Association of XPD Genetic Variations with Prostate Cancer Risk: Consolidated Results from 31 case-control studies

Association of XPD Genetic Variations with Prostate Cancer Risk: Consolidated Results from 31 case-control studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of XPD Genetic Variations with Prostate Cancer Risk: Consolidated Results from 31 case-control studies Nima Narimani, Mohammad Mehdi Atarod, Mehdi Khosravi-Mashizi, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5770719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aims to perform a comprehensive meta-analysis of existing literature to elucidate the associations between genetic variations in the XPD gene and the risk of prostate cancer development. Methods A systematic search of multiple databases, including PubMed, Scopus, EMBASE, and CNKI, was executed up until January 1, 2025, to collect pertinent information. The search utilized relevant keywords and MeSH terms pertaining to prostate cancer and genetic factors. Inclusion criteria were established for original case-control, longitudinal, or cohort studies. Associations were assessed as odds ratios (ORs) with 95% confidence intervals (CIs) employing Comprehensive Meta-Analysis software. Results This review included 31 case-control studies featuring 11,632 prostate cancer cases and 14,661 healthy controls. The polymorphism rs13181 (Lys751Gln) was studied in 16 studies with 5,295 cases and 7,199 controls, rs1799793 (Asp312Asn) in 10 studies with 3,316 cases and 4,310 controls, and rs238406 (Arg156Arg) in five studies with 3,021 cases and 3,152 controls. Pooled analyses indicated a significant association between the XPD rs13181 polymorphism and prostate cancer risk across all genetic models assessed. However, both rs238406 and rs1799793 polymorphisms showed no overall association with prostate cancer risk. Subgroup analyses revealed a significant link between rs13181 and rs238406 polymorphisms and prostate cancer risk in Asian populations, with no such association found in Caucasian or African groups. Furthermore, rs1799793 did not show a significant association with prostate cancer when examined by ethnicity. Conclusions The analysis of polymorphisms XPD rs13181 and rs238406 shows that the rs13181 G allele is significantly associated with increased prostate cancer risk, particularly in the Asian population. The rs238406 polymorphism displays a marginal association in the overall population but significant risk among Asians. Conversely, the rs1799793 polymorphism does not show meaningful associations in any group. These findings indicate that genetic factors may influence prostate cancer risk, with varying associations across ethnicities. Prostate Cancer XPD Gene Genetic Variations Polymorphisms Meta-Analysis Figures Figure 1 Figure 2 Figure 3 Introduction Prostate cancer is one of the most common malignant tumors affecting the male genitourinary system. Prostate cancer remains a significant health concern for men, particularly as they age [ 1 , 2 ]. In 2021, it was the second leading cause of cancer death among men aged 65 to 79 in the United States, resulting in 32,563 deaths, highlighting the urgent need for awareness and early detection in this demographic. The disease poses a lower risk for younger males, with only 531 deaths in the 20 to 39 age group and 15,794 in those aged 50 to 64. This age-related variation in mortality underscores the importance of regular screenings and further research into effective prevention and treatment strategies [ 3 ]. Moreover, the impact of prostate cancer incidence rates varies significantly among different racial and ethnic groups, with Black men experiencing rates 68% higher than White men and two to three times higher than American Indian/Alaska Native, Hispanic, and Asian American/Pacific Islander men. According to 2022 GLOBOCAN estimates, age-standardized prostate cancer incidence rates varied by a factor of 13 across regions, while mortality rates varied by 9.5 times. High incidence rates were particularly noted in Australia/New Zealand, North America, and Northern Europe, whereas sub-Saharan Africa and Latin America/Caribbean reported the highest mortality rates. Over the past five years, incidence rates have increased in 11 of 50 countries analyzed, with mortality rates rising in nine, mainly in Africa, Asia, and Latin America/Caribbean, while mortality rates decreased in 38 countries, primarily in Europe and Oceania [ 4 ]. DNA repair genes are linked to an increased risk of prostate cancer, influencing both the likelihood of developing the disease and its progression [ 5 ]. The BRCA2 gene, a crucial tumor suppressor in DNA repair, is associated with a 40% lifetime risk of prostate cancer and a 2.5- to 8.6-fold increased risk by age 65. It is the most prevalent DNA repair variant in prostate cancer, with studies showing that BRCA2 variant carriers have a higher positive predictive value for biopsy results. Similarly, BRCA1 variants raise the lifetime risk to 20%, particularly among younger individuals [ 6 , 7 ]. Other significant genes include ATM, which has a 2.18 to 4.4 odds ratio for prostate cancer, and CHEK2, where variants, especially the 1100delC variant common in Polish populations, are linked to a risk range of 1.8 to 8.2. PALB2 variants are also associated with a 3.5-fold increased risk and aggressive disease [ 8 , 9 ]. Mismatch repair (MMR) genes like MSH2 and MSH6 indicate a 2- to 10-fold increased risk, particularly in younger patients [ 7 ]. Additionally, the HOXB13 G84E variant significantly raises risk by 2.93-fold, especially in those with a family history of prostate cancer [ 10 ]. These genetic findings highlight the need for targeted screening and personalized risk assessment strategies for individuals carrying these variants [ 11 ]. XPD, also known as excision repair cross-complementation group 2 (ERCC2), is a vital protein in the nucleotide excision repair (NER) pathway, which is essential for repairing DNA damage inflicted by ionizing radiation and various cytotoxic agents [ 12 ]. The XPD gene is located on chromosome 19q13.3 and comprises 23 exons that encode a DNA helicase crucial for the NER process [ 13 ]. Among the significant polymorphisms within the XPD gene, the Lys751Gln variant (rs13181) results in an amino acid substitution at codon 751 in exon 23, changing lysine to glutamine. This alteration affects the protein's functionality, particularly diminishing helicase activity by impacting the ATP-binding site without influencing gene transcription [ 14 ]. Other polymorphisms, such as Asp312Asn (rs1799793) and p.Arg156Arg (rs238406), have also been extensively examined concerning their effects on DNA repair mechanisms [ 15 ]. While a number of studies have investigated the connection between XPD polymorphisms and prostate cancer risk, the findings have been inconsistent and inconclusive. These discrepancies may result from factors such as ethnic diversity, environmental influences, methodological variations, and sample sizes in genetic epidemiology. Future research is recommended to involve larger, multi-ethnic cohorts and standardized methodologies to better elucidate the associations between XPD polymorphisms and prostate cancer susceptibility. This meta-analysis aims to synthesize data from various studies and populations to assess the relationship between XPD genetic variations and prostate cancer risk. Materials and Methods Search Strategy A systematic literature search was performed across a wide range of databases, including MEDLINE, PubMed, PubMed Central (PMC), Europe PubMed Central (Europe PMC), Scopus, Cochrane Library, Google Scholar, Web of Science, Elsevier, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ResearchGate, ClinicalTrials.gov, SciELO, MedNexus, MedRxiv, Chinese Biomedical Database (CBD), Chinese National Knowledge Infrastructure (CNKI), Wanfang Data Company, Chaoxing, Circumpolar Health Bibliographic Database (CHBD), China/Asia On Demand (CAOD), Indian Citation Index (ICI), Chinese Medical Citation Index (CMCI), Semantic Scholar, Egyptian Knowledge Bank (EKB), VIP Information Consultancy Company (VIP), Chinese Medical Current Contents (CMCC), and Weipu Periodical Database.The objective was to identify relevant articles assessing the correlation between XPD polymorphisms and prostate cancer risk, focusing on studies available until December 30, 2024. The investigation utilized a combination of MeSH terms and keywords related to prostate cancer and XPD polymorphisms, including 'ERCC2 Lys751Gln', 'XPD codon 751', 'rs13181', 'c.2251A > C', 'A20337C', 'ERCC2 Asp312Asn', 'XPD codon 312', 'rs1799793', 'c.934G > A', 'G7988A', 'ERCC2 Arg156Arg', 'XPD codon 156', 'rs238406', 'c.468A > C', as well as genetic terms like 'Gene', 'Genotype', 'Allele', 'Polymorphism', 'Single nucleotide polymorphisms', 'SNP', 'Variation', 'Mutation', 'Cancer genetics', and 'Tumor markers'. To identify relevant publications, we manually examined references from eligible studies, prior meta-analyses, and review articles. The search focused on human studies, emphasizing reputable publications and significant reviews as supplementary sources. When multiple studies by the same authors had overlapping data, we prioritized the study with the largest sample size or the most recent publication date. There were no restrictions on language or publication year. Additionally, references from relevant reviews and articles were reviewed to find additional studies. Non-English articles were included following a defined translation process and specific quality assessment criteria. Inclusion and exclusion Criteria Studies were selected for inclusion based on the following criteria: 1) availability of full-text articles; 2) classification as either case-control or cohort studies; 3) examination of the relationship between XPD polymorphisms and prostate cancer risk; and 4) provision of sufficient data for calculating odds ratios (OR) or relative risks, accompanied by 95% confidence intervals (CI). Exclusion criteria encompassed studies that: 1) did not investigate the association between XPD polymorphisms and prostate cancer risk; 2) concentrated on animal or in vitro research; 3) lacked adequate data for analysis; 4) were case-only studies without control groups; 5) involved linkage or family-based designs, including studies of twins and siblings; 6) were case reports, abstracts, comments, conference abstracts, editorials, reviews, or meta-analyses; and 7) constituted duplicates of other studies. In cases of duplicate publications by the same author utilizing the same patient sample, only the study with the largest sample size was incorporated into the meta-analysis. Data Extraction Two authors independently evaluated all eligible articles and extracted pertinent information based on the established inclusion criteria. Any discrepancies that arose during this process were resolved through discussion; if a consensus was not reached, a third researcher was consulted, with decisions ultimately made by majority vote. Each eligible study was required to provide details regarding the primary author, publication date, country of origin, ethnic backgrounds of participants (categorized as Asian, Caucasian, African, Hispanic, and Mixed), genotyping methodologies employed, sources of healthy subjects, total sample size, allele and genotype frequencies of XPD variants among healthy subjects and cases, Minor Allele Frequencies (MAFs), and Hardy-Weinberg equilibrium (HWE) among healthy controls. Additionally, studies published in languages other than English underwent a thorough translation to ensure accurate interpretation of results. This meta-analysis considered separate case-control groups or cohorts within a single publication as distinct studies. Quality Score Assessment The Newcastle-Ottawa Score (NOS) was utilized to evaluate the quality of studies included in a meta-analysis by examining key methodological aspects of observational research, such as case selection, group comparability, and exposure determination, with each category comprising eight specific criteria. Studies were awarded one star for excellent selection and exposure, while comparability could earn a maximum of two stars. Overall quality assessment was conducted on a nine-star scale, where a score of zero indicated poor quality and a score of nine represented high quality. Studies that achieved a score of seven or above were classified as high quality, while those scoring at least five points were deemed appropriate for inclusion in the meta-analysis. Any disagreements regarding scoring were resolved through discussion and consensus among the evaluators. Statistical analysis Crude odds ratios (ORs) and 95% confidence intervals (CIs) were computed to assess the association between XPD polymorphisms and prostate cancer risk within the overall population. Combined ORs were derived according to five genetic comparison models: allele (A vs. B), homozygote (AA vs. BB), heterozygote (BA vs. BB), dominant (AA + BA vs. BB), and recessive (AA vs. BA + BB). To evaluate study heterogeneity, Cochran's Q test was utilized, with P-values less than 0.1 indicating the absence of heterogeneity, further quantified using the I² statistic, which categorized heterogeneity levels as low, moderate, or high at thresholds of 25%, 50%, and 75%, respectively. For studies exhibiting significant heterogeneity, a random effects model based on the DerSimonian and Laird approach was adopted, whereas a fixed effects model (Mantel-Haenszel) was applied when heterogeneity was not significant. Meta-regression analyses were conducted to identify potential confounding factors contributing to heterogeneity. The Hardy-Weinberg equilibrium (HWE) among controls was evaluated using a chi-square goodness-of-fit test [ 16 ]. To ensure the robustness of the findings, sensitivity analyses were performed by systematically excluding individual studies. Potential publication bias was assessed using both Egger’s and Begg’s tests, with P-values below 0.05 indicative of significant bias. The non-parametric "trim-and-fill" method proposed by Duval and Tweedie was employed to adjust for publication bias, facilitating a comparison between the adjusted pooled ORs and the original estimates. All statistical analyses were performed using Comprehensive Meta-Analysis (CMA) software version 2.0 (Biostat, USA), with statistical significance determined using the Z-test, where P-values below 0.05 were considered significant. Results Characteristics of eligible studies Figure 1 illustrates the study selection methodology for this analysis. An initial search across various online databases identified 482 articles. After removing duplicates, 311 unique articles remained. A review of titles and abstracts excluded 163 studies that did not meet the inclusion criteria, leaving 148 for full-text assessment. This thorough review further eliminated 117 studies due to issues like inappropriate control groups, insufficient data, and focus on different XPD gene polymorphic loci. Ultimately, 31 case-control studies from 16 publications [17, 18, 27–32, 19–26] were included, comprising 11,632 cases of PrCr and 14,661 healthy controls. Table 1 summarizes these studies, highlighting specific XPD gene polymorphisms: rs13181 (Lys751Gln) in 16 studies (5,295 cases, 7,199 controls), rs1799793 (Asp312Asn) in 10 studies (3,316 cases, 4,310 controls), and rs238406 (Arg156Arg) in five studies (3,021 cases, 3,152 controls). The studies published between 2004 and 2024 were in English, enhancing their accessibility. The sample sizes for cases in the studies included in the meta-analysis ranged from 40 to 1,233, while the number of controls varied from 40 to 1,055. This meta-analysis included research from countries such as the United States, China, India, Australia, Poland, Brazil, Turkey, Saudi Arabia, and Bangladesh, providing a global perspective. It comprised 11 studies focusing on Caucasian populations, eight on Asian populations, five on African populations, and one on a mixed population. This diversity strengthens the generalizability of the findings. A total of 12 studies used population-based controls, while 8 studies used hospital-based controls. The genotyping methods used—ranging from ARMS-PCR, MALDI-TOF, RFLP-PCR, SNPlex™, TaqMan assays, DS (Direct Sequencing), and qRT-PCR—underscore methodological rigor. Notably, all but two studies adhered to HWE in their control groups, essential for accurate genetic analysis interpretation. Quality of Studies The quality of studies in the meta-analysis can be assessed through several key factors: study design, sample size, genotyping methods, MAFs, adherence to HWE, and NOS scores. Most studies utilize robust designs, primarily through population-based or hospital-based sampling methods that minimize bias, though each approach has its own advantages. Sample sizes vary significantly; larger samples, such as Agalliu 2010 (1233 cases, 1228 controls), generally yield more reliable results, whereas smaller studies, like Balkan 2020 (40 cases, 40 controls), are more prone to errors. Various genotyping techniques are applied, each with particular strengths and weaknesses, with standardized methods favored. MAFs indicate genetic diversity, but extreme values may raise accuracy concerns. Adherence to HWE is vital, as seen in Bau 2007 (HWE ≤0.001), which shows significant deviations that might suggest genotyping errors or sample bias. NOS scores typically range from 5 to 8, indicating moderate to high quality, although a higher score does not eliminate the risk of bias. Overall, while many studies exhibit strong methodologies, smaller sample sizes, HWE deviations, and variability in genotyping methods require careful consideration to ensure valid interpretations in the meta-analysis. HWE HWE is a critical measure in genetic studies, providing insight into the genetic variation within and between populations. The analysis of HWE by polymorphism shows diverse results across different countries and ethnicities. For instance, the rs13181 polymorphism exhibited a significant HWE deviation in a Caucasian population from the USA, with a p-value of 0.017, indicating potential disturbances in genetic equilibrium. Conversely, both African populations in the USA revealed variability, with HWE p-values of 0.242 and 0.784 indicating acceptable equilibrium. In Asian populations, the rs13181 polymorphism exhibited a strikingly low HWE p-value (≤0.001) in a Chinese sample, suggesting a strong deviation from equilibrium. When examining rs1799793, the Caucasian population from the USA also showed notable HWE deviations (p-value 0.017), while African cohorts exhibited more stable frequencies with p-values of 0.242 and 0.332. Additionally, the rs238406 polymorphism displayed diverse profiles, with a Caucasian population from Poland demonstrating substantial equilibrium (p-value ≤0.001). In contrast, the African cohort in the USA revealed a p-value of 0.323, indicating a less stable genetic structure. These observations highlight the importance of considering both ethnicity and geographic origin when assessing HWE, as variations underscore the underlying genetic complexities and population histories. Synthesis of Quantitative Data Table 2 presents a summary analysis of the association between the XPD polymorphism and prostate cancer risk. rs13181: The analysis of the association between the XPD rs13181 polymorphism and prostate cancer risk revealed several significant findings. In the overall population, the G allele was associated with an increased risk of prostate cancer, with an odds ratio (OR) of 1.278 (95% CI: 1.098-1.489, p = 0.002, Fig 2A). Notably, individuals with the GG genotype compared to the AA genotype exhibited a higher risk (OR = 1.511, 95% CI: 1.143-1.999, p = 0.004, Fig 2B). Similarly, the GA genotype also showed increased risk relative to AA (OR = 1.276, 95% CI: 1.046-1.557, p = 0.016). When combining GG and GA genotypes against AA, the OR was 1.344 (95% CI: 1.099-1.642, p = 0.004). In contrast, a fixed model indicated a marginally significant association for GG versus GA+AA (OR = 1.139, 95% CI: 1.001-1.295, p = 0.049). In the Caucasian subgroup, however, no significant associations were observed across various genetic models. In the Asian subgroup, significant associations were noted, with the GG genotype showing a strong increased risk (OR = 2.158, 95% CI: 1.553-2.999, p ≤ 0.001). Other models in the Asian group also indicated heightened risk, confirming the risk associated with the G allele. Conversely, the African subgroup displayed no significant associations across all genetic models, with ORs consistently below 1, indicating a potential protective effect of the G allele in this population. Overall, these results suggest that the XPD rs13181 polymorphism may contribute to prostate cancer susceptibility, particularly in Asian populations. rs13181: The association between the XPD rs1799793 polymorphism and prostate cancer risk was analyzed across different subgroups. In the overall population, the genetic model revealed an odds ratio (OR) of 1.252 for the A vs. C comparison, with a 95% confidence interval (CI) of 0.031-1.684 and a p-value of 0.137, indicating no significant association. Subgroup analyses demonstrated similar patterns. In Caucasians, the A vs. C comparison yielded an OR of 1.284 (95% CI: 0.809-2.038, p = 0.289), while for Asians, the OR was 1.226 (95% CI: 0.786-1.913, p = 0.369), both suggesting a lack of significant risk. Among Africans, the fixed model for the A vs. C comparison showed an OR of 1.230 (95% CI: 0.777-1.947, p = 0.377). Individual genotype comparisons (AA vs. CC and AC vs. CC) displayed wide confidence intervals indicating considerable variability in estimates, particularly in the African group. Overall, these results suggest that the XPD rs1799793 polymorphisms do not significantly influence prostate cancer risk across the studied populations. rs238406: The results of the association between XPD rs238406 polymorphisms and prostate cancer risk were analyzed across different subgroups. In the overall population, no significant associations were observed, with odds ratios (OR) close to 1, indicating a lack of increased risk. Specifically, the comparison of the C vs. T genotype yielded an OR of 1.071 (95% CI: 0.997-1.151) with a p-value of 0.061, suggesting a marginal trend. Subgroup analysis revealed notable differences in the Asian population, where the C vs. T comparison showed a significant OR of 1.146 (95% CI: 1.019-1.289) and a p-value of 0.023, indicating a statistically significant association. Additionally, the CC vs. TT genotype comparison in Asians also revealed an OR of 1.298 (95% CI: 1.026-1.644) with a p-value of 0.030, further suggesting a higher risk of prostate cancer associated with the CC genotype. Other comparisons within the Asian subgroup displayed trends towards significance but did not reach conventional levels. In contrast, the Caucasian subgroup showed no significant associations, with ORs remaining close to 1 across various genetic models. Overall, these findings suggest that XPD rs238406 polymorphisms may be associated with increased prostate cancer risk, particularly in Asian populations. Z-test Results A Z-test assesses significant differences in odds ratios between case and control groups. The results for XPD polymorphisms show varying levels of statistical significance in their association with prostate cancer risk across different genetic models and populations. For rs13181, several models yielded significant Z-values, especially in the overall group (Z = 3.160, p = 0.002) and the Asian subgroup, where the GG vs. AA comparison showed a Z-value of 4.582 (p < 0.001). In contrast, the Caucasian subgroup yielded mostly non-significant results, including the G vs. A model (Z = 1.490, p = 0.136). For rs1799793, the overall group results showed stronger Z-values, with the AA vs. CC model indicating Z = 1.545 (p = 0.122), suggesting a trend toward significance but not meeting traditional thresholds. Conversely, rs238406 consistently showed Z-values below 2, indicating a lack of significant association across all models and populations. Overall, rs13181 appears to have the strongest association with prostate cancer risk, particularly in Asian populations, while rs238406 shows no significant impact. Heterogeneity In the analysis of the association between XPD polymorphisms and prostate cancer risk, heterogeneity across different genetic models and subgroups was evaluated using the I² statistic and its corresponding p-values. For the rs13181 polymorphism, significant heterogeneity was observed in the overall group across all genetic models with I² values ranging from 63.87% to 79.25% and p-values ≤0.001, indicating variability in the studies examined. This trend was consistent within the Caucasian subgroup, where substantial heterogeneity was present in several models, while the Asian subgroup also showed significant heterogeneity, especially in the G vs. A and GA vs. AA models. In contrast, the African subgroup exhibited fixed effects across all models, with I² values of 0.00% and p-values ranging from 0.322 to 0.512, suggesting no significant heterogeneity. For the rs1799793 polymorphism, a high degree of heterogeneity was noted in the overall group, with I² values reaching 96.07% in the A vs. C model, indicating considerable variability among studies. This heterogeneity persisted across subgroups, particularly in Caucasian individuals. Conversely, the African subgroup showed no heterogeneity, similar to the findings for rs238406, which displayed I² values consistently at 0.00% across all models and both subgroups tested. This lack of variability suggests that the African cohort exhibited a uniform response to the rs238406 polymorphism concerning prostate cancer risk. Publication Bias The assessment of publication bias for XPD polymorphisms in prostate cancer risk reveals varying degrees of bias across different genetic models and subgroups. For the rs13181 polymorphism, the overall analysis indicated significant publication bias in the genetic models "G vs. A" (PBeggs = 0.444, PEggers = 0.038, Fig 3A) and "GG vs. AA" (PBeggs = 1.000, PEggers = 0.046, Fig 3B), suggesting potential biases in the available literature. When focusing on the Caucasian subgroup, particularly stark publication bias was evident for "GG vs. AA" (PBeggs = 0.024, PEggers = 0.007) and "GA vs. AA" (PBeggs = 0.259, PEggers = 0.118). Conversely, the Asian subgroup displayed minimal publication bias across most models, particularly for "GG vs. AA" (PBeggs = 0.133, PEggers = 0.218). The publication bias for rs1799793 showed a relatively consistent lack of bias in the Caucasian subgroup with all models exhibiting PBeggs around 0.806, though the Asian subgroup revealed a significant publication bias for "A vs. C" (PBeggs = 0.296, PEggers = 0.016). The rs238406 polymorphism did not allow for a reliable evaluation of publication bias in both Caucasian and Asian subgroups due to a lack of available data. Overall, the presence of publication bias, particularly in certain genetic models and subgroups, underscores the importance of considering potential biases when interpreting the association between XPD polymorphisms and prostate cancer risk. Sensitivity analyses Sensitivity analyses were conducted to evaluate the robustness of the results regarding the association between XPD polymorphisms and prostate cancer risk. By examining the odds ratios across different genetic models and subgroups, we found that the overall analysis for polymorphism rs13181 showed a significant association with prostate cancer risk in the G vs. A genetic model, yielding an odds ratio of 1.278 (95% CI: 1.098-1.489, p=0.002). This trend was also observed in the subgroup analyses, particularly among Asians, where GG vs. AA resulted in a notably higher odds ratio of 2.158 (95% CI: 1.553-2.999, p ≤ 0.001). In contrast, the African subgroup displayed a lack of association across all models, indicating that genetic variability within populations may influence susceptibility to prostate cancer. Similar patterns emerged for rs1799793 and rs238406, where significant associations were found primarily in Asian populations. Overall, these sensitivity analyses highlight the importance of considering population stratification and genetic background when interpreting the results of genetic association studies. MAFs MAFs of various polymorphisms exhibit notable variations across different countries and ethnicities as demonstrated in the meta-analysis. For the rs13181 polymorphism, the MAFs among Caucasian populations in the USA ranged from 0.355 to 0.377, while African Americans showed similar frequencies at 0.360. In contrast, the Asian populations in India and China presented MAFs of 0.206 and 0.075, respectively. The rs1799793 polymorphism displayed a MAF of 0.339 in the Caucasian cohort from the USA, whereas it was markedly lower in the African group with a MAF of 0.151. Additionally, MAFs for rs238406 were reported at 0.448 for Caucasian populations in the USA and 0.375 in a Chinese cohort, both reflecting a higher prevalence than reported in African populations, where it was as low as 0.099. Overall, these findings suggest that MAFs can vary significantly between ethnic groups and are influenced by geographic location, emphasizing the need for further studies to explore these genetic variations in diverse populations. Discussion XPD is a key gene in the NER pathway, essential for maintaining genomic stability, and its genetic variations have been studied for their potential link to prostate cancer risk, yielding mixed results. The Asn312Asp variant at codon 312 has been associated with a significant increase in prostate cancer risk, particularly in Taiwanese patients, showing a 1.81-fold increase for individuals with the Asn/Asn genotype compared to the Asp/Asp genotype (p = 0.003) [ 17 , 19 ]. In contrast, the Lys751Gln variant at codon 751 has yielded inconsistent findings, with a Brazilian study showing a significant association and an odds ratio of 2.36 (p < 0.001) [ 19 ], while other studies reported no significant link [ 33 ]. Additionally, research suggests that the combined effects of multiple SNPs, such as the XPD codon 312 Asn variant with other genetic variations like XRCC1, may produce a greater risk than individual polymorphisms [ 17 ]. Furthermore, the relationship between XPD polymorphisms and prostate cancer risk appears to vary across populations, highlighting the necessity to factor in genetic backgrounds and environmental influences when evaluating cancer susceptibility. Thus, we conducted a pooled analysis of 31 case-control studies, involving 11,632 prostate cancer cases and 14,661 healthy controls, to evaluate the association between XPD gene polymorphisms and prostate cancer. The rs13181 (Lys751Gln) variant in the ERCC2 gene has been linked to prostate cancer, primarily through its effects on DNA repair mechanisms. This variant replaces lysine with glutamine at position 751, which may impair NER efficiency, essential for repairing DNA damage [ 34 ]. Evidence indicates that individuals carrying the Gln allele could have elevated DNA adduct levels and reduced repair activity, leading to increased genomic instability, a precursor to cancer [ 28 ]. Several studies have identified a significant association between the rs13181 variant and prostate cancer risk, including a Brazilian case-control study that reported an odds ratio of 2.36 for Gln allele carriers [ 27 ]. Furthermore, a correlation exists between this variant and higher Gleason scores, suggesting a potential impact on cancer aggressiveness. However, the influence of the Lys751Gln variant on prostate cancer risk differs across populations; while some ethnic groups exhibit a strong association, others, such as findings from Turkey, show no significant link [ 28 ]. This variability underscores the importance of environmental factors and genetic backgrounds in cancer risk. Our pooled analysis of 16 studies involving 5,295 cases and 7,199 controls found a significant increase in prostate cancer risk linked to the G allele of rs13181, especially in Asian populations, where individuals with the GG genotype had an odds ratio of 2.158. No significant association was observed in Caucasians, while the African subgroup suggested a possible protective effect of the G allele. Meta-analyses by Liu et al. (2018) [ 35 ], which included 11 studies with 4,456 cases, and by Fu et al. (2017) [ 36 ], based on ten case-control studies, found no significant link between the rs13181 polymorphism and prostate cancer risk, even when analyzed by ethnicity. Similarly, Ma et al. (2013) conducted a pooled analyses that also reported no significant association across various studies [ 37 ]. These findings suggest that the Lys751Gln variant may not universally influence prostate cancer susceptibility, and its effects might differ by ethnicity. The protective effect observed in the African subgroup complicates this narrative, indicating that genetic variants can have varying impacts based on ethnic backgrounds. This variability highlights the need for more nuanced research that considers both genetic polymorphisms and the environmental and lifestyle factors influencing cancer susceptibility. The rs1799793 (Asp312Asn) polymorphism in the XPD gene involves a G to A substitution at codon 312, resulting in a change from aspartic acid (Asp) to asparagine (Asn). While this variant may influence helicase activity and impact DNA repair capacity, its overall functional consequences are not as established as those of other variations, such as rs13181 [ 38 ]. This polymorphism is linked to cancer risk primarily through its effects on DNA repair efficiency, particularly in the context of NER, which is vital for addressing bulky DNA lesions and maintaining genomic integrity [ 39 ]. Research suggests that individuals with the Asn allele might experience decreased DNA repair capacity, leading to increased DNA damage and heightened cancer risk. Some studies indicate that carriers of the Asn allele face significantly elevated risks for several cancers, including prostate cancer, with reported risks as high as 1.84-fold compared to those with the Asp/Asp genotype [ 38 ]. In our meta-analysis of rs1799793 across 10 studies (3,316 cases, 4,310 controls), no significant impact on prostate cancer risk was found among any populations studied, with odds ratios consistently around 1. This suggests that genetic variations at this locus may have minimal influence on susceptibility to prostate cancer, despite various studies supporting the association between the Asp312Asn polymorphism and increased prostate cancer risk. Other meta-analyses, like those by Liu et al. (2018) [ 35 ] and Fu et al. (2017) [ 36 ], highlighted a lack of overall association but noted increased risks in specific subpopulations, particularly Asian and African individuals. Further investigation by Ma et al. (2013) reinforced this link, especially when analyzing different ethnicities [ 37 ]. These mixed findings underscore the potential role of the Asp312Asn polymorphism in prostate cancer susceptibility, warranting further research with larger, more diverse cohorts to better understand the gene-environment interactions and genetic influences on cancer risks. The inconsistency across studies indicates a need for a nuanced approach to unravel how genetic variations like Asp312Asn interact with environmental factors and other genetic components in different populations. The rs238406 (Arg156Arg) polymorphism is a silent G > T mutation at codon 156 that does not alter the amino acid sequence but may influence mRNA splicing or expression levels of the ERCC2 gene, potentially affecting protein functionality [ 35 ]. While this SNP does not directly impact enzymatic function, studies suggest it could modulate ERCC2 protein levels via mRNA processing [ 40 ]. Results across multiple studies have been inconsistent, reflecting the complexity of genetic analyses. Our pooled analysis of rs238406 from five studies (3,021 cases, 3,152 controls) showed varying effects, with a significant association in the Asian population where individuals with the CC genotype faced increased risk. This inconsistency aligns with previous meta-analyses, including those by Liu et al. (2018) [ 35 ] and Fu et al. (2017) [ 36 ] which found no significant link between the Arg156Arg polymorphism and prostate cancer risk among over 2,000 subjects. This suggests that while certain SNPs might show associations in specific populations, their overall impact may be minimal when considering diverse ethnicities and genetic variations. The complexity of genetic data highlights the need for careful interpretation, underscoring the importance of larger, more diverse studies to clarify the role of the rs238406 polymorphism in cancer susceptibility. These varying results emphasize the intricate genetic influences on prostate cancer and the need for further investigation, particularly with larger sample sizes, to understand how these polymorphisms interact with environmental factors in cancer development. The heterogeneity analysis in this study reveals a complex landscape regarding the association between XPD polymorphisms and prostate cancer risk, underscoring the variability in genetic influences across different populations and genetic models. For the rs13181 polymorphism, substantial heterogeneity was observed in most comparisons, particularly in the overall population and the Caucasian subgroup, indicating differing effects of the genetic variant on prostate cancer risk among the studies considered. In contrast, the Asian and African populations displayed lower heterogeneity for certain models, specifically regarding the GG vs. GA + AA comparison for rs13181, suggesting a more consistent genetic-risk association in these groups. The rs1799793 polymorphism, while also showing significant heterogeneity, particularly in the overall and Caucasian groups, had consistently lower heterogeneity in the African subgroup, indicative of similar risk patterns within this population. Meanwhile, for the rs238406 polymorphism, the absence of heterogeneity across all comparisons suggests a uniform pattern which could indicate a shared genetic background or environmental factors mitigating the influence of this variant on prostate cancer risk. These findings highlight the importance of considering population-specific contexts and genetic model variations when evaluating polymorphisms' impact on cancer susceptibility. Limiations The meta-analysis of XPD genetic variations and prostate cancer risk is strengthened by comprehensive data pooling from diverse studies across different ethnicities. However, these studies also present several notable limitations. 1) There is significant variation in ethnic representation among the populations examined, with most studies primarily focused on Caucasian and Asian groups, while African, Mixed, or other ethnic backgrounds are underrepresented, leading to challenges in generalizability. 2) The small sample sizes in studies on the XPD rs238406 polymorphism reduce the statistical power needed to establish a true correlation with prostate cancer risk, necessitating larger and more robust study designs. 3) A potential publication bias exists, as studies demonstrating positive correlations are more likely to be published, distorting meta-analysis results. 4) The reliance on published literature from English and Chinese databases may exclude important unpublished studies and those in other languages, resulting in systematic bias. 5) Insufficient sample sizes for studies on specific ethnic groups hinder definitive conclusions about broader population relationships. 6) Variability in genotyping techniques across studies, such as ARMS-PCR, RFLP-PCR, and SNPlex™, may introduce discrepancies in results due to differences in sensitivity and specificity. 7) Geographical and ethnic diversity among participants may lead to confounding factors that are not adequately controlled, including age, family history, ethnicity, diet, obesity, physical activity, smoking, hormone levels, chemical exposure, and inflammation. 8) The lack of long-term follow-up data limits the assessment of causal relationships and the role of XPD polymorphisms over time. 9) Insufficient data for stratified analyses concerning confounding factors like age, gender, smoking habits, and prostate cancer types restricts the evaluation of their influence on XPD polymorphisms and prostate cancer risk. 10) Lastly, the primary studies inadequately address complex gene-environment interactions due to a lack of information, emphasizing the need for larger-scale studies involving diverse populations with comprehensive data to better understand the interplay of gene-gene and gene-environment interactions in the relationship between polymorphisms and prostate cancer risk. Conclusion The analysis of the XPD gene polymorphisms rs13181, rs1799793, and rs238406 in relation to prostate cancer risk revealed significant associations, particularly in Asian populations. The rs13181 polymorphism demonstrated a marked increase in prostate cancer risk linked to the G allele, with substantial odds ratios for both GG and GA genotypes, while no significant associations were found in the Caucasian and African subgroups. In contrast, the rs1799793 polymorphism indicated no significant influence on prostate cancer risk across all studied populations. The rs238406 polymorphism exhibited a slight increased risk overall but showed significant associations specifically within the Asian subgroup, highlighting the potential role of genetic variability in cancer susceptibility. Collectively, these findings underscore the importance of considering population-specific genetic factors in understanding an individual's risk for prostate cancer. Declarations Funding: There is no funding source. Conflicts of interest/Competing interests: The authors declare that they have no conflict of interest. Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate: Not applicable for this manuscript. Availability of data and material: The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request. Acknowledgments: The authors wish to extend their heartfelt appreciation to all the contributors of the articles incorporated in this meta-analysis. Their invaluable insights and efforts were crucial to the successful completion of this manuscript. Authors' contributions: N.N., M.M.A., M.K.-M., and H.M.: Methodology, conceptualization, investigation. S.F. and M.A.: Methodology, investigation, writing, original draft preparation. H.N. and M.A.: Formal analysis, investigation. K.A. and H.N.: Investigation, writing. M.M. and S.F.: Investigation, writing. M.B. and H.M.: Investigation. S.A.D.: Methodology, software. K.A.: Investigation, writing. S.A.D. and H.M.: Project administration. M.K.-M. and M.B.: Writing, reviewing, editing. References Pinho S, Coelho JMP, Gaspar MM, Reis CP. Advances in localized prostate cancer: A special focus on photothermal therapy. Eur J Pharmacol. 2024;983:176982. Chen S, Lu C, Lin S, Sun C, Wen Z, Ge Z, et al. A panel based on three-miRNAs as diagnostic biomarker for prostate cancer. Front Genet. 2024;15:1371441. Siegel Mph RL, Giaquinto AN, Ahmedin |, Dvm J, Siegel RL, Cancer statistics. 2024. CA: A Cancer Journal for Clinicians. 2024;74:12–49. 10.3322/CAAC.21820 Schafer EJ, Laversanne M, Sung H, Soerjomataram I, Briganti A, Dahut W, et al. Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update. Eur Urol. 2024. 10.1016/J.EURURO.2024.11.013 . Lukashchuk N, Barnicle A, Adelman CA, Armenia J, Kang J, Barrett JC, et al. Impact of DNA damage repair alterations on prostate cancer progression and metastasis. Front Oncol. 2023;13:1162644. 10.3389/FONC.2023.1162644 . Bancroft EK, Raghallaigh HN, Page EC, Eeles RA. Updates in Prostate Cancer Research and Screening in Men at Genetically Higher Risk. Curr Genetic Med Rep 2021. 2021;9:4. 10.1007/S40142-021-00202-5 . Hall R, Bancroft E, Pashayan N, Kote-Jarai Z, Eeles RA. Genetics of prostate cancer: a review of latest evidence. J Med Genet. 2024;61:915–26. 10.1136/JMG-2024-109845 . Southey MC, Goldgar DE, Winqvist R, Pylkäs K, Couch F, Tischkowitz M, et al. PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet. 2016;53:800–11. 10.1136/JMEDGENET-2016-103839 . Karlsson Q, Brook MN, Dadaev T, Wakerell S, Saunders EJ, Muir K, et al. Rare Germline Variants in ATM Predispose to Prostate Cancer: A PRACTICAL Consortium Study. Eur Urol Oncol. 2021;4:570–9. 10.1016/J.EUO.2020.12.001 . Kote-Jarai Z, Mikropoulos C, Leongamornlert DA, Dadaev T, Tymrakiewicz M, Saunders EJ, et al. Prevalence of theHOXB13 G84E germline mutation in British men and correlation with prostate cancer risk, tumour characteristics and clinical outcomes. Ann Oncol. 2015;26:756–61. Aghasipour M, Asadian F, Dastgheib SA, Shirinzadeh-Dastgiri A, Vakili-Ojarood M, Narimani N, et al. Familial Hereditary Prostate Cancer: Genetic, Screening, and Treatment Strategies. Eurasian J Med Oncol. 2024;8:250–66. Hashemzehi A, Ghadyani M, Asadian F, Dastgheib SA, Kargar S, Neamatzadeh H, et al. Association of polymorphisms in nucleotide excision repair pathway genes with susceptibility to cutaneous melanoma. Klinicka onkologie. 2021;34:350–5. 10.48095/CCKO2021350 . Zeng W, Xu W, Long W. The association between XPD rs13181 and rs1799793 polymorphism and oral cancer risk: evidence from a meta-analysis. BMC Cancer. 2024;24:738. 10.1186/S12885-024-12503-3 . Matullo G, Palli D, Peluso M, Guarrera S, Carturan S, Celentano E, et al. XRCC1, XRCC3, XPD gene polymorphisms, smoking and 32P-DNA adducts in a sample of healthy subjects. Carcinogenesis. 2001;22:1437–45. Rouissi K, Bahria IB, Bougatef K, Marrakchi R, Stambouli N, Hamdi K, et al. The effect of tobacco, XPC, ERCC2 and ERCC5 genetic variants in bladder cancer development. BMC Cancer. 2011;11:101. 10.1186/1471-2407-11-101 . Neamatzadeh H, Dastgheib SA, Mazaheri M, Masoudi A, Shiri A, Omidi A, et al. Hardy-Weinberg Equilibrium in Meta-Analysis Studies and Large-Scale Genomic Sequencing Era. Asian Pac J cancer prevention: APJCP. 2024;25:2229–35. 10.31557/APJCP.2024.25.7.2229 . Rybicki BA, Conti DV, Moreira A, Cicek M, Casey G, Witte JS. DNA repair gene XRCC1 and XPD polymorphisms and risk of prostate cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2004;13:23–9. 10.1158/1055-9965.EPI-03-0053 Ritchey JD, Huang WY, Chokkalingam AP, Gao YT, Deng J, Levine P et al. Genetic variants of DNA repair genes and prostate cancer: a population-based study. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2005;14:1703–9. 10.1158/1055-9965.EPI-04-0809 Bau D, Wu H-C, Chiu C, Lin C, Hsu C, Wang C, et al. Association of XPD polymorphisms with prostate cancer in Taiwanese patients. Anticancer Res. 2007;27:2893–6. Agalliu I, Kwon EM, Salinas CA, Koopmeiners JS, Ostrander EA, Stanford JL. Genetic variation in DNA repair genes and prostate cancer risk: results from a population-based study. Cancer causes control: CCC. 2010;21:289–300. 10.1007/S10552-009-9461-5 . Mandal RK, Gangwar R, Mandhani A, Mittal RD. DNA repair gene X-ray repair cross-complementing group 1 and xeroderma pigmentosum group D polymorphisms and risk of prostate cancer: a study from North India. DNA Cell Biol. 2010;29:183–90. 10.1089/DNA.2009.0956 . Lavender NA, Komolafe OO, Benford M, Brock G, Moore JH, VanCleave TT, et al. No association between variant DNA repair genes and prostate cancer risk among men of African descent. Prostate. 2010;70:113–9. 10.1002/PROS.21048 . Gao R, Price DK, Dahut WL, Reed E, Figg WD. Genetic polymorphisms in XRCC1 associated with radiation therapy in prostate cancer. Cancer Biol Ther. 2010;10:13–8. 10.4161/CBT.10.1.12172 . Sobti RC, Berhane N, Melese S, Mahdi SA, Gupta L, Thakur H, et al. Impact of XPD gene polymorphism on risk of prostate cancer on north Indian population. Mol Cell Biochem. 2012;362:263–8. 10.1007/S11010-011-1152-3 . Mirecka A, Paszkowska-Szczur K, Scott RJ, Górski B, van de Wetering T, Wokołorczyk D, et al. Common variants of xeroderma pigmentosum genes and prostate cancer risk. Gene. 2014;546:156–61. 10.1016/J.GENE.2014.06.026 . Wang M, Li Q, Gu C, Zhu Y, Yang Y, Wang J, et al. Polymorphisms in nucleotide excision repair genes and risk of primary prostate cancer in Chinese Han populations. Oncotarget. 2017;8:24362–71. 10.18632/ONCOTARGET.13848 . Cypriano AS, Alves G, Ornellas AA, Scheinkman J, Almeida R, Scherrer L, et al. Relationship between XPD, RAD51, and APEX1 DNA repair genotypes and prostate cancer risk in the male population of Rio de Janeiro, Brazil. Genet Mol biology. 2017;40:751–8. 10.1590/1678-4685-GMB-2017-0039 . Balkan E, Bilici M, Gundogdu B, Aksungur N, Kara A, Yaşar E, et al. ERCC2 Lys751Gln rs13181 and XRCC2 Arg188His rs3218536 Gene Polymorphisms Contribute to Subsceptibility of Colon, Gastric, HCC, Lung And Prostate Cancer. J BUON. 2020;25:574–81. Al Abdulmonem W, Rasheed Z, Alsagaby SA, Aljohani ASM, Alkhamiss AS, Ahmed AA. Impact of ERCC2 Lys751Gln (rs13181), ERCC2 Asp312Asn (rs1799793) and XRCC1 Arg399Gln (rs25487) polymorphisms on the risk of prostate cancer among cases from the central region of Saudi Arabia. Gene Rep. 2021;24:101278. Ahmed N, Islam MA, Hossain MM, Kabir Y. XRCC1 and XPD polymorphisms: clinical outcomes and risk of prostate cancer in Bangladeshi population. Mol Biol Rep. 2024;51. 10.1007/S11033-024-09707-Y . Dhillon VS, Yeoh E, Fenech M. DNA repair gene polymorphisms and prostate cancer risk in South Australia–results of a pilot study. Urol Oncol. 2011;29:641–6. 10.1016/J.UROLONC.2009.08.013 . Zhou C, Xie L-P, Lin Y-W, Yang K, Mao Q-Q, Cheng Y. Susceptibility of XPD and hOGG1 genetic variants to prostate cancer. Biomedical Rep. 2013;1:679–83. Mi Y, Zhang L, Feng N, Wu S, You X, Shao H, et al. Impact of two common xeroderma pigmentosum group D (XPD) gene polymorphisms on risk of prostate cancer. PLoS ONE. 2012;7:e44756. 10.1371/journal.pone.0044756 . Wu Y, Lu ZP, Zhang JJ, Liu DF, Shi GD, Zhang C, et al. Association between ERCC2 Lys751Gln polymorphism and the risk of pancreatic cancer, especially among Asians: evidence from a meta-analysis. Oncotarget. 2017;8:50124. 10.18632/ONCOTARGET.15394 . Liu Y, Hu Y, Zhang M, Jiang R, Liang C. Polymorphisms in ERCC2 and ERCC5 and Risk of Prostate Cancer: A Meta-Analysis and Systematic Review. J Cancer. 2018;9:2786. 10.7150/JCA.25356 . Fu W, Xiao F, Zhang R, Li J, Zhao D, Lin X, et al. Association Between the Asp312Asn, Lys751Gln, and Arg156Arg Polymorphisms in XPD and the Risk of Prostate Cancer. Technol Cancer Res Treat. 2017;16:692–704. 10.1177/1533034617724678 . Ma Q, Qi C, Tie C, Guo Z. Genetic polymorphisms of xeroderma pigmentosum group D gene Asp312Asn and Lys751Gln and susceptibility to prostate cancer: a systematic review and meta-analysis. Gene. 2013;530:309–14. 10.1016/J.GENE.2013.08.053 . Xiao F, Pu J, Wen Q, Huang Q, Zhang Q, Huang B, et al. Association between the ERCC2 Asp312Asn polymorphism and risk of cancer. Oncotarget. 2017;8:48488. 10.18632/ONCOTARGET.17290 . Mehdinejad M, Sobhan MR, Mazaheri M, Shehneh MZ, Neamatzadeh H, Kalantar SM. Genetic association between ERCC2, NBN, RAD51 gene variants and osteosarcoma risk: A systematic review and meta-analysis. Asian Pac J Cancer Prev. 2017;18:1315–21. Huang LM, Shi X, Yan DF, Zheng M, Deng YJ, Zeng WC, et al. Association between ERCC2 polymorphisms and glioma risk: a meta-analysis. Asian Pac J cancer prevention: APJCP. 2014;15:4417–22. 10.7314/APJCP.2014.15.11.4417 . Tables Table 1: Characteristics of studies included in the meta-analysis. First Author/Year Country (Ethnicity) SOC Genotyping Technique Case/Control Cases Controls MAFs HWE NOS Genotypes Allele Genotypes Allele rs13181 AA AG GG A G AA AG GG A G Rybicki 2004 USA(Caucasian) PB ARMS-PCR 571/435 223 273 75 719 423 178 205 52 561 309 0.355 0.547 7 Rybicki 2004 USA(African) PB ARMS-PCR 65/43 34 24 7 92 38 18 19 6 55 31 0.360 0.784 6 Ritchey 2005 USA(Asian) PB MALDI-TOF 160/285 141 19 0 301 19 251 33 1 535 35 0.061 0.938 6 Bau 2007 China(Asian) HB RFLP-PCR 123/517 111 10 2 232 14 479 33 5 991 43 0.042 ≤0.001 6 Agalliu 2010 USA(Caucasian) PB SNPlex™ 1233/1228 505 575 153 1585 881 480 571 177 1531 925 0.377 0.732 8 Agalliu 2010 USA(African) PB SNPlex™ 146/83 87 48 11 222 70 50 28 5 128 38 0.229 0.685 6 Mandal 2010 India(Asian) PB RFLP-PCR 171/311 73 84 14 230 112 200 94 17 494 128 0.206 0.183 6 Lavender 2010 USA(Caucasian) HB TaqMan 183/897 110 60 13 280 86 665 194 38 1524 270 0.151 ≤0.001 7 Gao 2010 USA(Caucasian) PB DS 428/118 186 178 64 550 306 49 56 13 154 82 0.347 0.612 6 Sobti 2012 India(Asian) HB RFLP-PCR 150/469 62 67 21 191 109 300 139 30 739 199 0.177 0.186 5 Mirecka 2014 Poland(Caucasian) PB RFLP-PCR 655/925 231 302 122 764 546 319 444 162 1082 768 0.415 0.725 8 Wang 2017 China(Asian) HB TaqMan 1004/1055 845 153 6 1843 165 907 138 10 1952 158 0.075 0.069 8 Cypriano 2017 Brazil(Mixed) HB RFLP-PCR 110/200 40 55 15 135 85 115 68 17 298 102 0.255 0.137 6 Balkan 2020 Turkey(Caucasian) HB qRT-PCR 40/40 6 14 20 26 54 21 0 19 42 38 0.475 ≤0.001 7 Abdulmonem 2021 KSA(Asian) HB RFLP-PCR 124/458 47 58 19 152 96 208 211 39 627 289 0.316 0.153 6 Ahmed 2024 Bangladesh(Asian) HB RFLP-PCR 132/135 64 54 14 182 82 68 61 6 197 73 0.270 0.091 6 rs1799793 CC CT TT C T CC CT TT C T Rybicki 2004 USA(Caucasian) PB ARMS-PCR 571/437 230 269 72 729 413 180 218 39 578 296 0.339 0.017 7 Rybicki 2004 USA(African) PB ARMS-PCR 65/43 47 17 1 111 19 30 13 0 73 13 0.151 0.242 6 Bau 2007 China(Asian) HB RFLP-PCR 123/479 62 39 22 163 83 310 106 63 726 232 0.242 ≤0.001 6 Agalliu 2010 USA(Caucasian) PB SNPlex™ 1240/1221 545 575 120 1665 815 527 528 166 1582 860 0.352 0.067 8 Agalliu 2010 USA(African) PB SNPlex™ 144/82 106 31 7 243 45 65 15 2 145 19 0.116 0.332 6 Mandal 2010 India(Asian) PB RFLP-PCR 171/200 76 56 39 208 134 99 81 20 279 121 0.303 0.569 6 Lavender 2010 USA(Caucasian) HB TaqMan 208/665 146 39 5 331 49 510 116 5 1136 126 0.100 0.567 7 Dhillon 2011 Australia(Caucasian) HB RFLP-PCR 118/132 71 37 8 179 53 80 42 10 202 62 0.235 0.187 6 Mirecka 2014 Poland(Caucasian) PB RFLP-PCR 572/627 199 249 124 647 497 377 218 32 972 282 0.225 0.946 8 Abdulmonem 2021 KSA(Asian) HB RFLP-PCR 124/458 66 51 7 183 65 215 198 45 628 288 0.314 0.952 6 rs238406 GG GA AA G A GG GA AA G A Agalliu 2010 USA(Caucasian) PB SNPlex™ 1261/1238 365 636 260 1366 1156 383 600 255 1366 1110 0.448 0.476 7 Agalliu 2010 USA(African) PB SNPlex™ 144/81 112 29 3 253 35 65 16 0 146 16 0.099 0.323 6 Zhou 2013 China(Asian) HB RFLP-PCR 100/100 26 53 21 105 95 38 49 13 125 75 0.375 0.650 6 Mirecka 2014 Poland(Caucasian) PB RFLP-PCR 512/678 113 300 99 526 498 141 411 126 693 663 0.489 ≤0.001 8 Wang 2017 China(Asian) HB TaqMan 1004/1055 310 480 214 1100 908 358 497 200 1213 897 0.425 0.239 8 Abbreviations: SOC - Source of Control, PB - Population-Based, HB - Hospital-Based, PCR - Polymerase Chain Reaction, RFLP - Restriction Fragment Length Polymorphism, DS - Direct Sequencing, ARMS - Amplification Refractory Mutation System, MAF - Minor Allele Frequency, HWE - Hardy-Weinberg Equilibrium, NOS - Newcastle-Ottawa Scale. Table 2: summary of results on the association between XPD polymorphisms and prostate cancer risk. Subgroup Genetic Model Type of Model Heterogeneity Odds Ratio Publication Bias I 2 (%) P H OR 95% CI Z test P OR P Beggs P Eggers rs13181 Overall G vs. A Random 79.02 ≤0.001 1.278 1.098-1.489 3.160 0.002 0.444 0.038 GG vs. AA Random 63.87 ≤0.001 1.511 1.143-1.999 2.894 0.004 1.000 0.046 GA vs. AA Random 75.81 ≤0.001 1.276 1.046-1.557 2.403 0.016 0.300 0.090 GG+GA vs. AA Random 79.25 ≤0.001 1.344 1.099-1.642 2.883 0.004 0.620 0.063 GG vs. GA+AA Fixed 38.80 0.057 1.139 1.001-1.295 1.969 0.049 0.752 0.080 Caucasian G vs. A Random 78.94 ≤0.001 1.153 0.956-1.391 1.490 0.136 0.132 0.046 GG vs. AA Random 62.32 0.021 1.207 0.892-1.632 1.220 0.222 0.024 0.007 GA vs. AA Random 77.58 ≤0.001 1.118 0.850-1.470 0.797 0.425 0.259 0.118 GG+GA vs. AA Random 81.13 ≤0.001 1.205 0.916-1.586 1.331 0.183 0.132 0.077 GG vs. GA+AA Fixed 24.51 0.250 1.021 0.882-1.182 0.276 0.782 0.452 0.094 Asian G vs. A Random 67.00 0.006 1.423 1.136-1.783 3.065 0.002 1.000 0.841 GG vs. AA Fixed 27.17 0.221 2.158 1.553-2.999 4.582 ≤0.001 0.133 0.218 GA vs. AA Random 68.96 0.004 1.428 1.062-1.921 2.359 0.018 1.000 0.994 GG+GA vs. AA Random 72.35 0.001 1.488 1.102-2.009 2.596 0.009 1.000 0.895 GG vs. GA+AA Fixed 2.751 0.404 1.790 1.306-2.453 3.619 ≤0.001 0.229 0.296 African G vs. A Fixed 0.00 0.322 0.924 0.647-1.319 -0.438 0.662 NA NA GG vs. AA Fixed 0.00 0.454 0.867 0.539-1.396 -0.586 0.558 NA NA GA vs. AA Fixed 0.00 0.397 0.916 0.401-2.092 -0.208 0.835 NA NA GG+GA vs. AA Fixed 0.00 0.356 0.885 0.565-1.386 -0.534 0.593 NA NA GG vs. GA+AA Fixed 0.00 0.512 0.989 0.446-2.197 -0.026 0.979 NA NA rs1799793 Overall A vs. C Random 92.17 ≤0.001 1.252 0.031-1.684 1.487 0.137 0.858 0.738 AA vs. CC Random 90.81 ≤0.001 1.681 0.870-3.247 1.545 0.122 1.000 0.531 AC vs. CC Random 74.42 ≤0.001 1.169 0.927-1.476 1.320 0.187 0.474 0.851 AA+AC vs. CC Random 86.88 ≤0.001 1.243 0.921-1.676 1.423 0.155 0.720 0.953 AA vs. AC+CC Random 89.19 ≤0.001 1.580 0.879-2.840 1.530 0.126 0.858 0.446 Caucasian A vs. C Random 96.07 ≤0.001 1.284 0.809-2.038 1.059 0.289 0.806 0.713 AA vs. CC Random 85.01 ≤0.001 1.223 0.878-1.704 1.192 0.233 0.806 0.921 AC vs. CC Random 95.44 ≤0.001 1.855 0.650-5.292 1.156 0.248 0.806 0.545 AA+AC vs. CC Random 93.23 ≤0.001 1.297 0.817-2.059 1.104 0.269 0.806 0.816 AA vs. AC+CC Random 94.48 ≤0.001 1.701 0.680-4.252 1.136 0.256 0.806 0.493 Asian A vs. C Random 84.04 0.002 1.226 0.786-1.913 0.899 0.369 0.296 0.016 AA vs. CC Random 78.71 0.009 1.378 0.602-3.154 0.758 0.448 1.000 0.348 AC vs. CC Random 72.42 0.027 1.110 0.683-1.802 0.421 0.674 0.296 0.547 AA+AC vs. CC Random 76.69 0.014 1.197 0.740-1.938 0.734 0.463 1.000 0.843 AA vs. AC+CC Random 78.84 0.009 1.342 0.605-2.977 0.724 0.469 1.000 0.471 African A vs. C Fixed 0.00 0.430 1.230 0.777-1.947 0.883 0.377 NA NA AA vs. CC Fixed 0.00 0.953 2.101 0.500-8.824 1.014 0.311 NA NA AC vs. CC Fixed 0.00 0.456 1.075 0.629-1.839 0.264 0.792 NA NA AA+AC vs. CC Fixed 0.00 0.421 1.165 0.696-1.952 0.581 0.561 NA NA AA vs. AC+CC Fixed 0.00 0.996 2.040 0.488-8.523 0.977 0.329 NA NA rs238406 Overall C vs. T Fixed 14.54 0.322 1.071 0.997-1.151 1.875 0.061 0.806 0.276 CC vs. TT Fixed 18.24 0.299 1.142 0.984-1.324 1.753 0.080 0.462 0.219 CT vs. TT Fixed 0.00 0.571 1.089 0.968-1.224 1.418 0.156 0.806 0.759 CC+CT vs. TT Fixed 0.00 0.416 1.107 0.990-1.237 1.237 0.074 0.806 0.555 CC vs. CT+TT Fixed 0.00 0.486 1.083 0.954-1.230 1.230 0.219 0.220 0.106 Caucasian C vs. T Fixed 0.00 0.611 1.025 0.935-1.123 0.518 0.605 NA NA CC vs. TT Fixed 0.00 0.687 1.044 0.863-1.264 0.444 0.657 NA NA CT vs. TT Fixed 24.13 0.251 1.051 0.901-1.226 0.634 0.526 NA NA CC+CT vs. TT Fixed 4.024 0.307 1.050 0.907-1.215 0.649 0.516 NA NA CC vs. CT+TT Fixed 0.00 0.790 1.016 0.864-1.194 0.192 0.848 NA NA Asian C vs. T Fixed 49.88 0.158 1.146 1.019-1.289 2.268 0.023 NA NA CC vs. TT Fixed 51.09 0.153 1.298 1.026-1.644 2.170 0.030 NA NA CT vs. TT Fixed 6.16 0.302 1.150 0.953-1.389 1.459 0.145 NA NA CC+CT vs. TT Fixed 40.52 0.195 1.192 0.999-1.422 1.947 0.052 NA NA CC vs. CT+TT Fixed 12.75 0.284 1.196 0.972-1.472 1.692 0.091 NA NA Additional Declarations No competing interests reported. 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Atarod","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYFACHjApZ3+8AUgZWBCvxZjhzAGQFgnitSQ23EgA0URokZ+Re/jFxx02xowzn1/d8KNAgoG/vTsBrxaDG3lpljPPpMkxS+eU3ewBOkzizNkN+LVI5JgZ87YdNmaTzkm7wQPUYiCRi1+L/Ayglr9t/xN7JM+k3fxDjBaGGznGjxnbDiTOkGA/dpsoWwzOvEtj7G1LNjbgyWG7LWMgwUPQL/LtuYc//GyzkzNgP/7s5ps/NnL87b0EHMbAwAaNCx4DMElIOQgwf4DQ7A+IUT0KRsEoGAUjEAAAytdIbxi3uPAAAAAASUVORK5CYII=","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Mehdi","lastName":"Atarod","suffix":""},{"id":398867953,"identity":"4eefa3a5-7f8f-4a2f-8ea4-e7a094f45e86","order_by":2,"name":"Mehdi Khosravi-Mashizi","email":"","orcid":"","institution":"Hazrat-e Rasool General Hospital, Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Khosravi-Mashizi","suffix":""},{"id":398867954,"identity":"7ff3701a-51cf-442d-b49a-c83464488d8e","order_by":3,"name":"Hadi Maleki","email":"","orcid":"","institution":"Alborz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hadi","middleName":"","lastName":"Maleki","suffix":""},{"id":398867956,"identity":"bcf4e0a6-2fe1-4f77-903d-29539081e724","order_by":4,"name":"Saman Farshid","email":"","orcid":"","institution":"Imam Khomeini Hospital, Urmia University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Saman","middleName":"","lastName":"Farshid","suffix":""},{"id":398867958,"identity":"4a15dff2-8b28-419c-adae-1ffae652fe87","order_by":5,"name":"Seyed Alireza Dastgheib","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Alireza","lastName":"Dastgheib","suffix":""},{"id":398867960,"identity":"c901dddb-d6f4-4e57-9c11-ee9edada1d10","order_by":6,"name":"Abolhasan Alijanpour","email":"","orcid":"","institution":"Babol University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abolhasan","middleName":"","lastName":"Alijanpour","suffix":""},{"id":398867961,"identity":"c6f90881-52e1-4700-94bf-309b227d40d3","order_by":7,"name":"Maedeh Barahman","email":"","orcid":"","institution":"Firoozgar Hospital, Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maedeh","middleName":"","lastName":"Barahman","suffix":""},{"id":398867962,"identity":"4a3545e3-aded-46be-9819-1e9b2269ea7b","order_by":8,"name":"Amirhossein Rahmani","email":"","orcid":"","institution":"Iranshahr University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amirhossein","middleName":"","lastName":"Rahmani","suffix":""},{"id":398867963,"identity":"96714c0a-339c-4bf9-81a8-9baaa46401df","order_by":9,"name":"Maryam Aghasipour","email":"","orcid":"","institution":"University of Cincinnati","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Aghasipour","suffix":""},{"id":398867968,"identity":"da3a880a-f7bf-4564-82b6-d99d0a530009","order_by":10,"name":"Kazem Aghili","email":"","orcid":"","institution":"Shahid Rahnamoun Hospital, Shahid Sadoughi University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kazem","middleName":"","lastName":"Aghili","suffix":""},{"id":398867969,"identity":"18e2f53d-afb7-49e0-a9ff-d0ffb16a7d35","order_by":11,"name":"Hossein Neamatzadeh","email":"","orcid":"","institution":"Shahid Sadoughi University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Neamatzadeh","suffix":""}],"badges":[],"createdAt":"2025-01-06 05:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5770719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5770719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73519050,"identity":"9570f8fa-b0da-489d-99cd-51098393522c","added_by":"auto","created_at":"2025-01-10 18:07:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234470,"visible":true,"origin":"","legend":"\u003cp\u003eStudy selection and inclusion process.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770719/v1/cdd540380c3636776d9f92ed.jpg"},{"id":73520285,"identity":"57bc7500-bb44-46bc-9891-f8196a44d869","added_by":"auto","created_at":"2025-01-10 18:15:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":842038,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots showing the association between the XPD rs13181 polymorphism and prostate cancer risk in the overall population: A. allele model (G vs. A); B. homozygote model (GG vs. AA); C. recessive model (GG vs. GA+AA).\u003c/p\u003e","description":"","filename":"Fig2A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770719/v1/df0982facd14f132208d95c6.jpg"},{"id":73519051,"identity":"8fc3de17-f10f-416d-baab-1e96234153a3","added_by":"auto","created_at":"2025-01-10 18:07:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184105,"visible":true,"origin":"","legend":"\u003cp\u003eBegg’s funnel plots examining publication bias for the XPD rs13181 polymorphism and prostate cancer risk: A. allele model (G vs. A); B. homozygote model (GG vs. AA).\u003c/p\u003e","description":"","filename":"Fig3A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770719/v1/3123122433e461c78d3f2820.jpg"},{"id":101753756,"identity":"869a2aeb-942e-4543-9f5c-7b9503aed172","added_by":"auto","created_at":"2026-02-03 10:40:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2802800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5770719/v1/b6e9c16e-674d-490e-9ef0-fe3ccf3db0ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of XPD Genetic Variations with Prostate Cancer Risk: Consolidated Results from 31 case-control studies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is one of the most common malignant tumors affecting the male genitourinary system. Prostate cancer remains a significant health concern for men, particularly as they age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In 2021, it was the second leading cause of cancer death among men aged 65 to 79 in the United States, resulting in 32,563 deaths, highlighting the urgent need for awareness and early detection in this demographic. The disease poses a lower risk for younger males, with only 531 deaths in the 20 to 39 age group and 15,794 in those aged 50 to 64. This age-related variation in mortality underscores the importance of regular screenings and further research into effective prevention and treatment strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, the impact of prostate cancer incidence rates varies significantly among different racial and ethnic groups, with Black men experiencing rates 68% higher than White men and two to three times higher than American Indian/Alaska Native, Hispanic, and Asian American/Pacific Islander men. According to 2022 GLOBOCAN estimates, age-standardized prostate cancer incidence rates varied by a factor of 13 across regions, while mortality rates varied by 9.5 times. High incidence rates were particularly noted in Australia/New Zealand, North America, and Northern Europe, whereas sub-Saharan Africa and Latin America/Caribbean reported the highest mortality rates. Over the past five years, incidence rates have increased in 11 of 50 countries analyzed, with mortality rates rising in nine, mainly in Africa, Asia, and Latin America/Caribbean, while mortality rates decreased in 38 countries, primarily in Europe and Oceania [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDNA repair genes are linked to an increased risk of prostate cancer, influencing both the likelihood of developing the disease and its progression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The BRCA2 gene, a crucial tumor suppressor in DNA repair, is associated with a 40% lifetime risk of prostate cancer and a 2.5- to 8.6-fold increased risk by age 65. It is the most prevalent DNA repair variant in prostate cancer, with studies showing that BRCA2 variant carriers have a higher positive predictive value for biopsy results. Similarly, BRCA1 variants raise the lifetime risk to 20%, particularly among younger individuals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Other significant genes include ATM, which has a 2.18 to 4.4 odds ratio for prostate cancer, and CHEK2, where variants, especially the 1100delC variant common in Polish populations, are linked to a risk range of 1.8 to 8.2. PALB2 variants are also associated with a 3.5-fold increased risk and aggressive disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mismatch repair (MMR) genes like MSH2 and MSH6 indicate a 2- to 10-fold increased risk, particularly in younger patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, the HOXB13 G84E variant significantly raises risk by 2.93-fold, especially in those with a family history of prostate cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These genetic findings highlight the need for targeted screening and personalized risk assessment strategies for individuals carrying these variants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eXPD, also known as excision repair cross-complementation group 2 (ERCC2), is a vital protein in the nucleotide excision repair (NER) pathway, which is essential for repairing DNA damage inflicted by ionizing radiation and various cytotoxic agents [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The XPD gene is located on chromosome 19q13.3 and comprises 23 exons that encode a DNA helicase crucial for the NER process [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among the significant polymorphisms within the XPD gene, the Lys751Gln variant (rs13181) results in an amino acid substitution at codon 751 in exon 23, changing lysine to glutamine. This alteration affects the protein's functionality, particularly diminishing helicase activity by impacting the ATP-binding site without influencing gene transcription [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Other polymorphisms, such as Asp312Asn (rs1799793) and p.Arg156Arg (rs238406), have also been extensively examined concerning their effects on DNA repair mechanisms [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While a number of studies have investigated the connection between XPD polymorphisms and prostate cancer risk, the findings have been inconsistent and inconclusive. These discrepancies may result from factors such as ethnic diversity, environmental influences, methodological variations, and sample sizes in genetic epidemiology. Future research is recommended to involve larger, multi-ethnic cohorts and standardized methodologies to better elucidate the associations between XPD polymorphisms and prostate cancer susceptibility. This meta-analysis aims to synthesize data from various studies and populations to assess the relationship between XPD genetic variations and prostate cancer risk.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy\u003c/h2\u003e \u003cp\u003eA systematic literature search was performed across a wide range of databases, including MEDLINE, PubMed, PubMed Central (PMC), Europe PubMed Central (Europe PMC), Scopus, Cochrane Library, Google Scholar, Web of Science, Elsevier, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ResearchGate, ClinicalTrials.gov, SciELO, MedNexus, MedRxiv, Chinese Biomedical Database (CBD), Chinese National Knowledge Infrastructure (CNKI), Wanfang Data Company, Chaoxing, Circumpolar Health Bibliographic Database (CHBD), China/Asia On Demand (CAOD), Indian Citation Index (ICI), Chinese Medical Citation Index (CMCI), Semantic Scholar, Egyptian Knowledge Bank (EKB), VIP Information Consultancy Company (VIP), Chinese Medical Current Contents (CMCC), and Weipu Periodical Database.The objective was to identify relevant articles assessing the correlation between XPD polymorphisms and prostate cancer risk, focusing on studies available until December 30, 2024. The investigation utilized a combination of MeSH terms and keywords related to prostate cancer and XPD polymorphisms, including 'ERCC2 Lys751Gln', 'XPD codon 751', 'rs13181', 'c.2251A\u0026thinsp;\u0026gt;\u0026thinsp;C', 'A20337C', 'ERCC2 Asp312Asn', 'XPD codon 312', 'rs1799793', 'c.934G\u0026thinsp;\u0026gt;\u0026thinsp;A', 'G7988A', 'ERCC2 Arg156Arg', 'XPD codon 156', 'rs238406', 'c.468A\u0026thinsp;\u0026gt;\u0026thinsp;C', as well as genetic terms like 'Gene', 'Genotype', 'Allele', 'Polymorphism', 'Single nucleotide polymorphisms', 'SNP', 'Variation', 'Mutation', 'Cancer genetics', and 'Tumor markers'. To identify relevant publications, we manually examined references from eligible studies, prior meta-analyses, and review articles. The search focused on human studies, emphasizing reputable publications and significant reviews as supplementary sources. When multiple studies by the same authors had overlapping data, we prioritized the study with the largest sample size or the most recent publication date. There were no restrictions on language or publication year. Additionally, references from relevant reviews and articles were reviewed to find additional studies. Non-English articles were included following a defined translation process and specific quality assessment criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eStudies were selected for inclusion based on the following criteria: 1) availability of full-text articles; 2) classification as either case-control or cohort studies; 3) examination of the relationship between XPD polymorphisms and prostate cancer risk; and 4) provision of sufficient data for calculating odds ratios (OR) or relative risks, accompanied by 95% confidence intervals (CI). Exclusion criteria encompassed studies that: 1) did not investigate the association between XPD polymorphisms and prostate cancer risk; 2) concentrated on animal or in vitro research; 3) lacked adequate data for analysis; 4) were case-only studies without control groups; 5) involved linkage or family-based designs, including studies of twins and siblings; 6) were case reports, abstracts, comments, conference abstracts, editorials, reviews, or meta-analyses; and 7) constituted duplicates of other studies. In cases of duplicate publications by the same author utilizing the same patient sample, only the study with the largest sample size was incorporated into the meta-analysis.\u003c/p\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003eTwo authors independently evaluated all eligible articles and extracted pertinent information based on the established inclusion criteria. Any discrepancies that arose during this process were resolved through discussion; if a consensus was not reached, a third researcher was consulted, with decisions ultimately made by majority vote. Each eligible study was required to provide details regarding the primary author, publication date, country of origin, ethnic backgrounds of participants (categorized as Asian, Caucasian, African, Hispanic, and Mixed), genotyping methodologies employed, sources of healthy subjects, total sample size, allele and genotype frequencies of XPD variants among healthy subjects and cases, Minor Allele Frequencies (MAFs), and Hardy-Weinberg equilibrium (HWE) among healthy controls. Additionally, studies published in languages other than English underwent a thorough translation to ensure accurate interpretation of results. This meta-analysis considered separate case-control groups or cohorts within a single publication as distinct studies.\u003c/p\u003e\n\u003ch3\u003eQuality Score Assessment\u003c/h3\u003e\n\u003cp\u003eThe Newcastle-Ottawa Score (NOS) was utilized to evaluate the quality of studies included in a meta-analysis by examining key methodological aspects of observational research, such as case selection, group comparability, and exposure determination, with each category comprising eight specific criteria. Studies were awarded one star for excellent selection and exposure, while comparability could earn a maximum of two stars. Overall quality assessment was conducted on a nine-star scale, where a score of zero indicated poor quality and a score of nine represented high quality. Studies that achieved a score of seven or above were classified as high quality, while those scoring at least five points were deemed appropriate for inclusion in the meta-analysis. Any disagreements regarding scoring were resolved through discussion and consensus among the evaluators.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCrude odds ratios (ORs) and 95% confidence intervals (CIs) were computed to assess the association between XPD polymorphisms and prostate cancer risk within the overall population. Combined ORs were derived according to five genetic comparison models: allele (A vs. B), homozygote (AA vs. BB), heterozygote (BA vs. BB), dominant (AA\u0026thinsp;+\u0026thinsp;BA vs. BB), and recessive (AA vs. BA\u0026thinsp;+\u0026thinsp;BB). To evaluate study heterogeneity, Cochran's Q test was utilized, with P-values less than 0.1 indicating the absence of heterogeneity, further quantified using the I\u0026sup2; statistic, which categorized heterogeneity levels as low, moderate, or high at thresholds of 25%, 50%, and 75%, respectively. For studies exhibiting significant heterogeneity, a random effects model based on the DerSimonian and Laird approach was adopted, whereas a fixed effects model (Mantel-Haenszel) was applied when heterogeneity was not significant. Meta-regression analyses were conducted to identify potential confounding factors contributing to heterogeneity. The Hardy-Weinberg equilibrium (HWE) among controls was evaluated using a chi-square goodness-of-fit test [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To ensure the robustness of the findings, sensitivity analyses were performed by systematically excluding individual studies. Potential publication bias was assessed using both Egger\u0026rsquo;s and Begg\u0026rsquo;s tests, with P-values below 0.05 indicative of significant bias. The non-parametric \"trim-and-fill\" method proposed by Duval and Tweedie was employed to adjust for publication bias, facilitating a comparison between the adjusted pooled ORs and the original estimates. All statistical analyses were performed using Comprehensive Meta-Analysis (CMA) software version 2.0 (Biostat, USA), with statistical significance determined using the Z-test, where P-values below 0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of eligible studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the study selection methodology for this analysis. An initial search across various online databases identified 482 articles. After removing duplicates, 311 unique articles remained. A review of titles and abstracts excluded 163 studies that did not meet the inclusion criteria, leaving 148 for full-text assessment. This thorough review further eliminated 117 studies due to issues like inappropriate control groups, insufficient data, and focus on different XPD gene polymorphic loci. Ultimately, 31 case-control studies from 16 publications [17, 18, 27\u0026ndash;32, 19\u0026ndash;26]\u0026nbsp; were included, comprising 11,632 cases of PrCr and 14,661 healthy controls. Table 1 summarizes these studies, highlighting specific XPD gene polymorphisms: rs13181 (Lys751Gln) in 16 studies (5,295 cases, 7,199 controls), rs1799793 (Asp312Asn) in 10 studies (3,316 cases, 4,310 controls), and rs238406 (Arg156Arg) in five studies (3,021 cases, 3,152 controls). The studies published between 2004 and 2024 were in English, enhancing their accessibility. The sample sizes for cases in the studies included in the meta-analysis ranged from 40 to 1,233, while the number of controls varied from 40 to 1,055. This meta-analysis included research from countries such as the United States, China, India, Australia, Poland, Brazil, Turkey, Saudi Arabia, and Bangladesh, providing a global perspective. It comprised 11 studies focusing on Caucasian populations, eight on Asian populations, five on African populations, and one on a mixed population. This diversity strengthens the generalizability of the findings. A total of 12 studies used population-based controls, while 8 studies used hospital-based controls. The genotyping methods used\u0026mdash;ranging from ARMS-PCR, MALDI-TOF, RFLP-PCR, SNPlex\u0026trade;, TaqMan assays, DS (Direct Sequencing), and qRT-PCR\u0026mdash;underscore methodological rigor. Notably, all but two studies adhered to HWE in their control groups, essential for accurate genetic analysis interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality of Studies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quality of studies in the meta-analysis can be assessed through several key factors: study design, sample size, genotyping methods, MAFs, adherence to HWE, and NOS scores. Most studies utilize robust designs, primarily through population-based or hospital-based sampling methods that minimize bias, though each approach has its own advantages. Sample sizes vary significantly; larger samples, such as Agalliu 2010 (1233 cases, 1228 controls), generally yield more reliable results, whereas smaller studies, like Balkan 2020 (40 cases, 40 controls), are more prone to errors. Various genotyping techniques are applied, each with particular strengths and weaknesses, with standardized methods favored. MAFs indicate genetic diversity, but extreme values may raise accuracy concerns. Adherence to HWE is vital, as seen in Bau 2007 (HWE \u0026le;0.001), which shows significant deviations that might suggest genotyping errors or sample bias. NOS scores typically range from 5 to 8, indicating moderate to high quality, although a higher score does not eliminate the risk of bias. Overall, while many studies exhibit strong methodologies, smaller sample sizes, HWE deviations, and variability in genotyping methods require careful consideration to ensure valid interpretations in the meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHWE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;HWE is a critical measure in genetic studies, providing insight into the genetic variation within and between populations. The analysis of HWE by polymorphism shows diverse results across different countries and ethnicities. For instance, the rs13181 polymorphism exhibited a significant HWE deviation in a Caucasian population from the USA, with a p-value of 0.017, indicating potential disturbances in genetic equilibrium. Conversely, both African populations in the USA revealed variability, with HWE p-values of 0.242 and 0.784 indicating acceptable equilibrium. In Asian populations, the rs13181 polymorphism exhibited a strikingly low HWE p-value (\u0026le;0.001) in a Chinese sample, suggesting a strong deviation from equilibrium. When examining rs1799793, the Caucasian population from the USA also showed notable HWE deviations (p-value 0.017), while African cohorts exhibited more stable frequencies with p-values of 0.242 and 0.332. Additionally, the rs238406 polymorphism displayed diverse profiles, with a Caucasian population from Poland demonstrating substantial equilibrium (p-value \u0026le;0.001). In contrast, the African cohort in the USA revealed a p-value of 0.323, indicating a less stable genetic structure. These observations highlight the importance of considering both ethnicity and geographic origin when assessing HWE, as variations underscore the underlying genetic complexities and population histories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynthesis of Quantitative Data \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents a summary analysis of the association between the XPD polymorphism and prostate cancer risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ers13181:\u003c/strong\u003e The analysis of the association between the XPD rs13181 polymorphism and prostate cancer risk revealed several significant findings. In the overall population, the G allele was associated with an increased risk of prostate cancer, with an odds ratio (OR) of 1.278 (95% CI: 1.098-1.489, p = 0.002, Fig 2A). Notably, individuals with the GG genotype compared to the AA genotype exhibited a higher risk (OR = 1.511, 95% CI: 1.143-1.999, p = 0.004, Fig 2B). Similarly, the GA genotype also showed increased risk relative to AA (OR = 1.276, 95% CI: 1.046-1.557, p = 0.016). When combining GG and GA genotypes against AA, the OR was 1.344 (95% CI: 1.099-1.642, p = 0.004). In contrast, a fixed model indicated a marginally significant association for GG versus GA+AA (OR = 1.139, 95% CI: 1.001-1.295, p = 0.049). In the Caucasian subgroup, however, no significant associations were observed across various genetic models. In the Asian subgroup, significant associations were noted, with the GG genotype showing a strong increased risk (OR = 2.158, 95% CI: 1.553-2.999, p \u0026le; 0.001). Other models in the Asian group also indicated heightened risk, confirming the risk associated with the G allele. Conversely, the African subgroup displayed no significant associations across all genetic models, with ORs consistently below 1, indicating a potential protective effect of the G allele in this population. Overall, these results suggest that the XPD rs13181 polymorphism may contribute to prostate cancer susceptibility, particularly in Asian populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ers13181:\u003c/strong\u003e The association between the XPD rs1799793 polymorphism and prostate cancer risk was analyzed across different subgroups. In the overall population, the genetic model revealed an odds ratio (OR) of 1.252 for the A vs. C comparison, with a 95% confidence interval (CI) of 0.031-1.684 and a p-value of 0.137, indicating no significant association. Subgroup analyses demonstrated similar patterns. In Caucasians, the A vs. C comparison yielded an OR of 1.284 (95% CI: 0.809-2.038, p = 0.289), while for Asians, the OR was 1.226 (95% CI: 0.786-1.913, p = 0.369), both suggesting a lack of significant risk. Among Africans, the fixed model for the A vs. C comparison showed an OR of 1.230 (95% CI: 0.777-1.947, p = 0.377). Individual genotype comparisons (AA vs. CC and AC vs. CC) displayed wide confidence intervals indicating considerable variability in estimates, particularly in the African group. Overall, these results suggest that the XPD rs1799793 polymorphisms do not significantly influence prostate cancer risk across the studied populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ers238406:\u003c/strong\u003e The results of the association between XPD rs238406 polymorphisms and prostate cancer risk were analyzed across different subgroups. In the overall population, no significant associations were observed, with odds ratios (OR) close to 1, indicating a lack of increased risk. Specifically, the comparison of the C vs. T genotype yielded an OR of 1.071 (95% CI: 0.997-1.151) with a p-value of 0.061, suggesting a marginal trend. Subgroup analysis revealed notable differences in the Asian population, where the C vs. T comparison showed a significant OR of 1.146 (95% CI: 1.019-1.289) and a p-value of 0.023, indicating a statistically significant association. Additionally, the CC vs. TT genotype comparison in Asians also revealed an OR of 1.298 (95% CI: 1.026-1.644) with a p-value of 0.030, further suggesting a higher risk of prostate cancer associated with the CC genotype. Other comparisons within the Asian subgroup displayed trends towards significance but did not reach conventional levels. In contrast, the Caucasian subgroup showed no significant associations, with ORs remaining close to 1 across various genetic models. Overall, these findings suggest that XPD rs238406 polymorphisms may be associated with increased prostate cancer risk, particularly in Asian populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZ-test Results\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Z-test assesses significant differences in odds ratios between case and control groups. The results for XPD polymorphisms show varying levels of statistical significance in their association with prostate cancer risk across different genetic models and populations. For rs13181, several models yielded significant Z-values, especially in the overall group (Z = 3.160, p = 0.002) and the Asian subgroup, where the GG vs. AA comparison showed a Z-value of 4.582 (p \u0026lt; 0.001). In contrast, the Caucasian subgroup yielded mostly non-significant results, including the G vs. A model (Z = 1.490, p = 0.136). For rs1799793, the overall group results showed stronger Z-values, with the AA vs. CC model indicating Z = 1.545 (p = 0.122), suggesting a trend toward significance but not meeting traditional thresholds. Conversely, rs238406 consistently showed Z-values below 2, indicating a lack of significant association across all models and populations. Overall, rs13181 appears to have the strongest association with prostate cancer risk, particularly in Asian populations, while rs238406 shows no significant impact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the analysis of the association between XPD polymorphisms and prostate cancer risk, heterogeneity across different genetic models and subgroups was evaluated using the I\u0026sup2; statistic and its corresponding p-values. For the rs13181 polymorphism, significant heterogeneity was observed in the overall group across all genetic models with I\u0026sup2; values ranging from 63.87% to 79.25% and p-values \u0026le;0.001, indicating variability in the studies examined. This trend was consistent within the Caucasian subgroup, where substantial heterogeneity was present in several models, while the Asian subgroup also showed significant heterogeneity, especially in the G vs. A and GA vs. AA models. In contrast, the African subgroup exhibited fixed effects across all models, with I\u0026sup2; values of 0.00% and p-values ranging from 0.322 to 0.512, suggesting no significant heterogeneity. For the rs1799793 polymorphism, a high degree of heterogeneity was noted in the overall group, with I\u0026sup2; values reaching 96.07% in the A vs. C model, indicating considerable variability among studies. This heterogeneity persisted across subgroups, particularly in Caucasian individuals. Conversely, the African subgroup showed no heterogeneity, similar to the findings for rs238406, which displayed I\u0026sup2; values consistently at 0.00% across all models and both subgroups tested. This lack of variability suggests that the African cohort exhibited a uniform response to the rs238406 polymorphism concerning prostate cancer risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe assessment of publication bias for XPD polymorphisms in prostate cancer risk reveals varying degrees of bias across different genetic models and subgroups. For the rs13181 polymorphism, the overall analysis indicated significant publication bias in the genetic models \u0026quot;G vs. A\u0026quot; (PBeggs = 0.444, PEggers = 0.038, Fig 3A) and \u0026quot;GG vs. AA\u0026quot; (PBeggs = 1.000, PEggers = 0.046, Fig 3B), suggesting potential biases in the available literature. When focusing on the Caucasian subgroup, particularly stark publication bias was evident for \u0026quot;GG vs. AA\u0026quot; (PBeggs = 0.024, PEggers = 0.007) and \u0026quot;GA vs. AA\u0026quot; (PBeggs = 0.259, PEggers = 0.118). Conversely, the Asian subgroup displayed minimal publication bias across most models, particularly for \u0026quot;GG vs. AA\u0026quot; (PBeggs = 0.133, PEggers = 0.218). The publication bias for rs1799793 showed a relatively consistent lack of bias in the Caucasian subgroup with all models exhibiting PBeggs around 0.806, though the Asian subgroup revealed a significant publication bias for \u0026quot;A vs. C\u0026quot; (PBeggs = 0.296, PEggers = 0.016). The rs238406 polymorphism did not allow for a reliable evaluation of publication bias in both Caucasian and Asian subgroups due to a lack of available data. Overall, the presence of publication bias, particularly in certain genetic models and subgroups, underscores the importance of considering potential biases when interpreting the association between XPD polymorphisms and prostate cancer risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were conducted to evaluate the robustness of the results regarding the association between XPD polymorphisms and prostate cancer risk. By examining the odds ratios across different genetic models and subgroups, we found that the overall analysis for polymorphism rs13181 showed a significant association with prostate cancer risk in the G vs. A genetic model, yielding an odds ratio of 1.278 (95% CI: 1.098-1.489, p=0.002). This trend was also observed in the subgroup analyses, particularly among Asians, where GG vs. AA resulted in a notably higher odds ratio of 2.158 (95% CI: 1.553-2.999, p \u0026le; 0.001). In contrast, the African subgroup displayed a lack of association across all models, indicating that genetic variability within populations may influence susceptibility to prostate cancer. Similar patterns emerged for rs1799793 and rs238406, where significant associations were found primarily in Asian populations. Overall, these sensitivity analyses highlight the importance of considering population stratification and genetic background when interpreting the results of genetic association studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAFs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMAFs of various polymorphisms exhibit notable variations across different countries and ethnicities as demonstrated in the meta-analysis. For the rs13181 polymorphism, the MAFs among Caucasian populations in the USA ranged from 0.355 to 0.377, while African Americans showed similar frequencies at 0.360. In contrast, the Asian populations in India and China presented MAFs of 0.206 and 0.075, respectively. The rs1799793 polymorphism displayed a MAF of 0.339 in the Caucasian cohort from the USA, whereas it was markedly lower in the African group with a MAF of 0.151. Additionally, MAFs for rs238406 were reported at 0.448 for Caucasian populations in the USA and 0.375 in a Chinese cohort, both reflecting a higher prevalence than reported in African populations, where it was as low as 0.099. Overall, these findings suggest that MAFs can vary significantly between ethnic groups and are influenced by geographic location, emphasizing the need for further studies to explore these genetic variations in diverse populations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eXPD is a key gene in the NER pathway, essential for maintaining genomic stability, and its genetic variations have been studied for their potential link to prostate cancer risk, yielding mixed results. The Asn312Asp variant at codon 312 has been associated with a significant increase in prostate cancer risk, particularly in Taiwanese patients, showing a 1.81-fold increase for individuals with the Asn/Asn genotype compared to the Asp/Asp genotype (p\u0026thinsp;=\u0026thinsp;0.003) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, the Lys751Gln variant at codon 751 has yielded inconsistent findings, with a Brazilian study showing a significant association and an odds ratio of 2.36 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], while other studies reported no significant link [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, research suggests that the combined effects of multiple SNPs, such as the XPD codon 312 Asn variant with other genetic variations like XRCC1, may produce a greater risk than individual polymorphisms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, the relationship between XPD polymorphisms and prostate cancer risk appears to vary across populations, highlighting the necessity to factor in genetic backgrounds and environmental influences when evaluating cancer susceptibility. Thus, we conducted a pooled analysis of 31 case-control studies, involving 11,632 prostate cancer cases and 14,661 healthy controls, to evaluate the association between XPD gene polymorphisms and prostate cancer.\u003c/p\u003e \u003cp\u003eThe rs13181 (Lys751Gln) variant in the ERCC2 gene has been linked to prostate cancer, primarily through its effects on DNA repair mechanisms. This variant replaces lysine with glutamine at position 751, which may impair NER efficiency, essential for repairing DNA damage [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Evidence indicates that individuals carrying the Gln allele could have elevated DNA adduct levels and reduced repair activity, leading to increased genomic instability, a precursor to cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Several studies have identified a significant association between the rs13181 variant and prostate cancer risk, including a Brazilian case-control study that reported an odds ratio of 2.36 for Gln allele carriers [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, a correlation exists between this variant and higher Gleason scores, suggesting a potential impact on cancer aggressiveness. However, the influence of the Lys751Gln variant on prostate cancer risk differs across populations; while some ethnic groups exhibit a strong association, others, such as findings from Turkey, show no significant link [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This variability underscores the importance of environmental factors and genetic backgrounds in cancer risk. Our pooled analysis of 16 studies involving 5,295 cases and 7,199 controls found a significant increase in prostate cancer risk linked to the G allele of rs13181, especially in Asian populations, where individuals with the GG genotype had an odds ratio of 2.158. No significant association was observed in Caucasians, while the African subgroup suggested a possible protective effect of the G allele. Meta-analyses by Liu et al. (2018) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which included 11 studies with 4,456 cases, and by Fu et al. (2017) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], based on ten case-control studies, found no significant link between the rs13181 polymorphism and prostate cancer risk, even when analyzed by ethnicity. Similarly, Ma et al. (2013) conducted a pooled analyses that also reported no significant association across various studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These findings suggest that the Lys751Gln variant may not universally influence prostate cancer susceptibility, and its effects might differ by ethnicity. The protective effect observed in the African subgroup complicates this narrative, indicating that genetic variants can have varying impacts based on ethnic backgrounds. This variability highlights the need for more nuanced research that considers both genetic polymorphisms and the environmental and lifestyle factors influencing cancer susceptibility.\u003c/p\u003e \u003cp\u003eThe rs1799793 (Asp312Asn) polymorphism in the XPD gene involves a G to A substitution at codon 312, resulting in a change from aspartic acid (Asp) to asparagine (Asn). While this variant may influence helicase activity and impact DNA repair capacity, its overall functional consequences are not as established as those of other variations, such as rs13181 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This polymorphism is linked to cancer risk primarily through its effects on DNA repair efficiency, particularly in the context of NER, which is vital for addressing bulky DNA lesions and maintaining genomic integrity [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Research suggests that individuals with the Asn allele might experience decreased DNA repair capacity, leading to increased DNA damage and heightened cancer risk. Some studies indicate that carriers of the Asn allele face significantly elevated risks for several cancers, including prostate cancer, with reported risks as high as 1.84-fold compared to those with the Asp/Asp genotype [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our meta-analysis of rs1799793 across 10 studies (3,316 cases, 4,310 controls), no significant impact on prostate cancer risk was found among any populations studied, with odds ratios consistently around 1. This suggests that genetic variations at this locus may have minimal influence on susceptibility to prostate cancer, despite various studies supporting the association between the Asp312Asn polymorphism and increased prostate cancer risk. Other meta-analyses, like those by Liu et al. (2018) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and Fu et al. (2017) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], highlighted a lack of overall association but noted increased risks in specific subpopulations, particularly Asian and African individuals. Further investigation by Ma et al. (2013) reinforced this link, especially when analyzing different ethnicities [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These mixed findings underscore the potential role of the Asp312Asn polymorphism in prostate cancer susceptibility, warranting further research with larger, more diverse cohorts to better understand the gene-environment interactions and genetic influences on cancer risks. The inconsistency across studies indicates a need for a nuanced approach to unravel how genetic variations like Asp312Asn interact with environmental factors and other genetic components in different populations.\u003c/p\u003e \u003cp\u003eThe rs238406 (Arg156Arg) polymorphism is a silent G\u0026thinsp;\u0026gt;\u0026thinsp;T mutation at codon 156 that does not alter the amino acid sequence but may influence mRNA splicing or expression levels of the ERCC2 gene, potentially affecting protein functionality [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. While this SNP does not directly impact enzymatic function, studies suggest it could modulate ERCC2 protein levels via mRNA processing [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Results across multiple studies have been inconsistent, reflecting the complexity of genetic analyses. Our pooled analysis of rs238406 from five studies (3,021 cases, 3,152 controls) showed varying effects, with a significant association in the Asian population where individuals with the CC genotype faced increased risk. This inconsistency aligns with previous meta-analyses, including those by Liu et al. (2018) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and Fu et al. (2017) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] which found no significant link between the Arg156Arg polymorphism and prostate cancer risk among over 2,000 subjects. This suggests that while certain SNPs might show associations in specific populations, their overall impact may be minimal when considering diverse ethnicities and genetic variations. The complexity of genetic data highlights the need for careful interpretation, underscoring the importance of larger, more diverse studies to clarify the role of the rs238406 polymorphism in cancer susceptibility. These varying results emphasize the intricate genetic influences on prostate cancer and the need for further investigation, particularly with larger sample sizes, to understand how these polymorphisms interact with environmental factors in cancer development.\u003c/p\u003e \u003cp\u003eThe heterogeneity analysis in this study reveals a complex landscape regarding the association between XPD polymorphisms and prostate cancer risk, underscoring the variability in genetic influences across different populations and genetic models. For the rs13181 polymorphism, substantial heterogeneity was observed in most comparisons, particularly in the overall population and the Caucasian subgroup, indicating differing effects of the genetic variant on prostate cancer risk among the studies considered. In contrast, the Asian and African populations displayed lower heterogeneity for certain models, specifically regarding the GG vs. GA\u0026thinsp;+\u0026thinsp;AA comparison for rs13181, suggesting a more consistent genetic-risk association in these groups. The rs1799793 polymorphism, while also showing significant heterogeneity, particularly in the overall and Caucasian groups, had consistently lower heterogeneity in the African subgroup, indicative of similar risk patterns within this population. Meanwhile, for the rs238406 polymorphism, the absence of heterogeneity across all comparisons suggests a uniform pattern which could indicate a shared genetic background or environmental factors mitigating the influence of this variant on prostate cancer risk. These findings highlight the importance of considering population-specific contexts and genetic model variations when evaluating polymorphisms' impact on cancer susceptibility.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimiations\u003c/h2\u003e \u003cp\u003eThe meta-analysis of XPD genetic variations and prostate cancer risk is strengthened by comprehensive data pooling from diverse studies across different ethnicities. However, these studies also present several notable limitations. 1) There is significant variation in ethnic representation among the populations examined, with most studies primarily focused on Caucasian and Asian groups, while African, Mixed, or other ethnic backgrounds are underrepresented, leading to challenges in generalizability. 2) The small sample sizes in studies on the XPD rs238406 polymorphism reduce the statistical power needed to establish a true correlation with prostate cancer risk, necessitating larger and more robust study designs. 3) A potential publication bias exists, as studies demonstrating positive correlations are more likely to be published, distorting meta-analysis results. 4) The reliance on published literature from English and Chinese databases may exclude important unpublished studies and those in other languages, resulting in systematic bias. 5) Insufficient sample sizes for studies on specific ethnic groups hinder definitive conclusions about broader population relationships. 6) Variability in genotyping techniques across studies, such as ARMS-PCR, RFLP-PCR, and SNPlex\u0026trade;, may introduce discrepancies in results due to differences in sensitivity and specificity. 7) Geographical and ethnic diversity among participants may lead to confounding factors that are not adequately controlled, including age, family history, ethnicity, diet, obesity, physical activity, smoking, hormone levels, chemical exposure, and inflammation. 8) The lack of long-term follow-up data limits the assessment of causal relationships and the role of XPD polymorphisms over time. 9) Insufficient data for stratified analyses concerning confounding factors like age, gender, smoking habits, and prostate cancer types restricts the evaluation of their influence on XPD polymorphisms and prostate cancer risk. 10) Lastly, the primary studies inadequately address complex gene-environment interactions due to a lack of information, emphasizing the need for larger-scale studies involving diverse populations with comprehensive data to better understand the interplay of gene-gene and gene-environment interactions in the relationship between polymorphisms and prostate cancer risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe analysis of the XPD gene polymorphisms rs13181, rs1799793, and rs238406 in relation to prostate cancer risk revealed significant associations, particularly in Asian populations. The rs13181 polymorphism demonstrated a marked increase in prostate cancer risk linked to the G allele, with substantial odds ratios for both GG and GA genotypes, while no significant associations were found in the Caucasian and African subgroups. In contrast, the rs1799793 polymorphism indicated no significant influence on prostate cancer risk across all studied populations. The rs238406 polymorphism exhibited a slight increased risk overall but showed significant associations specifically within the Asian subgroup, highlighting the potential role of genetic variability in cancer susceptibility. Collectively, these findings underscore the importance of considering population-specific genetic factors in understanding an individual's risk for prostate cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e There is no funding source.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e\u0026nbsp; This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Not applicable for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors wish to extend their heartfelt appreciation to all the contributors of the articles incorporated in this meta-analysis. Their invaluable insights and efforts were crucial to the successful completion of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eN.N., M.M.A., M.K.-M., and H.M.: Methodology, conceptualization, investigation. S.F. and M.A.: Methodology, investigation, writing, original draft preparation. H.N. and M.A.: Formal analysis, investigation. K.A. and H.N.: Investigation, writing. M.M. and S.F.: Investigation, writing. M.B. and H.M.: Investigation. S.A.D.: Methodology, software. K.A.: Investigation, writing. S.A.D. and H.M.: Project administration. M.K.-M. and M.B.: Writing, reviewing, editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePinho S, Coelho JMP, Gaspar MM, Reis CP. Advances in localized prostate cancer: A special focus on photothermal therapy. Eur J Pharmacol. 2024;983:176982.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Lu C, Lin S, Sun C, Wen Z, Ge Z, et al. A panel based on three-miRNAs as diagnostic biomarker for prostate cancer. Front Genet. 2024;15:1371441.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel Mph RL, Giaquinto AN, Ahmedin |, Dvm J, Siegel RL, Cancer statistics. 2024. CA: A Cancer Journal for Clinicians. 2024;74:12\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/CAAC.21820\u003c/span\u003e\u003cspan address=\"10.3322/CAAC.21820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchafer EJ, Laversanne M, Sung H, Soerjomataram I, Briganti A, Dahut W, et al. Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update. Eur Urol. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.EURURO.2024.11.013\u003c/span\u003e\u003cspan address=\"10.1016/J.EURURO.2024.11.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLukashchuk N, Barnicle A, Adelman CA, Armenia J, Kang J, Barrett JC, et al. Impact of DNA damage repair alterations on prostate cancer progression and metastasis. Front Oncol. 2023;13:1162644. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/FONC.2023.1162644\u003c/span\u003e\u003cspan address=\"10.3389/FONC.2023.1162644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBancroft EK, Raghallaigh HN, Page EC, Eeles RA. Updates in Prostate Cancer Research and Screening in Men at Genetically Higher Risk. Curr Genetic Med Rep 2021. 2021;9:4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/S40142-021-00202-5\u003c/span\u003e\u003cspan address=\"10.1007/S40142-021-00202-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall R, Bancroft E, Pashayan N, Kote-Jarai Z, Eeles RA. Genetics of prostate cancer: a review of latest evidence. J Med Genet. 2024;61:915\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/JMG-2024-109845\u003c/span\u003e\u003cspan address=\"10.1136/JMG-2024-109845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouthey MC, Goldgar DE, Winqvist R, Pylk\u0026auml;s K, Couch F, Tischkowitz M, et al. PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS. J Med Genet. 2016;53:800\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/JMEDGENET-2016-103839\u003c/span\u003e\u003cspan address=\"10.1136/JMEDGENET-2016-103839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarlsson Q, Brook MN, Dadaev T, Wakerell S, Saunders EJ, Muir K, et al. Rare Germline Variants in ATM Predispose to Prostate Cancer: A PRACTICAL Consortium Study. Eur Urol Oncol. 2021;4:570\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.EUO.2020.12.001\u003c/span\u003e\u003cspan address=\"10.1016/J.EUO.2020.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKote-Jarai Z, Mikropoulos C, Leongamornlert DA, Dadaev T, Tymrakiewicz M, Saunders EJ, et al. Prevalence of theHOXB13 G84E germline mutation in British men and correlation with prostate cancer risk, tumour characteristics and clinical outcomes. Ann Oncol. 2015;26:756\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAghasipour M, Asadian F, Dastgheib SA, Shirinzadeh-Dastgiri A, Vakili-Ojarood M, Narimani N, et al. Familial Hereditary Prostate Cancer: Genetic, Screening, and Treatment Strategies. Eurasian J Med Oncol. 2024;8:250\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashemzehi A, Ghadyani M, Asadian F, Dastgheib SA, Kargar S, Neamatzadeh H, et al. Association of polymorphisms in nucleotide excision repair pathway genes with susceptibility to cutaneous melanoma. Klinicka onkologie. 2021;34:350\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48095/CCKO2021350\u003c/span\u003e\u003cspan address=\"10.48095/CCKO2021350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng W, Xu W, Long W. The association between XPD rs13181 and rs1799793 polymorphism and oral cancer risk: evidence from a meta-analysis. BMC Cancer. 2024;24:738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/S12885-024-12503-3\u003c/span\u003e\u003cspan address=\"10.1186/S12885-024-12503-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatullo G, Palli D, Peluso M, Guarrera S, Carturan S, Celentano E, et al. XRCC1, XRCC3, XPD gene polymorphisms, smoking and 32P-DNA adducts in a sample of healthy subjects. Carcinogenesis. 2001;22:1437\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouissi K, Bahria IB, Bougatef K, Marrakchi R, Stambouli N, Hamdi K, et al. The effect of tobacco, XPC, ERCC2 and ERCC5 genetic variants in bladder cancer development. BMC Cancer. 2011;11:101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2407-11-101\u003c/span\u003e\u003cspan address=\"10.1186/1471-2407-11-101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeamatzadeh H, Dastgheib SA, Mazaheri M, Masoudi A, Shiri A, Omidi A, et al. Hardy-Weinberg Equilibrium in Meta-Analysis Studies and Large-Scale Genomic Sequencing Era. Asian Pac J cancer prevention: APJCP. 2024;25:2229\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31557/APJCP.2024.25.7.2229\u003c/span\u003e\u003cspan address=\"10.31557/APJCP.2024.25.7.2229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRybicki BA, Conti DV, Moreira A, Cicek M, Casey G, Witte JS. DNA repair gene XRCC1 and XPD polymorphisms and risk of prostate cancer. Cancer epidemiology, biomarkers \u0026amp; prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2004;13:23\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.EPI-03-0053\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-03-0053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchey JD, Huang WY, Chokkalingam AP, Gao YT, Deng J, Levine P et al. Genetic variants of DNA repair genes and prostate cancer: a population-based study. Cancer epidemiology, biomarkers \u0026amp; prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2005;14:1703\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1055-9965.EPI-04-0809\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-04-0809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBau D, Wu H-C, Chiu C, Lin C, Hsu C, Wang C, et al. Association of XPD polymorphisms with prostate cancer in Taiwanese patients. Anticancer Res. 2007;27:2893\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgalliu I, Kwon EM, Salinas CA, Koopmeiners JS, Ostrander EA, Stanford JL. Genetic variation in DNA repair genes and prostate cancer risk: results from a population-based study. Cancer causes control: CCC. 2010;21:289\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/S10552-009-9461-5\u003c/span\u003e\u003cspan address=\"10.1007/S10552-009-9461-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandal RK, Gangwar R, Mandhani A, Mittal RD. DNA repair gene X-ray repair cross-complementing group 1 and xeroderma pigmentosum group D polymorphisms and risk of prostate cancer: a study from North India. DNA Cell Biol. 2010;29:183\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/DNA.2009.0956\u003c/span\u003e\u003cspan address=\"10.1089/DNA.2009.0956\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavender NA, Komolafe OO, Benford M, Brock G, Moore JH, VanCleave TT, et al. No association between variant DNA repair genes and prostate cancer risk among men of African descent. Prostate. 2010;70:113\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/PROS.21048\u003c/span\u003e\u003cspan address=\"10.1002/PROS.21048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao R, Price DK, Dahut WL, Reed E, Figg WD. Genetic polymorphisms in XRCC1 associated with radiation therapy in prostate cancer. Cancer Biol Ther. 2010;10:13\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4161/CBT.10.1.12172\u003c/span\u003e\u003cspan address=\"10.4161/CBT.10.1.12172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSobti RC, Berhane N, Melese S, Mahdi SA, Gupta L, Thakur H, et al. Impact of XPD gene polymorphism on risk of prostate cancer on north Indian population. Mol Cell Biochem. 2012;362:263\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/S11010-011-1152-3\u003c/span\u003e\u003cspan address=\"10.1007/S11010-011-1152-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirecka A, Paszkowska-Szczur K, Scott RJ, G\u0026oacute;rski B, van de Wetering T, Wokołorczyk D, et al. Common variants of xeroderma pigmentosum genes and prostate cancer risk. Gene. 2014;546:156\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.GENE.2014.06.026\u003c/span\u003e\u003cspan address=\"10.1016/J.GENE.2014.06.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Li Q, Gu C, Zhu Y, Yang Y, Wang J, et al. Polymorphisms in nucleotide excision repair genes and risk of primary prostate cancer in Chinese Han populations. Oncotarget. 2017;8:24362\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/ONCOTARGET.13848\u003c/span\u003e\u003cspan address=\"10.18632/ONCOTARGET.13848\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCypriano AS, Alves G, Ornellas AA, Scheinkman J, Almeida R, Scherrer L, et al. Relationship between XPD, RAD51, and APEX1 DNA repair genotypes and prostate cancer risk in the male population of Rio de Janeiro, Brazil. Genet Mol biology. 2017;40:751\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1678-4685-GMB-2017-0039\u003c/span\u003e\u003cspan address=\"10.1590/1678-4685-GMB-2017-0039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalkan E, Bilici M, Gundogdu B, Aksungur N, Kara A, Yaşar E, et al. ERCC2 Lys751Gln rs13181 and XRCC2 Arg188His rs3218536 Gene Polymorphisms Contribute to Subsceptibility of Colon, Gastric, HCC, Lung And Prostate Cancer. J BUON. 2020;25:574\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Abdulmonem W, Rasheed Z, Alsagaby SA, Aljohani ASM, Alkhamiss AS, Ahmed AA. Impact of ERCC2 Lys751Gln (rs13181), ERCC2 Asp312Asn (rs1799793) and XRCC1 Arg399Gln (rs25487) polymorphisms on the risk of prostate cancer among cases from the central region of Saudi Arabia. Gene Rep. 2021;24:101278.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed N, Islam MA, Hossain MM, Kabir Y. XRCC1 and XPD polymorphisms: clinical outcomes and risk of prostate cancer in Bangladeshi population. Mol Biol Rep. 2024;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/S11033-024-09707-Y\u003c/span\u003e\u003cspan address=\"10.1007/S11033-024-09707-Y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhillon VS, Yeoh E, Fenech M. DNA repair gene polymorphisms and prostate cancer risk in South Australia\u0026ndash;results of a pilot study. Urol Oncol. 2011;29:641\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.UROLONC.2009.08.013\u003c/span\u003e\u003cspan address=\"10.1016/J.UROLONC.2009.08.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou C, Xie L-P, Lin Y-W, Yang K, Mao Q-Q, Cheng Y. Susceptibility of XPD and hOGG1 genetic variants to prostate cancer. Biomedical Rep. 2013;1:679\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMi Y, Zhang L, Feng N, Wu S, You X, Shao H, et al. Impact of two common xeroderma pigmentosum group D (XPD) gene polymorphisms on risk of prostate cancer. PLoS ONE. 2012;7:e44756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0044756\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0044756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Lu ZP, Zhang JJ, Liu DF, Shi GD, Zhang C, et al. Association between ERCC2 Lys751Gln polymorphism and the risk of pancreatic cancer, especially among Asians: evidence from a meta-analysis. Oncotarget. 2017;8:50124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/ONCOTARGET.15394\u003c/span\u003e\u003cspan address=\"10.18632/ONCOTARGET.15394\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Hu Y, Zhang M, Jiang R, Liang C. Polymorphisms in ERCC2 and ERCC5 and Risk of Prostate Cancer: A Meta-Analysis and Systematic Review. J Cancer. 2018;9:2786. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/JCA.25356\u003c/span\u003e\u003cspan address=\"10.7150/JCA.25356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu W, Xiao F, Zhang R, Li J, Zhao D, Lin X, et al. Association Between the Asp312Asn, Lys751Gln, and Arg156Arg Polymorphisms in XPD and the Risk of Prostate Cancer. Technol Cancer Res Treat. 2017;16:692\u0026ndash;704. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1533034617724678\u003c/span\u003e\u003cspan address=\"10.1177/1533034617724678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Q, Qi C, Tie C, Guo Z. Genetic polymorphisms of xeroderma pigmentosum group D gene Asp312Asn and Lys751Gln and susceptibility to prostate cancer: a systematic review and meta-analysis. Gene. 2013;530:309\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.GENE.2013.08.053\u003c/span\u003e\u003cspan address=\"10.1016/J.GENE.2013.08.053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao F, Pu J, Wen Q, Huang Q, Zhang Q, Huang B, et al. Association between the ERCC2 Asp312Asn polymorphism and risk of cancer. Oncotarget. 2017;8:48488. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/ONCOTARGET.17290\u003c/span\u003e\u003cspan address=\"10.18632/ONCOTARGET.17290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehdinejad M, Sobhan MR, Mazaheri M, Shehneh MZ, Neamatzadeh H, Kalantar SM. Genetic association between ERCC2, NBN, RAD51 gene variants and osteosarcoma risk: A systematic review and meta-analysis. Asian Pac J Cancer Prev. 2017;18:1315\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang LM, Shi X, Yan DF, Zheng M, Deng YJ, Zeng WC, et al. Association between ERCC2 polymorphisms and glioma risk: a meta-analysis. Asian Pac J cancer prevention: APJCP. 2014;15:4417\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7314/APJCP.2014.15.11.4417\u003c/span\u003e\u003cspan address=\"10.7314/APJCP.2014.15.11.4417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 1:\u003c/strong\u003e Characteristics of studies included in the meta-analysis.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Author/Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Ethnicity)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotyping\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTechnique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase/Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAFs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHWE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers13181\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRybicki 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eARMS-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e571/435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRybicki 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(African)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eARMS-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRitchey 2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMALDI-TOF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160/285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBau 2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123/517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgalliu 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNPlex\u0026trade;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1233/1228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgalliu 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(African)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNPlex\u0026trade;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146/83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMandal 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndia(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171/311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLavender 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183/897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGao 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e428/118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSobti 2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndia(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150/469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMirecka 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoland(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e655/925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWang 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1004/1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCypriano 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBrazil(Mixed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110/200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBalkan 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTurkey(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eqRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40/40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAbdulmonem 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKSA(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124/458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAhmed 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBangladesh(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132/135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers1799793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRybicki 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eARMS-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e571/437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRybicki 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(African)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eARMS-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBau 2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123/479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgalliu 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNPlex\u0026trade;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1240/1221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgalliu 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(African)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNPlex\u0026trade;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144/82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMandal 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndia(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e171/200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLavender 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e208/665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDhillon 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAustralia(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118/132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMirecka 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoland(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e572/627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAbdulmonem 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKSA(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124/458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers238406\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgalliu 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNPlex\u0026trade;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1261/1238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgalliu 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUSA(African)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNPlex\u0026trade;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144/81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eZhou 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100/100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMirecka 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoland(Caucasian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRFLP-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e512/678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWang 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChina(Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTaqMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1004/1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e SOC - Source of Control, PB - Population-Based, HB - Hospital-Based, PCR - Polymerase Chain Reaction, RFLP - Restriction Fragment Length Polymorphism, DS - Direct Sequencing, ARMS - Amplification Refractory Mutation System, MAF - Minor Allele Frequency, HWE - Hardy-Weinberg Equilibrium, NOS - Newcastle-Ottawa Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 2:\u0026nbsp;\u003c/strong\u003esummary of results on the association between XPD polymorphisms and prostate cancer risk.\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeterogeneity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePublication Bias\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003csup\u003e2\u003c/sup\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eH\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eZ\u003csub\u003etest\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eOR\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eBeggs\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csub\u003eEggers\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers13181\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG vs. A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.098-1.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.143-1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.046-1.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG+GA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.099-1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. GA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.001-1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCaucasian\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG vs. A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.956-1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.892-1.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.850-1.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG+GA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.916-1.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. GA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882-1.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAsian\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG vs. A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.136-1.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.553-2.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.062-1.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG+GA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.102-2.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. GA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.306-2.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAfrican\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eG vs. A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.647-1.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.539-1.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.401-2.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG+GA vs. AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.565-1.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG vs. GA+AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.446-2.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers1799793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA vs. C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.031-1.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.870-3.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.927-1.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA+AC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.921-1.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. AC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.879-2.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCaucasian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA vs. C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.809-2.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e85.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.878-1.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.650-5.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA+AC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.817-2.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. AC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026le;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.680-4.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAsian\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA vs. C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.786-1.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.602-3.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.683-1.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA+AC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.740-1.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. AC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.605-2.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAfrican\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA vs. C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.777-1.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.500-8.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.629-1.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA+AC vs. CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.696-1.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAA vs. AC+CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.488-8.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers238406\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC vs. T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.997-1.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.984-1.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCT vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.968-1.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC+CT vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.990-1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC vs. CT+TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.954-1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCaucasian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC vs. T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.935-1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.863-1.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCT vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.901-1.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC+CT vs. TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.907-1.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC vs. CT+TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.864-1.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n 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\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate Cancer, XPD Gene, Genetic Variations, Polymorphisms, Meta-Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5770719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5770719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aims to perform a comprehensive meta-analysis of existing literature to elucidate the associations between genetic variations in the XPD gene and the risk of prostate cancer development.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA systematic search of multiple databases, including PubMed, Scopus, EMBASE, and CNKI, was executed up until January 1, 2025, to collect pertinent information. The search utilized relevant keywords and MeSH terms pertaining to prostate cancer and genetic factors. Inclusion criteria were established for original case-control, longitudinal, or cohort studies. Associations were assessed as odds ratios (ORs) with 95% confidence intervals (CIs) employing Comprehensive Meta-Analysis software.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis review included 31 case-control studies featuring 11,632 prostate cancer cases and 14,661 healthy controls. The polymorphism rs13181 (Lys751Gln) was studied in 16 studies with 5,295 cases and 7,199 controls, rs1799793 (Asp312Asn) in 10 studies with 3,316 cases and 4,310 controls, and rs238406 (Arg156Arg) in five studies with 3,021 cases and 3,152 controls. Pooled analyses indicated a significant association between the XPD rs13181 polymorphism and prostate cancer risk across all genetic models assessed. However, both rs238406 and rs1799793 polymorphisms showed no overall association with prostate cancer risk. Subgroup analyses revealed a significant link between rs13181 and rs238406 polymorphisms and prostate cancer risk in Asian populations, with no such association found in Caucasian or African groups. Furthermore, rs1799793 did not show a significant association with prostate cancer when examined by ethnicity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe analysis of polymorphisms XPD rs13181 and rs238406 shows that the rs13181 G allele is significantly associated with increased prostate cancer risk, particularly in the Asian population. The rs238406 polymorphism displays a marginal association in the overall population but significant risk among Asians. Conversely, the rs1799793 polymorphism does not show meaningful associations in any group. These findings indicate that genetic factors may influence prostate cancer risk, with varying associations across ethnicities.\u003c/p\u003e","manuscriptTitle":"Association of XPD Genetic Variations with Prostate Cancer Risk: Consolidated Results from 31 case-control studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:59:43","doi":"10.21203/rs.3.rs-5770719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32d6b98e-2ceb-4368-9713-bb0156a0a9f0","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T12:42:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:59:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5770719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5770719","identity":"rs-5770719","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}