Browsing by Author "Uren, Caitlin"

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    Investigating southern African genetic diversity and its role in TB susceptibility
    (Stellenbosch : Stellenbosch University, 2017-12) Uren, Caitlin; Moller, Marlo; Henn, Brenna; Hoal, Eileen; Stellenbosch University. Faculty of Health Sciences. Dept. of Biomedical Sciences: Molecular Biology and Human Genetics.
    ENGLISH ABSTRACT: Recent genetic studies have established that the KhoeSan populations of southern Africa are the earliest known indigenous inhabitants of the region and distinct from all other African populations. Owing to the region’s 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: there is a known correlation between ancestry and tuberculosis (TB) susceptibility. Research presented in this thesis consolidates information from various population genetic studies that characterized admixture patterns in southern Africa with the aim to improve the understanding of differences in adverse disease phenotypes observed among populations. Further to previous studies, genome-wide polymorphism data from more than 20 southern African populations were analysed to investigate the fine-scale population structure in the area. The analyses revealed fine-scale population structure in and around the Kalahari Desert, which does not always correspond to linguistic or subsistence categories, but rather reflects the role of ecogeographic boundaries. In addition, we showed that the Khoe adopted their pastoralism through a process of largely cultural diffusion rather than demic diffusion as previously thought. The proportion and origin of KhoeSan genetic ancestry in southern African populations is of particular relevance to disease, because the KhoeSan exhibit greater variation in genetic diversity than other African populations, including unusual variation in genes with demonstrable immune function. Utilizing data from several TB genome-wide association studies (GWAS), a bioinformatics pipeline was employed to detect regulatory polymorphisms in linkage disequilibrium with variants previously implicated in TB susceptibility. A total of 133 predicted regulatory variants were found. Association analyses were performed in TB cases and healthy controls and yielded six intronic functionally relevant variants. The post-GWAS approach, which included ancestry as a confounder, demonstrated the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with TB susceptibility. In addition to classical association studies, selection scans have the ability to identify genomic regions associated with a phenotype. Signals of natural selection in southern African populations was studied using high-coverage exome sequence data. Selection signals were identified in genes associated with immune response to foreign pathogens introduced from the 12th century onwards. In addition, signals of selection were identified in pathways associated with focal adhesion and ECM receptor interaction. It is clear that there are distinct immune-related signals of positive selection present in southern African populations. This research not only provided insight into the genetic basis and biology of human TB susceptibility, but also harnessed the unique ancestry present in southern African populations. The addition of population genetics information was shown to greatly shape and improve our investigations of TB susceptibility and may also apply to other phenotypes unique to southern Africa. Since the era of personalised medicine is imminent, more investigations of understudied southern African populations most severely affected by TB are required.
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    A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility
    (Public Library of Science, 2017) Uren, Caitlin; Henn, Brenna M.; Franke, Andre; Wittig, Michael; Van Helden, Paul D.; Hoal, Eileen G.; Moller, Marlo
    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.
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    Putting RFMix and ADMIXTURE to the test in a complex admixed population
    (BMC (part of Springer Nature), 2020) Uren, Caitlin; Hoal, Eileen G.; Moller, Marlo
    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 worldwide complex admixture scenarios particularly when including these estimates in association studies.