Pharmacogenetic Study of Anti-TB Drugs in the Native Ancestry Peruvian Population

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Horton, Julio A. Poterico, Cristina M Lanata, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5742453/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 In Peru, 33 113 individuals were diagnosed with tuberculosis (TB) in 2023. While TB treatments are generally effective, 3.4–13% of cases are associated with significant adverse drug reactions, with drug-induced liver injury (DILI) being the most prevalent. Limited data exist on genetic risk factors for DILI in Latin America; even less is known about these factors in native Peruvian populations. This study aimed to determine the prevalence of TB drug-metabolizing genotypes in these populations. A cross-sectional analysis was conducted using genetic data from 254 participants from the Peruvian Genome Project (PGP) representing three subpopulations: Coast, Andes, and Amazon. Twenty-three genes associated with TB treatment, include isoniazid, rifampin, ethambutol, and pyrazinamide, as identified in the PharmGKB database, were analysed. Significant differences were observed in genotype frequencies among subpopulations for AGBL4, NAT2, GSTP1, SCOLB1, NOS , and CYP2B6 genes. The Amazonian population demonstrated a higher risk of DILI due to the increased prevalence of hepatotoxic alleles in AGBL4, GSTP1 , and SLCO1B1 . In contrast, alleles in the NOS gene indicated a lower risk of hepatotoxicity in the Andean population. However, the high-risk genotypes identified in the study’s native Peruvian populations exhibit distinct prevalence patterns compared to those reported in the 1 000 Genomes Project. These findings can inform the development of personalized therapeutic strategies to improve TB treatment outcomes among Peru’s diverse subpopulations. Tuberculosis Genotype Pharmacogenetics Native Population BACKGROUND Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis and is among the top 10 leading causes of death worldwide ( 1 ). TB affects anyone at any age but has a greater impact on the working-age population ( 2 ). In 2022, TB caused approximately 1.3 million deaths worldwide. It predominantly affects lower-income populations, though it can impact individuals across all socioeconomic levels. In Peru, 33 113 people were diagnosed with TB in 2023 ( 1 ). In Peru, first-line drug treatment had a success rate of 87.2%, according to the World Health Organization (WHO) in 2016 ( 1 ). Although TB treatments are effective, 3.4–13% are associated with significant adverse drug reactions, with drug-induced liver injury (DILI) considered the most predominant ( 1 ). DILI is a significant adverse reaction associated with anti-tuberculosis treatment, creating delays in therapy and increasing the likelihood of drug resistance. In the human liver, isoniazid (INH) is first acetylated by NAT2 to acetylhydrazine, then oxidized into toxic intermediates by CYP2E1 ( 3 ). The toxic compounds produced are detoxified through acetylation by NAT2 and conjugation reactions catalysed by GST enzymes. Several risk factors for DILI have been identified, including co-infection with HIV, hepatitis B or C, advanced age, and female gender ( 4 , 5 ). Additionally, genetic variations can influence drug metabolism, leading to differences in antibiotic concentrations in the blood. For example, individuals with genetic traits associated with increased metabolism may process antibiotics too quickly, reducing their effectiveness, while slow metabolizers may have prolonged drug exposure, increasing the risk of toxicity ( 6 , 7 ). Genes such as NAT2, CYP2E1 have been evaluated in different populations worldwide, including Americans, Africans, Europeans, and Asians ( 8 – 10 ). A multinational study that included individuals from Peru reported that the NAT2*4,*1,*12,*13,*18 genotypes were associated with intermediate and fast metabolizers. However, this study did not provide sufficient evidence to optimize the concentration of the drug and reduce the side effects of isoniazid in the Peruvian populations diagnosed with tuberculosis ( 11 ). Studies of genetic diversity in populations are important because they identify the frequencies of polymorphisms for each population. The basis of pharmacogenomics is that while standard drug selection and dosage guidelines apply across populations, the distribution of metabolizer phenotypes (e.g., intermediate metabolizers [IMs], poor metabolizers [PMs]) varies. For example, genetic variations in NAT 2 affect the metabolism of isoniazid, a first-line drug for tuberculosis. Populations with a higher prevalence of slow acetylators (NAT2 slow metabolizers) may experience increased drug toxicity, whereas fast acetylators may have reduced therapeutic efficacy. Genetic ancestry analysis of the Peruvian mestizo population, derived from multiple Native American communities, has revealed distinct genetic profiles ( 12 ). These differences highlight the limitations of extrapolating pharmacogenomic data from other populations. For example, variations in NAT2 alleles, which influence isoniazid metabolism in tuberculosis treatment, show different frequencies in Peruvian populations compared to others, affecting drug response and the risk of adverse effects ( 11 ). Thus, in 2018, the technical report by WHO experts underscores an important role in determining metabolization phenotypes during drug administration. For example, for the administration of isoniazid, in children and adults, the dose is defined as 10–15 mg/kg/day in multidrug-resistant tuberculosis (MDR-TB) regimens (the usual dose is 4–6 mg/kg/day). However, it notes that in North Asia, where most of the population has the rapid metabolizer, a dose of 15 mg/kg may be more effective ( 13 ). Several studies have reported that pharmacogenetic variations in NAT2 influence pharmacokinetics and contribute to differences in toxicity following isoniazid treatment ( 14 – 16 ). A previous study concludes that patients with a fast metabolizer should receive 50% more than the standard dose, while patients with a slow genotype should receive half the standard dose ( 17 ). Azuma et al. reported that 78% of the slow metabolizers experienced hepatotoxicity due to being treated with standard doses of INH, while none of those who received the modified dose reported liver damage ( 6 ). According to the above, the benefit of the modified treatment in reducing DILI according to the metabolization of INH was demonstrated ( 18 ). This study highlights the unmet need to identify individuals at genetic risk for DILI, enabling the development of personalized therapeutic regimens that mitigate these risks. By targeting high-risk genotypes, alternative treatments can be offered, reducing the burden of DILI and improving treatment adherence and outcomes in affected populations. Although the main mechanisms involved in developing hepatotoxicity are known, this field has not been significantly explored in Peru, particularly in native ancestry populations. This study aims to estimate the prevalence of metabolizing genotypes in patients during tuberculosis treatment in native Peruvian populations. PATIENTS AND METHODS Studied population and participants This research is a secondary analysis of data from a broader cross-sectional study conducted by the Peruvian Institute of Health (NIH). The primary study aimed to investigate genetic diversity in populations from Peru, reporting clinically relevant single nucleotide polymorphisms (SNPs) among Latin American groups ( 12 ). This secondary study investigate genetic diversity and pharmacogenetic variations in native Peruvian populations. By utilizing existing genetic datasets, this study aims to identify population-specific metabolic profiles that influence TB drug response, thus informing more effective, personalized treatment strategies. A total of 254 samples were analysed. Inclusion criteria consisted of individuals from PGP with more than 95% native ancestry based on bioinformatics analysis results (Coast, Andes, Amazon), and informed consent for genetic testing. Exclusion criteria included individuals with incomplete genetic data. Patient Consent Statement The study was approved by the Ethics in Research Committee of the Peruvian National Institute of Health (authorizations OI-003-11 and OI-087-13) and was conducted in accordance with the principles of the Declaration of Helsinki. Informed written consent was obtained from all participants. Genetic data and quality control Peripheral blood samples (4 mL) were collected from participants, and DNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, CA, USA). DNA samples were genotyped using the Illumina Omni2.5M array in three batches at the Peruvian NIH facilities. One batch, consisting of 50 individuals, was genotyped using GRch38. This batch was lifted to GRch37 using the UCSC lift-over tool ( https://genome.ucsc.edu/cgi-bin/hgLiftOver ) before merging with other GRch37 batches. After merging, we removed SNPs and individuals with more than 5% missing genotypes using PLINK ( 19 ). Variants with AT/CG genotypes or those out of Hardy-Weinberg equilibrium (HWE) (p < 10e-5) were excluded. Related individuals (first-degree relatives) were removed (king-cutoff 0.177) using PLINK. Array data was imputed against the TOPMed imputation panel ( https://imputation.biodatacatalyst.nhlbi.nih.gov/#! ). Briefly, the TOPMed imputation server performs the phasing of the query data using Eagle2 and imputation using minimac4. Admixture analysis We used ADMIXTURE ( 20 ) to explore genome-wide ancestry proportions In this analysis, K represents the number of ancestral populations assumed in the model. We tested values of K ranging from 4 to 8 in a subset of individuals that includes all samples (admixed and Native Americans), a subgroup of 1 000 Genomes Project (1KGP) high coverage populations with unrelated individuals (TSI, IBS, CEU, PEL, CLM, MXL, PUR, LWK, MSL, YRI, GWD, JPT, CHB, and CHS ), and Native Americans from Human Genome Diversity Project (HGDP) (Colombian, Pima, and Maya populations). This subset included unlinked single nucleotide variants and a minor allele frequency (MAF) > 5%. We performed ten runs per K values and plotted the run with the highest log-likelihood. We performed local ancestry by running RFMIXver2 ( 21 – 23 ) with reference panels from European, African, East Asian, and Native American populations. Individuals with > 95% Native American ancestry were classified into Andean, Coastal, or Amazonian groups based on recruitment location. We selected European (n = 404 individuals; populations included CEU, GBR, IBS, and TSI), African (n = 405 individuals; populations included YRI, ESN, GWD, and LWK), and East Asian (n = 411 individuals; populations included CHB, CHS, JPT, and KHV) individuals from 1KGP populations. Native American reference included 187 individuals from PGP with more than 99% NAT (Native American ancestry) based on ADMIXTURE K4 results. We ran RMIX using two expectation maximization steps. Measures and analysis : For this analysis, we only included individuals with > 95% NAT ancestry, resulting in 254 non-admixed participants. Individuals were classified into Coast, Andes, or Amazon native subpopulations depending on the location of their recruitment site. We explored the relationship between native subpopulations and 23 genetic markers associated with TB treatment identified in the PharmGKB database ( https://www.pharmgkb.org/ ) (Supplementary Table S1 ). A high-risk genotype is defined as a genetic variant or combination of variants associated with an increased likelihood of adverse drug reactions, particularly DILI, in the context of TB treatment. These genotypes are identified based on prior pharmacogenetic research and databases, such as PharmGKB, and are evaluated for their prevalence and association with hepatotoxicity. Statistical analysis, including chi-square tests and regression models, determines significant differences in the frequency of these high-risk genotypes across the studied subpopulations. To account for multiple testing, a Bonferroni correction was applied, adjusting the significance threshold by the number of independent tests performed. Statistical significance was set at p < 0.05 using Stata 15 (StataCorp, College Station, TX, USA). RESULTS A total of 27 SNPs associated with TB drug metabolism were analysed, as reported in the PharmGKB database (Supplementary Table S1 ). These SNPs span key pharmacogenetic genes such as NAT2, CYP2E1, GSTP1 , and SLCO1B1 , which play a crucial role in drug metabolism and hepatotoxicity risk during TB treatment ( https://www.pharmgkb.org/ ). Among these, six genes ( AGBL4, NAT2, GSTP1, SLCO1B1, NOS , and CYP2B6 ) demonstrated significant frequency differences across the studied populations, warranting further investigation (Tables 1 and 2 ), additional SNPs without significant differences are reported in Supplementary Tables S2 and S3, providing a broader view of genetic variability related to TB drug metabolism in native Peruvian populations. Table 1 Genotypic frequencies of Anti-TB drug metabolism genes with statistically significant differences among Native Peruvian Populations N (%) in populations Overall Amazon Andes Coast p-value Gene / SNP N = 254 N = 69 N = 99 N = 86 AGBL4 rs393994 GG 56 (22.0) 22 (31.9) 18 (18.2) 16 (18.6) 0.026 GA 146 (57.5) 41 (59.4) 58 (58.6) 47 (54.7) AA 52 (20.5) 6 (8.7) 23 (23.2) 23 (26.7) rs319952 GG 57 ( 22.4) 22 ( 31.9) 18 ( 18.2) 17 ( 19.8) 0.029 GA 145 ( 57.1) 41 ( 59.4) 58 ( 58.6) 46 ( 53.5) AA 52 ( 20.5) 6 ( 8.7) 23 ( 23.2) 23 ( 26.7) rs320003 AA 60 ( 23.6) 22 ( 31.9) 21 ( 21.2) 17 ( 19.8) 0.045 AG 142 ( 55.9) 41 ( 59.4) 55 ( 55.6) 46 ( 53.5) GG 52 ( 20.5) 6 ( 8.7) 23 ( 23.2) 23 ( 26.7) NAT2 rs1799929 TT 169 ( 66.5) 54 ( 78.3) 67 ( 67.7) 48 ( 55.8) 0.008 TC 73 ( 28.7) 14 ( 20.3) 24 ( 24.2) 35 ( 40.7) CC 12 ( 4.7) 1 ( 1.4) 8 ( 8.1) 3 ( 3.5) rs1799931 AA 122 ( 48.0) 28 ( 40.6) 50 ( 50.5) 44 ( 51.2) 0.028 AG 101 ( 39.8) 26 ( 37.7) 37 ( 37.4) 38 ( 44.2) GG 31 ( 12.2) 15 ( 21.7) 12 ( 12.1) 4 ( 4.7) GSTP1 rs1695 AA 133 ( 52.4) 25 ( 36.2) 57 ( 57.6) 51 ( 59.3) 0.010 AG 95 ( 37.4) 31 ( 44.9) 34 ( 34.3) 30 ( 34.9) GG 26 ( 10.2) 13 ( 18.8) 8 ( 8.1) 5 ( 5.8) SLCO1B1 rs4149032 TT 121 ( 47.6) 53 ( 76.8) 28 ( 28.3) 40 ( 46.5) < 0.001 TC 98 ( 38.6) 11 ( 15.9) 54 ( 54.5) 33 ( 38.4) CC 35 ( 13.8) 5 ( 7.2) 17 ( 17.2) 13 ( 15.1) NOS rs11080344 TT 132 (52.0) 39 (56.5) 42 (42.4) 51 ( 59.3) 0.018 TC 103 ( 40.6) 29 (42.0) 44 (44.4) 30 ( 34.9) CC 19 (7.5) 1 (1.4) 13 (13.1) 5 (5.8) CYP 2B6 rs3745274 TT 67 (26.4) 29 (42.0) 19 (19.2) 19 (22.1) 0.012 TG 132 (52.0) 30 (43.5) 57 ( 57.6) 45 (52.3) GG 55 (21.7) 10 ( 14.5) 23 ( 23.2) 22 (25.6) Table 2 Association between Native Subpopulations and High-Risk Genotypes with statistically significant differences AGBL4 gene - Rifampicin rs393994 Hepatotoxicity high risk genotype Total Yes ( AA / AG) No (GG) OR CI (95%) p-Value N % N % N % Coast 63 73.3 23 26.7 86 33.9 Ref Andean 76 76.8 23 23.2 99 39.0 1.21 0.59–2.48 0.582 Amazon 63 91.3 6 8.7 69 27.1 3.83 1.38–12.20 0.004 rs319952 Yes ( AA / AG) No (GG) Coast 63 73.3 23 26.7 86 33.9 Ref Andean 76 76.8 23 23.2 99 39.0 1.21 0.59–2.48 0.582 Amazon 63 91.3 6 8.7 69 27.1 3.83 1.38–12.20 0.004 rs320003 Yes ( GG / GA) No (AA) Coast 63 73.3 23 26.7 86 33.9 Ref Andean 76 76.8 23 23.2 99 39.0 1.21 0.59–2.48 0.582 Amazon 63 91.3 6 8.7 69 27.1 3.83 1.38–12.20 0.004 *High-Risk from 1000 Genomes Project Frequencies (%): PEL: 80.0, EUR: 75.0, EAS: 70.0, AFR: 65.0 GSTP gene- Pyrazinamide rs1695 Hepatotoxicity high risk genotype Total Yes (AA) No (AG / GG) OR CI (95%) p-Value N % N % N % Coast 5 5.8 81 94.2 86 33.9 Ref Andean 8 8.1 91 91.9 99 39.0 1.42 0.39–5.75 0.547 Amazon 13 18.8 56 81.2 69 27.1 3.76 1.16–14.13 0.012 *High-Risk from 1000 Genomes Project Frequencies (%):PEL: 10.0, EUR: 5.0, EAS: 5.0, AFR: 10.0 SLCO1B1 gene - Rifampicin rs4149032 Hepatotoxicity high risk genotype Total Yes (CC) No (TT / TC) OR CI (95%) p-Value N % N % N % Coast 40 46.5 46 53.5 86 33.9 Ref Andean 28 28.3 71 71.7 99 39.0 0.45 0.24–0.87 0.010 Amazon 53 76.8 16 23.2 69 27.1 3.81 1.80–8.23 0.012 *This SNP has a minor allele frequency (MAF) according to the 1000 Genomes database NOS gene - Ethambutol rs11080344 Hepatotoxicity high risk genotype Total Yes (CC) No (TT / TC) OR CI (95%) p-Value N % N % N % Coast 51 59.3 35 40.7 86 33.9 Ref Andean 42 42.4 57 57.6 99 39.0 0.51 0.27–0.95 0.022 Amazon 39 56.5 30 43.5 69 27.1 3.89 0.45–1.78 0.727 * This SNP is not available in the 1000 Genomes Project For AGBL4 , SNPs rs393994, rs319952, and rs320003 showed significant variations. The heterozygous genotype (AG) was the most frequent across all subpopulations. However, the AA genotype, associated with hepatotoxicity risk, was significantly more frequent in the Andean and Coastal populations compared to the Amazonian population. Despite this, the Amazonian population exhibited a higher prevalence of hepatotoxicity risk overall (OR = 3.83, p = 0.004). NAT2 genotyping identified the presence of rs4646244, rs1799929, rs1799930, rs1799931 , and rs1495741 SNPs. Statistically significant differences were observed for rs1799929 and rs1799931 . The homozygous genotype (AA) for rs1799931 was more frequent in the Andean (50.5%) and Coastal (51.2%) populations compared to the Amazonian population (40.6%). Meanwhile, the TT genotype for rs1799929 was most prevalent in the Amazonian population (78.3%), followed by the Andean (67.7%) and Coastal (55.8%) populations. For GSTP1 , the rs1695 SNP showed significant variation. The homozygous AA genotype was significantly less frequent in the Amazonian population (36.2%) compared to the Andean (57.6%) and Coastal (59.3%) populations. The heterozygous AG genotype was more common in the Amazonian group (44.9%). Hepatotoxic genotypes were significantly more frequent in the Amazon population compared to the Coast (OR = 3.76, p = 0.012). Analysis of SCOLB1 revealed significant differences for rs4149032 . The homozygous TT genotype was most frequent in the Amazonian population (76.8%), followed by the Coastal (46.5%) and Andean (28.3%) populations. The prevalence of the hepatotoxic CC genotype was significantly higher in the Amazonian group (OR = 3.81, p = 0.012) and lower in the Andean group (OR = 0.45, p = 0.010). For NOS , rs11080344 was the key SNP, showing significant differences. The homozygous TT genotype was most frequent in the Coastal population (59.3%), followed by the Amazonian (56.5%) and Andean (42.4%) populations. The heterozygous TC genotype, associated with reduced hepatotoxicity risk, was more frequent in the Andean population (44.4%), which had a significantly lower risk of hepatotoxicity compared to the Coast (OR = 0.51, p = 0.022). Finally, CYP2B6 genotyping identified rs3745274 as the primary SNP of interest. The heterozygous TG genotype was the most frequent across all populations. However, the TT genotype, which has been linked to increased hepatotoxicity risk, was significantly higher in the Amazonian population (42.0%) compared to the Andean (19.2%) and Coastal (22.1%) populations (p = 0.012). The Amazonian population exhibited a higher risk of developing DILI due to hepatotoxic alleles in the AGBL4, GSTP1, SCOLB1 , and NOS genes. In contrast, Andean populations showed a genetic profile associated with lower hepatotoxicity risk. RFMIX v2 analysis identified distinct ancestry proportions among the studied groups. The Andean population exhibited higher Native American ancestry, while the Coastal group had greater European admixture. These ancestry differences were reflected in pharmacogenetic variations, particularly in NAT2 and GSTP1 .These findings highlight the genetic variability underlying TB drug metabolism and the importance of tailoring treatments to specific subpopulations. DISCUSSION In this study, we identified differences in the prevalence of several genetic variants that affect TB drug metabolism across geographically distinct native non-admixed Peruvian populations. The Amazonian population demonstrated a higher prevalence of hepatotoxic alleles in the AGBL4, GSTP1 , and SLCO1B1 genes. Variants such as rs393994 in AGBL4 were associated with increased high-risk genotype (OR = 3.83, p = 0.004). Similar findings have been reported in other studies, linking these variants to impaired cellular deglutamylation processes, exacerbating liver toxicity when exposed to drugs like rifampin ( 24 ). In contrast, the Andean population exhibited a lower prevalence of DILI-associated alleles in the NOS gene, particularly rs11080344. This variant has been linked to reduced nitric oxide production, which may mitigate oxidative stress and hepatotoxicity ( 25 ). Several previous studies have explored the role of genetic polymorphisms in drug metabolism, particularly in relation to NAT2 and its impact on isoniazid metabolism. The NAT2 gene has been widely studied in different populations, with significant variability observed among ethnic groups. Studies in East Asian populations, for instance, have shown a higher prevalence of fast acetylator alleles, whereas South American Indigenous populations tend to exhibit a higher frequency of slow acetylators, increasing their risk of DILI ( 8 , 11 ). Similarly, genetic studies in African and European populations have reported diverse acetylation patterns that influence TB treatment outcomes ( 15 , 16 ). The NAT2 gene plays a critical role in the metabolism of isoniazid. Variants such as rs1799931 associated with slow acetylation phenotypes, were more prevalent in the Amazonian population. Slow acetylators are at an increased risk of DILI due to the accumulation of toxic metabolites​ ( 26 ). These findings align with previous studies that highlight the role of slow acetylation in TB treatment-related hepatotoxicity in populations with similar genetic backgrounds​ ( 27 , 28 ). The presence of NAT25, NAT26 , and NAT27 alleles has been consistently linked to slow metabolism in multiple populations, including Peruvians ( 29 ). Studies in Latin American mestizo populations have demonstrated distinct metabolic profiles compared to Indigenous groups, reinforcing the importance of population-specific pharmacogenomic studies ( 30 ). Moreover, in Peruvian TB patients, NAT25B and NAT27B were associated with higher DILI risk in mestizos, whereas NAT25G and NAT213A were protective in native populations, suggesting an evolutionary role in drug metabolism adaptations ( 31 ). The GSTP1 gene, responsible for detoxification and oxidative stress regulation, also showed population-specific differences. The rs1695 AA genotype, more common in the Amazonian population, has been linked to reduced glutathione activity and an increased risk of oxidative damage, corroborating findings from prior research​ ( 32 – 35 ). Similarly, polymorphisms in the SLCO1B1 gene, particularly rs4149032 , were significantly associated with DILI in the Amazonian population (OR = 3.81, p = 0.012). This gene plays a vital role in hepatic drug transport, and its variants can lead to higher plasma drug levels, increasing the risk of toxicity​ ( 36 – 38 ) Interestingly, no significant differences were observed in the CYP2B6 gene across the three subpopulations. While CYP2B6 has been implicated in the metabolism of drugs such as efavirenz, its limited variability in this study suggests that other genetic factors may play a more critical role in influencing DILI risk among these populations ( 39 – 41 ). Previous research has highlighted the impact of CYP2E1 polymorphisms on drug metabolism, particularly in populations with a high burden of TB. Tang et al. ( 10 ) found that individuals carrying CYP2E11D alleles had significantly altered enzyme activity, affecting isoniazid metabolism and hepatotoxicity risk. Similar findings have been reported in South American and Asian populations, suggesting a potential genetic basis for inter-individual variability in drug response. These findings reinforce the critical role of genetic diversity in influencing treatment outcomes. The genetic differentiation observed between the Andean and Amazonian populations is consistent with prior studies showing that geographic and cultural isolation has shaped distinct genetic profiles in South America ( 42 – 45 ). Genetic ancestry plays a central role in population pharmacogenomics ( 46 ). In a previous study, we identified the presence of adverse reactions during anti-tuberculosis treatment in the Peruvian population. We reported that 30% of the Peruvian populations are associated with the slow metabolism of isoniazid ( 29 ). We also identified haplotypes with divergent associations with DILI, based on the mestizo or native Peruvian population. For instance, we found evidence of NAT2*5B and NAT2*7B being associated with DILI risk in mestizos, while no such association has been observed in natives. Additionally, haplotypes NAT2*5G and NAT2*13A have only been negatively associated with DILI in the studied Native Peruvians ( 30 ). Another study revealed that this environmental and genetic differentiation between the Andean and Amazonian populations has allowed natural selection and other evolutionary forces to act over millennia, shaping differences in the frequencies of genetic variants, including genes related to the immune response ( CD45 and DUOX2 ), with thyroid ( DUOX2 ), cardiovascular ( HAND2-AS1 ) and haematological ( TMPRSS6 ) functions 4, as well as genes related to drug response ( 31 ). Our findings suggest that there are differences in the AGBL4, NAT2, GSTP1, SCOLB1, NOS and CYP2B6 genes between the native populations in Peru that is correlated with clinical reports about toxicity and treatment failure in Peruvian populations ( 47 – 49 ). These differences could have implications for the risk of hepatotoxicity associated with the use of antituberculosis drugs ( 50 , 51 ). This study has several strengths, including its focus on underrepresented native Peruvian populations, providing crucial insights into genetic variability affecting TB drug metabolism. The comprehensive analysis of 23 genes using advanced genotyping techniques ensures high data quality while including diverse subpopulations (Coast, Andes, Amazon) and offers valuable comparative perspectives. Clinically relevant findings, such as the prevalence of hepatotoxic alleles, have practical implications for personalized TB treatment strategies. However, the study also has limitations, such as a relatively small sample size that may restrict generalizability, the exclusion of admixed populations, and reliance on a cross-sectional design, which limits the ability to assess long-term impacts. Understanding these genetic variations is crucial for designing effective TB treatment regimens. The differences observed in this study align with previous findings that Indigenous South American populations exhibit distinct pharmacogenetic profiles compared to mestizo, European, African, and Asian populations. These findings further support the need for incorporating pharmacogenomics into TB treatment guidelines to reduce adverse effects and improve therapeutic outcomes ( 18 , 34 ). While our study identifies significant differences in allele frequencies among native Peruvian subpopulations, clinical studies are necessary to validate their impact on drug metabolism and treatment outcomes. Functional validation through pharmacokinetic analyses will determine if these variations influence tuberculosis treatment efficacy and adverse drug reactions. Only through clinical confirmation can these findings contribute to pharmacogenetic guideline development. Our results provide a foundation for future research but require further investigation in a clinical setting CONCLUSIONS Although our study results provide valuable insights into the frequency of metabolizing genotypes for anti-TB drugs in Peru, particularly among native populations, a deeper understanding of the factors associated with these genotypes is needed. Longitudinal studies including large samples have revealed that genetic polymorphisms play an important role in drug metabolism. Despite the limitations of a secondary study, our findings suggest that the subpopulation of Peruvian natives is associated with the metabolizing profile and AGBL4, NAT2, GSTP1, SCOLB1, NOS , and CYP2B6 alleles. Abbreviations AGBL4 ATP/GTP Binding Protein Like 4 CYP2B6 Cytochrome P450 Family 2 Subfamily B Member 6 DILI Drug-Induced Liver Injury GSTP1 Glutathione S-Transferase Pi 1 INH Isoniazid MDR-TB Multidrug-Resistant Tuberculosis NAT2 N-Acetyltransferase 2 NOS Nitric Oxide Synthase PGP Peruvian Genome Project SCOLB1 Solute Carrier Organic Anion Transporter Family Member 1B1 TB Tuberculosis WHO World Health Organization Declarations Ethics approval and consent to participate Our study was approved by the Ethics in Research Committee of the Peruvian National Institute of Health and follows the principles of the Declaration of Helsinki. Written informed consent was obtained from all the participant. Clinical Trial Not applicable Consent for publication Not applicable. This study does not contain any individual details, images, or videos. Availability of data and material The data that support the findings of this study are available from the corresponding author, LJ-V, upon reasonable request. Competing interests The authors declare no competing interests FUNDING This work was funded by the “Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC)” and the “Programa Nacional de Investigación Científica y Estudios Avanzados (PROCIENCIA)” within the framework of the competition “E067-2023-01 Proyectos Especiales: Proyectos de Incorporación de Investigadores Postdoctorales en Instituciones Peruanas” [contract number PE501084276-2023]. Authors' contributions Study design: LJ-V. Performed the experiments: LJ-V, MH. Analysed the data: LJ-V, MH, CL, HG. All authors have read and approved the final manuscript. ACKNOWLEDGMENTS This work was funded by the “Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC)” and the “Programa Nacional de Investigación Científica y Estudios Avanzados (PROCIENCIA)” within the framework of the competition “E067-2023-01 Proyectos Especiales: Proyectos de Incorporación de Investigadores Postdoctorales en Instituciones Peruanas” [contract number PE501084276-2023]. The funders had no role in the study design, data collection, and interpretation, or the decision to submit the work for publication. We thank Victor Borda for his contribution to the analysis of ancestry. References World Health Organization. Global tuberculosis report 2023. 265 p. https://iris.who.int/bitstream/handle/10665/363752/9789240061729-eng.pdf?sequence=1 Llanos-Zavalaga LF, Velásquez-Hurtado JE, García PJ, Gottuzzo E. Tuberculosis y salud pública: ¿derechos individuales o derechos colectivos? . Vol. 29, Revista Peruana de Medicina Experimental y Salud Publica. 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Dompreh A, Tang X, Zhou J, Yang H, Topletz A, Adu Ahwireng E, Antwi S, Enimil A, Langaee T, Peloquin CA, Court MH, Kwara A. Effect of Genetic Variation of NAT2 on Isoniazid and SLCO1B1 and CES2 on Rifampin Pharmacokinetics in Ghanaian Children with Tuberculosis. Antimicrob Agents Chemother. 2018 Feb 23;62(3):e02099-17. doi: 10.1128/AAC.02099-17. Kinzig-Schippers M, Tomalik-Scharte D, Jetter A, Scheidel B, Jakob V, Rodamer M, et al. Should We Use N-Acetyltransferase Type 2 Genotyping To Personalize Isoniazid Doses? Antimicrob Agents Chemother. 2005 May 1;49(5):1733–8. doi: 10.1128/AAC.49.5.1733-1738.2005 Guio H, Levano KS, Sánchez C, Tarazona D. [The role of pharmacogenomics in the tuberculosis treatment regime]. Rev Peru Med Exp Salud Publica. 2015 Oct;32(4):794–800. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26732931 Chang CC, et al. (2015) Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4:7 Alexander DH, Novembre J, Lange K. 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Functional segregation and emerging role of cilia-related cytosolic carboxypeptidases (CCPs). FASEB J. 2013;27:424–31. Nanashima K, Mawatari T, Tahara N, et al. Genetic variants in antioxidant pathway: risk factors for hepatotoxicity in tuberculosis patients. Tuberculosis (Edinburgh, Scotland). 2012 May;92(3):253-259. DOI: 10.1016/j.tube.2011.12.004. PMID: 22341855. Sánchez R, Acosta O, Laymito L, Oscanoa T, Guevara-Fujita M, Moscol S, Obispo D, Huerta D, Fujita R. Variants in the N-acetyltranferase 2 gene, acetylator phenotypes and their association with tuberculosis: Findings in Peruvian patients. J Clin Tuberc Other Mycobact Dis. 2024 Oct 16;37:100485. doi: 10.1016/j.jctube.2024.100485. PMID: 39502413; PMCID: PMC11535994. Walraven J, Zan Y, Trent J, Hein D. Structure/function evaluations of single nucleotide polymorphisms in human N-acetyltransferase 2. Curr Drug Metab 2008; 9(6):471–86. https://doi.org/10.2174/138920008784892065. Gra O, Kozhekbaeva Z, Skotnikova O, Litvinov V, Nasedkina T. Analysis of genetic predisposition to pulmonary tuberculosis in native Russians. Russ J Genet 2010;46 (2):230–8. https://doi.org/10.1134/S1022795410020146 Levano KS, Jaramillo-Valverde L, Tarazona DD, Sanchez C, Capristano S, Vásquez-Loarte T, Solari L, Mendoza-Ticona A, Soto A, Rojas C, Zegarra-Chapoñan R, Guio H. Allelic and genotypic frequencies of NAT2, CYP2E1, and AADAC genes in a cohort of Peruvian tuberculosis patients. Mol Genet Genomic Med. 2021 Oct;9(10):e1764. doi: 10.1002/mgg3.1764. Epub 2021 Sep 12. PMID: 34510815; PMCID: PMC8580101 Jaramillo-Valverde L, Levano KS, Tarazona DD, 612 Capristano S, Sanchez C, Poterico JA, Tarazona-Santos E, Guio H. Pharmacogenetic variability of tuberculosis biomarkers in native and mestizo Peruvian populations. Pharmacol Res Perspect. 2024 Jun;12(3):e1179. doi: 10.1002/prp2.1179. PMID: 38666760; PMCID: PMC11047445. Borda V, Alvim I, Mendes M, Silva-Carvalho C, Soares-Souza GB, Leal TP, Furlan V, Scliar MO, Zamudio R, Zolini C, Araújo GS, Luizon MR, Padilla C, Cáceres O, Levano K, Sánchez C, Trujillo O, Flores-Villanueva PO, Dean M, Fuselli S, Machado M, Romero PE, Tassi F, Yeager M, O'Connor TD, Gilman RH, Tarazona- Santos E, Guio H. The genetic structure and adaptation of Andean highlanders and Amazonians are influenced by the interplay between geography and culture. Proc Natl Acad Sci U S A. 2020 Dec 22;117(51):32557-32565. doi:10.1073/pnas.2013773117. Epub 2020 Dec 4. PMID: 33277433; PMCID:PMC7768732. Wu S, Wang Y-J, Tang X, Wang Y, Wu J, Ji G, et al. (2016) Genetic Polymorphisms of Glutathione S-Transferase P1 (GSTP1) and the Incidence of Anti-Tuberculosis Drug-Induced Hepatotoxicity. PLoS ONE 11(6): e0157478. doi:10.1371/journal.pone.0157478 Zimniak P, Nanduri B, Pikula S, Bandorowicz-Pikula J, Singhal SS, Srivastava SK, et al. Naturally occurring human glutathione S-transferase GSTP1-1 isoforms with isoleucine and valine in position 104 differ in enzymic properties. Eur J Biochem. 1994; 224(3):893–9. Epub 1994/09/15. PMID: 7925413. Hu X, Xia H, Srivastava SK, Pal A, Awasthi YC, Zimniak P, et al. Catalytic efficiencies of allelic variants of human glutathione S-transferase P1-1 toward carcinogenic anti-diol epoxides of benzo[c]phenanthrene and benzo[g]chrysene. Cancer research. 1998; 58(23):5340–3. Epub 1998/12/16. PMID: 9850062. Reszka E, Jablonowski Z, Wieczorek E, Gromadzinska J, Sosnowski M, Wasowicz W. GSTP1 mRNA expression in human circulating blood leukocytes is associated with GSTP1 genetic polymorphism. Clin Biochem. 2011; 44(13):1153–5. Epub 2011/06/15. doi: 10.1016/j.clinbiochem.2011.05.024 PMID: 21669193. Shabani S, Farnia P, Ghanavi J, Velayati AA, Farnia P. Pharmacogenetic study of drugs affecting Mycobacterium tuberculosis. Int J Mycobacteriol 2024;13:206-12 Weiner M, Gelfond J, Johnson‑Pais TL, Engle M, Peloquin CA, Johnson JL, et al. Elevated plasma moxifloxacin concentrations and SLCO1B1 g.‑11187G>A polymorphism in adults with pulmonary tuberculosis. Antimicrob Agents Chemother 2018;62:e01802‑17. Weiner M, Peloquin C, Burman W, Luo CC, Engle M, Prihoda TJ, et al. Effects of tuberculosis, race, and human gene SLCO1B1 polymorphisms on rifampin concentrations. Antimicrob Agents Chemother 2010;54:4192‑200 Kwara A, Lartey M, Sagoe KW, Xexemeku F, Kenu E, Oliver-Commey J, Boima V, Sagoe A, Boamah I, Greenblatt DJ, Court MH. Pharmacokinetics of efavirenz when co-administered with rifampin in TB/HIV co-infected patients: pharmacogenetic effect of CYP2B6 variation. J Clin Pharmacol. 2008 Sep;48(9):1032-40. doi: 10.1177/0091270008321790. PMID: 18728241; PMCID: PMC2679896. Hofmann MH, Blievernicht JK, Klein K, et al. Aberrant splicing caused by single nucleotide polymorphism c.516G>T [Q172H], a marker of CYP2B6*6, is responsible for decreased expression and activity of CYP2B6 in liver. J Pharmacol Exp Ther. 2008in press Hesse LM, He P, Krishnaswamy S, et al. Pharmacogenetic determinants of interindividual variability in bupropion hydroxylation by cytochrome P450 2B6 in human liver microsomes. Pharmacogenet 2004;14:225–238.2004 Tarazona-Santos E, Carvalho-Silva DR, Pettener D, Luiselli D, De Stefano GF, Labarga CM, Rickards O, Tyler-Smith C, Pena SD, Santos FR. Genetic differentiation in South Amerindians is related to environmental and cultural diversity: evidence from the Y chromosome. Am J Hum Genet. 2001 Jun;68(6):1485-96. doi: 10.1086/320601. Epub 2001 May 15. PMID: 11353402; PMCID: PMC1226135. Fuselli S, Tarazona-Santos E, Dupanloup I, Soto A, Luiselli D, Pettener D.. 2003. Mitochondrial DNA diversity in South America and the genetic history of Andean highlanders. Mol Biol Evol. 2010:1682–1691. Barbieri C, Heggarty P, Yang Yao D, Ferri G, De Fanti S, Sarno S, Ciani G, Boattini A, Luiselli D, Pettener D.. 2014. Between Andes and Amazon: the genetic profile of the Arawak-speaking Yanesha. Am J Phys Anthropol. 1554:600–609. Barbieri C, Barquera R, Arias L, Sandoval JR, Acosta O, Zurita C, Aguilar- Campos A, Tito-Álvarez AM, Serrano-Osuna R, Gray RD, Mafessoni F, Heggarty P, Shimizu KK, Fujita R, Stoneking M, Pugach I, Fehren-Schmitz L. The Current Genomic Landscape of Western South America: Andes, Amazonia, and Pacific Coast. Mol Biol Evol. 2019 Dec 1;36(12):2698-2713. doi: 10.1093/molbev/msz174. PMID: 31350885; PMCID: PMC6878948. Yang HC, Chen CW, Lin YT, Chu SK. Genetic ancestry plays a central role in population pharmacogenomics. Commun Biol. 2021 Feb 5;4(1):171. doi:10.1038/s42003-021-01681-6. Cuéllar L, Castañeda CA, Rojas K, et al. Características clínicas y toxicidad del tratamiento de tuberculosis en pacientes con cáncer [Clinical features and toxicity of tuberculosis treatment in patients with cancer]. Rev Peru Med Exp Salud Publica. 2015; 32(2): 272-277. Spanish. http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1726-46342015000200009 Lackey B, Seas C, Van der Stuyft P, Otero L. Patient characteristics associated with tuberculosis treatment default: a cohort study in a high-incidence area of Lima, Peru. PLoS One. 2015; 10(6):e0128541. doi:10.1371/journal.pone.0128541 Kawai V, Soto G, Gilman RH, et al. Tuberculosis mortality, drug resistance, and infectiousness in patients with and without HIV infection in Peru. Am J Trop Med Hyg. 2006; 75(6): 1027-1033. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912515/ Jaramillo-Valverde L, Levano KS, Tarazona DD, Capristano S, Zegarra- Chapoñan R, Sanchez C, Yufra-Picardo VM, Tarazona-Santos E, Ugarte-Gil C, Guio H. NAT2 and CYP2E1 polymorphisms and antituberculosis drug-induced hepatotoxicity in Peruvian patients. Mol Genet Genomic Med. 2022 Aug;10(8):e1987. doi: 10.1002/mgg3.1987. Epub 2022 Jun 24. PMID: 35751408; PMCID: PMC9356556. Jaramillo-Valverde L, Levano KS, Tarazona DD, Vasquez-Dominguez A, Toledo-Nauto A, Capristano S, Sanchez C, Tarazona-Santos E, Ugarte-Gil C, Guio H. GSTT1/GSTM1 Genotype and Anti-Tuberculosis Drug-Induced Hepatotoxicity in Peruvian Patients. Int J Mol Sci. 2022 Sep 20;23(19):11028. doi: 10.3390/ijms231911028. PMID: 36232322; PMCID: PMC9569363 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5742453","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433090239,"identity":"f0b46327-b6b6-4335-bf24-0ce292b65cca","order_by":0,"name":"Luis Jaramillo-Valverde","email":"data:image/png;base64,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","orcid":"","institution":"INBIOMEDIC, Research and Technological Center","correspondingAuthor":true,"prefix":"","firstName":"Luis","middleName":"","lastName":"Jaramillo-Valverde","suffix":""},{"id":433090240,"identity":"0b6a6c1a-99a5-4c85-9a5f-f8efba018eb5","order_by":1,"name":"Mary K. 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TB affects anyone at any age but has a greater impact on the working-age population (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In 2022, TB caused approximately 1.3\u0026nbsp;million deaths worldwide. It predominantly affects lower-income populations, though it can impact individuals across all socioeconomic levels. In Peru, 33 113 people were diagnosed with TB in 2023 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Peru, first-line drug treatment had a success rate of 87.2%, according to the World Health Organization (WHO) in 2016 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although TB treatments are effective, 3.4\u0026ndash;13% are associated with significant adverse drug reactions, with drug-induced liver injury (DILI) considered the most predominant (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). DILI is a significant adverse reaction associated with anti-tuberculosis treatment, creating delays in therapy and increasing the likelihood of drug resistance. In the human liver, isoniazid (INH) is first acetylated by \u003cem\u003eNAT2\u003c/em\u003e to acetylhydrazine, then oxidized into toxic intermediates by \u003cem\u003eCYP2E1\u003c/em\u003e (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The toxic compounds produced are detoxified through acetylation by \u003cem\u003eNAT2\u003c/em\u003e and conjugation reactions catalysed by \u003cem\u003eGST\u003c/em\u003e enzymes. Several risk factors for DILI have been identified, including co-infection with HIV, hepatitis B or C, advanced age, and female gender (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, genetic variations can influence drug metabolism, leading to differences in antibiotic concentrations in the blood. For example, individuals with genetic traits associated with increased metabolism may process antibiotics too quickly, reducing their effectiveness, while slow metabolizers may have prolonged drug exposure, increasing the risk of toxicity (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Genes such as \u003cem\u003eNAT2, CYP2E1\u003c/em\u003e have been evaluated in different populations worldwide, including Americans, Africans, Europeans, and Asians (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A multinational study that included individuals from Peru reported that the \u003cem\u003eNAT2*4,*1,*12,*13,*18\u003c/em\u003e genotypes were associated with intermediate and fast metabolizers. However, this study did not provide sufficient evidence to optimize the concentration of the drug and reduce the side effects of isoniazid in the Peruvian populations diagnosed with tuberculosis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies of genetic diversity in populations are important because they identify the frequencies of polymorphisms for each population. The basis of pharmacogenomics is that while standard drug selection and dosage guidelines apply across populations, the distribution of metabolizer phenotypes (e.g., intermediate metabolizers [IMs], poor metabolizers [PMs]) varies. For example, genetic variations in \u003cem\u003eNAT\u003c/em\u003e2 affect the metabolism of isoniazid, a first-line drug for tuberculosis. Populations with a higher prevalence of slow acetylators (NAT2 slow metabolizers) may experience increased drug toxicity, whereas fast acetylators may have reduced therapeutic efficacy. Genetic ancestry analysis of the Peruvian mestizo population, derived from multiple Native American communities, has revealed distinct genetic profiles (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These differences highlight the limitations of extrapolating pharmacogenomic data from other populations. For example, variations in NAT2 alleles, which influence isoniazid metabolism in tuberculosis treatment, show different frequencies in Peruvian populations compared to others, affecting drug response and the risk of adverse effects (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, in 2018, the technical report by WHO experts underscores an important role in determining metabolization phenotypes during drug administration. For example, for the administration of isoniazid, in children and adults, the dose is defined as 10\u0026ndash;15 mg/kg/day in multidrug-resistant tuberculosis (MDR-TB) regimens (the usual dose is 4\u0026ndash;6 mg/kg/day). However, it notes that in North Asia, where most of the population has the rapid metabolizer, a dose of 15 mg/kg may be more effective (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have reported that pharmacogenetic variations in NAT2 influence pharmacokinetics and contribute to differences in toxicity following isoniazid treatment (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A previous study concludes that patients with a fast metabolizer should receive 50% more than the standard dose, while patients with a slow genotype should receive half the standard dose (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Azuma et al. reported that 78% of the slow metabolizers experienced hepatotoxicity due to being treated with standard doses of INH, while none of those who received the modified dose reported liver damage (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). According to the above, the benefit of the modified treatment in reducing DILI according to the metabolization of INH was demonstrated (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study highlights the unmet need to identify individuals at genetic risk for DILI, enabling the development of personalized therapeutic regimens that mitigate these risks. By targeting high-risk genotypes, alternative treatments can be offered, reducing the burden of DILI and improving treatment adherence and outcomes in affected populations. Although the main mechanisms involved in developing hepatotoxicity are known, this field has not been significantly explored in Peru, particularly in native ancestry populations. This study aims to estimate the prevalence of metabolizing genotypes in patients during tuberculosis treatment in native Peruvian populations.\u003c/p\u003e"},{"header":"PATIENTS AND METHODS","content":"\u003cp\u003e \u003cstrong\u003eStudied population and participants\u003c/strong\u003e \u003cp\u003eThis research is a secondary analysis of data from a broader cross-sectional study conducted by the Peruvian Institute of Health (NIH). The primary study aimed to investigate genetic diversity in populations from Peru, reporting clinically relevant single nucleotide polymorphisms (SNPs) among Latin American groups (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This secondary study investigate genetic diversity and pharmacogenetic variations in native Peruvian populations. By utilizing existing genetic datasets, this study aims to identify population-specific metabolic profiles that influence TB drug response, thus informing more effective, personalized treatment strategies. A total of 254 samples were analysed. Inclusion criteria consisted of individuals from PGP with more than 95% native ancestry based on bioinformatics analysis results (Coast, Andes, Amazon), and informed consent for genetic testing. Exclusion criteria included individuals with incomplete genetic data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePatient Consent Statement\u003c/strong\u003e \u003cp\u003eThe study was approved by the Ethics in Research Committee of the Peruvian National Institute of Health (authorizations OI-003-11 and OI-087-13) and was conducted in accordance with the principles of the Declaration of Helsinki. Informed written consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGenetic data and quality control\u003c/strong\u003e \u003cp\u003ePeripheral blood samples (4 mL) were collected from participants, and DNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, CA, USA). DNA samples were genotyped using the Illumina Omni2.5M array in three batches at the Peruvian NIH facilities.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eOne batch, consisting of 50 individuals, was genotyped using GRch38. This batch was lifted to GRch37 using the UCSC lift-over tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/cgi-bin/hgLiftOver\u003c/span\u003e\u003cspan address=\"https://genome.ucsc.edu/cgi-bin/hgLiftOver\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) before merging with other GRch37 batches. After merging, we removed SNPs and individuals with more than 5% missing genotypes using PLINK (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Variants with AT/CG genotypes or those out of Hardy-Weinberg equilibrium (HWE) (p\u0026thinsp;\u0026lt;\u0026thinsp;10e-5) were excluded. Related individuals (first-degree relatives) were removed (king-cutoff 0.177) using PLINK. Array data was imputed against the TOPMed imputation panel (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imputation.biodatacatalyst.nhlbi.nih.gov/#!\u003c/span\u003e\u003cspan address=\"https://imputation.biodatacatalyst.nhlbi.nih.gov/#!\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, the TOPMed imputation server performs the phasing of the query data using Eagle2 and imputation using minimac4.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAdmixture analysis\u003c/strong\u003e \u003cp\u003eWe used ADMIXTURE (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) to explore genome-wide ancestry proportions In this analysis, K represents the number of ancestral populations assumed in the model. We tested values of K ranging from 4 to 8 in a subset of individuals that includes all samples (admixed and Native Americans), a subgroup of 1 000 Genomes Project (1KGP) high coverage populations with unrelated individuals (TSI, IBS, CEU, PEL, CLM, MXL, PUR, LWK, MSL, YRI, GWD, JPT, CHB, and CHS ), and Native Americans from Human Genome Diversity Project (HGDP) (Colombian, Pima, and Maya populations). This subset included unlinked single nucleotide variants and a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;5%. We performed ten runs per K values and plotted the run with the highest log-likelihood.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWe performed local ancestry by running RFMIXver2 (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) with reference panels from European, African, East Asian, and Native American populations. Individuals with \u0026gt;\u0026thinsp;95% Native American ancestry were classified into Andean, Coastal, or Amazonian groups based on recruitment location. We selected European (n\u0026thinsp;=\u0026thinsp;404 individuals; populations included CEU, GBR, IBS, and TSI), African (n\u0026thinsp;=\u0026thinsp;405 individuals; populations included YRI, ESN, GWD, and LWK), and East Asian (n\u0026thinsp;=\u0026thinsp;411 individuals; populations included CHB, CHS, JPT, and KHV) individuals from 1KGP populations. Native American reference included 187 individuals from PGP with more than 99% NAT (Native American ancestry) based on ADMIXTURE K4 results. We ran RMIX using two expectation maximization steps.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasures and analysis\u003c/b\u003e: For this analysis, we only included individuals with \u0026gt;\u0026thinsp;95% NAT ancestry, resulting in 254 non-admixed participants. Individuals were classified into Coast, Andes, or Amazon native subpopulations depending on the location of their recruitment site. We explored the relationship between native subpopulations and 23 genetic markers associated with TB treatment identified in the PharmGKB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pharmgkb.org/\u003c/span\u003e\u003cspan address=\"https://www.pharmgkb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A high-risk genotype is defined as a genetic variant or combination of variants associated with an increased likelihood of adverse drug reactions, particularly DILI, in the context of TB treatment. These genotypes are identified based on prior pharmacogenetic research and databases, such as PharmGKB, and are evaluated for their prevalence and association with hepatotoxicity. Statistical analysis, including chi-square tests and regression models, determines significant differences in the frequency of these high-risk genotypes across the studied subpopulations. To account for multiple testing, a Bonferroni correction was applied, adjusting the significance threshold by the number of independent tests performed. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using Stata 15 (StataCorp, College Station, TX, USA).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 27 SNPs associated with TB drug metabolism were analysed, as reported in the PharmGKB database (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These SNPs span key pharmacogenetic genes such as \u003cem\u003eNAT2, CYP2E1, GSTP1\u003c/em\u003e, and \u003cem\u003eSLCO1B1\u003c/em\u003e, which play a crucial role in drug metabolism and hepatotoxicity risk during TB treatment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pharmgkb.org/\u003c/span\u003e\u003cspan address=\"https://www.pharmgkb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Among these, six genes (\u003cem\u003eAGBL4, NAT2, GSTP1, SLCO1B1, NOS\u003c/em\u003e, and \u003cem\u003eCYP2B6\u003c/em\u003e) demonstrated significant frequency differences across the studied populations, warranting further investigation (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), additional SNPs without significant differences are reported in Supplementary Tables S2 and S3, providing a broader view of genetic variability related to TB drug metabolism in native Peruvian populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotypic frequencies of Anti-TB drug metabolism genes with statistically significant differences among Native Peruvian Populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eN (%) in populations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAndes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene / SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGBL4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers393994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers319952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 ( 22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 ( 31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 ( 18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 ( 19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 ( 57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 ( 59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 ( 58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 ( 53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 ( 20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 ( 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 ( 23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 ( 26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers320003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 ( 23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 ( 31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 ( 21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 ( 19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 ( 55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 ( 59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 ( 55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 ( 53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 ( 20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 ( 8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 ( 23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 ( 26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNAT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1799929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 ( 66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 ( 78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 ( 67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 ( 55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 ( 28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 ( 20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 ( 24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 ( 40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 ( 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 ( 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 ( 8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 ( 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1799931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 ( 48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 ( 40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 ( 50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 ( 51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 ( 39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 ( 37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 ( 37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 ( 44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 ( 12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 ( 21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 ( 12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 ( 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSTP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 ( 52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 ( 36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 ( 57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 ( 59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 ( 37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 ( 44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 ( 34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 ( 34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 ( 10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 ( 18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 ( 8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 ( 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSLCO1B1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4149032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 ( 47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 ( 76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 ( 28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 ( 46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 ( 38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 ( 15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 ( 54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33 ( 38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 ( 13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 ( 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 ( 17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 ( 15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNOS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11080344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 ( 59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 ( 40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 ( 34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCYP 2B6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3745274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 ( 57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 ( 14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 ( 23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between Native Subpopulations and High-Risk Genotypes with statistically significant differences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAGBL4 gene - Rifampicin\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers393994\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHepatotoxicity high risk genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes ( AA / AG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo (GG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.59\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u0026ndash;12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers319952\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes ( AA / AG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo (GG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.59\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u0026ndash;12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers320003\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes ( GG / GA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo (AA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.59\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.38\u0026ndash;12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e*High-Risk from 1000 Genomes Project Frequencies (%): PEL: 80.0, EUR: 75.0, EAS: 70.0, AFR: 65.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSTP gene- Pyrazinamide\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers1695\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHepatotoxicity high risk genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes (AA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo (AG / GG)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.39\u0026ndash;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.16\u0026ndash;14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e*High-Risk from 1000 Genomes Project Frequencies (%):PEL: 10.0, EUR: 5.0, EAS: 5.0, AFR: 10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSLCO1B1 gene - Rifampicin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers4149032\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHepatotoxicity high risk genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes (CC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo (TT / TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u0026ndash;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.80\u0026ndash;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e*This SNP has a minor allele frequency (MAF) according to the 1000 Genomes database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNOS gene - Ethambutol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ers11080344\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHepatotoxicity high risk genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes (CC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNo (TT / TC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.45\u0026ndash;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e* This SNP is not available in the 1000 Genomes Project\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor \u003cem\u003eAGBL4\u003c/em\u003e, SNPs \u003cem\u003ers393994, rs319952, and rs320003\u003c/em\u003e showed significant variations. The heterozygous genotype (AG) was the most frequent across all subpopulations. However, the AA genotype, associated with hepatotoxicity risk, was significantly more frequent in the Andean and Coastal populations compared to the Amazonian population. Despite this, the Amazonian population exhibited a higher prevalence of hepatotoxicity risk overall (OR\u0026thinsp;=\u0026thinsp;3.83, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNAT2\u003c/em\u003e genotyping identified the presence of \u003cem\u003ers4646244, rs1799929, rs1799930, rs1799931\u003c/em\u003e, and \u003cem\u003ers1495741\u003c/em\u003e SNPs. Statistically significant differences were observed for \u003cem\u003ers1799929\u003c/em\u003e and \u003cem\u003ers1799931\u003c/em\u003e. The homozygous genotype (AA) for \u003cem\u003ers1799931\u003c/em\u003e was more frequent in the Andean (50.5%) and Coastal (51.2%) populations compared to the Amazonian population (40.6%). Meanwhile, the TT genotype for rs1799929 was most prevalent in the Amazonian population (78.3%), followed by the Andean (67.7%) and Coastal (55.8%) populations.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eGSTP1\u003c/em\u003e, the \u003cem\u003ers1695\u003c/em\u003e SNP showed significant variation. The homozygous AA genotype was significantly less frequent in the Amazonian population (36.2%) compared to the Andean (57.6%) and Coastal (59.3%) populations. The heterozygous AG genotype was more common in the Amazonian group (44.9%). Hepatotoxic genotypes were significantly more frequent in the Amazon population compared to the Coast (OR\u0026thinsp;=\u0026thinsp;3.76, p\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e \u003cp\u003eAnalysis of \u003cem\u003eSCOLB1\u003c/em\u003e revealed significant differences for \u003cem\u003ers4149032\u003c/em\u003e. The homozygous TT genotype was most frequent in the Amazonian population (76.8%), followed by the Coastal (46.5%) and Andean (28.3%) populations. The prevalence of the hepatotoxic CC genotype was significantly higher in the Amazonian group (OR\u0026thinsp;=\u0026thinsp;3.81, p\u0026thinsp;=\u0026thinsp;0.012) and lower in the Andean group (OR\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eNOS\u003c/em\u003e, \u003cem\u003ers11080344\u003c/em\u003e was the key SNP, showing significant differences. The homozygous TT genotype was most frequent in the Coastal population (59.3%), followed by the Amazonian (56.5%) and Andean (42.4%) populations. The heterozygous TC genotype, associated with reduced hepatotoxicity risk, was more frequent in the Andean population (44.4%), which had a significantly lower risk of hepatotoxicity compared to the Coast (OR\u0026thinsp;=\u0026thinsp;0.51, p\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e \u003cp\u003eFinally, \u003cem\u003eCYP2B6\u003c/em\u003e genotyping identified \u003cem\u003ers3745274\u003c/em\u003e as the primary SNP of interest. The heterozygous TG genotype was the most frequent across all populations. However, the TT genotype, which has been linked to increased hepatotoxicity risk, was significantly higher in the Amazonian population (42.0%) compared to the Andean (19.2%) and Coastal (22.1%) populations (p\u0026thinsp;=\u0026thinsp;0.012).\u003c/p\u003e \u003cp\u003eThe Amazonian population exhibited a higher risk of developing DILI due to hepatotoxic alleles in the \u003cem\u003eAGBL4, GSTP1, SCOLB1\u003c/em\u003e, and \u003cem\u003eNOS\u003c/em\u003e genes. In contrast, Andean populations showed a genetic profile associated with lower hepatotoxicity risk. RFMIX v2 analysis identified distinct ancestry proportions among the studied groups. The Andean population exhibited higher Native American ancestry, while the Coastal group had greater European admixture. These ancestry differences were reflected in pharmacogenetic variations, particularly in \u003cem\u003eNAT2\u003c/em\u003e and \u003cem\u003eGSTP1\u003c/em\u003e.These findings highlight the genetic variability underlying TB drug metabolism and the importance of tailoring treatments to specific subpopulations.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we identified differences in the prevalence of several genetic variants that affect TB drug metabolism across geographically distinct native non-admixed Peruvian populations. The Amazonian population demonstrated a higher prevalence of hepatotoxic alleles in the \u003cem\u003eAGBL4, GSTP1\u003c/em\u003e, and \u003cem\u003eSLCO1B1\u003c/em\u003e genes. Variants such as \u003cem\u003ers393994\u003c/em\u003e in \u003cem\u003eAGBL4\u003c/em\u003e were associated with increased high-risk genotype (OR\u0026thinsp;=\u0026thinsp;3.83, p\u0026thinsp;=\u0026thinsp;0.004). Similar findings have been reported in other studies, linking these variants to impaired cellular deglutamylation processes, exacerbating liver toxicity when exposed to drugs like rifampin (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In contrast, the Andean population exhibited a lower prevalence of DILI-associated alleles in the \u003cem\u003eNOS\u003c/em\u003e gene, particularly rs11080344. This variant has been linked to reduced nitric oxide production, which may mitigate oxidative stress and hepatotoxicity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral previous studies have explored the role of genetic polymorphisms in drug metabolism, particularly in relation to \u003cem\u003eNAT2\u003c/em\u003e and its impact on isoniazid metabolism. The \u003cem\u003eNAT2\u003c/em\u003e gene has been widely studied in different populations, with significant variability observed among ethnic groups. Studies in East Asian populations, for instance, have shown a higher prevalence of fast acetylator alleles, whereas South American Indigenous populations tend to exhibit a higher frequency of slow acetylators, increasing their risk of DILI (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Similarly, genetic studies in African and European populations have reported diverse acetylation patterns that influence TB treatment outcomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eNAT2\u003c/em\u003e gene plays a critical role in the metabolism of isoniazid. Variants such as \u003cem\u003ers1799931\u003c/em\u003e associated with slow acetylation phenotypes, were more prevalent in the Amazonian population. Slow acetylators are at an increased risk of DILI due to the accumulation of toxic metabolites​ (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These findings align with previous studies that highlight the role of slow acetylation in TB treatment-related hepatotoxicity in populations with similar genetic backgrounds​ (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The presence of \u003cem\u003eNAT25, NAT26\u003c/em\u003e, and \u003cem\u003eNAT27\u003c/em\u003e alleles has been consistently linked to slow metabolism in multiple populations, including Peruvians (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Studies in Latin American mestizo populations have demonstrated distinct metabolic profiles compared to Indigenous groups, reinforcing the importance of population-specific pharmacogenomic studies (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Moreover, in Peruvian TB patients, \u003cem\u003eNAT25B\u003c/em\u003e and \u003cem\u003eNAT27B\u003c/em\u003e were associated with higher DILI risk in mestizos, whereas \u003cem\u003eNAT25G\u003c/em\u003e and \u003cem\u003eNAT213A\u003c/em\u003e were protective in native populations, suggesting an evolutionary role in drug metabolism adaptations (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eGSTP1\u003c/em\u003e gene, responsible for detoxification and oxidative stress regulation, also showed population-specific differences. The \u003cem\u003ers1695\u003c/em\u003e AA genotype, more common in the Amazonian population, has been linked to reduced glutathione activity and an increased risk of oxidative damage, corroborating findings from prior research​ (\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Similarly, polymorphisms in the \u003cem\u003eSLCO1B1\u003c/em\u003e gene, particularly \u003cem\u003ers4149032\u003c/em\u003e, were significantly associated with DILI in the Amazonian population (OR\u0026thinsp;=\u0026thinsp;3.81, p\u0026thinsp;=\u0026thinsp;0.012). This gene plays a vital role in hepatic drug transport, and its variants can lead to higher plasma drug levels, increasing the risk of toxicity​ (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eInterestingly, no significant differences were observed in the \u003cem\u003eCYP2B6\u003c/em\u003e gene across the three subpopulations. While \u003cem\u003eCYP2B6\u003c/em\u003e has been implicated in the metabolism of drugs such as efavirenz, its limited variability in this study suggests that other genetic factors may play a more critical role in influencing DILI risk among these populations (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Previous research has highlighted the impact of \u003cem\u003eCYP2E1\u003c/em\u003e polymorphisms on drug metabolism, particularly in populations with a high burden of TB. Tang et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) found that individuals carrying \u003cem\u003eCYP2E11D\u003c/em\u003e alleles had significantly altered enzyme activity, affecting isoniazid metabolism and hepatotoxicity risk. Similar findings have been reported in South American and Asian populations, suggesting a potential genetic basis for inter-individual variability in drug response.\u003c/p\u003e \u003cp\u003eThese findings reinforce the critical role of genetic diversity in influencing treatment outcomes. The genetic differentiation observed between the Andean and Amazonian populations is consistent with prior studies showing that geographic and cultural isolation has shaped distinct genetic profiles in South America (\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Genetic ancestry plays a central role in population pharmacogenomics (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In a previous study, we identified the presence of adverse reactions during anti-tuberculosis treatment in the Peruvian population. We reported that 30% of the Peruvian populations are associated with the slow metabolism of isoniazid (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). We also identified haplotypes with divergent associations with DILI, based on the mestizo or native Peruvian population. For instance, we found evidence of NAT2*5B and NAT2*7B being associated with DILI risk in mestizos, while no such association has been observed in natives. Additionally, haplotypes NAT2*5G and NAT2*13A have only been negatively associated with DILI in the studied Native Peruvians (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Another study revealed that this environmental and genetic differentiation between the Andean and Amazonian populations has allowed natural selection and other evolutionary forces to act over millennia, shaping differences in the frequencies of genetic variants, including genes related to the immune response (\u003cem\u003eCD45\u003c/em\u003e and \u003cem\u003eDUOX2\u003c/em\u003e), with thyroid (\u003cem\u003eDUOX2\u003c/em\u003e), cardiovascular (\u003cem\u003eHAND2-AS1\u003c/em\u003e) and haematological (\u003cem\u003eTMPRSS6\u003c/em\u003e) functions 4, as well as genes related to drug response (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings suggest that there are differences in the \u003cem\u003eAGBL4, NAT2, GSTP1, SCOLB1, NOS\u003c/em\u003e and \u003cem\u003eCYP2B6\u003c/em\u003e genes between the native populations in Peru that is correlated with clinical reports about toxicity and treatment failure in Peruvian populations (\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). These differences could have implications for the risk of hepatotoxicity associated with the use of antituberculosis drugs (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). This study has several strengths, including its focus on underrepresented native Peruvian populations, providing crucial insights into genetic variability affecting TB drug metabolism. The comprehensive analysis of 23 genes using advanced genotyping techniques ensures high data quality while including diverse subpopulations (Coast, Andes, Amazon) and offers valuable comparative perspectives. Clinically relevant findings, such as the prevalence of hepatotoxic alleles, have practical implications for personalized TB treatment strategies.\u003c/p\u003e \u003cp\u003eHowever, the study also has limitations, such as a relatively small sample size that may restrict generalizability, the exclusion of admixed populations, and reliance on a cross-sectional design, which limits the ability to assess long-term impacts. Understanding these genetic variations is crucial for designing effective TB treatment regimens. The differences observed in this study align with previous findings that Indigenous South American populations exhibit distinct pharmacogenetic profiles compared to mestizo, European, African, and Asian populations. These findings further support the need for incorporating pharmacogenomics into TB treatment guidelines to reduce adverse effects and improve therapeutic outcomes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile our study identifies significant differences in allele frequencies among native Peruvian subpopulations, clinical studies are necessary to validate their impact on drug metabolism and treatment outcomes. Functional validation through pharmacokinetic analyses will determine if these variations influence tuberculosis treatment efficacy and adverse drug reactions. Only through clinical confirmation can these findings contribute to pharmacogenetic guideline development. Our results provide a foundation for future research but require further investigation in a clinical setting\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eAlthough our study results provide valuable insights into the frequency of metabolizing genotypes for anti-TB drugs in Peru, particularly among native populations, a deeper understanding of the factors associated with these genotypes is needed. Longitudinal studies including large samples have revealed that genetic polymorphisms play an important role in drug metabolism. Despite the limitations of a secondary study, our findings suggest that the subpopulation of Peruvian natives is associated with the metabolizing profile and \u003cem\u003eAGBL4, NAT2, GSTP1, SCOLB1, NOS\u003c/em\u003e, and \u003cem\u003eCYP2B6\u003c/em\u003e alleles.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eAGBL4\u0026nbsp;\u003c/em\u003e ATP/GTP Binding Protein Like 4\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCYP2B6\u0026nbsp;\u003c/em\u003e Cytochrome P450 Family 2 Subfamily B Member 6\u003c/p\u003e\n\u003cp\u003eDILI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Drug-Induced Liver Injury\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGSTP1\u0026nbsp;\u003c/em\u003e Glutathione S-Transferase Pi 1\u003c/p\u003e\n\u003cp\u003eINH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Isoniazid\u003c/p\u003e\n\u003cp\u003eMDR-TB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multidrug-Resistant Tuberculosis\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNAT2\u0026nbsp;\u003c/em\u003e N-Acetyltransferase 2\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNOS\u003c/em\u003e Nitric Oxide Synthase\u003c/p\u003e\n\u003cp\u003ePGP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Peruvian Genome Project\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSCOLB1\u0026nbsp;\u003c/em\u003e Solute Carrier Organic Anion Transporter Family Member 1B1\u003c/p\u003e\n\u003cp\u003eTB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tuberculosis\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was approved by the Ethics in Research Committee of the Peruvian National Institute of Health\u0026nbsp;and follows the principles of the Declaration of Helsinki. Written informed consent was obtained from all the participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual details, images, or videos.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, LJ-V, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the “Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC)” and the “Programa Nacional de Investigación Científica y Estudios Avanzados (PROCIENCIA)” within the framework of the competition “E067-2023-01 Proyectos Especiales: Proyectos de Incorporación de Investigadores Postdoctorales en Instituciones Peruanas” [contract number PE501084276-2023].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: LJ-V. Performed the experiments: LJ-V, MH. Analysed the data: LJ-V, MH, CL, HG. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the “Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC)” and the “Programa Nacional de Investigación Científica y Estudios Avanzados (PROCIENCIA)” within the framework of the competition “E067-2023-01 Proyectos Especiales: Proyectos de Incorporación de Investigadores Postdoctorales en Instituciones Peruanas” [contract number PE501084276-2023]. The funders had no role in the study design, data collection, and interpretation, or the decision to submit the work for publication. We thank Victor Borda for his contribution to the analysis of ancestry.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global tuberculosis report 2023. 265 p. https://iris.who.int/bitstream/handle/10665/363752/9789240061729-eng.pdf?sequence=1\u003c/li\u003e\n\u003cli\u003eLlanos-Zavalaga LF, Vel\u0026aacute;squez-Hurtado JE, Garc\u0026iacute;a PJ, Gottuzzo E. Tuberculosis y salud p\u0026uacute;blica: \u0026iquest;derechos individuales o derechos colectivos? . Vol. 29, Revista Peruana de Medicina Experimental y Salud Publica. Instituto Nacional de Salud; 2002 . 259\u0026ndash;264 p. Available from: http://www.scielo.org.pe/scielo.php?script=sci_arttext\u0026amp;pid=S1726-46342012000200016\u003c/li\u003e\n\u003cli\u003eChamorro JG, Castagnino JP, Musella RM, Nogueras M, Aranda FM, Fr\u0026iacute;as A, et al. Sex, ethnicity, and slow acetylator profile are the major causes of hepatotoxicity induced by antituberculosis drugs. 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Russ J Genet 2010;46 (2):230\u0026ndash;8. https://doi.org/10.1134/S1022795410020146\u003c/li\u003e\n\u003cli\u003eLevano KS, Jaramillo-Valverde L, Tarazona DD, Sanchez C, Capristano S, V\u0026aacute;squez-Loarte T, Solari L, Mendoza-Ticona A, Soto A, Rojas C, Zegarra-Chapo\u0026ntilde;an R, Guio H. Allelic and genotypic frequencies of NAT2, CYP2E1, and AADAC genes in a cohort of Peruvian tuberculosis patients. Mol Genet Genomic Med. 2021 Oct;9(10):e1764. doi: 10.1002/mgg3.1764. Epub 2021 Sep 12. PMID: 34510815; PMCID: PMC8580101\u003c/li\u003e\n\u003cli\u003eJaramillo-Valverde L, Levano KS, Tarazona DD, 612 Capristano S, Sanchez C, Poterico JA, Tarazona-Santos E, Guio H. Pharmacogenetic variability of tuberculosis biomarkers in native and mestizo Peruvian populations. Pharmacol Res Perspect. 2024 Jun;12(3):e1179. doi: 10.1002/prp2.1179. PMID: 38666760; PMCID: PMC11047445.\u003c/li\u003e\n\u003cli\u003eBorda V, Alvim I, Mendes M, Silva-Carvalho C, Soares-Souza GB, Leal TP, Furlan V, Scliar MO, Zamudio R, Zolini C, Ara\u0026uacute;jo GS, Luizon MR, Padilla C, C\u0026aacute;ceres O, Levano K, S\u0026aacute;nchez C, Trujillo O, Flores-Villanueva PO, Dean M, Fuselli S, Machado M, Romero PE, Tassi F, Yeager M, O\u0026apos;Connor TD, Gilman RH, Tarazona- Santos E, Guio H. The genetic structure and adaptation of Andean highlanders and Amazonians are influenced by the interplay between geography and culture. Proc Natl Acad Sci U S A. 2020 Dec 22;117(51):32557-32565. doi:10.1073/pnas.2013773117. Epub 2020 Dec 4. PMID: 33277433; PMCID:PMC7768732.\u003c/li\u003e\n\u003cli\u003eWu S, Wang Y-J, Tang X, Wang Y, Wu J, Ji G, et al. (2016) Genetic Polymorphisms of Glutathione S-Transferase P1 (GSTP1) and the Incidence of Anti-Tuberculosis Drug-Induced Hepatotoxicity. PLoS ONE 11(6): e0157478. doi:10.1371/journal.pone.0157478\u003c/li\u003e\n\u003cli\u003eZimniak P, Nanduri B, Pikula S, Bandorowicz-Pikula J, Singhal SS, Srivastava SK, et al. Naturally occurring human glutathione S-transferase GSTP1-1 isoforms with isoleucine and valine in position 104 differ in enzymic properties. Eur J Biochem. 1994; 224(3):893\u0026ndash;9. Epub 1994/09/15. PMID: 7925413.\u003c/li\u003e\n\u003cli\u003eHu X, Xia H, Srivastava SK, Pal A, Awasthi YC, Zimniak P, et al. Catalytic efficiencies of allelic variants of human glutathione S-transferase P1-1 toward carcinogenic anti-diol epoxides of benzo[c]phenanthrene and benzo[g]chrysene. Cancer research. 1998; 58(23):5340\u0026ndash;3. Epub 1998/12/16. PMID: 9850062.\u003c/li\u003e\n\u003cli\u003eReszka E, Jablonowski Z, Wieczorek E, Gromadzinska J, Sosnowski M, Wasowicz W. GSTP1 mRNA expression in human circulating blood leukocytes is associated with GSTP1 genetic polymorphism. Clin Biochem. 2011; 44(13):1153\u0026ndash;5. Epub 2011/06/15. doi: 10.1016/j.clinbiochem.2011.05.024 PMID: 21669193.\u003c/li\u003e\n\u003cli\u003eShabani S, Farnia P, Ghanavi J, Velayati AA, Farnia P. Pharmacogenetic study of drugs affecting Mycobacterium tuberculosis. Int J Mycobacteriol 2024;13:206-12\u003c/li\u003e\n\u003cli\u003eWeiner M, Gelfond J, Johnson‑Pais TL, Engle M, Peloquin CA, Johnson JL, et al. Elevated plasma moxifloxacin concentrations and SLCO1B1 g.‑11187G\u0026gt;A polymorphism in adults with pulmonary tuberculosis. Antimicrob Agents Chemother 2018;62:e01802‑17.\u003c/li\u003e\n\u003cli\u003eWeiner M, Peloquin C, Burman W, Luo CC, Engle M, Prihoda TJ, et al. Effects of tuberculosis, race, and human gene SLCO1B1 polymorphisms on rifampin concentrations. Antimicrob Agents Chemother 2010;54:4192‑200\u003c/li\u003e\n\u003cli\u003eKwara A, Lartey M, Sagoe KW, Xexemeku F, Kenu E, Oliver-Commey J, Boima V, Sagoe A, Boamah I, Greenblatt DJ, Court MH. 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Am J Phys Anthropol. 1554:600\u0026ndash;609.\u003c/li\u003e\n\u003cli\u003eBarbieri C, Barquera R, Arias L, Sandoval JR, Acosta O, Zurita C, Aguilar- Campos A, Tito-\u0026Aacute;lvarez AM, Serrano-Osuna R, Gray RD, Mafessoni F, Heggarty P, Shimizu KK, Fujita R, Stoneking M, Pugach I, Fehren-Schmitz L. The Current Genomic Landscape of Western South America: Andes, Amazonia, and Pacific Coast. Mol Biol Evol. 2019 Dec 1;36(12):2698-2713. doi: 10.1093/molbev/msz174. PMID: 31350885; PMCID: PMC6878948.\u003c/li\u003e\n\u003cli\u003eYang HC, Chen CW, Lin YT, Chu SK. Genetic ancestry plays a central role in population pharmacogenomics. Commun Biol. 2021 Feb 5;4(1):171. doi:10.1038/s42003-021-01681-6.\u003c/li\u003e\n\u003cli\u003eCu\u0026eacute;llar L, Casta\u0026ntilde;eda CA, Rojas K, et al. Caracter\u0026iacute;sticas cl\u0026iacute;nicas y toxicidad del tratamiento de tuberculosis en pacientes con c\u0026aacute;ncer [Clinical features and toxicity of tuberculosis treatment in patients with cancer]. Rev Peru Med Exp Salud Publica. 2015; 32(2): 272-277. Spanish. http://www.scielo.org.pe/scielo.php?script=sci_arttext\u0026amp;pid=S1726-46342015000200009\u003c/li\u003e\n\u003cli\u003eLackey B, Seas C, Van der Stuyft P, Otero L. Patient characteristics associated with tuberculosis treatment default: a cohort study in a high-incidence area of Lima, Peru. PLoS One. 2015; 10(6):e0128541. doi:10.1371/journal.pone.0128541\u003c/li\u003e\n\u003cli\u003eKawai V, Soto G, Gilman RH, et al. Tuberculosis mortality, drug resistance, and infectiousness in patients with and without HIV infection in Peru. Am J Trop Med Hyg. 2006; 75(6): 1027-1033. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912515/\u003c/li\u003e\n\u003cli\u003eJaramillo-Valverde L, Levano KS, Tarazona DD, Capristano S, Zegarra- Chapo\u0026ntilde;an R, Sanchez C, Yufra-Picardo VM, Tarazona-Santos E, Ugarte-Gil C, Guio H. NAT2 and CYP2E1 polymorphisms and antituberculosis drug-induced hepatotoxicity in Peruvian patients. Mol Genet Genomic Med. 2022 Aug;10(8):e1987. doi: 10.1002/mgg3.1987. Epub 2022 Jun 24. PMID: 35751408; PMCID: PMC9356556.\u003c/li\u003e\n\u003cli\u003eJaramillo-Valverde L, Levano KS, Tarazona DD, Vasquez-Dominguez A, Toledo-Nauto A, Capristano S, Sanchez C, Tarazona-Santos E, Ugarte-Gil C, Guio H. GSTT1/GSTM1 Genotype and Anti-Tuberculosis Drug-Induced Hepatotoxicity in Peruvian Patients. Int J Mol Sci. 2022 Sep 20;23(19):11028. doi: 10.3390/ijms231911028. PMID: 36232322; PMCID: PMC9569363\u003c/li\u003e\n\u003c/ol\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":"Tuberculosis, Genotype, Pharmacogenetics, Native Population","lastPublishedDoi":"10.21203/rs.3.rs-5742453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5742453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Peru, 33 113 individuals were diagnosed with tuberculosis (TB) in 2023. While TB treatments are generally effective, 3.4\u0026ndash;13% of cases are associated with significant adverse drug reactions, with drug-induced liver injury (DILI) being the most prevalent. Limited data exist on genetic risk factors for DILI in Latin America; even less is known about these factors in native Peruvian populations. This study aimed to determine the prevalence of TB drug-metabolizing genotypes in these populations. A cross-sectional analysis was conducted using genetic data from 254 participants from the Peruvian Genome Project (PGP) representing three subpopulations: Coast, Andes, and Amazon. Twenty-three genes associated with TB treatment, include isoniazid, rifampin, ethambutol, and pyrazinamide, as identified in the PharmGKB database, were analysed. Significant differences were observed in genotype frequencies among subpopulations for \u003cem\u003eAGBL4, NAT2, GSTP1, SCOLB1, NOS\u003c/em\u003e, and \u003cem\u003eCYP2B6\u003c/em\u003e genes. The Amazonian population demonstrated a higher risk of DILI due to the increased prevalence of hepatotoxic alleles in \u003cem\u003eAGBL4, GSTP1\u003c/em\u003e, and \u003cem\u003eSLCO1B1\u003c/em\u003e. In contrast, alleles in the \u003cem\u003eNOS\u003c/em\u003e gene indicated a lower risk of hepatotoxicity in the Andean population. However, the high-risk genotypes identified in the study\u0026rsquo;s native Peruvian populations exhibit distinct prevalence patterns compared to those reported in the 1 000 Genomes Project. These findings can inform the development of personalized therapeutic strategies to improve TB treatment outcomes among Peru\u0026rsquo;s diverse subpopulations.\u003c/p\u003e","manuscriptTitle":"Pharmacogenetic Study of Anti-TB Drugs in the Native Ancestry Peruvian Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 07:26:17","doi":"10.21203/rs.3.rs-5742453/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":"65683be4-a3dc-4809-8ce1-218849141230","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-02T13:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 07:26:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5742453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5742453","identity":"rs-5742453","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}