Population structure and infectious disease risk in southern Africa

An imprecise probability approach-based determination of over-represented southern African plant genera and families used in ethnopharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

The analyses of patterns of over-representation of southern African traditional medicinal plants at the genus and family level provide information about the differences in plant use among southern African countries and disease categories. 'Over-representation' refers to the phenomenon whereby the proportion of plants belonging to a taxonomic group is higher in ethnobotanical pharmacopoeia than in the total flora.

AIM OF THE STUDY

This study aimed to use the Imprecise Dirichlet Model (IDM) to analyse data from ten southern African countries to establish how over-represented medicinal plant families compare with over-represented genera, how over-represented medicinal taxa differ among countries in this region of Africa, and how over-represented taxa differ among six major disease categories.

MATERIALS AND METHODS

Floral data for the total species composition of each country were obtained from online databases. Medicinal plant species lists were generated from published surveys, inventories, and books. IDM calculations were executed using the inverse of the cumulative beta probability density function in Microsoft Excel™. Python programming language source code was used to calculate Pearson correlation (r) values and Jaccard coefficients (J).

RESULTS

Nine of forty-two over-represented medicinal plant families in southern Africa (group 1) do not have over-represented genera. Seven of the forty genera with the highest margins of over-representation belong to under-represented families. Nineteen of the forty-two over-represented families have margins of over-representation smaller than the cumulative margins of their over-represented genera. Groups of countries with similar overall flora (J ≥ 0.333) are Botswana and Namibia (group 2), Malawi, Mozambique, Zambia and Zimbabwe (group 3). The families and genera with the highest margins of over-representation are Loganiaceae and Albizia in group 1, Combretaceae and Vachellia in group 2, Dioscoreaceae and Senna in group 3, and Sapotaceae and Solanum in group 4 (South Africa). The families and genera with the highest margins of over-representation across disease categories are Ebenaceae and Albizia, Canellaceae and Dicoma, Combretaceae and Pterocelastrus, Ebenaceae and Bersama, Francoaceae and Erythrina, and Aristolochiaceae and Strychnos for plants used in the treatment of STIs, febrile and mosquito-vector diseases, microbial infections, pain, skin conditions, and female sexual/reproductive problems, respectively.

CONCLUSIONS

Genus-level calculations are more efficient in generating taxonomic lists that can be used for ethnopharmacological investigations due to the exclusion of under-represented genera. Limiting the size of geographical areas from which medicinal plant lists are sampled and targeting plants used to treat specific types of disease prevents the underestimation of niche over-represented taxa.

Challenges of Diagnosing Mendelian Susceptibility to Mycobacterial Diseases in South Africa

Inborn errors of immunity (IEI) are genetic disorders with extensive clinical presentations. They can range from increased susceptibility to infections to significant immune dysregulation that results in immune impairment. While IEI cases are individually rare, they collectively represent a significant burden of disease, especially in developing countries such as South Africa, where infectious diseases like tuberculosis (TB) are endemic. This is particularly alarming considering that certain high penetrance mutations that cause IEI, such as Mendelian Susceptibility to Mycobacterial Disease (MSMD), put individuals at higher risk for developing TB and other mycobacterial diseases. MSMD patients in South Africa often present with different clinical phenotypes than those from the developed world, therefore complicating the identification of disease-associated variants in this setting with a high burden of infectious diseases. The lack of available data, limited resources, as well as variability in clinical phenotype are the reasons many MSMD cases remain undetected or misdiagnosed. This article highlights the challenges in diagnosing MSMD in South Africa and proposes the use of transcriptomic analysis as a means of potentially identifying dysregulated pathways in affected African populations.

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.

Bantu-speaker migration and admixture in southern Africa

The presence of Early and Middle Stone Age human remains and associated archeological artifacts from various sites scattered across southern Africa, suggests this geographic region to be one of the first abodes of anatomically modern humans. Although the presence of hunter-gatherer cultures in this region dates back to deep times, the peopling of southern Africa has largely been reshaped by three major sets of migrations over the last 2000 years. These migrations have led to a confluence of four distinct ancestries (San hunter-gatherer, East-African pastoralist, Bantu-speaker farmer and Eurasian) in populations from this region. In this review, we have summarized the recent insights into the refinement of timelines and routes of the migration of Bantu-speaking populations to southern Africa and their admixture with resident southern African Khoe-San populations. We highlight two recent studies providing evidence for the emergence of fine-scale population structure within some South-Eastern Bantu-speaker groups. We also accentuate whole genome sequencing studies (current and ancient) that have both enhanced our understanding of the peopling of southern Africa and demonstrated a huge potential for novel variant discovery in populations from this region. Finally, we identify some of the major gaps and inconsistencies in our understanding and emphasize the importance of more systematic studies of southern African populations from diverse ethnolinguistic groups and geographic locations.

Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis

In today's society, the development of information technology is very rapid, and the transmission and sharing of information has become a development trend. The results of data analysis and research are gradually applied to various fields of social development, structured analysis, and research. Data mining of electronic medical records in the medical field is gradually valued by researchers and has become a major work in the medical field. In the course of clinical treatment, electronic medical records are edited, including all personal health and treatment information. This paper mainly introduces the research of diabetes risk data mining method based on electronic medical record analysis and intends to provide some ideas and directions for the research of diabetes risk data mining method. This paper proposes a research strategy of diabetes risk data mining method based on electronic medical record analysis, including data mining and classification rule mining based on electronic medical record analysis, which are used in the research experiment of diabetes risk data mining method based on electronic medical record analysis. The experimental results in this paper show that the average prediction accuracy of the decision tree is 91.21%, and the results of the training set and the test set are similar, indicating that there is no overfitting of the training set.

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.

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.

The critical needs and challenges for genetic architecture studies in Africa

Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date. Additionally, African investigators need to be meaningfully included, as greater inclusivity and enhanced research capacity afford enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages, challenges, and examples of genetic architecture studies of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.

Genetic Resistance to Mycobacterium tuberculosis Infection and Disease

Natural history studies of tuberculosis (TB) have revealed a spectrum of clinical outcomes after exposure to Mycobacterium tuberculosis, the cause of TB. Not all individuals exposed to the bacterium will become diseased and depending on the infection pressure, many will remain infection-free. Intriguingly, complete resistance to infection is observed in some individuals (termed resisters) after intense, continuing M. tuberculosis exposure. After successful infection, the majority of individuals will develop latent TB infection (LTBI). This infection state is currently (and perhaps imperfectly) defined by the presence of a positive tuberculin skin test (TST) and/or interferon gamma release assay (IGRA), but no detectable clinical disease symptoms. The majority of healthy individuals with LTBI are resistant to clinical TB, indicating that infection is remarkably well-contained in these non-progressors. The remaining 5-15% of LTBI positive individuals will progress to active TB. Epidemiological investigations have indicated that the host genetic component contributes to these infection and disease phenotypes, influencing both susceptibility and resistance. Elucidating these genetic correlates is therefore a priority as it may translate to new interventions to prevent, diagnose or treat TB. The most successful approaches in resistance/susceptibility investigation have focused on specific infection and disease phenotypes and the resister phenotype may hold the key to the discovery of actionable genetic variants in TB infection and disease. This review will not only discuss lessons from epidemiological studies, but will also focus on the contribution of epidemiology and functional genetics to human genetic resistance to M. tuberculosis infection and disease.

African genetic diversity provides novel insights into evolutionary history and local adaptations

Genetic variation and susceptibility to disease are shaped by human demographic history and adaptation. We can now study the genomes of extant Africans and uncover traces of population migration, admixture, assimilation and selection by applying sophisticated computational algorithms. There are four major ethnolinguistic divisions among present day Africans: Hunter-gatherer populations in southern and central Africa; Nilo-Saharan speakers from north and northeast Africa; Afro-Asiatic speakers from north and east Africa; and Niger-Congo speakers who are the predominant ethnolinguistic group spread across most of sub-Saharan Africa. The enormous ethnolinguistic diversity in sub-Saharan African populations is largely paralleled by extensive genetic diversity and until a decade ago, little was known about detailed origins and divergence of these groups. Results from large-scale population genetic studies, and more recently whole genome sequence data, are unravelling the critical role of events like migration and admixture and environmental factors including diet, infectious diseases and climatic conditions in shaping current population diversity. It is now possible to start providing quantitative estimates of divergence times, population size and dynamic processes that have affected populations and their genetic risk for disease. Finally, the availability of ancient genomes from Africa provides historical insights of unprecedented depth. In this review, we highlight some key interpretations that have emerged from recent African genome studies.

Different Selected Mechanisms Attenuated the Inhibitory Interaction of KIR2DL1 with C2+ HLA-C in Two Indigenous Human Populations in Southern Africa

The functions of human NK cells in defense against pathogens and placental development during reproduction are modulated by interactions of killer cell Ig-like receptors (KIRs) with HLA-A, -B and -C class I ligands. Both receptors and ligands are highly polymorphic and exhibit extensive differences between human populations. Indigenous to southern Africa are the KhoeSan, the most ancient group of modern human populations, who have highest genomic diversity worldwide. We studied two KhoeSan populations, the Nama pastoralists and the ≠Khomani San hunter-gatherers. Comprehensive next-generation sequence analysis of HLA-A, -B, and -C and all KIR genes identified 248 different KIR and 137 HLA class I, which assort into ∼200 haplotypes for each gene family. All 74 Nama and 78 ≠Khomani San studied have different genotypes. Numerous novel KIR alleles were identified, including three arising by intergenic recombination. On average, KhoeSan individuals have seven to eight pairs of interacting KIR and HLA class I ligands, the highest diversity and divergence of polymorphic NK cell receptors and ligands observed to date. In this context of high genetic diversity, both the Nama and the ≠Khomani San have an unusually conserved, centromeric KIR haplotype that has arisen to high frequency and is different in the two KhoeSan populations. Distinguishing these haplotypes are independent mutations in KIR2DL1, which both prevent KIR2DL1 from functioning as an inhibitory receptor for C2⁺ HLA-C. The relatively high frequency of C2⁺ HLA-C in the Nama and the ≠Khomani San appears to have led to natural selection against strong inhibitory C2-specific KIR.

The role of human host genetics in tuberculosis resistance

INTRODUCTION

Tuberculosis (TB) remains a public health problem: the latest estimate of new incident cases per year is a staggering 10.4 million. Despite this overwhelming number, the majority of the immunocompetent population can control infection with Mycobacterium tuberculosis. The human genome underlies the immune response and contributes to the outcome of TB infection. Areas covered: Investigations of TB resistance in the general population have closely mirrored those of other infectious diseases and initially involved epidemiological observations. Linkage and association studies, including studies of VDR, SLC11A1 and HLA-DRB1 followed. Genome-wide association studies of common variants, not necessarily sufficient for disease, became possible after technological advancements. Other approaches involved the identification of those individuals with rare disease-causing mutations that strongly predispose to TB, epistasis and the role of ethnicity in disease. Despite these efforts, infection outcome, on an individual basis, cannot yet be predicted. Expert commentary: The early identification of future disease progressors is necessary to stem the TB epidemic. Human genetics may contribute to this endeavour and could in future suggest pathways to target for disease prevention. This will however require concerted efforts to establish large, well-phenotyped cohorts from different ethnicities, improved genomic resources and a better understanding of the human genome architecture.