Improvements in Algorithms for Phenotype Inference: The NAT2 Example

Title:Improvements in Algorithms for Phenotype Inference: The NAT2 Example

Volume: 15 Issue: 2

Author(s): Silvia Selinski, Meinolf Blaszkewicz, Katja Ickstadt, Jan G. Hengstler and Klaus Golka

Affiliation:

Keywords: Anti-tuberculosis drug-induced hepatoxicity, bladder cancer, ethnic differences, haplotype phasing, haplotype reconstruction, individualized medicine, NAT2 activity, ultra-slow acetylators.

Abstract: Numerous studies have analyzed the impact of N-acetyltransferase 2 (NAT2) polymorphisms on drug efficacy, side effects as well as cancer risk. Here, we present the state of the art of deriving haplotypes from polymorphisms and discuss the available software. PHASE v2.1 is currently considered a gold standard for NAT2 haplotype assignment. In vitro studies have shown that some slow acetylation genotypes confer reduced protein stability. This has been observed particularly for G191A, T341C and G590A. Substantial ethnic variations of the acetylation status have been described. Probably, upcoming agriculture and the resulting change in diet caused a selection pressure for slow acetylation. In recent years much research has been done to reduce the complexity of NAT2 genotyping. Deriving the haplotype from seven SNPs is still considered a gold standard. However, meanwhile several studies have shown that a two-SNP combination, C282T and T341C, results in a similarly good distinction in Caucasians. However, attempts to further reduce complexity to only one 'tagging SNP' (rs1495741) may lead to wrong predictions where phenotypically slow acetylators were genotyped as intermediate or rapid. Numerous studies have shown that slow NAT2 haplotypes are associated with increased urinary bladder cancer risk and increased risk of anti-tuberculosis drug-induced hepatotoxicity. A drawback of the current practice of solely discriminating slow, intermediate and rapid genotypes for phenotype inference is limited resolution of differences between slow acetylators. Future developments to differentiate between slow and ultra-slow genotypes may further improve individualized drug dosing and epidemiological studies of cancer risk.

Cite this article as:

Selinski Silvia, Blaszkewicz Meinolf, Ickstadt Katja, Hengstler G. Jan and Golka Klaus, Improvements in Algorithms for Phenotype Inference: The NAT2 Example, Current Drug Metabolism 2014; 15 (2) . https://dx.doi.org/10.2174/1389200215666140202215717