The impact of alcohol and illicit substance use on the pharmacokinetics of first-line TB drugs.

BACKGROUND: In South Africa, an estimated 11% of the population have high alcohol use, a major risk factor for TB. Alcohol and other substance use are also associated with poor treatment response, with a potential mechanism being altered TB drug pharmacokinetics. OBJECTIVES: To investigate the impact of alcohol and illicit substance use on the pharmacokinetics of first-line TB drugs in participants with pulmonary TB. METHODS: We prospectively enrolled participants ≥15 years old, without HIV, and initiating drug-susceptible TB treatment in Worcester, South Africa. Alcohol use was measured via self-report and blood biomarkers. Other illicit substances were captured through a urine drug test. Plasma samples were drawn 1 month into treatment pre-dose, and 1.5, 3, 5 and 8 h post-dose. Non-linear mixed-effects modelling was used to describe the pharmacokinetics of rifampicin, isoniazid, pyrazinamide and ethambutol. Alcohol and drug use were tested as covariates. RESULTS: The study included 104 participants, of whom 70% were male, with a median age of 37 years (IQR 27-48). Alcohol use was high, with 42% and 28% of participants having moderate and high alcohol use, respectively. Rifampicin and isoniazid had slightly lower pharmacokinetics compared with previous reports, whereas pyrazinamide and ethambutol were consistent. No significant alcohol use effect was detected, other than 13% higher ethambutol clearance in participants with high alcohol use. Methaqualone use reduced rifampicin bioavailability by 19%. CONCLUSION: No clinically relevant effect of alcohol use was observed on the pharmacokinetics of first-line TB drugs, suggesting that poor treatment outcome is unlikely due to pharmacokinetic alterations. That methaqualone reduced rifampicin means dose adjustment may be beneficial.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies.

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Strong Effect of Demographic Changes on Tuberculosis Susceptibility in South Africa.

South Africa is among the world's top eight TB burden countries, and despite a focus on HIV-TB co-infection, most of the population living with TB are not HIV co-infected. The disease is endemic across the country with 80-90% exposure by adulthood. We investigated epidemiological risk factors for tuberculosis (TB) in the Northern Cape Province, South Africa: an understudied TB endemic region with extreme TB incidence (645/100,000) and the lowest provincial population density. We leveraged the population's high TB incidence and community transmission to design a case-control study with population-based controls, reflecting similar mechanisms of exposure between the groups. We recruited 1,126 participants with suspected TB from 12 community health clinics, and generated a cohort of 878 individuals (cases =374, controls =504) after implementing our enrollment criteria. All participants were GeneXpert Ultra tested for active TB by a local clinic. We assessed important risk factors for active TB using logistic regression and random forest modeling. Additionally, a subset of individuals were genotyped to determine genome-wide ancestry components. Male gender had the strongest effect on TB risk (OR: 2.87 [95% CI: 2.1-3.8]); smoking and alcohol consumption did not significantly increase TB risk. We identified two interactions: age by socioeconomic status (SES) and birthplace by residence locality on TB risk (OR = 3.05, p = 0.016) - where rural birthplace but town residence was the highest risk category. Finally, participants had a majority Khoe-San ancestry, typically greater than 50%. Epidemiological risk factors for this cohort differ from other global populations. The significant interaction effects reflect rapid changes in SES and mobility over recent generations and strongly impact TB risk in the Northern Cape of South Africa. Our models show that such risk factors combined explain 16% of the variance (r2) in case/control status.

Pharmacogenetics as part of recommended precision medicine for tuberculosis treatment in African populations: Could it be a reality?

Globally, tuberculosis (TB) is the second most lethal infectious disease. However, in sub-Saharan Africa, TB has the largest disease burden, with drug-resistant TB increasingly becoming a concern. The social and economic impact of TB should not be overlooked, especially in areas where healthcare systems are overburdened, and resources need to be allocated judiciously. The aim of pharmacogenetics (PGx) is to improve therapeutic response and to minimize adverse drug reactions by selecting the most optimal drug and dosage for the individual patient. Implementation of PGx into routine clinical care has been slow, especially in resource-limited settings, because of perceived high costs relative to uncertain benefit. Given the impact of TB on the disease and disability burden in these regions, a better understanding and optimization of TB treatment in understudied African populations is vital. The first weeks of treatment are the most crucial for treatment success, and a point-of-care pre-emptive PGx test could start patients on the most bactericidal and least toxic drug combination. This may potentially reduce the number of patients returning to clinical care and streamline the use of limited resources across the healthcare system. This review explores the status of TB PGx in Africa, the utility of existing TB PGx testing panels, and the economic feasibility in developing a clinically valuable, cost-effective, pre-emptive PGx test to guide optimized, new dosing regimens specifically for African population groups. TB is a disease of poverty, but investment in PGx research in African populations could ensure improved treatments and long-term cost savings.

Epidemiological correlates of overweight and obesity in the Northern Cape Province, South Africa.

Background: In the past several decades, obesity has become a major public health issue worldwide, associated with increased rates of chronic disease and death. Like many developing nations, South Africa is experiencing rapid increases in BMI, and as a result, evidence-based preventive strategies are needed to reduce the increasing burden of overweight and obesity. This study aimed to determine the prevalence and predictors of overweight and obesity among a multi-ethnic cohort from the rural Northern Cape of South Africa. Methods: These data were collected as part of a tuberculosis (TB) case-control study, with 395 healthy control participants included in the final analysis. Overweight and obesity were defined according to WHO classification. Multivariate linear models of BMI were generated using sex, age, education level, smoking, alcohol consumption, and diabetes as predictor variables. We also used multivariable logistic regression analysis to assess the relationship of these factors with overweight and obesity. Results: The average BMI in our study cohort was 25.2. The prevalence of overweight was 18.0% and the prevalence of obesity was 25.0%. We find that female sex, being older, having more years of formal education, having diabetes, and being in a rural area are all positively associated with BMI in our dataset. Women (OR = 5.6, 95% CI [3.3-9.8]), rural individuals (OR = 3.3, 95% CI [1.9-6.0]), older individuals (OR = 1.02, 95% CI [1-1.04]), and those with more years of education (OR = 1.2, 95% CI [1.09-1.32]) were all more likely to be overweight or obese. Alternatively, being a smoker is negatively associated with BMI and decreases one's odds of being overweight or obese (OR = 0.28, 95% CI [0.16-0.46]). Conclusions: We observed a high prevalence of overweight and obesity in this study. The odds of being overweight and obese were higher in women, those living in rural areas, and those with more education, and increases with age. Community-based interventions to control obesity in this region should pay special attention to these groups.

The immunogenetics of tuberculosis (TB) susceptibility.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains the leading cause of death due to a single bacterial agent, with approximately 10.6 million people developing active disease and 1.6 million deaths reported globally in 2021. After exposure, some, but not all individuals, will become infected with the bacillus. However, only a small fraction (approximately 5 to 15%) of these individuals will progress to clinical disease, while in the remainder, infection is seemingly contained, and no signs of clinical disease are shown. Numerous observations have advocated for the role of host genetics in the display of these inter-individual variabilities in infection and disease phenotypes. In this review, we will provide an overview of the approaches, findings and limitations of the very first studies investigating TB genetic susceptibility to more recent studies. Lastly, we highlight several approaches, namely, linkage analyses and association studies, proposed to discover genetic markers associated with TB susceptibility. This review also explored the concept of polygenic risk scores (PRS) for prediction of tuberculosis susceptibility. The identification of host genetic factors influencing TB susceptibility/resistance is paramount to not only better understand the physiopathology of the disease but also explore more effective approaches for the development of both optimal preventive measures (i.e. better vaccines) and treatments of TB disease.

GWAS in the southern African context.

Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan et al. (2018) first described local ancestry adjusted allelic (LAAA) analysis as a robust method for discovering association signals, while producing minimal false positive hits. Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in genome-wide association studies (GWAS). We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false positive hits which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.

The recombination landscape of the Khoe-San likely represents the upper limits of recombination divergence in humans.

BACKGROUND: Recombination maps are important resources for epidemiological and evolutionary analyses; however, there are currently no recombination maps representing any African population outside of those with West African ancestry. We infer the demographic history for the Nama, an indigenous Khoe-San population of southern Africa, and derive a novel, population-specific recombination map from the whole genome sequencing of 54 Nama individuals. We hypothesise that there are no publicly available recombination maps representative of the Nama, considering the deep population divergence and subsequent isolation of the Khoe-San from other African groups. RESULTS: We show that the recombination landscape of the Nama does not cluster with any continental groups with publicly available representative recombination maps. Finally, we use selection scans as an example of how fine-scale differences between the Nama recombination map and the combined Phase II HapMap recombination map can impact the outcome of selection scans. CONCLUSIONS: Fine-scale differences in recombination can meaningfully alter the results of a selection scan. The recombination map we infer likely represents an upper bound on the extent of divergence we expect to see for a recombination map in humans and would be of interest to any researcher that wants to test the sensitivity of population genetic or GWAS analysis to recombination map input.

Local Ancestry Adjusted Allelic Association Analysis Robustly Captures Tuberculosis Susceptibility Loci.

Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis, is a complex disease. The risk of developing active TB is in part determined by host genetic factors. Most genetic studies investigating TB susceptibility fail to replicate association signals particularly across diverse populations. South African populations arose because of multi-wave genetic admixture from the indigenous KhoeSan, Bantu-speaking Africans, Europeans, Southeast Asian-and East Asian populations. This has led to complex genetic admixture with heterogenous patterns of linkage disequilibrium and associated traits. As a result, precise estimation of both global and local ancestry is required to prevent both false positive and false-negative associations. Here, 820 individuals from South Africa were genotyped on the SNP-dense Illumina Multi-Ethnic Genotyping Array (∼1.7M SNPs) followed by local and global ancestry inference using RFMix. Local ancestry adjusted allelic association (LAAA) models were utilized owing to the extensive genetic heterogeneity present in this population. Hence, an interaction term, comprising the identification of the minor allele that corresponds to the ancestry present at the specific locus under investigation, was included as a covariate. One SNP (rs28647531) located on chromosome 4q22 was significantly associated with TB susceptibility and displayed a SNP minor allelic effect (G allele, frequency = 0.204) whilst correcting for local ancestry for Bantu-speaking African ancestry (p-value = 5.518 × 10-7; OR = 3.065; SE = 0.224). Although no other variants passed the significant threshold, clear differences were observed between the lead variants identified for each ancestry. Furthermore, the LAAA model robustly captured the source of association signals in multi-way admixed individuals from South Africa and allowed the identification of ancestry-specific disease risk alleles associated with TB susceptibility that have previously been missed.

Local ancestry inference in heterogeneous populations-Are recent recombination events more relevant?

To date, numerous software tools have been developed to infer recombination maps. Many of these software tools infer the recombination rate from linkage disequilibrium, and therefore they infer recombination many generations into the past. Other recently developed methods rely on the inference of recent recombination events to determine the recombination rate, such as identity by descent- and local ancestry inference (LAI)-based tools. Methods that mainly use recent recombination events to infer the recombination rate might be more relevant for certain analyses like LAI. We therefore describe a protocol for creating high-resolution, population-specific recombination maps using methods that mainly use recent recombination events and a method that uses recent and distant recombination events for recombination rate inference. Subsequently, we compared the effect of using maps inferred by these two paradigms on LAI accuracy.

Inferring recombination patterns in African populations.

Although several high-resolution recombination maps exist for European-descent populations, the recombination landscape of African populations remains relatively understudied. Given that there is high genetic divergence among groups in Africa, it is possible that recombination hotspots also diverge significantly. Both limitations and opportunities exist for developing recombination maps for these populations. In this review, we discuss various recombination inference methods, and the strengths and weaknesses of these methods in analyzing recombination in African-descent populations. Furthermore, we provide a decision tree and recommendations for which inference method to use in various research contexts. Establishing an appropriate methodology for recombination rate inference in a particular study will improve the accuracy of various downstream analyses including but not limited to local ancestry inference, haplotype phasing, fine-mapping of GWAS loci and genome assemblies.

Mycobacterium tuberculosis complex and human coadaptation: a two-way street complicating host susceptibility to TB.

For centuries, the Mycobacterium tuberculosis complex (MTBC) has infected numerous populations, both human and non-human, causing symptomatic tuberculosis (TB) in some hosts. Research investigating the MTBC and how it has evolved with its host over time is sparse and has not resulted in many significant findings. There are even fewer studies investigating adaptation of the human host susceptibility to TB and these have largely focused on genome-wide association and candidate gene association studies. However, results emanating from these association studies are rarely replicated and appear to be population specific. It is, therefore, necessary to relook at the approach taken to investigate the relationship between the MTBC and the human host. Understanding that the evolution of the pathogen is coupled to the evolution of the host might be the missing link needed to effectively investigate their relationship. We hypothesize that this knowledge will bolster future efforts in combating the disease.

Hyperphosphatasia with mental retardation syndrome type 4 in three unrelated South African patients.

Hyperphosphatasia with mental retardation syndrome (HPMRS) is a rare autosomal recessive disorder caused by pathogenic variants in genes involved in glycosylphosphatidylinositol metabolism that result in a similar phenotype. We describe the first three patients with HPMRS from sub-Saharan Africa. Detection was assisted by Face2Gene phenotype matching and confirmed by the presence of elevated serum alkaline phosphatase. All three patients had severe intellectual disability, absent speech, hypotonia and palatal abnormality (cleft palate in two, very high-arched palate in one), no or minimal brachytelephalangy, and high serum alkaline phosphatase levels. Additional findings included seizures in two, and brain imaging abnormalities in two. In all three patients HPMRS was a top-20 gestalt match using Face2Gene. The overall phenotype is consistent with descriptions in the literature of HPMRS type 4, although not specific to it. Whole exome sequencing in the index patient and his mother detected a candidate variant in a homozygous state in the index patient (PGAP3:c.557G>C, p.Arg186Thr) and heterozygous in the mother. Further variant interpretation indicated pathogenicity. Sanger sequencing of another two patients identified the same homozygous, pathogenic variant, confirming a diagnosis of HPMRS type 4. The shared homozygous variant in apparently unrelated families, and in the absence of consanguinity, suggests the possibility of genetic drift due to a population bottleneck effect, and further research is recommended.

Prospective avenues for human population genomics and disease mapping in southern Africa.

Population substructure within human populations is globally evident and a well-known confounding factor in many genetic studies. In contrast, admixture mapping exploits population stratification to detect genotype-phenotype correlations in admixed populations. Southern Africa has untapped potential for disease mapping of ancestry-specific disease risk alleles due to the distinct genetic diversity in its populations compared to other populations worldwide. This diversity contributes to a number of phenotypes, including ancestry-specific disease risk and response to pathogens. Although the 1000 Genomes Project significantly improved our understanding of genetic variation globally, southern African populations are still severely underrepresented in biomedical and human genetic studies due to insufficient large-scale publicly available data. In addition to a lack of genetic data in public repositories, existing software, algorithms and resources used for imputation and phasing of genotypic data (amongst others) are largely ineffective for populations with a complex genetic architecture such as that seen in southern Africa. This review article, therefore, aims to summarise the current limitations of conducting genetic studies on populations with a complex genetic architecture to identify potential areas for further research and development.

Putting RFMix and ADMIXTURE to the test in a complex admixed population.

BACKGROUND: Global and local ancestry inference in admixed human populations can be performed using computational tools implementing distinct algorithms. The development and resulting accuracy of these tools has been tested largely on populations with relatively straightforward admixture histories but little is known about how well they perform in more complex admixture scenarios. RESULTS: Using simulations, we show that RFMix outperforms ADMIXTURE in determining global ancestry proportions even in a complex 5-way admixed population, in addition to assigning local ancestry with an accuracy of 89%. The ability of RFMix to determine global and local ancestry to a high degree of accuracy, particularly in admixed populations provides the opportunity for more accurate association analyses. CONCLUSION: This study highlights the utility of the extension of computational tools to become more compatible to genetically structured populations, as well as the need to expand the sampling of diverse world-wide populations. This is particularly noteworthy as modern-day societies are becoming increasingly genetically complex and some genetic tools and commonly used ancestral populations are less appropriate. Based on these caveats and the results presented here, we suggest that RFMix be used for both global and local ancestry estimation in world-wide complex admixture scenarios particularly when including these estimates in association studies.

Primary immunodeficiency diseases in a tuberculosis endemic region: challenges and opportunities.

While individual primary immunodeficiency diseases (PIDs) are rare, collectively they represent a significant burden of disease. Recent estimates show that about one million people in Africa suffer from a PID. However, data from African PID registries reflect only a small percentage of the estimated prevalence. This disparity is partly due to the lack of PID awareness and the masking of PIDs by the endemic pathogens. Over three million tuberculosis (TB) cases were reported in Africa in 2016, with many of these from southern Africa. Despite concerted efforts to address this high burden of disease, the underlying genetic correlates of susceptibility to TB remain poorly understood. High penetrance mutations in immune system genes can cause PIDs that selectively predispose individuals to TB and other mycobacterial diseases. Additionally, the identification of individuals at a heightened risk of developing TB or of presenting with severe or disseminated TB due to their genetic ancestry is crucial to promote a positive treatment outcome. The screening for and identification of PID mutations in TB-endemic regions by next-generation sequencing (NGS) represents a promising approach to improve the understanding of what constitutes an effective immune response to TB, as well as the range of associated PIDs and phenotypes.

A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility.

Utilizing data from published tuberculosis (TB) genome-wide association studies (GWAS), we use a bioinformatics pipeline to detect all polymorphisms in linkage disequilibrium (LD) with variants previously implicated in TB disease susceptibility. The probability that these variants had a predicted regulatory function was estimated using RegulomeDB and Ensembl's Variant Effect Predictor. Subsequent genotyping of these 133 predicted regulatory polymorphisms was performed in 400 admixed South African TB cases and 366 healthy controls in a population-based case-control association study to fine-map the causal variant. We detected associations between tuberculosis susceptibility and six intronic polymorphisms located in MARCO, IFNGR2, ASHAS2, ACACA, NISCH and TLR10. Our post-GWAS approach demonstrates the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with this infectious disease.

Population structure and infectious disease risk in southern Africa.

The KhoeSan populations are the earliest known indigenous inhabitants of southern Africa. The relatively recent expansion of Bantu-speaking agropastoralists, as well as European colonial settlement along the south-west coast, dramatically changed patterns of genetic diversity in a region which had been largely isolated for thousands of years. Owing to this unique history, population structure in southern Africa reflects both the underlying KhoeSan genetic diversity as well as differential recent admixture. This population structure has a wide range of biomedical and sociocultural implications; such as changes in disease risk profiles. Here, we consolidate information from various population genetic studies that characterize admixture patterns in southern Africa with an aim to better understand differences in adverse disease phenotypes observed among groups. Our review confirms that ancestry has a direct impact on an individual's immune response to infectious diseases. In addition, we emphasize the importance of collaborative research, especially for populations in southern Africa that have a high incidence of potentially fatal infectious diseases such as HIV and tuberculosis.

Fine-Scale Human Population Structure in Southern Africa Reflects Ecogeographic Boundaries.

Recent genetic studies have established that the KhoeSan populations of southern Africa are distinct from all other African populations and have remained largely isolated during human prehistory until ∼2000 years ago. Dozens of different KhoeSan groups exist, belonging to three different language families, but very little is known about their population history. We examine new genome-wide polymorphism data and whole mitochondrial genomes for >100 South Africans from the ≠Khomani San and Nama populations of the Northern Cape, analyzed in conjunction with 19 additional southern African populations. Our analyses reveal fine-scale population structure in and around the Kalahari Desert. Surprisingly, this structure does not always correspond to linguistic or subsistence categories as previously suggested, but rather reflects the role of geographic barriers and the ecology of the greater Kalahari Basin. Regardless of subsistence strategy, the indigenous Khoe-speaking Nama pastoralists and the N|u-speaking ≠Khomani (formerly hunter-gatherers) share ancestry with other Khoe-speaking forager populations that form a rim around the Kalahari Desert. We reconstruct earlier migration patterns and estimate that the southern Kalahari populations were among the last to experience gene flow from Bantu speakers, ∼14 generations ago. We conclude that local adoption of pastoralism, at least by the Nama, appears to have been primarily a cultural process with limited genetic impact from eastern Africa.