To study the cohort’s population affinities and to detect possible population structure in the cohort, PCA was carried out on a set of 793 independent SNPs pruned down (at an r2 cutoff of 0.6) from the set of 968 SNPs that were found across all three datasets: KGP , AGVP  and our dataset. For this analysis, we included two populations from the AGVP (Zulu, and Wolayta from Ethiopia) and the KGP populations (YRI, LWK, ASW, CEU, TSI, CHB and JPT). The quality/resolution of PCA depends on the number of SNPs included in the study. Although many PCAs in recent population genetic studies are based on hundreds of thousands of SNPs [1,45,46], we could find a clear separation between populations from Asia, Africa, and Europe, despite the use of a relatively small number of SNPs (Supplementary Fig. 2, Supplemental digital content 1, http://links.lww.com/FPC/B353). The Wolyata and ASW, as expected because of strong Eurasian admixture, clustered between African and European populations . Our study cohort showed overlap with the other three Bantu-speaking groups (Zulu, LWK, and YRI) (Supplementary Fig. 2, Supplemental digital content 1, http://links.lww.com/FPC/B353). Moreover, the analysis suggested four of the individuals to have potential Eurasian ancestry possibly as a consequence of a relatively recent admixture, which is not uncommon in various populations from these regions [45,46] (Supplementary Fig. 2, Supplemental digital content 1, http://links.lww.com/FPC/B353). Another possible source of ancestry in the cohort (particularly for these four individuals) was Khoesan; however, potential Khoesan admixture could not be investigated because of little overlap between the SNPs sequenced in our study and those genotyped in Schlebusch et al. . Genes with important biological functions, such as ADME genes, are highly conserved with a relatively low genetic variation. This was observed to be the case for the more closely related Bantu-speaking populations (Zulu, LWK and YRI), resulting in minimal separation as determined by PCA. Analysis of a larger subset of more diverse genes would thus be expected to identify distinguishing signatures of each population among these populations.
For the SNPs that were shared between our cohort and other datasets, we estimated the FST for each in comparison with populations from the same geographic area [AGVP-ZUL and Central-West Africa (KGP-YRI)], and with other continents (KGP-CEU and KGP-CHB). Supplementary Fig. 3 (Supplemental digital content 1, http://links.lww.com/FPC/B353), illustrates the number of SNPs with FST scores higher than 0.15 (moderate genetic differentiation) and 0.25 (high genetic differentiation) in the various population comparisons. As expected, few SNPs were found to show very high FST scores between the cohort and other African populations (Supplementary Fig. 3, Supplemental digital content 1, http://links.lww.com/FPC/B353). A detailed list of SNPs is provided in Supplementary Table 4 (Supplemental digital content 5, http://links.lww.com/FPC/B357). A similar analysis of weighted FST across 10 kb blocks along the genome identified regions in chromosomes 1 (DPYD), 6 (HLA-C) and 13 (ABCC4) with high differentiation between our population and other African populations (Fig. 3 and Supplementary Table 5, Supplemental digital content 6, http://links.lww.com/FPC/B358).
Gene variants were examined for their potential pharmacological impact using data contained within the PharmGKB database. Allele frequency data representing 34 key variants, located within 16 important pharmacogenes, are summarized in Fig. 4. Assessment of these variants was based primarily on their relevance to drugs commonly prescribed within the broader population of our representative cohort, including notably those used to treat HIV and TB. Among these variants, one of the most frequent variants [rs1208 (NAT2*12)] occurred at a MAF of 0.389, being comparatively common in ethnically related cohorts but relatively rare in others, for example, within the Chinese population (Fig. 4). The variant is associated with a rapid acetylator phenotype, affecting drugs such as isoniazid, sulphamethazine, sulphamethoxazole and trimethoprim commonly used to treat bacterial infections including TB. In contrast, a well-documented ABCB1 variant (rs2032582, level 2A) is relatively rare among African populations (including our cohort), but frequent in individuals of Chinese (MAF = 0.493) and European (MAF = 0.433) descent. Close to two-thirds [22/34 (64.7%)] of the variants were relatively common (MAF ≥ 0.10) in our cohort. Of these, CYP2B6*6 (rs3745274, MAF = 0.264) is implicated widely in dosage-based variant–drug interactions (level 1B), notably during treatment of HIV infection with EFV. Several of the less frequent variants identified in our study have known or predicted pharmacogenomic relevance and some are either incorporated into prescribing guidelines or health systems guidelines [level 1A, namely CYP2C19*2 (rs4244285), CYP2D6*4 (rs3892097) and SLCO1B1*5 (rs4149056)], highlighting their potential clinical impact. For example, a novel, moderately frequent (MAF = 0.075) missense SLCO3A1 variant (15:92694224 T/C) was assigned a deleterious (SIFT score 0)/probably damaging (PolyPhen 2.0 score 0.99) phenotype, and a rarer (MAF = 0.013), novel missense variant (2:234669414 G/T) in UGT1A8 had a PolyPhen 2.0 score of 1. The functional validation of these variants remains to be performed.
Using high-throughput targeted sequencing, we sequenced 65 key ADME-related genes in 40 Bantu ancestry individuals and identified 1662 high-confidence unique variants, of which 129 were novel.
PCA analysis fdemonstrated that our cohort was most closely related to the Bantu-speaker populations represented in the AGVP and KGP (Supplementary Fig. 2, Supplemental digital content 1, http://links.lww.com/FPC/B353). Through FST analysis of 10 kb genomic regions, we investigated the extent of genetic differentiation with other populations, and several putative signatures of positive selection were identified among the pharmacogenes. Clear distinctions were observed between African populations and those of European and Asian descent. A broad range of variants within these clusters are known to significantly influence drug therapy; this genetic heterogeneity may explain the differences in the response among populations to the same drug, supporting the hypothesis that genetic heterogeneity may underlie notable discrepancies where these populations respond differently to the same drugs. Interestingly, evidence of selection for HLA variants was observed among African populations, possibly reflecting benefits to immune function to be gained from such diversity, given the nature of the continent’s diverse disease burden. Conversely, FST analysis identified relatively high differentiation between our population and other African populations for gene regions along chromosomes 1 (DPYD), chromosome 6 (HLA–C) and 13 (ABCC4). These genes play important roles in pyrimidine metabolism, HIV disease progression and organic anion transport, respectively, and variants thereof are implicated in adverse drug reactions associated with antiretroviral therapy and oncotherapy [47–49].
While investigating the evidence for population-based ADME variation, we confirmed the presence of variants of clinical importance, furthermore observing notable allele frequency differences in our cohort compared with other populations. This was observed for multiple variants implicated in the pharmacology of antiretrovirals, antimicrobials, antimalarials, anticoagulants, chemotherapeutic drugs and antiepileptics. This is the case for variants in CYP2B6 that are implicated in the altered metabolism of several drugs, notably EFV. Mirroring findings for several other African populations , and those of European (CEU) descent, CYP2B6*6 (rs3745274) was relatively more common (MAF = 0.264) compared with the Han Chinese (CHB) populations (MAFs = 0.004). A similar MAF (0.20) was noted in the Xhosa and Cape mixed-ancestry populations (CMA) . This variant has increasingly been studied in Southern African populations, given the widespread use of EFV and is associated with susceptibility to EFV-induced adverse events [24,26,51]. In the case of another widely prescribed antiretroviral drug, tenofovir disoproxil fumarate (TDF), acute kidney injury (AKI) poses a notable challenge to HIV management [52–57]. The most common variant implicated in AKI, rs717620, located in the ABCC2 gene [58–60], was rare in our cohort (MAF = 0.013), suggesting that in the broader population, its influence may not be as widespread compared with other populations. In contrast, another commonly implicated ABCC2 variant, rs2273697, showed moderate frequency (MAF = 0.181). Other variants that affect TDF treatment were found to be more frequent in our cohort, such as rs1751034 located in ABCC4 (MAF = 0.333), which is associated with the increased intracellular concentration of TDF . There is thus merit in prioritizing the analysis of such variants in the broader population to establish their clinical relevance to such therapy.
HIV management is increasingly complicated by concomitant diseases, in particular, TB co-infection. Coadministration of antiretrovirals and anti-TB drugs is associated with major complications, including immune reconstitution inflammatory response  and DILI. In South Africa, up to 8.3% of admissions and 2.9% of hospital deaths have been attributed to adverse reactions associated with TDF, rifampicin and co-trimoxazole coadministration [52,54,62]. Identification of valid genetic biomarkers that can guide treatment and prevent such outcomes has therefore become highly warranted. Isoniazid is commonly used as part of the anti-TB treatment regimen; variants of the NAT2 gene are implicated widely in the variable metabolism rates observed for this drug because of rapid, intermediate or slow metabolism (acetylation) phenotypes . Functional variants have been described for NAT2, with studies indicating significant differences in functional classes among ethnically diverse populations . Rapid acetylators are individuals at risk of potential drug resistance  and slow acetylators show reduced drug clearance coupled with increased isoniazid and hydrazine exposure, and as a result are at increased risk of hepatotoxicity, liver injury or hepatitis induced by anti-TB treatment . The incidence of drug-induced hepatotoxicity in Africa ranges from 8 to 21.2% [65,66] in patients receiving anti-TB treatment. Interestingly, slow-acetylator phenotypes are most prevalent within the European and African populations ; variants representing this phenotype were comparatively common in our cohort (Fig. 4). Another relatively common important NAT2 variant in our cohort (NAT2*11 A; rs1799929) is associated with anti-TB treatment outcome. Recent data have shown how a NAT2 genotype-guided regimen that includes this variant, combined with variants represented in ABCB1 such as rs1045642, can reduce isoniazid-induced liver injury (DILI) and early treatment failure in TB-HIV coinfected patients [30,31]. Given the increasingly widespread use of anti-TB drugs among Bantu-speaking patients, NAT2 variants present important candidates for further studies to determine their clinical effect on TB drug outcome, even more so during concomitant antiretroviral use.
Effective malaria treatment represents another clinical intervention on the continent where optimal drug regimens are critical for success, given the rapid pace of infection and the potential for disease resistance following inadequate treatment. Variants in several genes encoding members of the cytochrome P450 (CYP) enzyme family are implicated widely in altered antimalarial metabolism, resulting for example in increased drug serum concentration levels because of poor metabolism [68,69]. This is the case for CYP2C19, where variable activity influences prescription guidelines for a number of drugs [70–72]. On the basis of CYP2C19 activity levels, individuals can be classified as ultrarapid metabolizer, rapid metabolizer, extensive metabolizer, intermediate metabolizer or PM. Kaneko et al.  first identified an association between CYP2C19 variants and the PM of proguanil. PM individuals have two LoF alleles (*2/*2, *2/*3, *3/*3), resulting in markedly reduced or absent CYP2C19 activity. Conversely, UM individuals have two gain-of-function alleles (*1/*17, *17/*17), resulting in increased enzyme activity [70,74]. Of these, rs4244285 (*2) occurs at a MAF of 0.083 in our cohort (Fig. 4). Another important allele implicated in the PM of antimalarials is CYP2C8*2 . Several African studies have noted distinct inter-ethnic differences in the frequency of CYP2C8*2 [75,76]. We noted similar allele frequencies to a Bantu cohort from Botswana (0.194 and 0.175, respectively) . Given the scale of antimalarial use in the region, these prevalence rates merit studies to assess the relevance of considering genotype status before treatment or prophylaxis. Such strategies have become increasingly important as efforts grow to eliminate the disease, where failed treatment because of suboptimal dosing could precipitate the emergence of resistant parasite strains .
Although infectious disease represents a significant proportion of the continent’s disease burden, noncommunicable illnesses are increasingly important. An important enzyme in this respect is CYP2C9, involved in the oxidation of drugs including warfarin, losartan, and phenytoin. CYP2C9 variants, notably CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), CYP2C9*5 (rs28371686), CYP2C9*8 (rs7900194) and CYP2C9*11 (rs28371685), are commonly associated with reduced warfarin clearance, affecting the dosing of this drug [78–81]. CYP2C9*2 and CYP2C9*3 are more frequent in European and American (including African-American) populations; however, these variants are rare in African populations (including our study population). In contrast, CYP2C9*5, CYP2C9*8, and CYP2C9*11 are more frequent in African populations (Fig. 4). Given their prevalence, such genetic biomarkers could be exploited for improving warfarin efficacy in populations of African ancestry [79,81].
Other important variants that warrant consideration for this population on the basis of their PharmGKB classification include rs4244285 (CYP2C19*2, level 1A), which influences clopidogrel (cardiovascular disease) and amitriptyline (depression) efficacy, and rs1045642 (ABCB1, level 2A), which is associated with toxicity and adverse events during the treatment of lymphomas with methotrexate. Identification of the vast majority of important pharmacogene variants in individuals of Bantu ancestry now provides a broad basis for prioritizing the future investigation of these and other variants with a potential influence on drug treatment outcome for noncommunicable diseases in this population.
Despite the important new insights gained in this study through AmpliSeq-based sequencing, we acknowledge the technology’s limitations. One challenge is in completely capturing homopolymer sequences, that fortuitously are rare across the gene regions that we targeted. In addition, although it is expected that most pharmacogenetically important variants exist within coding regions, we would have missed potentially relevant variants in the intronic, upstream and regulatory regions. One example is CYP2C19*17 (rs12248560), an upstream variant associated with an UM phenotype. Similarly, copy number variants were not investigated. Although this study investigated the majority of the key ADME-related variants, future studies would benefit from the inclusion of variants in other similar genes that may be important to drug pharmacology. Notwithstanding this, we confirmed the presence of high pharmacogenetic diversity in an African population and highlighted the need for further research upon which to develop improved strategies for tailored pharmacological intervention.
Populations across SSA are genetically diverse, but relatively little is known in terms of the extent to which inter-ethnic differences impact upon drug-based therapeutic outcome. We mapped the variant composition of 65 pharmacologically important genes in a cohort of Bantu ancestry, resulting in the identification of 1662 variants of high confidence, of which 129 were found to be novel. On the basis of in-silico analysis, several of these are predicted to result in functional changes, providing motivation for follow-up studies to characterize and determine their clinical pharmacological effects. Ultimately, validation of their clinical relevance or otherwise, in conjuction with our knowledge of the prevalence of known variants of clinical relevance, will prove instrumental in guiding new policies for drug selection and dosing in African populations on the basis of pharmacogenetic principles and strategies aimed at improving drug safety and efficacy.
The authors thank Turflos Netshilindi for extracting the DNA, and Inger Jonasson, Susana Haggqvist and Adam Ameur at the National Genomics Infrastructure, SciLifeLab Department of Immunology, Genetics and Pathology, Uppsala University, Sweden, for sequencing our libraries on the Ion S5 sequencer. They also thank the nursing staff for patient recruitment and sample collection. They are grateful and indebted to all participants in this study.
Funding was provided by the Department of Science and Technology (grant # V6YET50) and a CSIR parliamentary (grant # V1YBT96) (N.B.K, D.M., S.T.). N.M. was supported by Perinatal HIV Research Unit funding. A.C. was supported by the AWI-Gen Collaborative Centre funded by the NIH (U54HG006938) as part of the H3Africa Consortium. M.R. is a South African Research Chair in Genomics and Bioinformatics of African populations hosted by the University of the Witwatersrand, funded by the Department of Science and Technology and administered by the National Research Foundation of South Africa (NRF).
There are no conflicts of interest.
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