Correction
23 Mar 2015: The PLOS ONE Staff (2015) Correction: Exploring the Distribution of Genetic Markers of Pharmacogenomics Relevance in Brazilian and Mexican Populations. PLOS ONE 10(3): e0122161. https://doi.org/10.1371/journal.pone.0122161 View correction
Figures
Abstract
Studies of pharmacogenomics-related traits are increasingly being performed to identify loci that affect either drug response or susceptibility to adverse drug reactions. However, the effect of the polymorphisms can differ in magnitude or be absent depending on the population being assessed. We used the Affymetrix Drug Metabolizing Enzymes and Transporters (DMET) Plus array to characterize the distribution of polymorphisms of pharmacogenetics and pharmacogenomics (PGx) relevance in two samples from the most populous Latin American countries, Brazil and Mexico. The sample from Brazil included 268 individuals from the southeastern state of Rio de Janeiro, and was stratified into census categories. The sample from Mexico comprised 45 Native American Zapotecas and 224 self-identified Mestizo individuals from 5 states located in geographically distant regions in Mexico. We evaluated the admixture proportions in the Brazilian and Mexican samples using a panel of Ancestry Informative Markers extracted from the DMET array, which was validated with genome-wide data. A substantial variation in ancestral proportions across census categories in Brazil, and geographic regions in Mexico was identified. We evaluated the extent of genetic differentiation (measured as FST values) of the genetic markers of the DMET Plus array between the relevant parental populations. Although the average levels of genetic differentiation are low, there is a long tail of markers showing large frequency differences, including markers located in genes belonging to the Cytochrome P450, Solute Carrier (SLC) and UDP-glucuronyltransferase (UGT) families as well as other genes of PGx relevance such as ABCC8, ADH1A, CHST3, PON1, PPARD, PPARG, and VKORC1. We show how differences in admixture history may have an important impact in the distribution of allele and genotype frequencies at the population level.
Citation: Bonifaz-Peña V, Contreras AV, Struchiner CJ, Roela RA, Furuya-Mazzotti TK, Chammas R, et al. (2014) Exploring the Distribution of Genetic Markers of Pharmacogenomics Relevance in Brazilian and Mexican Populations. PLoS ONE 9(11): e112640. https://doi.org/10.1371/journal.pone.0112640
Editor: Yan Gong, University of Florida, United States of America
Received: July 8, 2014; Accepted: October 9, 2014; Published: November 24, 2014
Copyright: © 2014 Bonifaz-Peña et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.
Funding: FUNDERS: (1) Funding supported by Conselho Nacional do Desenvolvimento Científico (CNPq) CNPq no. 304063/2011-8, http://www.cnpq.br (GS-K). (2) Funding supported by Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) FAPERJ no. E-26/102796/2012, URL: http://www.faperj.br; FAPERJ no. E-26/110683/2012, URL: http://www.faperj.br (GS-K). (3) Funding supported by Financiadora de Estudos e Projetos (FINEP) FINEP no. 01.08.0123.00, URL: http://www.finep.gov.br (GS-K). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SPONSORSHIPS: (1) The Affymetrix DMETplus chips were provided by the Pharmacogenetics for every nation initiative (PGENI), a USA-based global health non-profit organization. PGENI is supported by individual donors and does not solicit funding from health care-oriented companies. (2) The Affymetrix DMET Plus array data for the European, African and East Asian HapMap samples were provided by Affymetrix Inc.
Competing interests: The authors have read the journal's policy and have the following competing interests to declare: Co-author Roger Chammas has a potential conflict of interest as a member of the academic editorial board of the journal. Co-author Laura Uribe-Figueroa is an employee of the commercial company Affymetrix Inc. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.
Introduction
The development of high-throughput and rapid genotyping technologies in parallel with the completion of the Human Genome Project led to a wealth of information on human genetic diversity and its impact on disease susceptibility and drug response. Pharmacogenetics and pharmacogenomics research explores the contribution of genetic individuality to variability in drug response, to provide relevant information for personalized drug therapy. A common finding in PGx studies is that the frequency of genetic variants associated with drug response differs across and within populations. One distinct example is the VKORC1 3673G>A transition, a major determinant of warfarin dose requirement for appropriate anticoagulation [1]. The frequency of the 3673A allele, associated with low warfarin dose, ranges from <10% in sub-Saharan Africans to>90% in Southeast Asian populations [2], and from 10 to 40% among the predominant population strata of Brazil [3]. This variability has a major impact on the usefulness of the VKORC1 3673G>A SNP to predict warfarin dosage across populations worldwide [1], [4], [5]. A corollary to human diversity is that differences in frequency of clinically-relevant genetic variants might be used advantageously for PGx-informed drug therapy. Either to prevent adverse effects (e.g. carbamazepine-induced Stevens-Johnson syndrome in Southeast Asian population) to maximize clinical benefit or to rescue withdrawn medicines for use in populations which might not be adversely affected. The Pharmacogenetics for Every Nation Initiative (PGENI) addresses these goals, through the creation of an international consortium to assess the prevalence of genetic variants related to pharmacological response in an ethnically diverse set of samples, coming from different participating countries [6]. These include Brazil and Mexico, the two most populous countries in Latin America, with 194 million [7] and 112 million [8] people, respectively, which are the focus of the present study. Admixture of Native American, European and sub-Saharan African ancestral roots is extensive in both countries, but their population structure is quite different: European and sub-Saharan ancestry predominate largely over Native American ancestry in Brazilians [9], [10] whereas in Mexico the main ancestral contribution is European and Native American, and the African contribution is relatively small [11], [12], [13]. Together, Brazilian and Mexican populations provide an interesting illustration of the heterogeneity of Latin American peoples, regarding the kaleidoscopic combinations of individual proportions of Native American, European and sub-Saharan African ancestries.
In this study, we used the Affymetrix Drug Metabolizing Enzymes and Transporters (DMET) Plus array to characterize the distribution of PGx polymorphisms in a combined cohort of Brazilians and Mexicans. The DMET array interrogates variants in 231 genes involved in drug pharmacokinetics [14]. We evaluated the admixture proportions in the Brazilian and Mexican samples using a panel of Ancestry Informative Markers extracted from the DMET chip, which was validated with genome-wide data. We discuss the implications of the history of admixture in Brazil and Mexico for the distribution of genetic variants of PGx relevance.
Materials and Methods
Study participants and sample collection
The Brazilian cohort consisted of 268 healthy, unrelated adults recruited in the city of Rio de Janeiro in the Southeast region. The study protocol was approved by the Ethics Committee of the Instituto Nacional de Câncer, Rio de Janeiro. Each individual signed an informed consent to participate, and was asked to self-identify according to the “race/color” classification scheme adopted by the official Brazilian Census [7]. The cohort comprised 89 individuals in the branco (White), 90 individuals in the pardo (Brown) and 89 individuals in the preto (Black) Census categories. These “Color” categories will be capitalized to call attention to their special meaning in the context of the Brazilian census classification.
The Mexican cohort included 45 Native American Zapotecas (NAT) individuals and 224 self-identified Mestizo individuals (MEX) from 5 states located in geographically distant regions in Mexico. In our study, the Native American parental frequencies were estimated based on a Zapoteca sample recruited in the State of Oaxaca. Previous research has indicated that Zapotecas were the most useful population for building an admixture mapping map for Latino populations [11], [13], [15]. However, it is important point out there is not a single native group that could represent the full ancestry of Native American component in the Americas. Unfortunately, the Zapoteca sample was the only Native American sample that was available to us, and we could not evaluate the extent to which the allele frequencies observed for the panel of AIMs selected from the DMET array may differ between the Zapotecas and other Native American groups throughout the Americas. The Mestizos were recruited in the States of Sonora in the north, Guerrero in the south-Pacific, Guanajuato in the center, Yucatan in the southeast and Veracruz in the center-Gulf. The protocol was approved as part of a comprehensive genotypic characterization within the Mexican Genome Diversity Project (MGDP) [11] by the Scientific, Ethics, and Biosafety Review Boards of the National Institute of Genomic Medicine, where all Mexican participants signed an informed consent in their native language.
Genotyping with the DMET Plus platform
Samples were genotyped using the Affymetrix DMET Plus platform, using standard protocols. This platform interrogates 1,936 genetic variants across 231 genes of PGx relevance, including biallelic and triallelic SNPs, copy-number variants and insertion/deletions. Quality control tests were performed on data using PLINK [16]. We excluded individuals with more than 5% missing genotypes and included only SNPs with at least 95% genotyping rate (<5% missing). The final dataset was based on 1,647 genetic markers in 214 genes and 537 individuals that comprise Brazilians, and Mexican Native Americans and Mestizos populations. Data are provided as Data S1 (Supporting Information - Compressed/ZIP File Archive: Supporting_information_file-1-genotyping_data.zip).
Estimation and validation of ancestry proportions
In order to evaluate the ancestry proportions of the Brazilian and Mexican samples, we identified 71 unlinked Ancestry Informative Markers (AIMs) from the DMET Plus array. The selection of AIMs was based on frequency differences between the three main parental groups relevant for Latin American populations: European, African and Native American. We used as representatives of the parental groups 59 individuals from Europe (Hapmap CEU from northwestern Europe), 208 individuals from Africa (Hapmap YRI from Ibadan, Nigeria combined with Hapmap LWK from Webuye in Kenia) [17], and 45 Native Americans (Zapotecas from the State of Oaxaca, Mexico).
To validate the DMET Latin American AIMs panel, we used the program STRUCTURE v 2.3.4 [18], [19], [20] to estimate, based on the DMET AIMs panel, the individual ancestry proportions of the 224 Mexican Mestizo individuals analyzed in this project. The estimates obtained were then compared with genome-wide estimates (based on the Affymetrix 100K and Illumina 550K arrays) available for a subset of the samples [11], [21], [22].
Principal Component Analysis and estimates
A Principal Component Analysis (PCA) was carried out with the program EIGENSOFT [23]. Analysis based on genotype data for the Brazilian and Mexican admixed samples, as well as representatives of the three parental populations: HapMap Europeans CEU (EUR), Africans YRI+LWK (AFR) and Native Americans (NAT). The program EIGENSOFT was also used to obtain FST estimates for all the pairwise comparisons.
Genetic differentiation analysis
The program PHASE v 2.1.1 [24], [25], [26] was used to estimate haplotype frequencies in the genes included in the DMET Plus microarray. Allele frequencies were estimated with the toolset PLINK [16]. This program was also used to perform chi-square tests evaluating allele differences between populations. The p-values were adjusted for multiple testing using Bonferroni's correction. We applied Wright's FST to allele frequencies to characterize differentiation at specific SNPs as previously reported [27].
Graphical representation of the log odds of having a variant of PGx relevance depending on admixture proportions
We assessed the relationship between allele frequencies and ancestry proportions by fitting non-linear piece-wise smooth logistic regression models [28]. In these models, the response variable was the presence/absence of the relevant PGx variant, and ancestry was entered in the model as a predictor variable transformed as a linear tail-restricted cubic spline allowing for nonlinear contributions. The result of the model fitting exercise is presented as surface plots describing the predicted relationship between the frequency of the relevant variant, expressed as the natural log odds [i.e. ln(p/(1–p), where p is the proportion of variant alleles] and ancestry. This method, described in Harrell [28] is implemented as function ‘lrm’ available in the R package ‘rms’ [29]. Analysis of variance tables describe the Wald statistics for testing the model components.
Results
Estimation and validation of ancestry proportions
We estimated ancestry proportions in the Brazilian and Mexican samples using a panel of 71 unlinked, highly informative AIMs included in the DMET Plus array. The panel was identified based on the frequency differences between the relevant parental populations (African, European and Native American). Detailed information about the panel of AIMs, including parental frequencies, is provided in Table S1. The program STRUCTURE was used to estimate the individual admixture proportions, as well as the average admixture proportions in each sample.
Table 1 shows the average African, European and Native American genetic contributions to the Brazilian sample stratified by census categories, and to the Mexican sample stratified by State. The average African, European and Native American contributions to the Brazilian sample were estimated to be 29%, 62% and 7% respectively. The African contribution is highest for Black Brazilians (54% vs. 25.3% in Brown and 9.7% in White individuals). Conversely, the European contribution is highest for the White category (84.6% vs. 64.7% in Brown and 38% in Black individuals). The average Native American ancestral contributions range between 5.6% and 10% across the 3 Color groups. In contrast to Brazil, where the major ancestral contributions are from Europe and Africa, in Mexico the predominant ancestral contributions are Native American and European (55.9% and 38.8%, respectively), with a relatively small contribution from Africa (5.3%). There is evidence of geographic variation in ancestral contributions in Mexico, with higher European admixture in the Mestizo sample from the State of Sonora in the North (70% vs. 18% in Guerrero) and higher Native American contributions in the state of Guerrero (74% vs. 26% in Sonora). We observed a substantial variation of individual admixture proportions in the samples of Mexico and Brazil, as depicted in Figure S1, which shows the individual admixture estimates using a bar plot.
In order to evaluate the discriminatory power of the AIMs panel, we estimated the correlation of the individual admixture estimates obtained with the panel of 71 DMET AIMs with estimates based on genome-wide data for Mexican Mestizo individuals for which data were available (74 individuals genotyped with the Illumina 550K array and 68 genotyped with the Affymetrix 100K array, Figure S2). We observed very high R2 values for the major ancestral components of the Mexican Mestizo population (Illumina 550K: 0.92 for Native American and 0.91 for European; Affymetrix 100K: 0.91 for Native American and 0.89 for European). The R2 value observed for the African ancestral contribution was lower (Illumina 550K: 0.42 and Affymetrix 100K: 0.32), as expected given the relatively small average African ancestry observed in the Mexican Mestizo sample.
A principal component analysis (PCA) was performed to describe the diversity of Mexican and Brazilian populations in relation to the three ancestral populations (Figure 1). The PCA plot highlights the differences in admixture history between the Brazilian and Mexican samples, in agreement with the admixture results observed with the program STRUCTURE. The samples from Brazil are widely distributed between the samples from Europe and Africa. In contrast, Mexican Mestizos are located between the Native American and European samples in the plots.
The first two PCs were plotted. The plots are based on data for 1,647 SNPs available for two HapMap samples (EUR, AFR), Black, Brown, White Brazilians (BZ.BK, BZ.BN, BZ.WT) and Mexican Natives and Mestizos (NAT, MEX).
Genetic differentiation and haplotype diversity for DMET Plus markers
We calculated the FST statistic, which is a measure of the degree of genetic differentiation between populations, for all the markers included in the DMET Plus array. FST was calculated for all possible pairwise population combinations of the parental samples (African-European, African-Native American and European-Native American). Table S2 shows the average and minimum-maximum range of FST values for all the pairwise comparisons. Figure S3 shows the distribution of FST values for all the pairwise comparisons in a graphical format. The average FST values are low, indicating reduced genetic differentiation. However, it is important to note the broad distribution of FST values. While most of the genetic markers have low genetic differentiation, there are many outliers with high FST values. The highest average FST values are observed between the African and the Native American samples (FST = 0.085). The FST values observed for the African-European and European-Native American comparisons were 0.059 and 0.062, respectively. The average genetic differentiation between the Brazilian and Mexican samples with respect to the parental samples is lower than the genetic differentiation observed between the parental samples (FST = 0.012), as expected given the history of admixture in Brazil and Mexico.
We also explored haplotype diversity using the program PHASE. We used this program to estimate haplotype frequencies based on 1,647 biallelic SNPs located on 214 DMET genes. We restricted our analysis to common haplotypes with frequencies equal or higher than 5% in each sample. We identified slightly more than 1,000 different haplotypes with these characteristics. The African sample shows the highest haplotype diversity (more than 68% of the common haplotypes are found in the African sample) and the Native American sample shows the lowest haplotype diversity (only 46.5% of the common haplotypes are found in the Native American sample). In agreement with expectations based on population history, the Brazilian sample has intermediate haplotype diversity between the African and European samples, and the Mexican Mestizo sample has intermediate haplotype diversity between the European and Native American samples (Figure S4).
In Table 2, we show the frequencies of alleles/haplotypes of PGx relevance for the CYP2D6, UGT1A1 and VKORC1 genes, which can be inferred based on the markers of the DMET Plus array. Although, unfortunately the array does not capture all the functional variants described for these genes, such as UGT1A1*28. The allele frequencies of several CYP2D6 variants of functional importance are known to vary amongst ethnic groups, and this explains variation in interindividual drug response [30], [31]. The frequencies of the non-functional CYP2D6*4 allele, and the decreased-activity allele CYP2D6*41 are considerably higher in Europeans than in Native American or West African populations. As expected, the relative frequencies for these alleles are intermediate in the admixed samples, and the variants are present in frequencies that are proportional to the relative admixture proportions. In the case of UGT1A1, the frequencies of variants that have been associated with irinotecan response (−3156G>A/−349C>T and UGT1A1*60) [32], [33], [34], [35] are also higher in European than Native American and West African populations. The consistent higher frequencies of the functional CYP2D6 and UGT1A1 alleles/haplotypes observed in Europeans vs. the other two populations are probably the result of ascertainment bias, because most of the studies exploring variants with potentially functional effects have been carried out in European populations. Finally, for VKORC1 there are also clear differences across populations in the distribution of haplotypes associated with low (H1 and H2) and high (H7/H8 and H9) warfarin dose requirements [36]. Again, in the admixed samples the frequencies follow a gradient depending on admixture proportions.
Identification of DMET Plus markers showing extreme genetic differentiation
We identified the genetic markers included in the DMET Plus array that show the most extreme genetic differentiation (e.g. highest FST values) between the parental populations that are most relevant for the Brazilian (European and African populations) and Mexican Mestizo (European and Native American) samples. Tables 3 and 4 list the 18 SNPs with the most extreme genetic differentiation between Europeans and either Africans or Native Americans, respectively. All these SNPs have a drug association reported in the Pharmacogenomics Knowledge Database (PharmGKb, www.pharmgkb.org), and their Bonferroni adjusted p-values are less than 1×10−20 in the European-African comparison and less than 1×10−6 in the European-Native American comparison. The SNPs listed in Tables 3 and 4 are located in genes belonging to the Cytochrome P450, Solute Carrier (SLC) and UDP-glucuronyltransferase (UGT) families as well as other genes of PGx relevance such as ABCC8, ADH1A, CHST3, PON1, PPARD, PPARG, and VKORC1. Of note, 6 of the SNPs identified in the African-European comparison are also present in the European-Native American comparison: rs2470890 at CYP1A2, rs2242480 at CYP3A4, rs1050152 at SLC22A4, rs2242046 at SLC28A1, rs1060896 at SLC28A2 and rs7867504 at SLC28A3. The list of markers with high genetic differentiaton for Brazilians and Mexicans is presented in Tables S3 and S4. All frequencies data can be consulted in Table S5.
We applied non-linear piece-wise smooth logistic regression modeling (see Materials and methods section) to explore how individual ancestry proportions influence allele frequencies for CYP1A2 rs2470890, which is one of the six SNPs that showed extreme genetic divergence between Europeans and both Africans and Native Americans. The resulting three-dimensional plot is presented in Figure 2.
The relative proportions of Native American and African ancestry are plotted in the x and y axes as appropriately labeled, whereas the relative proportion of European ancestry is inferred as the remaining proportion: 1- (af+nat). The plotted surface corresponds to the natural logarithm of the odds of having the variant rs2470890 T allele, depending on the relative admixture proportions of the parental populations. The odds refer to the ratio of the variant:wild-type alleles. For example the odds of having a variant allele frequency of 0.1 (wild-type frequency = 0.9) is 01/09 or 0.111. The corresponding Ln odds is −2.197. The circles correspond to the average ancestral proportions for Black Brazilians (black circle), Brown Braziilans (grey), White Brazilians (white), Mexicans (red), EUR (yellow), AFR (green) and NAT (blue).
Impact of admixture history at the population level on PGx implementation
Eighteen SNPs in the DMET array are included in the published guidelines of the Clinical Pharmacogenetics Implementation Consortium (CPIC, www.pharmgkb.org/page/cpic). We selected these SNPs to examine the impact of admixture on the distribution of clinically-important PGx polymorphisms in Brazilians and Mexicans. Initially, we calculated the FST values for each locus in pair-wise comparisons of proxy parental populations, namely EUR versus AFR (most relevant for Brazilians) and EUR versus NAT (most relevant for Mexicans). The average FST for the 18 markers was 0.037 in EUR versus AFR and 0.044 in EUR versus NAT (Table 5). Similarly to what was found for all the markers surveyed by the DMET Plus array (see above), these FST values point to low genetic differentiation. However, some SNPs displayed substantially higher levels of genetic differentiation (Table 5). Thus, 4 markers in the EUR - AFR comparison and 3 markers in the EUR - NAT comparison showed moderate divergence (FST>0.05), whereas large genetic divergence (FST>0.15) was observed for rs1135840 in CYP2D6 between EUR and NAT, and for rs9923231 in VKORC1 between EUR and AFR. We show the three-dimensional plots for these two markers in Figures 3 and 4, respectively. Figure S5 shows a three-dimensional plot for a marker, rs16947 in CYP2D6, which shows moderate genetic divergence between EUR and both AFR and NAT.
The relative proportions of Native American, African ancestry and European ancestry are presented as described in Figure 2. The plotted surfaces correspond to the natural logarithm of the odds of having the variant rs1135840 G allele, depending on the relative admixture proportions of the parental populations. The circles correspond to the average ancestral proportions for Black Brazilians (black circle), Brown Braziilans (grey), White Brazilians (white), Mexicans (red), EUR (yellow), AFR (green) and NAT (blue).
The relative proportions of Native American, African and European ancestry are presented as described in Figure 2. The plotted surfaces correspond to the natural logarithm of the odds of having the variant rs9923231 G allele, depending on the relative admixture proportions of the parental populations. The circles correspond to the average ancestral proportions for Black Brazilians (black circle), Brown Brazilians (grey), White Brazilians (white), Mexicans (red), EUR (yellow), AFR (green) and NAT (blue).
Discussion
Here, we describe the distribution of genetic markers of PGx relevance in two samples from the two most populous countries in Latin America, Brazil and Mexico. The Brazilian sample comes from the State of Rio de Janeiro, in Southeast Brazil, and was stratified according to the census categories used in this country. The Mexican sample comprised individuals from an indigenous population, the Zapotecas, and also Mestizo individuals from five States located in different regions of Mexico.
We evaluated the distribution of FST values of the genetic markers included in the DMET Plus array between pairs of ancestral populations. The FST statistic provides an indication of the level of genetic differentiation between populations. The distribution of FST values of the DMET Plus markers mirrors the FST distribution that has been described for much denser panels of markers [37], [38]. Overall, the amount of genetic differentiation is low, but there is a long tail of markers showing substantial genetic differentiation (Figure S3). The highest average levels of genetic differentiation were found between the African and Native American samples (average FST = 0.085). The estimates of genetic (haplotype) diversity based on the DMET Plus data are also consistent with evidence based on dense genome-wide data [39], [40]. The highest haplotype diversity is found in the African sample and the lowest in the Native American sample. These differences in genetic diversity have been attributed to serial founder effects after the migration of modern humans out of Africa [41], [42]. As expected, the haplotype diversities in the admixed samples from Brazil and Mexico are consistent with the known history of admixture of each region. The Brazilian samples have intermediate levels of diversity between the African and European samples, and the Mexican samples have intermediate levels of diversity between the Native American and European samples (Figure S4).
The admixture proportions of the individuals of Brazil and Mexico were estimated using a panel of highly informative AIMs included in the array. We selected 71 unlinked markers based on the degree of genetic differentiation between the relevant ancestral populations. The number of AIMs was limited due to the relatively low number of markers included in the DMET array (approximately 2,000) and the fact that many of the markers are in linkage disequilibrium and do not provide independent information. However, the ancestry information content of the panel is high and the individual admixture estimates obtained with the DMET AIMs panel are highly correlated with admixture estimates based on genome-wide data (Affy 100K and Illumina 550K) in a subset of the Mexican samples (see Results section above). The estimates of the average proportions of European, African and Native American ancestry in the Brazilian sample from Rio de Janeiro are very similar to those reported by Pena et al. [10] using an independent set of AIMs for a different cohort of White, Brown and Black Brazilians from the same geographical region.
Two issues must be highlighted regarding the results of the admixture analysis. First of all, there is a broad distribution of individual ancestry proportions in the samples from Brazil and Mexico (Figure 1 and Figure S1). Secondly, there are substantial differences in admixture proportions between census groups in Brazil and geographic regions in Mexico (Table 1). In Brazil, the average contribution of European ancestry decreased progressively from self-reported White (84.6%), to Brown (64.7%) and then to Black individuals (38.1%), whereas the opposite trend was observed with respect to African ancestry, which averaged 9.7%, 25% and 54% in White, Brown and Black persons, respectively. Native ancestry ranged from 5.6 to 10% across the three groups. The Brazilian individuals included in this study came exclusively from Rio de Janeiro (Southeast Brazil), so it was not possible to evaluate geographic variation in admixture proportions in Brazil. However, there is evidence pointing to the presence of substantial variation in admixture proportions in different regions of Brazil, even within census categories. For instance, [10] recently reported that self-classified Brown individuals from the North region had on average 68.6% European ancestry, compared to 44.4% in the South region. Similarly, European ancestry in self-reported Blacks ranged from 29.3% in the South to 53.9% in the Northeast region. This variability reflects the fact that self-identification based on the “race/Color” categorizations for Brazilians in our manuscript correspond to the categories adopted by the official Brazilian Census, where White, Brown and Black categories for Brazilians is influenced by phenotypic variables such as skin and eye pigmentation and facial features, as well as family history, sunlight exposure, income level, social class and schooling [10], [42], [43], [44]. Collectively, these factors underlie the tenuous correlation between self-reported color and biogeographical ancestry among Brazilian [10], [46].
Based on the results obtained with the panel of AIMs, in Mexico there is also evidence of regional variation in admixture proportions, in particular between the northern State of Sonora and the other States. The European admixture proportions in Sonora are 50% higher than the proportions observed in the Southern State of Guerrero. This geographic variation in admixture proportions within Mexico has also been described based on genome-wide panels of markers [11]. Indeed, when we analyzed the distribution of the DMET Plus markers by region within Mexico, we observed that a number of markers show substantial variation between regions, including a marker of clinical relevance (rs12248560) located in the CYP2C19 gene, which has been classified by CPIC as CPIC level A (annotation used to indicate that genetic information should be used to change prescribing of affected drug) and PharmGKB level 1A (annotation used for variant-drug combinations in a CPIC or medical society-endorsed PGx guideline). The CYP2C19 allele rs12248560 T (CYP2C19*17) is an ultrarapid metabolizer of drugs such as amitriptyline and clopidogrel. The frequency of this allele is more than 4-fold higher in states with high European admixture proportions (e.g Sonora: 0.144 and GUA: 0.136) than in states with high Native American admixture proportions (e.g. GUE: 0.033) (Figure 5).
We analyzed the distribution of the DMET Plus markers by region. CPIC marker rs12248560 shows much lower frequency for those states with stronger Native American component (GUE, VER) almost 4-fold when compared to that of SON. Whilst, rs1135840 shows that frequency for the state of SON is almost 2-fold when compared to that of GUE, a state with a high Native American component. This also correspond to what is observed at the ancestral population levels, over 4-fold difference between European and Native American.
As stated above, although the overall genetic differentiation of the markers interrogated in the DMET Plus array is low, there are many loci that show high levels of genetic differentiation between the parental populations relevant for contemporary Brazilians and Mexicans. Given the broad range of admixture proportions observed in Brazil (both in terms of census categories and geographic regions) and Mexico (geographic regions), this has clear PGx implications. We displayed graphically in Figure 2, 3 and 4 the impact of admixture proportions using three-dimensional graphs that depict the relationship of alleles and ancestry. The odds of having a variant of PGx relevance vary over ranges determined by the frequency of the polymorphisms in the relevant ancestral populations, i.e. mainly Europeans and Africans for Brazilians, and European and Native Americans for Mexicans. The larger the difference in frequency between the relevant parental populations, the more inappropriate it is to refer to a “Brazilian” or “Mexican” allele frequency. In reality, there will be variation in allele (and genotype) frequencies depending on geographic areas or census categories. Therefore, it may be misleading to extrapolate the allele (and genotype) frequencies found in a given geographic area or census group to another geographic area or census group. In addition, most of the available data are from people of European ancestry, so clinical translation in non-Europeans can be challenging [45], thus our results highlight the importance of studies in admixed populations because they present opportunities for discovery of genetic markers that would be missed.
The VKORC1 rs9923231 SNP, a major determinant of warfarin dose in CPIC guidelines, may be used as an example of the practical PGx implications of admixture history. The frequency of the rs9923231T allele, which associates with high warfarin sensitivity, varies 13-fold among proxy parental populations of Brazilians and Mexicans: 4.1% in sub-Saharan Africans (HapMap YRI and LWK), 38.1% in Europeans (HapMap CEU) and 52.2% in Native Americans (Zapotecas). Limdi et al [4] showed that the proportion of variation in warfarin dose explained by the VKORC1 rs9923231 SNP increases as the frequency of the A allele increased, such that VKORC1 explained greater variability in dose among Europeans compared to Africans and African Americans. Accordingly, warfarin-dosing algorithms including VKORC1 rs9923231 as a co-variate have considerably greater predictive power in Europeans compared to Africans [1]. However, the predictive power of two such algorithms did not differ between White and Black Brazilians: this was explained by the higher frequency of the rs9923231T allele in Black Brazilians, as a result of the extensive European- African admixture [5], [47].
Conclusions
In summary, our study emphasizes the remarkable population complexity found in Brazil and Mexico: there is a broad range of admixture proportions within census categories and geographic regions, which is a reflection of differences in population history. It is important to consider that, although Brazil and Mexico are the largest countries in Latin America, they only represent a subset of the diversity observed in this vast geographic area. It is therefore critical to have this rich diversity in mind when considering the PGx impact of admixture [45], [48]. In this sense, estimating admixture proportions at the population or individual level can be useful in two respects. At the population level, knowing average admixture proportions it is possible to infer the allele (and genotype) frequencies of relevant PGx variants. In turn, this distribution determines the proportion of the variance of traits of PGx importance explained by the polymorphisms. At the individual level, individual ancestry proportions will determine the probability of having a PGx relevant genotype. For these reasons, it is important to carry out further efforts to characterize admixture in the Americas. In particular, our understanding of the variation in admixture proportions within countries is still quite incomplete, and there are many gaps in our knowledge of the frequency distributions in the relevant parental populations (in particular, Native American groups).
Supporting Information
Figure S1.
Population structure analysis using 71 AIMs. Individual ancestry proportions in Brazilians (BR.BK, BR.BN, BR.WT) and Mexican Mestizo (MEX).
https://doi.org/10.1371/journal.pone.0112640.s001
(TIF)
Figure S2.
Analysis of correlation of individual ancestry estimates for Mexican Mestizo individuals for which data are available for the DMET Plus array and genome-wide arrays. Data for 74 individuals were available for both the DMET Plus array and the Illumina 550K array. Data for 68 individuals were available for both the DMET Plus array and the Affymetrix 100K array. Individual ancestry estimates for the Illumina 550K array were obtained with the program ADMIXTURE. Individual ancestry estimates for the Affymetrix 100K array were obtained with the program STRUCTURE using 1814 AIMs [11].
https://doi.org/10.1371/journal.pone.0112640.s002
(TIF)
Figure S3.
Distribution of FST values. Pairwise comparisons were done for the three ancestral populations and for the admixed populations Mexico and Brazil, a) Europe vs Africa; b) Europe vs Native American; c) Africa vs Native American; and d) Mexico vs Brazil.
https://doi.org/10.1371/journal.pone.0112640.s003
(TIF)
Figure S4.
Percentage of total common haplotypes observed per population. In order to prepare this plot, first we identified all the haplotypes present in each sample with a frequency equal or higher than 5%. The combined number of haplotypes observed was 1017. The Bar plots represent the percentage of haplotypes observed in each individual sample (e.g. 100% would correspond to the 1017 haplotypes observed in the combined sample). The data set presented includes all the Mexican admixed samples (MEX(MX)), all the Brazilian census groups (BZ.BK, BZ.BN, BZ.WT), the Native American Zapoteca sample (NAT(ZAP)), and several HapMap samples (EUR, AFR, MEX(LA), JPT.CHB).
https://doi.org/10.1371/journal.pone.0112640.s004
(TIF)
Figure S5.
Surface plots describing the predicted relationship between the frequency of rs16947 and parental ancestry, obtained by fitting piece-wise smooth logistic regression models to the 268 Brazilians and 224 Mexicans data. The relative proportions of Native American, African ancestry and European ancestry are presented as described in Figure 2. The plotted surfaces correspond to the natural logarithm of the odds of having the variant rs16947A allele, depending on the relative admixture proportions of the parental populations. The circles correspond to the average ancestral proportions for Black Brazilians (black circle), brown Braziilans (grey), White Brazilians (white), Mexicans (red), EUR (yellow), AFR (green) and NAT (blue).
https://doi.org/10.1371/journal.pone.0112640.s005
(TIF)
Table S1.
Allele Frequencies and FST values for 71 Ancestry Informative Markers (AIMs) in ancestral populations.
https://doi.org/10.1371/journal.pone.0112640.s006
(XLSX)
Table S2.
FST values for all pairwise comparisons among HapMap populations, Zapotecos (NAT), Brazilians (BK, BN, WT) and Mexicans (MEX).
https://doi.org/10.1371/journal.pone.0112640.s007
(XLSX)
Table S3.
Polymorphisms showing the highest genetic differentiation between Native Americans and the Admixed populations.
https://doi.org/10.1371/journal.pone.0112640.s008
(XLSX)
Table S4.
Polymorphisms showing the highest genetic differentiation between Africans and the Admixed populations.
https://doi.org/10.1371/journal.pone.0112640.s009
(XLSX)
Table S5.
Minor Allele Frequencies (MAF) for 1,647 genetic markers. The markers are those included in the DMET Plus array and for all populations: Europeans (Hapmap CEU), Africans (Hapmap YRI and LWK), Native Americans (Zapotecas), Brazilians and Mexican mestizos (Guanajuato (GUA); Guerrero (GUE); Sonora (SON); Veracruz (VER) and Yucatan (YUC)).
https://doi.org/10.1371/journal.pone.0112640.s010
(XLSX)
Data S1.
Genotype data with 1,647 genetic markers from 537 individuals form Brazil and Mexico.
https://doi.org/10.1371/journal.pone.0112640.s011
(ZIP)
Acknowledgments
The authors would like to thank all individuals from Brazil and Mexico for their participation in this study as well as Raúl Mojica-Espinosa and Dan J. Gutiérrez-Fuentes from the Microarray High Technology Unit at INMEGEN.
Author Contributions
Conceived and designed the experiments: JCFL GSK EJP VBP. Performed the experiments: RAR TKFM RC LUF. Analyzed the data: VBP AVC CJS EJP JCFL GSK. Contributed reagents/materials/analysis tools: VBP AVC CJS RAR TKFM RC CRE LUF MJGV HLM AHM EJP JCFL GSK. Wrote the paper: JCFL GSK EJP VBP AVC.
References
- 1. Klein TE, Altman RB, Eriksson N, Gage BF, Kimmel SE, et al. (2009) Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med 360:753–764.
- 2.
The Single Nucleotide Polymorphism database website. Available: http://www.ncbi.nlm.nih.gov/snp/?term=9923231. Accessed 2013 December 17.
- 3.
Rede Nacional de Farmacogenética website. Available: http://www.refargen.org.br/article.php3?id_article=50. Accessed 2014 January 14.
- 4. Limdi NA, Wadelius M, Cavallari L, Eriksson N, Crawford DC, et al. (2010) Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood 115:3827–3834.
- 5. Suarez-Kurtz G (2011) Population diversity and the performance of warfarin dosing algorithms. Br J Clin Pharmacol 72:451–453.
- 6. Ramos E, Doumatey A, Elkahloun AG, Shriner D, Huang H, et al. (2014) Pharmacogenomics, ancestry and clinical decision making for global populations. Pharmacogenomics J 14(3):217–22.
- 7.
Instituto Brasileiro de Geografia e Estatistica website. Available: www.ibge.gov.br/home/estatistica/populacao/estimativa2012/. Accessed 2013 December 12.
- 8.
Instituto Nacional de Estadística y Geografía website. Available: http://www.inegi.org.mx/sistemas/olap/proyectos/bd/consulta.asp?p=17118&c=27769&s=est. Accessed 2013 December 30.
- 9.
Salzano FM, Bortolini MC (2002) The Evolution and Genetics of Latin American Populations. Cambridge: Cambridge University Press.
- 10. Pena SD, Di Pietro G, Fuchshuber-Moraes M, Genro JP, Hutz MH, et al. (2011) The genomic ancestry of individuals from different geographical regions of Brazil is more uniform than expected. PLoS One 6:e17063.
- 11. Silva-Zolezzi I, Hidalgo-Miranda A, Estrada-Gil J, Fernandez-Lopez JC, Uribe-Figueroa L, et al. (2009) Analysis of genomic diversity in Mexican Mestizo populations to develop genomic medicine in Mexico. Proc Natl Acad Sci U S A 106:8611–8616.
- 12. Bryc K, Velez C, Karafet T, Moreno-Estrada A, Reynolds A, et al. (2010) Colloquium paper: genome-wide patterns of population structure and admixture among Hispanic/Latino populations. Proc Natl Acad Sci U S A. 11 107 Suppl 2: 8954–61.
- 13. Galanter JM, Fernandez-Lopez JC, Gignoux CR, Barnholtz-Sloan J, Fernandez-Rozadilla C, et al. (2012) Development of a panel of genome-wide ancestry informative markers to study admixture throughout the Americas. PLoS Genet 8(3):e1002554.
- 14. Burmester JK, Sedova M, Shapero MH, Mansfield E (2010) DMET microarray technology for pharmacogenomics-based personalized medicine. Methods Mol Biol 632:99–124.
- 15. Price AL, Patterson N, Yu F, Cox DR, Waliszewska A, et al. (2007) A Genomewide Admixture Map for Latino Populations. Am. J. Hum. Genet 80(6):1024–1036.
- 16. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575.
- 17. Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, et al; The International HapMap 3 Consortium (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58.
- 18. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959.
- 19. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587.
- 20. Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 9(5):1322–32.
- 21. Reich D, Patterson N, Campbell D, Tandon A, Mazieres S, et al. (2012) Reconstructing Native American population history. Nature 16 488(7411):370–4.
- 22. Moreno EA, Gignoux CR, Fernández JC, Zakharia F, Sikora M (2014) The genetics of Mexico recapitulates Native American substructure and affects biomedical traits. Science 344(6189)):1280–1285.
- 23. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2(12):e190.
- 24. Stephens M, Donnelly P (2003) A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet 73(5):1162–9.
- 25. Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68:978–989.
- 26. Stephens M, Scheet P (2005) Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet 76:449–462.
- 27. Suarez-Kurtz G, Pena SD, Hutz MH (2012) Application of the F(ST) statistics to explore pharmacogenomic diversity in the Brazilian population. Pharmacogenomics 13:771–777.
- 28.
Harrell FE (2002) Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer.
- 29.
R Development Core Team (2011) R: a language and environment for statistical computing. Vienna, Austria: the R Foundation for Statistical Computing.
- 30. Contreras AV, Monge-Cazares T, Alfaro-Ruiz L, Hernandez-Morales S, Miranda-Ortiz H, et al. (2011) Resequencing, haplotype construction and identification of novel variants of CYP2D6 in Mexican Mestizos. Pharmacogenomics 12:745–756.
- 31. Hicks JK, Swen JJ, Gaedigk A (2014) Challenges in CYP2D6 Phenotype Assignment from Genotype Data: A Critical Assessment and Call for Standardization. Curr Drug Metab 15:218–232.
- 32. Kim SY, S Hong Y, K Shim E, Kong SY, Shin A, et al. (2013) S-1 plus irinotecan and oxaliplatin for the first-line treatment of patients with metastatic colorectal cancer: a prospective phase II study and pharmacogenetic analysis. Br J Cancer 17 109(6):1420–7.
- 33. Cha PC, Mushiroda T, Zembutsu H, Harada H, Shinoda N, et al. (2009) Single nucleotide polymorphism in ABCG2 is associated with irinotecan-induced severe myelosuppression. J Hum Genet 54(10):572–80.
- 34. Côté JF, Kirzin S, Kramar A, Mosnier JF, Diebold MD, et al. (2007) UGT1A1 polymorphism can predict hematologic toxicity in patients treated with irinotecan. Clin Cancer Res 13(11):3269–75.
- 35. Saito Y, Sai K, Maekawa K, Kaniwa N, Shirao K, et al. (2009) Close association of UGT1A9 IVS1+399C> T with UGT1A1*28, *6, or *60 haplotype and its apparent influence on 7-ethyl-10-hydroxycamptothecin (SN-38) glucuronidation in Japanese. Drug Metab Dispos 37(2):272–6.
- 36. Rieder MJ, Reiner AP, Gage BF, Nickerson DA, Eby CS, et al. (2005) Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. N Engl J Med 352:2285–2293.
- 37. Weir BS, Cardon LR, Anderson AD, Nielsen DM, Hill WG (2005) Measures of human population structure show heterogeneity among genomic regions. Genome Res 15:1468–1476.
- 38. Elhaik E (2012) Empirical distributions of F(ST) from large-scale human polymorphism data. PLoS One 7:e49837.
- 39. Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, et al. (2008) Worldwide human relationships inferred from genome-wide patterns of variation. Science 319:1100–1104.
- 40. Auton A, Bryc K, Boyko AR, Lohmueller KE, Novembre J, et al. (2009) Global distribution of genomic diversity underscores rich complex history of continental human populations. Genome Res 19:795–803.
- 41. Henn BM, Cavalli-Sforza LL, Feldman MW (2012) The great human expansion. Proc Natl Acad Sci U S A 109:17758–17764.
- 42. Deshpande O, Batzoglou S, Feldman MW, Cavalli-Sforza LL (2009) A serial founder effect model for human settlement out of Africa. Proc Biol Sci 276:291–300.
- 43. Telles EE (2002) Racial Ambiguity among the Brazilian Population. Ethnic and Racial Studies 25(3):415–441.
- 44. Bastos JL, Peres MA, Peres KG, Dumith SC, Gigante DP (2008) [Socioeconomic differences between self- and interviewer-classification of color/race]. Rev Saude Publica 42:324–334.
- 45. Suarez-Kurtz G, Pena SD, Struchiner CJ, Hutz MH (2012) Pharmacogenomic Diversity among Brazilians: Influence of Ancestry, Self-Reported Color, and Geographical Origin. Front Pharmacol 3:191.
- 46. Perera MA, Cavallari LH, Johnson JA (2014) Warfarin pharmacogenetics: an illustration of the importance of studies in minority populations. Clin Pharmacol Ther 95(3):242–4.
- 47. Perini JA, Struchiner CJ, Silva-Assuncao E, Santana IS, Rangel F, et al. (2008) Pharmacogenetics of warfarin: development of a dosing algorithm for brazilian patients. Clin Pharmacol Ther 84:722–728.
- 48. Suarez-Kurtz G, Pena SD (2006) Pharmacogenomics in the Americas: the impact of genetic admixture. Curr Drug Targets 7(12):1649–58.