Inter-individual variation of drug metabolising enzymes (DMEs) leads to variable efficacy of many drugs and even adverse drug responses. Consequently, it would be desirable to test variants of many DMEs before drug treatment. Inter-ethnic differences in frequency mean that the choice of SNPs to test may vary across population groups. Here we examine the utility of testing representative groups as a way of assessing what variants might be tested. We show that publicly available population information is potentially useful for determining loci for pre-treatment genetic testing, and for determining the most prevalent risk haplotypes in defined groups. However, we also show that the NHS England classifications have limitations for grouping for these purposes, in particular for people of African descent. We conclude: (1) genotyping of hospital patients and people from the hospital catchment area confers no advantage over using samples from appropriate existing ethnic group collections or publicly available data, (2) given the current NHS England Black African grouping, a decision as to whether to test, would have to apply to all patients of recent Black African ancestry to cover reported risk alleles and (3) the current scarcity of available genome and drug effect data from Africans is a problem for both testing and treatment decisions.
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Data pertaining to the 1000G SNPs were extracted using VCFtools version 0.1.13 (https://vcftools.github.io/index.html), from the 1000G Phase 3 VCF files for the relevant chromosomes (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/). For systematic and automated haplotype analysis of large SNP data, we developed an R-based tool to convert PLINK PED/MAP files to PHASE input, and to summarise haplotype inference results in multiple groups from PHASE output. This tool is now publicly available on Github (https://github.com/nansari-pour/PLINKtoPHASE).
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We thank all the sample donors who participated in this study and the UCLH clinicians, Aroon Hingorani, Alastair Forbes, Simon Woldman, Steve Hurel, Clare Dollery and others who helped us with access to patient volunteers in their clinics. We also thank Mark Thomas for access to some of the samples and Pieta Nosanea, Esther Williams, Sarah Steward and Ayele Tarekegn for help with sample collections from African and Indian volunteers in their native countries; Ranji Areseratnam for technical assistance. This research was funded by the University College London Hospitals Comprehensive Biomedical Research Centre.
Conflict of interest
During this study NB had a controlling interest in a company interested in developing diagnostic technology to identify variation in drug metabolising enzymes to improve healthcare. Neither NB nor the company now have that objective. None of the other authors have any potential conflicts of interest to declare.
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Ingram, C.J.E., Ekong, R., Ansari-Pour, N. et al. Group-based pharmacogenetic prediction: is it feasible and do current NHS England ethnic classifications provide appropriate data?. Pharmacogenomics J (2020). https://doi.org/10.1038/s41397-020-0175-0