,535 people have declared themselves indigenous, accounting for 0.83% of the total Brazilian population. The country’s northern region has the largest contingent of indigenous people, with 753,357 individuals (44.4%), while the central-western region has the third-largest population, with 199,912 individuals (11.8%).

In the Brazilian states of Pará (PA) and Amapá (AP) (northern region), some Indigenous people live in isolated villages that are difficult to access due to local geography and are far from urban centers, such as Tiriyó (PA) and Waiampi (AP), located in the Amazon. In addition, in the state of Mato Grosso do Sul (MS) (central-west region), more than 100 thousand indigenous people are present. However, the state contains Brazil’s largest peri-urban indigenous area, located in the urban region of Dourados, where the villages of Jaguapiru and Bororó are located10.

Considering the diverse ethnic composition of the Brazilian population, establishing allelic frequencies and determining the ancestral genetic profile in a representative manner among the indigenous groups present in Brazil is necessary to understand their genetic diversity better and support studies that associate the ancestral genetic profile with biomedical correlations. Therefore, this study was conducted using AIMs to determine the contributions of three ancestral populations: African (AFR), Amerindian (AME), and European (EUR), to the genetic composition of Brazilian indigenous people from four villages in two macro-regions of Brazil: North (Tiriyó – PA and Waiampi—AP) and Central-West (Jaguapiru and Bororó – MS).

Methods

Study population

This study focused on the genetic profiles of indigenous populations across four villages, representing two key macro-regions in Brazil: the northern (Tiriyó and Waiampi villages) and central-western (Jaguapiru and Bororó villages) regions. These villages were selected based on their unique geographical, linguistic, and sociocultural attributes, offering a comprehensive framework to explore the diverse ancestral lineages of the Brazilian indigenous population.

The Waiampi predominantly comprise the Tupi-Guarani ethnic groups. This village spans the Brazil-French Guiana border. Dense tropical forests, rugged terrain, and small streams with numerous waterfalls characterize this area. The village is in the Amapá region of Brazil, nestled between the Jari, Oiapoque, and Araguari river basins11,12.

The Tiriyó village is composed of several ethnic subgroups, including the Wü tarëno, Txukuyana, Ewarhuyana, and Akuriyó. The Tiriyó population inhabits the Tumucumaque indigenous Land, which straddles the border between Brazil and Suriname. In Brazil, this area extends from the northern region of Pará to the northwest of Amapá, intersecting two major river systems: the Paru de Oeste and Merapi, as well as the Paru de Leste and Citaré11,13.

The Jaguapiru and Bororó villages in Dourados, Mato Grosso do Sul, comprise Brazil’s largest peri-urban Indigenous area. These communities predominantly comprise the Guarani-Kaiowá, Guarani-Nhandeva, and Terena ethnic groups11,14. The region’s proximity to the borders of Bolivia and Paraguay contributes to its cultural and historical diversity (Fig. 1).

Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
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Geographic position of the Brazilian Indigenous areas Tiriyó (Pará), Waiampi (Amapá), and Jaguapiru and Bororó (Mato Grosso do Sul) (A). Sample processing and analysis flowchart (B). Image created by PhoXo v.8.4 and Servier Medical Art CC 4.0.

Sample size

Sample size calculations were based on indigenous population estimates for each region: 753,000 in the northern region and 118,000 in the central-western region. A representative sample of 0.03% was selected from each region, totaling 302 participants across the four villages. Participants from Tiriyó village were self-declared as ethnic Wü Tarëno; Waiampi village of Waiãpi; Jaguapiru and Bororó ethnic groups Guarani-Kaiowá, Guarani-Nhandeva. Ethnicity data and family composition were collected during sample collection, excluding samples from genetically related individuals.

Ethical approval

This study was approved by the Research Ethics Committee of the Federal University of Grande Dourados (CAAE: 38,981,720.5.1001.5160; no. 4.584.624) and by the Gonçalo Moniz Research Center—FIOCRUZ-BA and CONEP (no. 234/2010). The participants provided their written informed consent to participate in this study, which was accomplished through an interview that presented the research objectives and addressed the volunteers’ questions. All those who agreed to participate received the free and informed consent form (TCLE) to sign their consent. All experiments were conducted according to the relevant guidelines and regulations, following the procedures the Committee on Human Research set forth.

Samples

Peripheral blood (4 mL) was collected in a tube containing EDTA anticoagulant (Vacuette) to obtain peripheral blood mononuclear cells (PBMCs). Cell separation was performed using the density gradient technique with Histopaque ® –1077 (SIGMA, UK). Genomic DNA was extracted from PBMCs using the PureLink™ Genomic DNA Mini Kit (Invitrogen ®; CA, USA), following the manufacturer’s recommendations.

AIMs and genotyping

Ten AIMS, including one insertion-deletion (INDEL), three Alu insertions, and six single nucleotide polymorphisms (SNPs), were analyzed (Table 1). The INDEL: AT3 I∕D primer sequences (F = 5’-CCACAGGTGTAACATTGTGT-3’ and R = 5’-GAGATAGTGTGATCTGAGGC-3’); Alu insertions: APO (F = 5’-AAGTGCTGTAGGCCATTTAGATTAG-3’ and R = 5’-AGTCTTCGATGACAGCGTATACAGA-3’); PV92 (F = 5’-AACTGGGAAAATTTGAAGAGAAAGT-3’ and R = 5’-TGAGTTCTCAACTCCTGTGTGTTAG-3’); and Sb19 (F = 5’-TCTAGCCCCAGATTTATGGTAACTG-3’ and R = 5’-AAGCACAATTGGTTATTTTCTGAC-3’) were performed by PCR and the products analyzed in 2% agarose gel stained with ethidium bromide or 6% polyacrylamide stained with silver nitrate.

Table 1 Loci, SNP ID (dbSNP—rs) from the database, alleles evaluated, and frequent alleles in the parental population.
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The SNP markers used (FYnull, CKMM, LPL, GC-1F, GC-1S, and CYP3A4) were identified using Taq-Man® Assays – SNP genotyping probes (Thermo Fisher, USA), following the manufacturer’s protocol and recommendations. PCR was performed using QuantStudio 3™ v1.5.2 software (Thermo Fisher Scientific, USA). These alleles were designated as allele1* and allele2*. In INDEL and Alu, the insertion of the 1* allele was marked by the presence of the insertion. This nucleotide abolished the restriction site for the 1* allele in the SNPs.

Parental population

The ancestral genetic reference population, composed of 399 individuals, is divided into Africans (134 Nigerians), Europeans (104 Germans and Spaniards), and Amerindians (242 Native Americans). Dr. Mark Shriver (Department of Anthropology—University of Pennsylvania) kindly provided these genotypic data to Dr. Kiyoko Abe Sandes (Laboratory of Immunology and Molecular Biology—Federal University of Bahia).

Statistical analysis

The GENEPOP v. 4.7 program was used to determine allele frequencies and evaluate Hardy–Weinberg equilibrium (EHW) and the Fixation Index (FST) 4.7 (RAYMOND; ROUSSET, 1995; https://genepop.curtin.edu.au/). To identify the parental ancestral contribution of each individual, Structure 2.2 was utilized to assess the ancestral contribution of each individual (study population) based on the genotypes of the Amerindian, European, and African ancestral populations. The software also estimated admixture using a tri-hybrid model (https://web.stanford.edu/group/pritchardlab/home.html). The Use Pop Info selection FLAG option enabled the precise determination of the groupings that included individuals belonging to the populations considered parental. In the Ancestry Model option, Use Population Information was selected. Analyses were performed with K = 3 as the predefined parameter for the number of populations assumed to be parental, with 30,000 interactions for the burn-in period and 100,000 additional interactions to obtain parameter estimates.

Results

The results highlight notable allele frequency variations among the populations studied. For the AT3 ID marker, the A1 allele is widespread in AFR (88.1%) but much lower in AME (15.6%) and EUR (26.1%), with Tiriyó showing the highest A1 frequency (31.7%) among Indigenous groups. In LPL, A1 predominates in AFR (97.4%) but appears less frequently in Indigenous groups, with Jaguapiru-Bororó and Tiriyó around 30%. The GC marker’s 1-F variant is most common in AFR (85.1%) but is less frequent in Indigenous populations, especially in WAI. For CYP3A4, A1 is highly prevalent in EUR and AME and is frequent in JAG/BOR (98.7%). This marker was not analyzed in Tiriyó and Waiampi due to sample availability. The PV92 marker shows that the A1 allele is frequent in AME and has high frequencies in all indigenous groups, while allele 2 is common in AFR and EUR. This indicates that PV92 is a good ancestral Amerindian genomic marker. In APO, A1 is fixed at 100% in WAI, suggesting a unique allele distribution in this group. Finally, the Fy-Null (A1) is highly frequent in EUR and AME (99.6%) and Indigenous groups (> 97%), whereas allele 2 (A2) is exclusively frequent in AFR. These results underscore the diversity in allele distributions among Indigenous groups, indicating varied ancestral influences. The allele frequencies of alleles 1 (reference allele) and 2 of the 10 markers are listed in Table 2.

Table 2 Allele frequencies for AIMs in the study population and Test for Hardy–Weinberg Equilibrium (HW) p-value.
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The markers used were effective in separating the parental groups. The ancestral genetic contributions (%) for Tiriyó Indigenous people are AME: 74.1, EUR: 14.7, AFR: 11.2; Waiampi: AME: 49.3, EUR: 35.7, AFR: 15.0; and Jaguapiru and Bororó: AME: 47.1, EUR: 42.8, AFR: 10.2 (Fig. 2).

Fig. 2
Fig. 2The alternative text for this image may have been generated using AI.
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Ancestral profile for ancestral populations Indigenous to the Waiampi, Tiriyó, Jaguapiru, and Bororó village. Percentage of ancestral profile by parental populations and Indigenous villages (2. A); Grouping by parental population: African – AFR (blue), Amerindian – AME (red), European – EUR (green), and Indigenous (yellow) (2. B).

The genetic ancestry distribution among indigenous groups was assessed. The Tiriyó showed more remarkable population similarity with the Amerindian parental population. Jaguapiru and Bororó cluster distinctly at the top, indicating a unique genetic composition compared to the others. The concentration of blue points suggests a relatively homogeneous genetic profile within the Jaguapiru and Bororó populations, with limited overlap with other groups. The Waiampi population has a unique genetic profile, minimal mixing with different groups. The Tiriyó shows a distinct genetic makeup as well. Like the Waiampi, the green points for the Tiriyó are relatively isolated, signifying limited gene flow with the other populations. The predominantly yellow Amerindian component represents a shared Amerindian genetic component among the populations. This suggests some degree of gene continuity or shared ancestry between the groups, though each group still maintains unique characteristics that position them toward specific edges of the triangle (Fig. 3).

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
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Amerindian parental (yellow) population grouped with the Tiriyó (green), Jaguapiru-Bororó (blue), and Waiampi (red) Indigenous village.

Based on the calculation of the Hardy–Weinberg equilibrium (Table 2), the loci that presented statistical significance (p < 0.05) Jaguapiru and Bororó village, CYP3A4: excess of homozygotes and GC: excess of heterozygotes; For Tiriyó village, AT3, LPL and PV92: excess of homozygotes; and for the Waiampi village, APO: excess of homozygotes.

To assess the genetic distance of populations through FST calculations, the values between the Indigenous peoples and the parental populations, the IND and AFR groups, showed more significant genetic differentiation (FST = 0.66). IND and AME presented the lowest values (FST = 0.36) compared with the other populations. However, the Sb19.3 and APO markers had values > 0.25 between the IND and EUR groups (FST = 0.50) (Fig. 4). The FST values used as references were described by Wright (1978)15: 0.0 to 0.05 indicates low, 0.05 to 0.15 indicates moderate, 0.15 to 0.25 indicates high, and greater than 0.25 shows very high genetic differentiation.

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
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Values to FST (Fixation Index) between Indigenous villages and parental population (Amerindian, European and African) and between Indigenous villages (Waiampi, Tiriyó and Jaguapiru-Bororó). Among the populations analyzed, the greatest difference between the analyzed pollutions was African and Indigenous (0.66), and the lowest value for Indigenous people from the villages of Waiampi and Tiriyó (0.11).

When comparing FST values between Indigenous villages, Tiriyó and Waiampi showed moderate genetic differentiation (FST = 0.11). The greatest difference was observed between Jaguapiru-Bororó and Waiampi (FST = 0.39), followed by Jaguapiru-Bororó and Tiriyó (FST = 0.31) (Fig. 4).

Discussion

We evaluated a set of ten AIMs in four indigenous villages (IND) distributed across Brazil’s northern and central-western regions through a panel of AIMs described in other studies1,2,5,6,18. The data revealed a high frequency of alleles common in AME observed in the IND group, with markedly lower frequencies of alleles characteristic of AFR populations. AIMs associated with EUR populations were also detected among the IND individuals, demonstrating that the selected AIMs effectively differentiate ancestral populations. While these markers clearly distinguished Africans and indigenous groups, they were less effective at distinguishing between Europeans and indigenous peoples.

The AME ancestry percentages were notably high in Tiriyó (74.1%), followed by Waiampi (49.3%), and Jaguapiru and Bororó (47.1%). These results reinforce the idea that native communities seem to preserve their ancestral genetic heritage. EUR contributions varied among the villages, being more prominent in Waiampi (> 30%) and Jaguapiru/Bororó (> 40%), while Tiriyó exhibited a notably lower EUR contribution (14.7%). This data can be justified, as Europeans and Amerindians are derived from the same migratory chain. In contrast, Amerindians share a common ancestor with human groups derived from Siberia16,17, making it challenging to differentiate alleles into groups with genetic similarities5,18.

Analysis of individual ancestry distribution showed that Tiriyó individuals had more homogeneous profiles resembling Native Americans, while Waiampi and Jaguapiru/Bororó displayed greater heterogeneity. The HW equilibrium analysis revealed deviations in some AIMs. For instance, CYP3A4 (Jaguapiru and Bororó) and PV92 (Tiriyó) showed an excess of homozygotes, likely reflecting the high frequency of these markers in Amerindian populations. Conversely, AT3 and LPL, frequent in AFR populations, exhibited a homozygote excess among Tiriyó. GC presented an excess of heterozygotes in Jaguapiru/Bororó, which the limited sample size may influence. Additionally, APO showed an excess of homozygotes in Waiampi; despite being a common EUR marker, it is also prevalent in AME populations. Although an ancestral marker is expected to present a high frequency, technical evaluations were performed to identify null alleles. Deviation from Hardy–Weinberg equilibrium due to genotyping errors cannot be ruled out since individuals may have some alteration in the primer binding region, and the target region will not amplify (allele drop-out)19. Or, if the proportion of primers with some alteration in their sequence is greater than expected (around 2%), amplification of some of the regions of interest may also be impaired by poor primer pairings in the region of interest.

The FST values provide insights into population structure20. Indigenous villages exhibit high genetic differentiation compared to parental populations (FST > 0.25), with the most significant differentiation observed between the AFR and IND populations (FST = 0.66). Differentiation was noted between the IND and AME populations (FST = 0.36), which is significant given that the parental AME data encompass genetically diverse Native American groups. Tiriyó/Waiampi showed moderate genetic differentiation (FST = 0.11), corroborating previous findings of interpopulation differences21 and suggesting limited admixture between these villages, indicating low mixing levels between Indigenous and non-Indigenous peoples. Higher FST values were observed between Waiampi/Jaguapiru-Bororó (FST = 0.39) and Tiriyó/Jaguapiru-Bororó (FST = 0.31), reflecting the influence of migratory flows on non-isolated populations22.

These findings underscore the impact of isolation on genetic homogeneity. Isolated villages like Tiriyó and Waiampi tend to maintain ethnic homogeneity through intra-group relationships. In contrast, villages like Jaguapiru and Bororó, located in peri-urban areas, have experienced greater admixture due to interactions with non-Indigenous populations. Such mixing is evident in the genomic ancestry of these individuals and aligns with historical migration patterns in the central-west region22,23. Although studies focusing on specific ethnic subgroups may not fully capture the ancestral diversity of broader populations, they are crucial for understanding the genomic heritage of Indigenous communities5,23. Our findings highlight the predominance of AME ancestry in these groups and the utility of AIMs for differentiating ancestral components, particularly between AME and EUR populations. The higher number of frequent AIMs between the AME and EUR groups may indicate greater informational value between these two populations.

It is essential to highlight theories about genetic diversity, such as the neutral theory, which proposes that genetic variations arise through mutations and genetic drift without the influence of natural selection24. This concept generates debate, with several theories trying to refute it25. Data report that genetic diversity tests observed in practice consistently reject the neutral theory since it would not be able to explain the maintenance of much of the genetic diversity observed in natural populations nor the patterns of adaptive evolution26. More complex evolutionary models are currently accepted, integrating natural selection and other factors, such as genetic drift, migration, and polymorphism equilibrium, to explain genetic variation in populations26. Regarding genetic ancestry, it is reasonable to assume that variations result from a broad spectrum of manifestations of populations originating from common ancestors. Genomic ancestry is identified through genetic markers considered neutral, such as mitochondrial DNA and certain regions of DNA. However, more recent studies suggest that Short Tandem Repeat and other molecular markers may have active biological functions27,28, questioning the idea of neutrality and suggesting that they may have originated as adaptations to specific geographic conditions. This paradox still creates gaps, and studies must advance to clarify this duality. Understanding the mechanisms involving genetic diversity and ancestry, including the interaction between mutations, genetic drift, and natural selection, is fundamental to resolving these questions. In addition, factors such as environmental changes and migrations must also be considered, as they may have influenced the genetic evolution of populations in complex ways25. Advances in research may provide a clearer view of the role of genetic markers and their possible adaptations, improving our understanding of humans’ evolutionary history.

Understanding the ancestral genomic profile of a homogeneous group, such as the one presented here, has social implications for the implementation of public policies aimed at these ethnicities and clinics, as it provides support for new studies that aim to evaluate or make associations between ancestry, ethnicity, and racial profiles of the Indigenous population in biomedical research. Furthermore, future studies should use the recorded allele frequencies as parental frequencies of Brazilian Amerindians from the northern and central-western regions.

This research can contribute to a deeper understanding of the unique genetic heritage and historical continuity of indigenous communities within Brazil. Additionally, these insights have broader implications, offering valuable foundations for studies across diverse scientific domains, including anthropology, public health, and population genetics, which can further illuminate the contributions of Indigenous populations to Brazilian society.