N-acetyltransferase 2 (NAT2) is involved in both carcinogen detoxification through hepatic N-acetylation and carcinogen activation through local O-acetylation. NAT2 slow acetylation status is significantly associated with increased bladder cancer risk among European populations, but its association in Asian populations is inconclusive.NAT2 acetylation status was determined by both single nucleotide polymorphisms (SNPs) and caffeine metabolic ratio (CMR), in a population-based study of 494 bladder cancer patients and 507 control subjects in Shanghai, China.The CMR, a functional measure of hepatic N-acetylation, was significantly reduced in a dose-dependent manner among both cases and controls possessing the SNP-inferred NAT2 slow acetylation status (all P-values<5.0×10-10). The CMR-determined slow N-acetylation status (CMR<0.34) was significantly associated with a 50% increased risk of bladder cancer (odds ratio = 1.50, 95% confidence interval = 1.10-2.06) whereas the SNP-inferred slow acetylation statuses were significantly associated with an approximately 50% decreased risk of bladder cancer. The genotype-disease association was strengthened after the adjustment for CMR and was primarily observed among never smokers.The apparent differential associations for phenotypic and genetic measures of acetylation statuses with bladder cancer risk may reflect dual functions of NAT2 in bladder carcinogenesis because the former only measures the capacity of carcinogen detoxification pathway while the latter represents both carcinogen activation and detoxification pathways. Future studies are warranted to ascertain the specific role of N- and O-acetylation in bladder carcinogenesis, particularly in populations exposed to different types of bladder carcinogens.

Epidemiological studies have identified positive family history and exposure to environmental toxins as risk factors for Parkinson's disease (PD). An inherited defect of xenobiotic metabolism could result in increased susceptibility to such toxins. We investigated the frequency of functionally relevant polymorphisms in six detoxification enzymes among patients with PD to elucidate the relation between these polymorphisms and the disease.We obtained brain-tissue samples from 100 patients with apparently sporadic PD and blood samples from 100 living patients with familial PD. For the control group, we extracted DNA from the tissue of 100 pathologically normal brains. The six enzymes analysed in these three groups were: CYP2D6, CYP2E1, NAD(P)H-menadione reductase, glutathione transferases M1 and T1, and N-acetyltransferase 2. We also investigated N-acetyltransferase 2 in 100 blood samples from patients with genetically proven Huntington's disease. We used PCR-based methods and restriction-enzyme analysis to detect polymorphisms.The slow acetylator genotype for N-acetyltransferase 2 was more common in the familial PD group (69%) than in all controls (37%). Even after correction for multiple comparisons, this result remained highly significant (p = 0.002) for familial PD compared with normal controls (odds ratio 3.79 [95% CI 2.08-6.90]) and compared with Huntington's disease (2.45 [1.37-4.38], p = 0.004). The slow acetylator frequency for N-acetyltransferase 2 for sporadic PD was between that for Huntington's disease and familial PD. The frequencies of all the other polymorphisms were similar in the two study groups and the normal control group.We found an association between the slow acetylator genotype for N-acetyltransferase 2 and familial PD. Further studies are needed to investigate the biological relevance of these findings, but slow acetylation could lead to impaired ability of patients with familial PD to handle neurotoxic substances.

The aim of this study is to evaluate the potential association between N-acetyltransferase type 2 (NAT2) polymorphisms and drug-induced liver injury during anti-TB treatment (AT-DILI).We conducted a systematic review and performed a meta-analysis to clarify the role of NAT2 polymorphism in AT-DILI. PubMed, Medline and EMBASE databases were searched for studies published in English to December 31, 2017, on the association between the NAT2 polymorphism and AT-DILI risk. Outcomes were pooled with random-effects meta-analysis. Details were registered in the PROSPERO register (number: CRD42016051722).Thirty-seven studies involving 1527 cases and 7184 controls were included in this meta-analysis. The overall odds ratio (OR) of AT-DILI associated with NAT2 slow acetylator phenotype was 3.15 (95% CI 2.58-3.84, I = 51.3%, P = 0.000). The OR varied between different ethnic populations, ranging from 6.42 (95% CI 2.41-17.10, I = 2.3%) for the West Asian population to 2.32 (95% CI 0.58-9.24, I = 80.3%) for the European population. Within the slow NAT2 genotype, variation was also observed; NAT2*6/*7 was associated with the highest risk of AT-DILI (OR = 1.68, 95% CI 1.09-2.59) compared to the other slow NAT2 acetylators combined.NAT2 slow acetylation was observed to increase the risk of AT-DILI in tuberculosis patients. Our results support the hypothesis that the slow NAT2 genotype is a risk factor for AT-DILI.© 2018 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.

To study the genetic basis of N-acetylatransferase polymorphism in Chinese.Genotypes in 120 healthy Han volunteers from 19 provinces of China were assayed. The 3 common mutant alleles (M1, M2, M3) and one normal wild-type (WT) allele of the N-acetyltransferase (NAT2) gene were detected by allele-specific polymerase chain reaction technique.The NAT2 allele frequencies in 120 Chinese (WT = 0.625, M1 = 0.0458, M2 = 0.188, M3 = 0.142) were different (P < 0.01). The NAT2 genotype distribution for all detected combinations of NAT2 alleles in 120 Chinese subjects was consisitent with Hardy-Weinberg equilibrium (chi 2 = 7.27, nu = 8, 0.7 > P > 0.5). Fifty subjects (41.7%) were homozygous wildtypes, 50 subjects (41.7%) were heterozygous mutants, and 20 subjects (16.7%) were homozygous mutants.The lower frequency of mutant M1 allele compared with that of Caucasians explains the low frequency of slow acylators in Chinese.

Current routine genotyping methods typically do not provide haplotype information, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by > 50%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.

Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.

In this report, we compare and contrast three previously published Bayesian methods for inferring haplotypes from genotype data in a population sample. We review the methods, emphasizing the differences between them in terms of both the models ("priors") they use and the computational strategies they employ. We introduce a new algorithm that combines the modeling strategy of one method with the computational strategies of another. In comparisons using real and simulated data, this new algorithm outperforms all three existing methods. The new algorithm is included in the software package PHASE, version 2.0, available online (http://www.stat.washington.edu/stephens/software.html).