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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2025.1631088</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Cytotoxic T lymphocyte-associated protein 4 gene polymorphisms are associated with ANCA-associated vasculitis in the Guangxi population of China</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Bu</surname>
<given-names>Kunpeng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2979741/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Binglan</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3185874/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>He</surname>
<given-names>Peigeng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Xue</surname>
<given-names>Chao</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1579258/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
<role content-type="https://credit.niso.org/contributor-roles/project-administration/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Endocrinology and Metabolism Nephrology Department, Guangxi Medical University Cancer Hospital</institution>, <addr-line>Nanning, Guangxi</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University</institution>, <addr-line>Nanning, Guangxi</addr-line>,&#xa0;<country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/623217/overview">Piero Ruscitti</ext-link>, University of L&#x2019;Aquila, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Micha&#x142; Jakubaszek, Rheumatology and Rehabilitation, Poland</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3076578/overview">Suresh Babu Sayana</ext-link>, Bhadradri Kothagudem, India</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Chao Xue, <email xlink:href="mailto:xuechao@stu.gxmu.edu.cn">xuechao@stu.gxmu.edu.cn</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>09</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1631088</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>11</day>
<month>08</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Bu, Yang, He and Xue.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Bu, Yang, He and Xue</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>
<sec>
<title>Introduction</title>
<p>Heredity and epigenetic factors contribute to the pathogenesis of neutrophil-cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Cytotoxic T lymphocyte-associated protein 4 (CTLA-4), an inhibitory receptor regulating T-cell homeostasis and maintaining self-tolerance, has emerged as a key target for immune screening and therapeutics in autoimmunity and cancer. CTLA-4 is associated with various autoimmune diseases; however, the relationship between CTLA-4 polymorphisms and AAV in the Guangxi population of China remains underexplored. In the present case&#x2013;control study, we evaluated the effects of CTLA-4 polymorphisms on AAV susceptibility in the Guangxi population of China.</p>
</sec>
<sec>
<title>Methods</title>
<p>A  total of 343 patients with AAV and 343 healthy controls were recruited. High-throughput sequencing was used to genotype <italic>CTLA4</italic> variants, and logistic regression analysis was used to assess their association with AAV risk. The relationship between the haplotypes of <italic>CTLA4</italic> single-nucleotide polymorphisms (SNPs) and AAV risk was assessed using the SHEsis platform.</p>
</sec>
<sec>
<title>Results</title>
<p>Three <italic>CTLA4</italic> SNPs&#x2014; <italic>rs62182595, rs16840252</italic>, and <italic>rs5742909</italic>&#x2014; showed significant association with AAV susceptibility. The ATT and GCC haplotypes, comprising these loci, were also associated with an increased risk of AAV.</p>
</sec>
<sec>
<title>Discussion</title>
<p>These findings suggest that <italic>CTLA4</italic> polymorphisms (<italic>rs62182595, rs16840252</italic>, and <italic>rs5742909</italic>) may contribute to AAV susceptibility in the Guangxi population and offer preliminary markers for risk assessment, early diagnosis, and personalized management of AAV.</p>
</sec>
</abstract>
<kwd-group>
<kwd>CTLA-4</kwd>
<kwd>vasculitis</kwd>
<kwd>T-cell</kwd>
<kwd>autoimmune diseases</kwd>
<kwd>single-nucleotide polymorphism</kwd>
</kwd-group>
<counts>
<fig-count count="1"/>
<table-count count="6"/>
<equation-count count="0"/>
<ref-count count="43"/>
<page-count count="10"/>
<word-count count="4356"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Autoimmune and Autoinflammatory Disorders: Autoinflammatory Disorders</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) comprises a group of small-vessel inflammatory disorders with largely unknown pathogenesis. AAV is serologically defined by autoantibodies against two specific neutrophil antigens: proteinase 3 and myeloperoxidase (<xref ref-type="bibr" rid="B1">1</xref>). Pathologically, AAV is characterized by necrotizing inflammation of vessel walls. Clinically, AAV includes three principal subtypes: granulomatosis with polyangiitis (<xref ref-type="bibr" rid="B2">2</xref>), microscopic polyangiitis (MPA) (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>), and eosinophilic granulomatosis with polyangiitis (<xref ref-type="bibr" rid="B5">5</xref>). Although the exact pathogenesis of AAV is unknown, immune dysregulation, which is influenced by genetic and environmental factors, plays a central role in AAV pathogenesis (<xref ref-type="bibr" rid="B6">6</xref>). Genome-wide analyses have identified several predisposing loci, mainly within the major histocompatibility complex region. Specifically, variants in the human leukocyte antigen-DQ segment show strong genetic associations with AAV pathogenesis (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B8">8</xref>). In Asia, AAV prevalence ranges from 46 to 421 per million (<xref ref-type="bibr" rid="B9">9</xref>), and in China, its prevalence is increasing annually, especially among the aging population. The current standard treatment for AAV involves systemic immunosuppressants targeting B and T cells, including glucocorticoids, cyclophosphamide, and rituximab (<xref ref-type="bibr" rid="B10">10</xref>); however, many patients with AAV remain unresponsive to the current standard of care and experience disease relapses (<xref ref-type="bibr" rid="B11">11</xref>). Therefore, identification of novel therapeutic targets to achieve long-term disease remission is necessary.</p>
<p>Cytotoxic T lymphocyte-associated protein 4 (CTLA-4), a crucial immune checkpoint molecule within the CD28 immunoglobulin superfamily, exhibits predominant expression in T lymphocytes (<xref ref-type="bibr" rid="B12">12</xref>). CTLA-4 is an important regulator of T-cell homeostasis and self-tolerance and can modulate the T-cell response by blocking CD28 co-stimulation of T-cells during T-cell activation (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>). CTLA-4 expression in T cells is tightly regulated, and the failure of the transport mechanism of CTLA-4 to the cell surface leads to autoimmune susceptibility (<xref ref-type="bibr" rid="B14">14</xref>). Given its central role in immune modulation, CTLA-4 has emerged as a promising therapeutic target across multiple disease domains, particularly in autoimmune conditions and oncology. Moreover, CTLA-4-targeted therapies through the successful implementation of CTLA-4 inhibitors have been validated to manage various immune-mediated disorders clinically (<xref ref-type="bibr" rid="B12">12</xref>). In AAV, CTLA-4&#x2013;Ig fusion proteins that block T-cell co-stimulation offer a novel therapeutic strategy potentially advantageous over traditional immunosuppressive regimens (<xref ref-type="bibr" rid="B15">15</xref>). However, genetic studies have reported inconsistent associations between <italic>CTLA4</italic> polymorphisms and AAV. For instance, a European case&#x2013;control study identified the association between <italic>CTLA4</italic> polymorphism&#x2014; rs3087243 &#x2014;and AAV (<xref ref-type="bibr" rid="B16">16</xref>). In contrast, a case&#x2014;control study of the Han Chinese population in Beijing, China, reported no association between <italic>CTLA4</italic> polymorphisms (rs231775 and rs5742909) and AAV susceptibility (<xref ref-type="bibr" rid="B17">17</xref>). These findings suggest the strong influence of ethnicity, population, and regional variation on genetic susceptibility; however, the association between single-nucleotide polymorphisms (SNPs) at the <italic>CTLA4</italic> locus and AAV susceptibility in the Guangxi population has not been reported.</p>
<p>To address this gap, we conducted a case&#x2014;control study involving 343 patients with AAV from Guangxi and selected three candidate <italic>CTLA4</italic> SNPs to evaluate their association with AAV risk in this population.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Ethics approval</title>
<p>This study was approved by the Medical Ethics Committee of The Second Affiliated Hospital of Guangxi Medical University (2018 KY-0100) and was conducted in compliance with the Declaration of Helsinki. We have obtained written consent from all participants.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Study population characteristics</title>
<p>The MPA investigation cohort constituted 343 patients diagnosed with AAV retrospectively enrolled from the Second Affiliated Hospital of Guangxi Medical University (2005-2024). The cases were confirmed through rigorous diagnostic protocols aligned with the Chapel Hill Consensus Conference classification criteria (<xref ref-type="bibr" rid="B18">18</xref>). Exclusion criteria strictly applied to individuals presenting with: (1) secondary vasculitides induced by infectious processes, neoplastic disorders, or pharmacological agents; (2) concurrent autoimmune conditions (including rheumatoid arthritis, systemic lupus erythematosus, Henoch-Sch&#xf6;nlein purpura). A parallel control group of 343 age- and sex-matched healthy volunteers was established, systematically excluding participants with autoimmune comorbidities or malignant histories.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>SNP selection and DNA extraction</title>
<p>We selected three <italic>CTLA4</italic> SNPs (<italic>rs62182595</italic>, <italic>rs16840252</italic>, and <italic>rs5742909</italic>) based on the following criteria: 1) all SNPs had a minor allele frequency (MAF) &#x2265; 0.05 (<xref ref-type="bibr" rid="B19">19</xref>) and 2) Chinese allele or genotype data of these SNPs were available in 1000 genomes (<uri xlink:href="https://grch37.ensembl.org/">https://grch37.ensembl.org/</uri>).</p>
<p>DNA extraction was performed using a Rapid Blood Genomic DNA Isolation Kit (Sangon, Shanghai, China), following the manufacturer&#x2019;s instructions. The concentration of the DNA was measured using a Qubit 2.0 (Thermo Fisher Scientific, Inc.) to ensure the extraction of adequate amounts of high-quality genomic DNA.</p>
</sec>
<sec id="s2_4">
<label>2.4</label>
<title>Statistical analyses</title>
<p>All statistical analyses were conducted within a rigorous statistical framework using IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA) (<xref ref-type="bibr" rid="B20">20</xref>). Initial demographic comparisons were performed using independent samples t-tests for continuous variables and chi-square (&#x3c7;&#xb2;) test for categorical variables. Hardy&#x2013;Weinberg equilibrium (HWE) for genotype and allele distribution was assessed using both &#x3c7;&#xb2; and Fisher&#x2019;s exact tests. Genetic association analysis was conducted using an unmatched case&#x2013;control paradigm through the SNPstats analytical platform (<ext-link ext-link-type="uri" xlink:href="https://www.snpstats.net/start.htm">https://www.snpstats.net/start.htm</ext-link>) (<xref ref-type="bibr" rid="B21">21</xref>), applying multivariate logistic regression with covariate adjustment (age, ethnicity, gender) to derive adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Haplotype architecture reconstruction (<xref ref-type="bibr" rid="B22">22</xref>) was performed using the SHEsis online suite (<xref ref-type="bibr" rid="B23">23</xref>), incorporating permutation-based significance testing. To control the Type I in multi-group comparisons, Bonferroni multiplicity correction was applied, maintaining a family-wise significance threshold set at &#x3b1;=0.05.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Demographic characteristics</title>
<p>The study cohort comprised equal numbers of patients with AAV (n = 343) and healthy controls (n = 343). Gender distribution showed comparable patterns across groups, with the AAV cohort comprising 130 male and 213 female participants, compared to 146 males and 197 females in the control group (P = 0.213). Age distribution revealed significant intergroup variation. The AAV group showed a mean age of 56.7 years (range, 8&#x2013;86 years; interquartile range (IQR), 49&#x2013;67 years), which was significantly higher than that of the control group (mean, 44.6 years, range: 18&#x2013;81 years; IQR, 35&#x2013;53 years) (P &lt; 0.05). Ethnic composition was comparable between the groups, with 202 Han Chinese and 141 non-Han Chinese participants in both cohorts (P = 0.738). To ensure analytical rigor, potential confounding variables, including age, sex, and ethnicity, were incorporated as covariates in the logistic regression analysis (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Demographic features of AAV cases and control group.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Variable</th>
<th valign="middle" align="center">Case (n=343)</th>
<th valign="middle" align="center">Control (n=343)</th>
<th valign="middle" align="center">
<italic>p</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">Gender (male/female)</td>
<td valign="middle" align="center">130/213</td>
<td valign="middle" align="center">146/197</td>
<td valign="middle" align="center">0.213</td>
</tr>
<tr>
<td valign="middle" align="center">Ethnicity (Han/non-Han)</td>
<td valign="middle" align="center">202/141</td>
<td valign="middle" align="center">251/184</td>
<td valign="middle" align="center">0.738</td>
</tr>
<tr>
<td valign="middle" align="center">Age (years)</td>
<td valign="middle" align="center">56.6(49,67) <sup>a</sup>
</td>
<td valign="middle" align="center">44.6 (35,53) <sup>a</sup>
</td>
<td valign="middle" align="center">&lt;0.01*</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AAV, Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis; n, number of people; a, The description of skewed distribution data statistics uses median (lower quartile, upper quartile) representation; <italic>p</italic>-value: The Student&#x2019;s t-test or the Chi-square test was used to compare variables in groups; * denotes statistical significance (p&lt;0.05).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Information for selected SNPs</title>
<p>Basic information for the selected <italic>CTLA4</italic> SNPs is shown in <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>. All loci are located on chromosome 2, specifically within the promoter region and 5&#x2019; upstream regulatory region (URR). The MAF for each SNP was greater than 0.05, indicating that variants are common in the Guangxi population. Additionally, all SNPs conformed to HWE (p &gt; 0.05).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Basic information of the selected SNPs.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="center">SNP</th>
<th valign="middle" rowspan="2" align="center">Chromosome</th>
<th valign="middle" rowspan="2" align="center">Position</th>
<th valign="middle" rowspan="2" align="center">Alleles</th>
<th valign="middle" rowspan="2" align="center">Gene</th>
<th valign="middle" rowspan="2" align="center">Location</th>
<th valign="middle" colspan="2" align="center">MAF</th>
<th valign="middle" rowspan="2" align="center">HWE-<italic>p</italic>
</th>
<th valign="middle" rowspan="2" align="center">Allele-<italic>p</italic>
</th>
<th valign="middle" rowspan="2" align="center">Genotype-<italic>p</italic>
</th>
</tr>
<tr>
<th valign="middle" align="center">Control</th>
<th valign="middle" align="center">Case</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">rs62182595</td>
<td valign="middle" align="center">Chr2</td>
<td valign="middle" align="center">204731188</td>
<td valign="middle" align="center">G&gt;A</td>
<td valign="middle" align="center">CTLA4</td>
<td valign="middle" align="center">promoter</td>
<td valign="middle" align="center">0.11</td>
<td valign="middle" align="center">0.10</td>
<td valign="middle" align="center">0.24</td>
<td valign="middle" align="center">0.42</td>
<td valign="middle" align="center">0.19</td>
</tr>
<tr>
<td valign="middle" align="center">rs16840252</td>
<td valign="middle" align="center">Chr2</td>
<td valign="middle" align="center">204731519</td>
<td valign="middle" align="center">C&gt;T</td>
<td valign="middle" align="center">CTLA4</td>
<td valign="middle" align="center">5&#x2019; URR</td>
<td valign="middle" align="center">0.12</td>
<td valign="middle" align="center">0.10</td>
<td valign="middle" align="center">0.13</td>
<td valign="middle" align="center">0.34</td>
<td valign="middle" align="center">0.29</td>
</tr>
<tr>
<td valign="middle" align="center">rs5742909</td>
<td valign="middle" align="center">Chr2</td>
<td valign="middle" align="center">204732347</td>
<td valign="middle" align="center">C&gt;T</td>
<td valign="middle" align="center">CTLA4</td>
<td valign="middle" align="center">promoter</td>
<td valign="middle" align="center">0.11</td>
<td valign="middle" align="center">0.10</td>
<td valign="middle" align="center">0.11</td>
<td valign="middle" align="center">0.48</td>
<td valign="middle" align="center">0.24</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AAV, Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis; SNP, single nucleotide polymorphism; MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; chr, chromosome; The <italic>p</italic>-value was calculated using Chi-square test and Fisher&#x2019;s exact test; Bonferroni correction was used for multiple comparison.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Correlation of SNPs with AAV susceptibility under different genetic models</title>
<p>The association between the SNPs and AAV risk were assessed using different genetic models (adjusted for age, sex, and race). The <italic>rs62182595</italic>, <italic>rs16840252</italic>, and <italic>rs5742909</italic> polymorphisms in <italic>CTLA4</italic> were significantly associated with AAV susceptibility in the Guangxi population under different genetic models (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). For <italic>rs62182595</italic>, the GA genotype in the codominant genetic model (OR = 0.58; 95% CI [0.38&#x2013;0.90]; p = 0.048), the GA-AA genotype in the dominant genetic model (0.61; 0.40&#x2013;0.93; p = 0.021), and GA genotypes in the overdominant genetic model (0.58; 0.38&#x2013;0.90; p = 0.014), were all associated with reduced AAV susceptibility in the Guangxi population. For <italic>rs16840252</italic>, the CT-TT genotype in the dominant genetic model (OR = 0.61; 95% CI [0.40&#x2013;0.93]; p = 0.022) and the C/T genotype in the overdominant genetic model (0.60; 0.39&#x2013;0.92; p = 0.019) were also associated with reduced risk of AAV. In contrast, only the CT genotype at <italic>rs5742909</italic> in the overdominant genetic model showed association with reduced susceptibility to AAV (OR =0.64; 95% CI [0.42&#x2013;0.99]; p = 0.044).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The relationship between the SNPs and the risk of AAV in Guangxi population in different genetic models.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">SNP</th>
<th valign="middle" align="center">Models</th>
<th valign="middle" align="center">Genotype/Allele</th>
<th valign="middle" align="center">Control(freq)</th>
<th valign="middle" align="center">Case(freq)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic> &#x2212;value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">rs62182595</td>
<td valign="middle" rowspan="2" align="center">Allele</td>
<td valign="middle" align="center">A</td>
<td valign="middle" align="left">75(10.9%)</td>
<td valign="middle" align="center">66(9.6%)</td>
<td valign="middle" rowspan="2" align="center">0.87(0.61-1.23)</td>
<td valign="middle" rowspan="2" align="center">0.42</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G</td>
<td valign="middle" align="left">611(89.1%)</td>
<td valign="middle" align="center">620(90.4%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="3" align="center">Codominant</td>
<td valign="middle" align="center">G/G</td>
<td valign="middle" align="left">270 (78.7%)</td>
<td valign="middle" align="center">282 (82.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="3" align="center">0.048*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A</td>
<td valign="middle" align="left">71 (20.7%)</td>
<td valign="middle" align="center">56 (16.3%)</td>
<td valign="middle" align="center">0.58 (0.38-0.90)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">A/A</td>
<td valign="middle" align="left">2 (0.6%)</td>
<td valign="middle" align="center">5 (1.5%)</td>
<td valign="middle" align="center">1.32 (0.21-8.34)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Dominant</td>
<td valign="middle" align="center">G/G</td>
<td valign="middle" align="left">270 (78.7%)</td>
<td valign="middle" align="center">282 (82.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.021*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A-A/A</td>
<td valign="middle" align="left">73 (21.3%)</td>
<td valign="middle" align="center">61 (17.8%)</td>
<td valign="middle" align="center">0.61 (0.40-0.93)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Recessive</td>
<td valign="middle" align="center">G/G-G/A</td>
<td valign="middle" align="left">341 (99.4%)</td>
<td valign="middle" align="center">338 (98.5%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.68</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">A/A</td>
<td valign="middle" align="left">2 (0.6%)</td>
<td valign="middle" align="center">5 (1.5%)</td>
<td valign="middle" align="center">1.46 (0.23-9.17)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Overdominant</td>
<td valign="middle" align="center">G/G-A/A</td>
<td valign="middle" align="left">272 (79.3%)</td>
<td valign="middle" align="center">287 (83.7%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.014*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A</td>
<td valign="middle" align="left">71 (20.7%)</td>
<td valign="middle" align="center">56 (16.3%)</td>
<td valign="middle" align="center">0.58 (0.38-0.90)</td>
</tr>
<tr>
<td valign="middle" align="center">rs16840252</td>
<td valign="middle" align="center">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="left">606 (88.3%)</td>
<td valign="middle" align="center">617 (89.9%)</td>
<td valign="middle" rowspan="2" align="center">1.18(0.84-1.66)</td>
<td valign="middle" rowspan="2" align="center">0.34</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="left">80 (11.7%)</td>
<td valign="middle" align="center">69 (10.1%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Codominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">267 (77.8%)</td>
<td valign="middle" align="center">280 (81.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.063</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">72 (21%)</td>
<td valign="middle" align="center">57 (16.6%)</td>
<td valign="middle" align="center">0.60 (0.39-0.92)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">4 (1.2%)</td>
<td valign="middle" align="center">6 (1.8%)</td>
<td valign="middle" align="center">0.88 (0.20-3.87)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">267 (77.8%)</td>
<td valign="middle" align="center">280 (81.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.022*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">CT-TT</td>
<td valign="middle" align="left">76 (22.2%)</td>
<td valign="middle" align="center">63 (18.4%)</td>
<td valign="middle" align="center">0.61 (0.40-0.93)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">C/C-C/T</td>
<td valign="middle" align="left">339 (98.8%)</td>
<td valign="middle" align="center">337 (98.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.98</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">4 (1.2%)</td>
<td valign="middle" align="center">6 (1.8%)</td>
<td valign="middle" align="center">0.98 (0.23-4.24)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Overdominant</td>
<td valign="middle" align="center">C/C-T/T</td>
<td valign="middle" align="left">271 (79%)</td>
<td valign="middle" align="center">286 (83.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.019*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">72 (21%)</td>
<td valign="middle" align="center">57 (16.6%)</td>
<td valign="middle" align="center">0.60 (0.39-0.92)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center">rs5742909</td>
<td valign="middle" align="center">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="left">610(88.9%)</td>
<td valign="middle" align="center">618(90.1%)</td>
<td valign="middle" rowspan="2" align="center">1.13(0.80-1.60)</td>
<td valign="middle" rowspan="2" align="center">0.48</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="left">76(11.1%)</td>
<td valign="middle" align="center">68(9.9%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Codominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">270 (78.7%)</td>
<td valign="middle" align="center">281 (81.9%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.13</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">70 (20.4%)</td>
<td valign="middle" align="center">56 (16.3%)</td>
<td valign="middle" align="center">0.64 (0.42-0.99)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">3 (0.9%)</td>
<td valign="middle" align="center">6 (1.8%)</td>
<td valign="middle" align="center">1.13 (0.22-5.70)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">270 (78.7%)</td>
<td valign="middle" align="center">281 (81.9%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.056</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T-T/T</td>
<td valign="middle" align="left">73 (21.3%)</td>
<td valign="middle" align="center">62 (18.1%)</td>
<td valign="middle" align="center">0.66 (0.43-1.01)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">C/C-C/T</td>
<td valign="middle" align="left">340 (99.1%)</td>
<td valign="middle" align="center">337 (98.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.8</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">3 (0.9%)</td>
<td valign="middle" align="center">6 (1.8%)</td>
<td valign="middle" align="center">1.23 (0.24-6.15)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Overdominant</td>
<td valign="middle" align="center">C/C-T/T</td>
<td valign="middle" align="left">273 (79.6%)</td>
<td valign="middle" align="center">287 (83.7%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.044*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">70 (20.4%)</td>
<td valign="middle" align="center">56 (16.3%)</td>
<td valign="middle" align="center">0.64 (0.42-0.99)</td>
<td valign="middle" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AAV, Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; The p-value, OR, and 95% CI were derived from a logistic regression model adjusted for age, ethnicity, and gender; * denotes statistical significance (<italic>p</italic>&lt;0.05).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Correlation between selected SNPs and risk of AAV development in different subgroups of the population</title>
<p>To assess the population subgroups in which the above <italic>CTLA4</italic> SNPs specifically influence the risk of developing AAV, subgroup analyses were performed by sex (male and female) and ethnicity (Han Chinese and non-Han Chinese). The analysis revealed a significant influence of sex and ethnicity on the association between the three SNPs and AAV risk. Specifically, across different genetic models, all three SNPs&#x2014;<italic>rs62182595, rs16840252</italic>, and <italic>rs5742909</italic>&#x2014; were significantly associated with reduced AAV susceptibility in the male subgroup (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>), whereas no significant association was observed in the female subgroup (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;1</bold>
</xref>). Regarding ethnicity, the strongest protective associations were observed in the Han Chinese subgroup (<xref ref-type="table" rid="T5">
<bold>Table&#xa0;5</bold>
</xref>, <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;2</bold>
</xref>), suggesting a population-specific genetic effect on AAV risk.</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>The relationship between the SNPs and the risk of AAV in male of Guangxi population in different genetic models.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">SNP</th>
<th valign="middle" align="center">Models</th>
<th valign="middle" align="center">Genotype/Allele</th>
<th valign="middle" align="center">Control(freq)</th>
<th valign="middle" align="center">Case(freq)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic> &#x2212;value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">rs62182595</td>
<td valign="middle" rowspan="2" align="center">Allele</td>
<td valign="middle" align="center">A</td>
<td valign="middle" align="left">35(12.0%)</td>
<td valign="middle" align="center">20(7.7%)</td>
<td valign="middle" rowspan="2" align="center">0.61(0.34~1.09)</td>
<td valign="middle" rowspan="2" align="center">0.092</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G</td>
<td valign="middle" align="left">257(88.0%)</td>
<td valign="middle" align="center">240(92.3%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="3" align="center">Codominant</td>
<td valign="middle" align="center">G/G</td>
<td valign="middle" align="left">112 (76.7%)</td>
<td valign="middle" align="center">111 (85.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="3" align="center">0.054</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A</td>
<td valign="middle" align="left">33 (22.6%)</td>
<td valign="middle" align="center">18 (13.8%)</td>
<td valign="middle" align="center">0.41 (0.20-0.87)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">A/A</td>
<td valign="middle" align="left">1 (0.7%)</td>
<td valign="middle" align="center">1 (0.8%)</td>
<td valign="middle" align="center">0.37 (0.01-18.35)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Dominant</td>
<td valign="middle" align="center">G/G</td>
<td valign="middle" align="left">112 (76.7%)</td>
<td valign="middle" align="center">111 (85.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.016*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A-A/A</td>
<td valign="middle" align="left">34 (23.3%)</td>
<td valign="middle" align="center">19 (14.6%)</td>
<td valign="middle" align="center">0.41 (0.20-0.86)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Recessive</td>
<td valign="middle" align="center">G/G-G/A</td>
<td valign="middle" align="left">145 (99.3%)</td>
<td valign="middle" align="center">129 (99.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.67</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">A/A</td>
<td valign="middle" align="left">1 (0.7%)</td>
<td valign="middle" align="center">1 (0.8%)</td>
<td valign="middle" align="center">0.44 (0.01-20.84)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Overdominant</td>
<td valign="middle" align="center">G/G-A/A</td>
<td valign="middle" align="left">113 (77.4%)</td>
<td valign="middle" align="center">112 (86.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.018*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A</td>
<td valign="middle" align="left">33 (22.6%)</td>
<td valign="middle" align="center">18 (13.8%)</td>
<td valign="middle" align="center">0.41(0.20-0.87)</td>
</tr>
<tr>
<td valign="middle" align="center">rs16840252</td>
<td valign="middle" align="center">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="left">255(87.3%)</td>
<td valign="middle" align="center">240(92.3%)</td>
<td valign="middle" rowspan="2" align="center">0.74(0.98~3.08)</td>
<td valign="middle" rowspan="2" align="center">0.055</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="left">37(12.7%)</td>
<td valign="middle" align="center">20(7.7%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Codominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">110 (75.3%)</td>
<td valign="middle" align="center">111 (85.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.034*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">35 (24%)</td>
<td valign="middle" align="center">18 (13.8%)</td>
<td valign="middle" align="center">0.39 (0.19-0.81)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">1 (0.7%)</td>
<td valign="middle" align="center">1 (0.8%)</td>
<td valign="middle" align="center">0.36 (0.01-18.15)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">110 (75.3%)</td>
<td valign="middle" align="center">111 (85.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0093*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T-T/T</td>
<td valign="middle" align="left">36 (24.7%)</td>
<td valign="middle" align="center">19 (14.6%)</td>
<td valign="middle" align="center">0.39 (0.19-0.81)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">C/C-C/T</td>
<td valign="middle" align="left">145 (99.3%)</td>
<td valign="middle" align="center">129 (99.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.67</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">1 (0.7%)</td>
<td valign="middle" align="center">1 (0.8%)</td>
<td valign="middle" align="center">0.44 (0.01-20.84)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Overdominant</td>
<td valign="middle" align="center">C/C-T/T</td>
<td valign="middle" align="left">111 (76%)</td>
<td valign="middle" align="center">112 (86.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.011*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">35 (24%)</td>
<td valign="middle" align="center">18 (13.8%)</td>
<td valign="middle" align="center">0.39 (0.19-0.82)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center">rs5742909</td>
<td valign="middle" align="center">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="left">254(87.0%)</td>
<td valign="middle" align="center">240(92.3%)</td>
<td valign="middle" rowspan="2" align="center">1.80(1.02-3.17)</td>
<td valign="middle" rowspan="2" align="center">0.04*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="left">38(13.0%)</td>
<td valign="middle" align="center">20(7.7%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Codominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">110 (75.3%)</td>
<td valign="middle" align="center">111 (85.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.019*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">34 (23.3%)</td>
<td valign="middle" align="center">18 (13.8%)</td>
<td valign="middle" align="center">0.37 (0.18-0.78)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">2 (1.4%)</td>
<td valign="middle" align="center">1 (0.8%)</td>
<td valign="middle" align="center">0.20 (0.01-5.04)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">110 (75.3%)</td>
<td valign="middle" align="center">111 (85.4%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0053*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T-T/T</td>
<td valign="middle" align="left">36 (24.7%)</td>
<td valign="middle" align="center">19 (14.6%)</td>
<td valign="middle" align="center">0.36 (0.17-0.75)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">C/C-C/T</td>
<td valign="middle" align="left">144 (98.6%)</td>
<td valign="middle" align="center">129 (99.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.37</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">2 (1.4%)</td>
<td valign="middle" align="center">1 (0.8%)</td>
<td valign="middle" align="center">0.24 (0.01-5.90)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Overdominant</td>
<td valign="middle" align="center">C/C-T/T</td>
<td valign="middle" align="left">112 (76.7%)</td>
<td valign="middle" align="center">112 (86.2%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0087*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">34 (23.3%)</td>
<td valign="middle" align="center">18 (13.8%)</td>
<td valign="middle" align="center">0.38 (0.18-0.80)</td>
<td valign="middle" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AAV, Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis; OR, odds ratio; CI, confidence interval; The <italic>p</italic>-value, OR, and 95% CI were derived from a logistic regression model adjusted for age and ethnicity; * denotes statistical significance (<italic>p</italic>&lt;0.05).</p>
</fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="T5" position="float">
<label>Table&#xa0;5</label>
<caption>
<p>The relationship between the SNPs and the risk of AAV in Han of ethnicity Guangxi population in different genetic models.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">SNP</th>
<th valign="middle" align="center">Models</th>
<th valign="middle" align="center">Genotype/Allele</th>
<th valign="middle" align="center">Control(freq)</th>
<th valign="middle" align="center">Case(freq)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic> &#x2212;value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">rs62182595</td>
<td valign="middle" rowspan="2" align="center">Allele</td>
<td valign="middle" align="center">A</td>
<td valign="middle" align="left">60(12.0%)</td>
<td valign="middle" align="center">26(06.4%)</td>
<td valign="middle" rowspan="2" align="center">0.50(0.31~0.82)</td>
<td valign="middle" rowspan="2" align="center">0.0048*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G</td>
<td valign="middle" align="left">442(88.0%)</td>
<td valign="middle" align="center">378(93.6%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="3" align="center">Codominant</td>
<td valign="middle" align="center">G/G</td>
<td valign="middle" align="left">193 (76.9%)</td>
<td valign="middle" align="center">176 (87.1%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="3" align="center">0.0037*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A</td>
<td valign="middle" align="left">56 (22.3%)</td>
<td valign="middle" align="center">26 (12.9%)</td>
<td valign="middle" align="center">0.43 (0.25-0.75)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">A/A</td>
<td valign="middle" align="left">2 (0.8%)</td>
<td valign="middle" align="center">0 (0%)</td>
<td valign="middle" align="center">0.00 (0.00-NA)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Dominant</td>
<td valign="middle" align="center">G/G</td>
<td valign="middle" align="left">193 (76.9%)</td>
<td valign="middle" align="center">176 (87.1%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.0014*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A-A/A</td>
<td valign="middle" align="left">58 (23.1%)</td>
<td valign="middle" align="center">26 (12.9%)</td>
<td valign="middle" align="center">0.42 (0.24-0.72)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Recessive</td>
<td valign="middle" align="center">G/G-G/A</td>
<td valign="middle" align="left">249 (99.2%)</td>
<td valign="middle" align="center">202 (100%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.17</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">A/A</td>
<td valign="middle" align="left">2 (0.8%)</td>
<td valign="middle" align="center">0 (0%)</td>
<td valign="middle" align="center">0.00 (0.00-NA)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" rowspan="2" align="center">Overdominant</td>
<td valign="middle" align="center">G/G-A/A</td>
<td valign="middle" align="left">195 (77.7%)</td>
<td valign="middle" align="center">176 (87.1%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" rowspan="2" align="center">0.0026*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">G/A</td>
<td valign="middle" align="left">56(22.3%)</td>
<td valign="middle" align="center">26(12.9%)</td>
<td valign="middle" align="center">0.43(0.25-0.76)</td>
</tr>
<tr>
<td valign="middle" align="center">rs16840252</td>
<td valign="middle" align="center">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="left">438(87.3%)</td>
<td valign="middle" align="center">377(93.3%)</td>
<td valign="middle" rowspan="2" align="center">2.04(1.27~3.27)</td>
<td valign="middle" rowspan="2" align="center">0.0025*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="left">64(12.7%)</td>
<td valign="middle" align="center">27(6.7%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Codominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">191 (76.1%)</td>
<td valign="middle" align="center">175 (86.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0029*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">56 (22.3%)</td>
<td valign="middle" align="center">27 (13.4%)</td>
<td valign="middle" align="center">0.45 (0.26-0.79)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">4 (1.6%)</td>
<td valign="middle" align="center">0 (0%)</td>
<td valign="middle" align="center">0.00 (0.00-NA)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">191 (76.1%)</td>
<td valign="middle" align="center">175 (86.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0018*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T-T/T</td>
<td valign="middle" align="left">60 (23.9%)</td>
<td valign="middle" align="center">27 (13.4%)</td>
<td valign="middle" align="center">0.43 (0.25-0.74)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">C/C-C/T</td>
<td valign="middle" align="left">247 (98.4%)</td>
<td valign="middle" align="center">202 (100%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.064</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">4 (1.6%)</td>
<td valign="middle" align="center">0 (0%)</td>
<td valign="middle" align="center">0.00 (0.00-NA)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Overdominant</td>
<td valign="middle" align="center">C/C-T/T</td>
<td valign="middle" align="left">195 (77.7%)</td>
<td valign="middle" align="center">175 (86.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0049*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">56 (22.3%)</td>
<td valign="middle" align="center">27 (13.4%)</td>
<td valign="middle" align="center">0.46 (0.26-0.80)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center">rs5742909</td>
<td valign="middle" align="center">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="left">444(88.4%)</td>
<td valign="middle" align="center">377(93.3%)</td>
<td valign="middle" rowspan="2" align="center">1.82(1.13~2.94)</td>
<td valign="middle" rowspan="2" align="center">0.012*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="left">58(11.6%)</td>
<td valign="middle" align="center">27(6.7%)</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Codominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">195 (77.7%)</td>
<td valign="middle" align="center">175 (86.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.02*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">54 (21.5%)</td>
<td valign="middle" align="center">27 (13.4%)</td>
<td valign="middle" align="center">0.49 (0.28-0.85)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">2 (0.8%)</td>
<td valign="middle" align="center">0 (0%)</td>
<td valign="middle" align="center">0.00 (0.00-NA)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">C/C</td>
<td valign="middle" align="left">195 (77.7%)</td>
<td valign="middle" align="center">175 (86.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.0076*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T-T/T</td>
<td valign="middle" align="left">56 (22.3%)</td>
<td valign="middle" align="center">27 (13.4%)</td>
<td valign="middle" align="center">0.48 (0.28-0.83)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">C/C-C/T</td>
<td valign="middle" align="left">249 (99.2%)</td>
<td valign="middle" align="center">202 (100%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.27</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">T/T</td>
<td valign="middle" align="left">2 (0.8%)</td>
<td valign="middle" align="center">0 (0%)</td>
<td valign="middle" align="center">0.00 (0.00-NA)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">Overdominant</td>
<td valign="middle" align="center">C/C-T/T</td>
<td valign="middle" align="left">197 (78.5%)</td>
<td valign="middle" align="center">175 (86.6%)</td>
<td valign="middle" align="center">1.00</td>
<td valign="middle" align="center">0.011*</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="left">54 (21.5%)</td>
<td valign="middle" align="center">27 (13.4%)</td>
<td valign="middle" align="center">0.49 (0.28-0.86)</td>
<td valign="middle" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>AAV, Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis; OR, odds ratio; CI, confidence interval; The <italic>p</italic>-value, OR, and 95% CI were derived from a logistic regression model adjusted for age and gender; * denotes statistical significance (<italic>p</italic>&lt;0.05).</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Linkage disequilibrium and haplotype analyses</title>
<p>To evaluate the interactions among <italic>CTLA4</italic> SNPs, we performed LD and haplotype analyses using the SHEsis online platform. Strong LD was observed between <italic>rs62182595</italic> and <italic>rs16840252</italic> (D&#x2019; = 1), whereas moderate LD was observed between <italic>rs62182595</italic> and <italic>rs5742909</italic> (D&#x2019; = 0.739) and between <italic>rs16840252</italic> and <italic>rs5742909</italic> (D&#x2019; = 0.735) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Graphical representation of the SNP locations and LD structure.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-16-1631088-g001.tif">
<alt-text content-type="machine-generated">A linkage disequilibrium plot displaying three genetic variants labeled rs62182595, rs16840252, and rs5742909. The diamonds are shaded, with values 99 and 73 indicating the strength of linkage between these loci.&lt;</alt-text>
</graphic>
</fig>
<p>The haplotype analysis identified six haplotypes based on the three SNPs (<xref ref-type="table" rid="T6">
<bold>Table&#xa0;6</bold>
</xref>). Four of these haplotypes had a frequency of less than 0.05 (<xref ref-type="bibr" rid="B24">24</xref>), suggesting a low frequency of occurrence in the population and unstable inheritance, and were therefore excluded from further analyses. Two haplotypes, ATT and GCC, with frequencies greater than 0.05 significantly elevated the risk of AAV development in the Guangxi population (ATT: OR=1.67, 95% CI: 1.113&#x2013;2.503, p = 0.01; GCC: 1.80, 1.300&#x2013;2.494, p = 0.000355).</p>
<table-wrap id="T6" position="float">
<label>Table&#xa0;6</label>
<caption>
<p>Correlations between the haplotypes of CTLA-4 gene and AAV susceptibility.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Geng</th>
<th valign="middle" align="center">SNP</th>
<th valign="middle" align="center">Haplotypes</th>
<th valign="middle" align="center">Control(freq)</th>
<th valign="middle" align="center">Case(freq)</th>
<th valign="middle" align="center">&#x3c7; 2</th>
<th valign="middle" align="center">p</th>
<th valign="middle" align="center">OR (95% CI)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="4" align="center">CTLA-4</td>
<td valign="middle" rowspan="4" align="center">rs62182595/rs16840252/<break/>rs5742909</td>
<td valign="middle" align="center">ATC</td>
<td valign="middle" align="center">33.99(0.050)</td>
<td valign="middle" align="center">0.00(0.000)</td>
<td valign="middle" align="center">34.958</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="center">ATT</td>
<td valign="middle" align="center">41.01(0.060)</td>
<td valign="middle" align="center">66.00(0.096)</td>
<td valign="middle" align="center">6.252</td>
<td valign="middle" align="center">0.012437*</td>
<td valign="middle" align="center">1.669 (1.113~2.503)</td>
</tr>
<tr>
<td valign="middle" align="center">GCC</td>
<td valign="middle" align="center">571.01(0.832)</td>
<td valign="middle" align="center">617.00(0.899)</td>
<td valign="middle" align="center">12.771</td>
<td valign="middle" align="center">0.000355*</td>
<td valign="middle" align="center">1.801 (1.300~2.494)</td>
</tr>
<tr>
<td valign="middle" align="center">GCT</td>
<td valign="middle" align="center">34.99(0.051)</td>
<td valign="middle" align="center">0.00(0.000)</td>
<td valign="middle" align="center">36.013</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GTC</td>
<td valign="middle" align="center">5.00(0.007)</td>
<td valign="middle" align="center">1.00(0.001)</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GTT</td>
<td valign="middle" align="center">0.00(0.000)</td>
<td valign="middle" align="center">2.00(0.003)</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; The p-value was calculated using Chi-square test and Fisher&#x2019;s exact test; Bonferroni correction was used for multiple comparison; * denotes statistical significance (p&lt;0.05); &#x2013; denotes frequency&lt;0.05 in both control &amp; case has been dropped.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>The pathogenesis of AAV involves the complex interplay of host immunological dysregulation (encompassing both innate and adaptive immunity) and genetic&#x2013;environmental synergism (<xref ref-type="bibr" rid="B4">4</xref>). The breakdown of ANCA tolerance, which is critical to disease initiation, is frequently driven by aberrant immune cell dynamics and humoral immunity deficiencies (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B26">26</xref>). Nevertheless, emerging evidence from large-scale genomic investigations has substantiated the pivotal role of genetic predisposition in AAV development (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B27">27</xref>). In this study, we focused on identifying the risk alleles associated with AAV mechanisms and evaluated the correlation between <italic>CTLA4</italic> SNPs and AAV risk in the Guangxi population in China. Our findings expand the current understanding of genetic loci associated with AAV susceptibility. CTLA-4 is a master immune checkpoint receptor that delivers co-inhibitory signals crucial for attenuating excessive immune activation (<xref ref-type="bibr" rid="B28">28</xref>) and orchestrating immune response termination (<xref ref-type="bibr" rid="B29">29</xref>). Although its central regulatory role in maintaining immune homeostasis and preventing autoreactivity is well-established, the precise molecular mechanisms governing CTLA-4-mediated immune modulation remain incompletely elucidated (<xref ref-type="bibr" rid="B30">30</xref>). <italic>CTLA4</italic> polymorphisms demonstrate significant pleiotropic effects, showing associations with multiple immune-mediated pathologies including systemic lupus erythematosus, rheumatoid arthritis, autoimmune diabetes, and allograft rejection episodes (<xref ref-type="bibr" rid="B31">31</xref>&#x2013;<xref ref-type="bibr" rid="B33">33</xref>).</p>
<p>Our data show that <italic>CTLA4 rs62182595</italic>, <italic>rs16840252</italic>, and <italic>rs5742909</italic> polymorphisms are significantly associated with AAV susceptibility in the Guangxi population under various genetic models. Specifically, the GA genotype of <italic>rs62182595</italic> in the codominant and overdominant genetic models and the GA-AA genotype in the dominant genetic model were associated with reduced AAV susceptibility in the Guangxi population. Similarly, the CT-TT and C/T genotypes of <italic>rs16840252</italic> (dominant genetic model) and the C/T genotype of <italic>rs5742909</italic> (overdominant genetic model) showed association with reduced AAV risk in the Guangxi population.</p>
<p>Next, we analyzed the synergistic effects of haplotype combinations to assess the possible interactions among <italic>CTLA4</italic> SNPs and determine the effects of these three SNPs on AAV susceptibility in the Guangxi population. Among the six haplotypes identified, ATT and GCC haplotypes significantly elevated susceptibility, suggesting an interaction between the three SNPs that increases the susceptibility to AAV in the Guangxi population, similar to the results of a previous study on CTLA-4 (<xref ref-type="bibr" rid="B34">34</xref>). Furthermore, previous studies suggest that SNP polymorphisms in the 5&#x2032; URR region may lead to altered selective splicing (<xref ref-type="bibr" rid="B35">35</xref>&#x2013;<xref ref-type="bibr" rid="B37">37</xref>), and alterations in selective splicing can lead to variation in <italic>CTLA4</italic>. Therefore, we hypothesized that alterations in bases at <italic>rs1684025</italic> may alter the selective splicing of both the soluble CTLA-4 (sCTLA-4) and the surface full-length CTLA-4 (flCTLA-4) isoforms, ultimately affecting CTLA-4 gene expression (<xref ref-type="bibr" rid="B38">38</xref>). The remaining two SNPs, <italic>rs5742909</italic> and <italic>rs62182595</italic>, located in the promoter region, may lead to different levels of mRNA transcription and protein translation and affect T cell homeostasis, leading to immune dysfunction and ultimately affecting the susceptibility of individuals to AAV (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>). Because <italic>rs62182595</italic>*G, <italic>rs16840252</italic>*C, and <italic>rs5742909</italic>*C showed strong LD, we hypothesize that these three loci are likely to amplify the synergistic effects between the loci through gene&#x2013;gene interactions (formation of different haplotypes). Such interactions could affect the expression level and protein structure of <italic>CTLA4</italic>, impair T-cell function, and disrupt immune homeostasis, ultimately influencing the susceptibility of individuals to AAV (<xref ref-type="bibr" rid="B41">41</xref>&#x2013;<xref ref-type="bibr" rid="B43">43</xref>). Overall, our study demonstrates a significant association between <italic>CTLA4</italic> SNPs and AAV risk in the Guangxi population. These findings not only lay the foundation for disease risk stratification (e.g., specific genes can serve as potential genetic markers) to help guide screening of high-risk populations and provide targets for subsequent precise prevention and early intervention, but also explore the potential of <italic>CTLA4</italic>-targeted precision therapy. It supports the rationale for using <italic>CTLA4</italic> as a therapeutic target and provides evidence for &#x201c;genotype-guided personalized immunotherapy.</p>
<p>Despite its strengths, the present study has certain limitations. First, although haplotype association analysis was conducted, we did&#xa0;not evaluate the expression levels of the <italic>CTLA4</italic> gene or protein.&#xa0;As gene expression is affected by numerous factors, such as transcriptional and translational regulations and gene&#x2013;environmental interactions, our findings require further experimental validation. Second, sample collection was limited due to the rarity of ANCA-associated vasculitis. Third, the study population was restricted to Guangxi, China, which may limit the generalizability of the findings to other populations. In the future, it is planned to carry out cross-regional multi-center collaborative studies. While expanding the sample size, populations of different ethnic groups will be included, and the expression levels of the <italic>CTLA4</italic> gene and protein will be detected simultaneously. Combined with the analysis of gene-environment interactions, the research conclusions will be comprehensively verified.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="s6" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Medical Ethics Committee of The Second Affiliated Hospital of Guangxi Medical University (2018 KY-0100). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>KB: Methodology, Writing &#x2013; original draft. BY: Writing &#x2013; original draft, Formal Analysis, Data curation. PH: Validation, Methodology, Writing &#x2013; original draft. CX: Writing &#x2013; review &amp; editing, Project administration.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the&#xa0;research and/or publication of this article. This work was supported by Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation under Grant (2025GXNSFAA069077), Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation under Grant (2024GXNSFAA999092), Guangxi Traditional Chinese medicine suitable technology development and promotion project (GZSY2025075).</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<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 id="s10" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
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<title>Publisher&#x2019;s note</title>
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</sec>
<sec id="s12" sec-type="supplementary-material">
<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/fimmu.2025.1631088/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fimmu.2025.1631088/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
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