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<article article-type="brief-report" dtd-version="2.3" xml:lang="EN" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">
<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
<issn pub-type="epub">1664-8021</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">1114742</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2023.1114742</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Genetics</subject>
<subj-group>
<subject>Brief Research Report</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Distribution of a novel <italic>CYP2C</italic> haplotype in Native American populations</article-title>
<alt-title alt-title-type="left-running-head">Fernandes et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fgene.2023.1114742">10.3389/fgene.2023.1114742</ext-link>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Fernandes</surname>
<given-names>Vanessa C&#xe2;mara</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1677295/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Pretti</surname>
<given-names>Marco Ant&#xf4;nio M.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/977985/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tsuneto</surname>
<given-names>Luiza Tamie</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Petzl-Erler</surname>
<given-names>Maria Luiza</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/89320/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Suarez-Kurtz</surname>
<given-names>Guilherme</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/11823/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Coordena&#xe7;&#xe3;o de Pesquisa</institution>, <institution>Instituto Nacional de C&#xe2;ncer</institution>, <addr-line>Rio de Janeiro</addr-line>, <country>Brazil</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Laborat&#xf3;rio de Bioinform&#xe1;tica e Biologia Computacional</institution>, <institution>Instituto Nacional de C&#xe2;ncer</institution>, <addr-line>Rio de Janeiro</addr-line>, <country>Brazil</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Departamento de An&#xe1;lises Cl&#xed;nicas</institution>, <institution>Universidade Estadual de Maring&#xe1;</institution>, <addr-line>Maring&#xe1;</addr-line>, <country>Brazil</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Programa de P&#xf3;s-Gradua&#xe7;&#xe3;o em Gen&#xe9;tica</institution>, <institution>Departamento de Gen&#xe9;tica</institution>, <institution>Universidade Federal do Paran&#xe1;</institution>, <addr-line>Curitiba</addr-line>, <country>Brazil</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1345614/overview">Marcos Leite Santoro</ext-link>, Universidade Federal de Sao Paulo, Brazil</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/224286/overview">Fernanda Rodrigues-Soares</ext-link>, Universidade Federal do Tri&#xe2;ngulo Mineiro, Brazil</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/128001/overview">Nancy Hakooz</ext-link>, The University of Jordan, Jordan</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Guilherme Suarez-Kurtz, <email>kurtz@inca.gov.br</email>
</corresp>
<fn fn-type="other">
<p>This article was submitted to Pharmacogenetics and Pharmacogenomics, a section of the journal Frontiers in Genetics</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>21</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1114742</elocation-id>
<history>
<date date-type="received">
<day>02</day>
<month>12</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>02</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Fernandes, Pretti, Tsuneto, Petzl-Erler and Suarez-Kurtz.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Fernandes, Pretti, Tsuneto, Petzl-Erler and Suarez-Kurtz</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>The <italic>CYP2C19</italic> gene, located in the <italic>CYP2C</italic> cluster, encodes the major drug metabolism enzyme CYP2C19. This gene is highly polymorphic and no-function (<italic>CYP2C19&#x2a;2</italic> and <italic>CYP2C19</italic>&#x2a;<italic>3</italic>), reduced function (<italic>CYP2C19&#x2a;9)</italic> and increased function (<italic>CYP2C19&#x2a;1</italic>7) star alleles (haplotypes) are commonly used to predict CYP2C19 metabolic phenotypes. <italic>CYP2C19&#x2a;17</italic> and the genotype-predicted rapid (RM) and ultrarapid (UM) CYP2C19 metabolic phenotypes are absent or rare in several Native American populations. However, discordance between genotype-predicted and pharmacokinetically determined CYP2C19 phenotypes in Native American cohorts have been reported. Recently, a haplotype defined by rs2860840T and rs11188059G alleles in the <italic>CYP2C</italic> cluster has been shown to encode increased rate of metabolism of the CYP2C19 substrate escitalopram, to a similar extent as <italic>CYP2C19&#x2a;17</italic>. We investigated the distribution of the <italic>CYP2C:TG</italic> haplotype and explored its potential impact on CYP2C19 metabolic activity in Native American populations. The study cohorts included individuals from the One Thousand Genomes Project AMR superpopulation (1&#xa0;KG_AMR), the Human Genome Diversity Project (HGDP), and from indigenous populations living in Brazil (Kaingang and Guarani). The frequency range of the <italic>CYP2C:TG</italic> haplotype in the study cohorts, 0.469 to 0.598, is considerably higher than in all 1&#xa0;KG superpopulations (range: 0.014&#x2014;to 0.340). We suggest that the high frequency of the <italic>CYP2C:TG</italic> haplotype might contribute to the reported discordance between <italic>CYP2C19-</italic>predicted and pharmacokinetically verified CYP2C19 metabolic phenotypes in Native American cohorts. However, functional studies involving genotypic correlations with pharmacokinetic parameters are warranted to ascertain the importance of the <italic>CYP2C:TG</italic> haplotype.</p>
</abstract>
<kwd-group>
<kwd>amerindians</kwd>
<kwd>CYP2C cluster</kwd>
<kwd>CYP2C19 metabolic phenotypes</kwd>
<kwd>native American populations</kwd>
<kwd>pharmacogenetics</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>The human CYP2C subfamily comprises four members, namely, CYP2C18, CYP2C19, CYP2C9 and CYP2C8, with encoding genes located in tandem in chromosome 10q23-24. CYP2C19 provides the major pathway for biotransformation of a variety of drugs from different therapeutic classes, including antidepressants, both tricyclic (e.g., imipramine) and selective serotonin reuptake inhibitors (escitalopram), antifungal (voriconazole), antimalarial (proguanil) and antiplatelet (clopidogrel) drugs, and proton pump inhibitors (omeprazole). CYP2C19-mediated metabolism may lead to drug inactivation (e.g., omeprazole and voriconazole) as well as to active metabolites, which account for the clinical effects of pro-drugs such as proguanil and clopidogrel (<xref ref-type="bibr" rid="B3">Botton et al., 2021</xref>). The clinical relevance of the CYP2C19 pathway is reflected in the CPIC (Clinical Pharmacogenetics Implementation Consortium) guidelines: five of the 26 guidelines currently available have dosing recommendations based on CYP2C19 metabolic phenotypes, predicted from <italic>CYP2C19</italic> genotypes (<xref ref-type="bibr" rid="B10">Hicks et al., 2015</xref>, <xref ref-type="bibr" rid="B11">2017</xref>; <xref ref-type="bibr" rid="B16">Moriyama et al., 2017</xref>; <xref ref-type="bibr" rid="B14">Lima et al., 2021</xref>; <xref ref-type="bibr" rid="B13">Lee et al., 2022</xref>).</p>
<p>The <italic>CYP2C19</italic> gene is highly polymorphic, with 39 star alleles (haplotypes) currently defined in the Pharmacogene Variation Consortium (<ext-link ext-link-type="uri" xlink:href="https://www.pharmvar.org/gene/CYP2C19">https://www.pharmvar.org/gene/CYP2C19</ext-link>). Five star alleles, namely, <italic>CYP2C19&#x2a;2</italic> and <italic>&#x2a;3</italic> (no-function alleles), <italic>CYP2C19&#x2a;9</italic> (reduced function) <italic>CYP2C19&#x2a;17</italic> (increased function) plus the default wildtype allele (<italic>CYP2C19&#x2a;1</italic>) are used in the CPIC guidelines to predict CYP2C19 metabolic phenotypes. However, a novel <italic>CYP2C</italic> haplotype, comprising the rs2860840T (<italic>CYP2C18</italic>, 3&#x2019; UTR) and rs11188059G (<italic>CYP2C18</italic>, intron 5) alleles in the <italic>CYP2C</italic> cluster has been recently shown to encode increased rate of metabolism of the CYP2C19 substrate escitalopram to at least a similar extent as <italic>CYP2C19&#x2a;17</italic> (<xref ref-type="bibr" rid="B4">Br&#xe5;ten et al., 2021</xref>).</p>
<p>The present study investigates the distribution of the <italic>CYP2C:TG</italic> haplotype and its potential impact on prediction of CYP2C19 metabolic phenotypes in Native American populations. Previous studies have shown that the <italic>CYP2C9&#x2a;17</italic> and, consequently, the genotype-assigned rapid (RM) and ultrarapid (UM) CYP2C19 metabolic phenotypes, are absent or rare in Native Americans (<xref ref-type="bibr" rid="B25">Vargens et al., 2012</xref>; <xref ref-type="bibr" rid="B2">Bonifaz-Pe&#xf1;a et al., 2014</xref>; <xref ref-type="bibr" rid="B6">de Andr&#xe9;s et al., 2021</xref>, <xref ref-type="bibr" rid="B7">2017</xref>; <xref ref-type="bibr" rid="B17">Naranjo et al., 2018</xref>; <xref ref-type="bibr" rid="B21">Rodrigues Soares et al., 2020</xref>; <xref ref-type="bibr" rid="B6">de Andr&#xe9;s et al., 2021</xref>). However, discordance between genotype-predicted and pharmacokinetically determined CYP2C19 phenotypes in Native American cohorts have been reported, such that individuals genotyped as <italic>CYP2C19&#x2a;1/&#x2a;1</italic> and assigned the normal metabolic (NM) phenotype showed greater CYP2C19 activity than UMs (<xref ref-type="bibr" rid="B7">de Andr&#xe9;s et al., 2017</xref>).</p>
</sec>
<sec sec-type="methods" id="s2">
<title>Methods</title>
<sec id="s2-1">
<title>Study populations</title>
<p>Four cohorts were investigated, namely, 1&#xa0;KG_NAT - a sub-cohort of the One Thousand Genomes Project Admixed American superpopulation (denoted 1&#xa0;KG_AMR; <xref ref-type="bibr" rid="B1">Auton et al., 2015</xref>) -, HGDP Native Americans (<xref ref-type="bibr" rid="B5">Cavalli-Sforza, 2005</xref>), and Kaingang and Guarani living in Brazil (<xref ref-type="bibr" rid="B20">Petzl-Erler et al., 1993</xref>; <xref ref-type="bibr" rid="B24">Tsuneto et al., 2003</xref>). The 1&#xa0;KG AMR comprises individuals from the South American countries Colombia (denoted CLM) and Peru (PEL), from Puerto Rico (PUR) as well as people of Mexican Ancestry (MXL) living in Los Angeles, United States of America. The 1&#xa0;KG_NAT comprised the 68 1&#xa0;KG_AMR individuals (58 PEL and 10 MXL) with the highest proportions of Native ancestry: median 94.5%, IQR 85.7%&#x2013;100% (<xref ref-type="bibr" rid="B22">Suarez-Kurtz et al., 2020</xref>). The HGDP cohort (n &#x3d; 61) is formed by samples of Native American groups, from Brazil (Surui and Karitiana), Mexico (Maya and Pima) and Colombia. Kaingang (KRC) and Guarani (GRC and GKW) are represented by adults enrolled in a population genetics study of Brazilian Amerindians, approved by the Brazilian National Ethics Committee (CONEP123/98). Kaingang and Guarani, the two major Amerindian tribes of southern Brazil, are culturally quite distinct from each other, the Guarani belonging to the Tupi linguistic group, while Kaingang are G&#xea;-speaking. The KRC and GRC live in different villages within the Rio das Cobras reservation (25&#xba;18&#x2032;S, 52&#xba;32&#x2032;W), whereas GKW are from the Amambai and Lim&#xe3;o Verde reservations (23&#xba;06&#x2032;S, 55&#xba;12&#x2032;W and 23&#xba;12&#x2032;S, 55&#xba;06&#x2032;W, respectively).</p>
</sec>
<sec id="s2-2">
<title>
<italic>CYP2C19</italic> and <italic>CYP2C</italic> alleles and haplotypes</title>
<p>We analyzed six single nucleotide polymorphisms (SNPs) in the <italic>CYP2C</italic> cluster, namely, rs2860840C&#x3e;T (GRCh38.13 chr 10:94735475) and rs11188059G&#x3e;A (GRCh38.13 chr 10:94709142) in <italic>CYP2C18</italic>, and rs4244285G&#x3e;A (GRCh38.13 chr 10:94781859, <italic>CYP2C19&#x2a;2</italic>), rs4986893G&#x3e;A (GRCh38.13 chr 10:94780653, <italic>CYP2C19&#x2a;3</italic>), rs17884712G&#x3e;A (GRCh38.13 chr 10:94775489, <italic>CYP2C19&#x2a;9</italic>), rs12248560C&#x3e;T (GRCh38.13 chr 10:94761900, <italic>CYP2C19&#x2a;17</italic>) in the <italic>CYP2C19</italic> gene<italic>.</italic> Genotype data from 1&#xa0;KG_AMR were retrieved at <ext-link ext-link-type="uri" xlink:href="https://www.ensembl.org/index.html">https://www.ensembl.org/index.html</ext-link> whereas aligned sequences for the 61 individuals from the Human Genome Diversity Project (HGDP) were retrieved at <ext-link ext-link-type="uri" xlink:href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGDP/">http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/HGDP/</ext-link>. The Kaingang and Guarani samples were genotyped using a 7500 Real-Time System and TaqMan allele discrimination probes for rs2860840 (C_11201742_10), rs11188059 (C_31983321_10), rs4244285 (C_25986767_70) and rs12248560 (C_469,857_10), according to the manufacturer&#x2019;s instructions. The <italic>CYP2C19&#x2a;1</italic> (wild-type allele) was assigned by default, i.e., absence of variant alleles at the <italic>CYP2C19</italic> loci genotyped.</p>
<p>Individual haplotypes and diplotypes were inferred using the HaploStats software implemented on the R platform. This software attributes a posterior probability value for the diplotype configuration for each individual on the basis of estimated haplotype frequencies. The minimal posterior probability value for inclusion of an individual in these analyses was set at 0.95. We adopted the labelling used by <xref ref-type="bibr" rid="B4">Br&#xe5;ten <italic>et al.</italic> (2021)</xref> to denote <italic>CYP2C</italic> haplotypes and diplotypes comprising the <italic>CYP2C18</italic> rs2860840 and rs11188059 SNPs.</p>
</sec>
<sec id="s2-3">
<title>Assignment of CYP2C19 metabolic phenotypes</title>
<p>CYP2C19 metabolic phenotypes were assigned according to either the <italic>CYP2C19</italic> guidelines (<xref ref-type="bibr" rid="B10">Hicks et al., 2015</xref>, <xref ref-type="bibr" rid="B11">2017</xref>; <xref ref-type="bibr" rid="B16">Moriyama et al., 2017</xref>; <xref ref-type="bibr" rid="B14">Lima et al., 2021</xref>; <xref ref-type="bibr" rid="B13">Lee et al., 2022</xref>), or as proposed by <xref ref-type="bibr" rid="B4">Br&#xe5;ten <italic>et al.</italic> (2021)</xref>.</p>
</sec>
<sec id="s2-4">
<title>Statistical analyses</title>
<p>Deviation of genotype distribution from Hardy-Weinberg equilibrium was assessed by the goodness-of-fit &#x3c7;<sup>2</sup> test. Chi square tests were applied to compare the distribution of <italic>CYP2C</italic> haplotypes and predicted CYP2C19 across cohorts. Significance level was set at <italic>p</italic> &#x3c; 0.05.</p>
</sec>
</sec>
<sec sec-type="results|discussion" id="s3">
<title>Results and discussion</title>
<p>There were no significant deviations from Hardy-Weinberg equilibrium at the <italic>CYP2C18</italic> and <italic>CYP2C19</italic> loci interrogated. The two <italic>CYP2C18</italic> SNPs were common in all study cohorts (<xref ref-type="table" rid="T1">Table 1</xref>): the minor allele frequency (MAF) of rs11188059G&#x3e;A ranged from 0.107 (HGDP) to 0.481 (Kaingang), whereas the frequency of the variant rs2860840&#xa0;T allele reached 0.963 in Kaingang, and ranged between 0.705&#x2013;0.757 in the other cohorts. These results are consistent with data for North and South American Native populations in the Allele Frequency Database (<ext-link ext-link-type="uri" xlink:href="https://alfred.med.yale.edu/">https://alfred.med.yale.edu/</ext-link>).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Distribution of <italic>CYP2C18</italic> alleles and <italic>CYP2C</italic> diplotypes in Native Americans.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Cohorts (n individuals)</th>
<th align="center">1&#xa0;KG_NAT (68)</th>
<th align="center">HGDP (61)</th>
<th align="center">Kaingang (54)</th>
<th align="center">Guarani (33)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">
<bold>
<italic>CYP2C18</italic> SNPs</bold>
</td>
<td colspan="4" align="center">Variant allele frequency</td>
</tr>
<tr>
<td align="left">rs2860840&#xa0;C&#x3e;T</td>
<td align="center">0.757</td>
<td align="center">0.705</td>
<td align="center">0.963</td>
<td align="center">0.727</td>
</tr>
<tr>
<td align="left">rs11188059&#xa0;G&#x3e;A</td>
<td align="center">0.265</td>
<td align="center">0.107</td>
<td align="center">0.481</td>
<td align="center">0.242</td>
</tr>
<tr>
<td align="left">
<bold>
<italic>CYP2C</italic> diplotypes</bold>
<xref ref-type="table-fn" rid="Tfn1">
<sup>&#x23;</sup>
</xref>
</td>
<td colspan="4" align="center">Diplotype frequency</td>
</tr>
<tr>
<td align="left">TG</td>
<td align="center">0.493</td>
<td align="center">0.598</td>
<td align="center">0.490</td>
<td align="center">0.469</td>
</tr>
<tr>
<td align="left">TA</td>
<td align="center">0.265</td>
<td align="center">0.107</td>
<td align="center">0.471</td>
<td align="center">0.250</td>
</tr>
<tr>
<td align="left">CG</td>
<td align="center">0.243</td>
<td align="center">0.295</td>
<td align="center">0.038</td>
<td align="center">0.281</td>
</tr>
<tr>
<td align="left">CA</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="Tfn1">
<label>
<sup>&#x23;</sup>
</label>
<p>comprising rs2860840&#xa0;C&#x3e;T and rs11188059&#xa0;G&#x3e;A, and denoted as in <xref ref-type="bibr" rid="B4">Br&#xe5;ten et al. (2021)</xref>
</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>Three haplotypes comprising rs2860840C&#x3e;T and rs11188059G&#x3e;A (<italic>CYP2C</italic> haplotypes) were identified in the study cohorts (<xref ref-type="table" rid="T1">Table 1</xref>): <italic>CYP2C:TG</italic> was the most common, with frequencies ranging from 0.469 to 0.598, while <italic>CYP2C:CG</italic> and <italic>CYP2C:TA</italic> frequencies ranged between 0.038&#x2013;0.295 and 0.107&#x2013;0.471, respectively. The frequencies of these <italic>CYP2C</italic> haplotypes in the study cohorts differed markedly from the African (1&#xa0;KG_AFR), European (1&#xa0;KG_EUR), East Asian (1&#xa0;KG_EAS) and South-Asian (1&#xa0;KG_SAS) 1&#xa0;KG superpopulations (<xref ref-type="sec" rid="s10">Supplementary Table S1</xref>): <italic>CYP2C:TG</italic> and, to a lesser extent, <italic>CYP2C:TA</italic> were more common in the study cohorts than in the 1&#xa0;KG superpopulations, whereas the opposite was observed for <italic>CYP2C:CG</italic>. The <italic>CYP2C:CA</italic> haplotype, absent or extremely rare in the 1&#xa0;KG superpopulations was absent in the Native American cohorts of our study. Pairwise comparisons of the frequency of <italic>CYP2C:TG, TA</italic> and <italic>CG</italic> haplotypes in any study cohort <italic>versus</italic> any <italic>1&#xa0;KG</italic> superpopulation disclosed highly significant differences (chi square <italic>p</italic> &#x3c; 0.0001).</p>
<p>The high frequency of <italic>CYP2C:TG</italic> in the study cohorts was of special interest to us in reference to the reported discordance observed in Native Americans, between CYP2C19 metabolic phenotypes predicted from <italic>CYP2C19</italic> diplotypes <italic>versus</italic> phenotypes determined by pharmacokinetic measurements (<xref ref-type="bibr" rid="B7">de Andr&#xe9;s et al., 2017</xref>; <xref ref-type="bibr" rid="B17">Naranjo et al., 2018</xref>). For example, <xref ref-type="bibr" rid="B7">de Andr&#xe9;s <italic>et al.</italic> (2017)</xref> observed that several Mexican Amerindians genotyped as <italic>CYP2C19&#x2a;1/&#x2a;1</italic> and assigned the normal metabolizer (NM) phenotype had higher CYP2C19 activity than genotype-predicted ultrarapid metabolizers (UMs), i.e., carriers of the <italic>CYP2C19&#x2a;17/&#x2a;17</italic> diplotype. We hypothesized that such discrepancy might result from linkage of <italic>CYP2C19&#x2a;1</italic> with the <italic>CYP2C:TG</italic> haplotype, as reported in the pivotal study of <xref ref-type="bibr" rid="B4">Br&#xe5;ten <italic>et al.</italic> (2021)</xref>.</p>
<p>To explore this hypothesis, we genotyped the <italic>CYP2C19</italic> star alleles &#x2a;2, &#x2a;3, &#x2a;4 and &#x2a;17 which are used in the CPIC guidelines to predict CYP2C19 metabolic phenotypes. As shown in <xref ref-type="table" rid="T2">Table 2</xref>, <italic>CYP2C19&#x2a;2</italic> was absent in Kaingang and its MAF ranged between 0.057 and 0.109 in the other three cohorts. <italic>CYP2C19&#x2a;3</italic> and <italic>CYP2C19&#x2a;9</italic> were not detected in 1&#xa0;KG_NAT and HGDP Native Americans and could not be interrogated in Kaingang and Guarani samples due to the limited amount of DNA available. <italic>CYP2C19&#x2a;17</italic> was absent in HGDP and Kaingang, and had MAF of 0.022 in 1&#xa0;KG_NAT and Guarani. These data are consistent with previous studies in other Native American groups (<xref ref-type="bibr" rid="B25">Vargens et al., 2012</xref>; <xref ref-type="bibr" rid="B2">Bonifaz-Pe&#xf1;a et al., 2014</xref>; <xref ref-type="bibr" rid="B6">de Andr&#xe9;s et al., 2021</xref>, <xref ref-type="bibr" rid="B7">2017</xref>; <xref ref-type="bibr" rid="B17">Naranjo et al., 2018</xref>; <xref ref-type="bibr" rid="B21">Rodrigues Soares et al., 2020</xref>). For example, <xref ref-type="bibr" rid="B21">Rodrigues-Soares et al. (2020)</xref> showed that <italic>CYP2C19&#x2a;2</italic> is absent in Guaymi from Costa Rica and Tzeltal from Mexico, <italic>CYP2C19&#x2a;17</italic> is absent in various Mayan and Uto-Aztecan groups from Mexico, as well as from Arawak and Quechuamara from Peru, while <italic>CYP2C19&#x2a;</italic>3 is not detected in the vast majority of Native American populations, including a Guarani cohort previously studied by our group (<xref ref-type="bibr" rid="B25">Vargens et al., 2012</xref>).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Distribution of <italic>CYP2C19</italic> alleles, diplotypes and assigned phenotypes.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Cohorts (n individuals)</th>
<th align="left"/>
<th align="center">1&#xa0;KG_NAT (68)</th>
<th align="center">HGDP (61)</th>
<th align="center">Kaingang (54)</th>
<th align="center">Guarani (33)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">
<bold>SNPs (star alleles)</bold>
</td>
<td align="left"/>
<td colspan="4" align="center">Allele frequency</td>
</tr>
<tr>
<td align="left">rs4244285&#xa0;G&#x3e;A (CYP2C19&#x2a;2)</td>
<td align="left"/>
<td align="center">0.066</td>
<td align="center">0.057</td>
<td align="center">0</td>
<td align="center">0.109</td>
</tr>
<tr>
<td align="left">rs4986893&#xa0;G&#x3e;A (CYP2C19&#x2a;3)</td>
<td align="left"/>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">ng</td>
<td align="center">ng</td>
</tr>
<tr>
<td align="left">rs28399504 A&#x3e;G (CYP2C19&#x2a;9)</td>
<td align="left"/>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">ng</td>
<td align="center">ng</td>
</tr>
<tr>
<td align="left">rs12248560&#xa0;C&#x3e;T (CYP2C19&#x2a;17)</td>
<td align="left"/>
<td align="center">0.022</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0.018</td>
</tr>
<tr>
<td align="left">
<bold>Diplotypes</bold>
</td>
<td align="center">Phenotypes<xref ref-type="table-fn" rid="Tfn2">
<sup>&#x23;</sup>
</xref>
</td>
<td colspan="4" align="center">Diplotype frquency</td>
</tr>
<tr>
<td align="left">&#x2a;1/&#x2a;1</td>
<td align="center">NM</td>
<td align="center">0.838</td>
<td align="center">0.885</td>
<td align="center">1.0</td>
<td align="center">0.758</td>
</tr>
<tr>
<td align="left">&#x2a;1/&#x2a;2</td>
<td align="center">IM</td>
<td align="center">0.118</td>
<td align="center">0.115</td>
<td align="center">0</td>
<td align="center">0.212</td>
</tr>
<tr>
<td align="left">&#x2a;2/17</td>
<td align="center">IM</td>
<td align="center">0.015</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0</td>
</tr>
<tr>
<td align="left">&#x2a;1/&#x2a;17</td>
<td align="center">RM</td>
<td align="center">0.029</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0.030</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="Tfn2">
<label>
<sup>&#x23;</sup>
</label>
<p>assigned according to the CPIC guidelines [2&#x2013;6]. NM, normal metabolizer; IM, intermediate metabolizer; RM, rapid metabolizer.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>We then examined the distribution of <italic>CYP2C19</italic> diplotypes and CYP2C19 phenotypes assigned according to the CPIC guidelines (<xref ref-type="table" rid="T2">Table 2</xref>): All Kaingang individuals were homozygous for the default <italic>CYP2C19&#x2a;1</italic> allele and therefore assigned the NM phenotype. In the 1&#xa0;KG_NAT, HGDP and Guarani cohorts, wildtype homozygosis (<italic>CYP2C19&#x2a;1/&#x2a;1;</italic> NM phenotype) ranged from 0.758 to 0.885, while other diplotypes and assigned phenotypes were distributed as follows: <italic>CYP2C19&#x2a;1/&#x2a;2</italic> (IM phenotype) ranged from 0.118, in 1&#xa0;KG_NAT and HGDP, to 0.212, in Guarani; <italic>CYP2C19&#x2a;2/&#x2a;17</italic> (IM) was detected in only one 1&#xa0;KG_NAT individual (0.015); <italic>CYP2C19&#x2a;1/&#x2a;17</italic> (RM phenotype) was absent in HGDP and rare (0.03) in 1&#xa0;KG_NAT and Guarani; homozygous <italic>CYP2C19&#x2a;2/&#x2a;2</italic> (PM) and <italic>CYP2C19&#x2a;17/&#x2a;17</italic> (UM phenotype<italic>)</italic> were not detected.</p>
<p>Next, we applied the Haplo-Stats software to infer the individual haplotypes and diplotypes formed by the <italic>CYP2C19</italic> star alleles and the <italic>CYP2C</italic> diplotypes (<xref ref-type="table" rid="T3">Table 3</xref>). Five haplotypes were identified, of which three had the wild-type <italic>CYP2C19&#x2a;1</italic> linked to one of <italic>CYP2C:CG</italic> (denoted haplotype <italic>&#x2a;1CG</italic>)<italic>, CYP2C:TA</italic> (<italic>&#x2a;1&#xa0;TA</italic>) or <italic>CYP2C:TG (&#x2a;1&#xa0;TG)</italic>; the two other haplotypes were formed by <italic>CYP2C:CG</italic> linked to either <italic>CYP2C19&#x2a;2</italic> (<italic>&#x2a;2CG</italic>) <italic>or CYP2C19&#x2a;17</italic> (<italic>&#x2a;17CG</italic>). The <italic>&#x2a;1&#xa0;TG</italic> haplotype was the most frequent in all cohorts (range 0.470&#x2013;0.598). While the <italic>&#x2a;1CG</italic> and <italic>&#x2a;1&#xa0;TA</italic> haplotypes ranged in frequency between 0.037&#x2013;0.238 and 0.107&#x2013;0.481, respectively of notice, the <italic>CYP2C:TG</italic> haplotype was never linked to either rs4244285 A (<italic>CYP2C19&#x2a;</italic>2) or rs12248560&#xa0;T (<italic>CYP2C19&#x2a;17</italic>) variant alleles, in concordance with the observation of <xref ref-type="bibr" rid="B4">Br&#xe5;ten <italic>et al.</italic> (2021)</xref>, that <italic>CYP2C:TG</italic> is in &#x201c;complete linkage disequilibrium with the c.991A&#x3e;G (I331V; CYP2C19&#x2a;1.002) variant, like the majority of CYP2C19&#x2a;1-alleles&#x201d;. Also, following these authors&#x2019; approach, the <italic>CYP2C:CG</italic> and <italic>CYP2C:TA</italic> haplotypes were merged for statistical analyses of the distribution of <italic>CYP2C19:CYP2C</italic> diplotypes and assigned metabolic phenotypes (<xref ref-type="table" rid="T3">Table 3</xref>). Eight diplotypes were observed, leading to assignment of NM, IM, RM and UM phenotypes. For visual comparison, plots of the distribution of CYP2C19 metabolic phenotypes predicted according to the <italic>CYP2C19</italic> (<xref ref-type="table" rid="T2">Table 2</xref>) or the <italic>CYP2C19-CYP2C</italic> diplotypes (<xref ref-type="table" rid="T3">Table 3</xref>) are shown in <xref ref-type="fig" rid="F1">Figure 1</xref>. The two assignment procedures resulted in highly significant differences in phenotype distribution in all cohorts (<italic>p</italic> &#x3c; 0.0001). The overall picture is the unveiling of UMs and large increases in frequency of RMs at the expense of NM, with no impact on IM frequency or on the absence of PMs when the <italic>CYP2C19-CYP2C</italic> diplotypes are used for phenotype assignment. This was observed in all cohorts, and most flagrant in Kaingang: the absence of the <italic>CYP2C19&#x2a;2</italic> or <italic>CYP2C19&#x2a;17</italic> alleles leads to assignment of the NM phenotype to all Kaingang individuals, according to <italic>CYP2C19</italic> diplotypes. By contrast, when phenotype assignment is based on <italic>CYP2C19-CYP2C</italic> diplotypes, NMs represent only 24% of the cohort, while RMs and UMs account for 56% and 20%, respectively.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Distribution of <italic>CYP2C19-CYP2C</italic> diplotypes and assigned CYP2C19 phenotypes.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">
<italic>CYP2C19-CYP2C</italic>
</th>
<th align="left"/>
<th align="center">1&#xa0;KG_NAT (68) <xref ref-type="table-fn" rid="Tfn3">
<sup>&#x23;</sup>
</xref>
</th>
<th align="center">HGDP (61)</th>
<th align="center">Kaingang (54)</th>
<th align="center">Guarani (33)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">
<bold>Haplotype</bold>
</td>
<td align="left"/>
<td colspan="4" align="center">Haplotype frequency</td>
</tr>
<tr>
<td align="center">&#x2a;1CG</td>
<td align="left"/>
<td align="center">0.154</td>
<td align="center">0.238</td>
<td align="center">0.037</td>
<td align="center">0.081</td>
</tr>
<tr>
<td align="center">&#x2a;1&#xa0;TA</td>
<td align="left"/>
<td align="center">0.265</td>
<td align="center">0.107</td>
<td align="center">0.481</td>
<td align="center">0.167</td>
</tr>
<tr>
<td align="center">&#x2a;1&#xa0;TG</td>
<td align="left"/>
<td align="center">0.493</td>
<td align="center">0.598</td>
<td align="center">0.481</td>
<td align="center">0.470</td>
</tr>
<tr>
<td align="center">&#x2a;2CG</td>
<td align="left"/>
<td align="center">0.066</td>
<td align="center">0.057</td>
<td align="center">0</td>
<td align="center">0.106</td>
</tr>
<tr>
<td align="center">&#x2a;17CG</td>
<td align="left"/>
<td align="center">0.022</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0.015</td>
</tr>
<tr>
<td align="center">
<bold>Diplotype</bold>
</td>
<td align="center">
<bold>Assigned phenotype</bold>
<xref ref-type="table-fn" rid="Tfn4">
<sup>&#x23;&#x23;</sup>
</xref>
</td>
<td colspan="4" align="center">Diplotype frequency</td>
</tr>
<tr>
<td align="center">&#x2a;1CG or TA/&#x2a;1CG or TA</td>
<td align="center">NM</td>
<td align="center">0.162</td>
<td align="center">0.180</td>
<td align="center">0.241</td>
<td align="center">0.182</td>
</tr>
<tr>
<td align="center">&#x2a;1CG or TA/<bold>&#x2a;1TG</bold>
</td>
<td align="center">RM</td>
<td align="center">0.426</td>
<td align="center">0.279</td>
<td align="center">0.556</td>
<td align="center">0.394</td>
</tr>
<tr>
<td align="center">
<bold>&#x2a;1TG/&#x2a;1TG</bold>
</td>
<td align="center">UM</td>
<td align="center">0.250</td>
<td align="center">0.426</td>
<td align="center">0.204</td>
<td align="center">0.182</td>
</tr>
<tr>
<td align="center">&#x2a;1CG or TA/&#x2a;2CG</td>
<td align="center">IM</td>
<td align="center">0.088</td>
<td align="center">0.049</td>
<td align="center">0</td>
<td align="center">0.030</td>
</tr>
<tr>
<td align="center">
<bold>&#x2a;1TG</bold>/&#x2a;2CG</td>
<td align="center">IM</td>
<td align="center">0.029</td>
<td align="center">0.066</td>
<td align="center">0</td>
<td align="center">0.182</td>
</tr>
<tr>
<td align="center">&#x2a;1CG or TA/&#x2a;17CG</td>
<td align="center">RM</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0.030</td>
</tr>
<tr>
<td align="center">
<bold>&#x2a;1TG</bold>/&#x2a;17CG</td>
<td align="center">UM</td>
<td align="center">0.029</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0</td>
</tr>
<tr>
<td align="center">&#x2a;2CG/&#x2a;17CG</td>
<td align="center">IM</td>
<td align="center">0.015</td>
<td align="center">0</td>
<td align="center">0</td>
<td align="center">0</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="Tfn3">
<label>
<sup>&#x23;</sup>
</label>
<p>number of individuals in brackets.</p>
</fn>
<fn id="Tfn4">
<label>
<sup>&#x23;&#x23;</sup>
</label>
<p>diplotypes were denoted and phenotypes were assigned as proposed by <xref ref-type="bibr" rid="B4">Br&#xe5;ten et al. (2021)</xref>.</p>
</fn>
<fn>
<p>NM, normal metabolizer; RM, rapid metabolizer, UM, ultrarapid metabolizer; IM, intermediate metabolizer; PM, poor metabolizer.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Distribution of predicted CYP2C19 metabolic phenotypes in the study cohorts. In each pair of columns, the left column shows phenotype prediction based on <italic>CYP2C19</italic> diplotypes (<xref ref-type="table" rid="T2">Table 2</xref>) and the right column shows phenotype prediction based on <italic>CYP2C19-CYP2C</italic> diplotypes (<xref ref-type="table" rid="T3">Table 3</xref>). The two assignment procedures resulted in highly significant differences in phenotype distribution in all cohorts (chi-square <italic>p</italic> &#x3c; 0.0001). NM, normal metabolizer; RM, rapid metabolizer; UM, ultrarapid metabolizer; IM intermediate metabolizer.</p>
</caption>
<graphic xlink:href="fgene-14-1114742-g001.tif"/>
</fig>
<p>The fact that all study individuals with <italic>CYP2C19:CYP2C</italic>-predicted RM or UM phenotypes carry the <italic>CYP2C19&#x2a;1/&#x2a;1</italic> diplotype might possibly offer an explanation for the reported discordance, alluded above, between <italic>CYP2C19</italic>-predicted and pharmacokinetically-verified phenotypes in Native Americans (<xref ref-type="bibr" rid="B7">de Andr&#xe9;s et al., 2017</xref>; <xref ref-type="bibr" rid="B17">Naranjo et al., 2018</xref>). However, we are fully aware that functional studies involving genotypic correlations with pharmacokinetic parameters are warranted to validate this suggestion. Modulation of CYP2C19 activity by the <italic>CYP2C:TG</italic> genotype was first observed in relation to escitalopram disposition in a cohort of predominantly &#x201c;White origin&#x201d; (<xref ref-type="bibr" rid="B4">Br&#xe5;ten et al., 2021</xref>), and was subsequently associated with failure of omeprazole treatment of New Zealand European GERD (gastroesophageal reflux disease) patients (<xref ref-type="bibr" rid="B12">Kee et al., 2022</xref>). There is no comparative data in Native American populations. The mechanism whereby the <italic>CYP2C:TG</italic> haplotype may increase CYP2C19-dependent metabolism needs to be addressed further. <xref ref-type="bibr" rid="B4">Br&#xe5;ten <italic>et al.</italic> (2021)</xref> suggested tentatively that the rs2860840&#xa0;T allele &#x201c;has a functional role as increasing the enhancer function and <italic>CYP2C19</italic> expression&#x201d; whereas the rs11188059 A variant abolishes this effect, such that the <italic>CYP2C:TG</italic>-haplotype, but not the <italic>CYP2C:TA</italic> haplotype associates with increased CYP2C19 activity. There is prior evidence for long-range haplotypes across the <italic>CYP2C</italic> cluster, that may form functional units, notably one defined by rs12777823, an intergenic polymorphism reported to be strongly associated with requirement of reduced warfarin doses among African Americans and black Africans (<xref ref-type="bibr" rid="B19">Perera et al., 2013</xref>; <xref ref-type="bibr" rid="B18">Ndadza et al., 2019</xref>).</p>
<p>We acknowledge the low number of individuals of distinct groups in the HGDP and Guarani cohorts as a limitation of our study. Practical and ethical difficulties are commonly encountered in recruiting participants from Native American populations, such that in a recent overview of the distribution of <italic>CYP2C19</italic> variants and predicted phenotypes among Native American groups, 9 out of the 19 studied cohorts had less than 50 individuals (<xref ref-type="bibr" rid="B21">Rodrigues-Soares et al., 2020</xref>). In addition, we caution that the present data should not be interpreted as representative of all extant Amerindian populations, in view of their high level of (pharmaco)genetic diversity (<xref ref-type="bibr" rid="B9">Gaspar et al., 2002</xref>; <xref ref-type="bibr" rid="B23">Suarez-Kurtz et al., 2019</xref>; <xref ref-type="bibr" rid="B8">Fernandes et al., 2022</xref>).</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s4">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="s10">Supplementary Material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s5">
<title>Ethics statement</title>
<p>The studies involving human participants were reviewed and approved by CONEP. The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s6">
<title>Author contributions</title>
<p>VF performed the allele discrimination genotyping, MP participated in data collection, LT and ME provided the Kaingang and Guarani samples, GK designed the study and wrote the original manuscript. All authors contributed to data analyses and to the final manuscript.</p>
</sec>
<sec id="s7">
<title>Funding</title>
<p>The authors acknowledge grant support from the Brazilian agencies Conselho Nacional de Desenvolvimento Cient&#xed;fico e Tecnol&#xf3;gico (CNPq) and Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado do Rio de Janeiro (FAPERJ).</p>
</sec>
<sec sec-type="COI-statement" id="s8">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s9">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranted or endorsed by the publisher.</p>
</sec>
<sec id="s10">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fgene.2023.1114742/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fgene.2023.1114742/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.DOCX" id="SM1" mimetype="application/DOCX" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<ref-list>
<title>References</title>
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