Browsing by Author "Chimusa, Emile R."

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    Concordance of genetic variation that increases risk for Tourette Syndrome and that influences its underlying neurocircuitry
    (Springer Nature, 2019) Mufford, Mary; Cheung, Josh; Jahanshad, Neda; Van Der Merwe, Celia; Ding, Linda; Groenewold, Nynke; Koen, Nastassja; Chimusa, Emile R.; Dalvie, Shareefa; Ramesar, Raj; Knowles, James A.; Lochner, Christine; Hibar, Derrek P.; Paschou, Peristera; Van Den Heuvel, Odile A.; Medland, Sarah E.; Scharf, Jeremiah M.; Mathews, Carol A.; Thompson, Paul M.; Stein, Dan J.; Psychiatric Genomics Consortium - Tourette Syndrome working group
    ENGLISH ABSTRACT: There have been considerable recent advances in understanding the genetic architecture of Tourette Syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variation that increases risk for TS—and its main symptom dimensions—influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS. We obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4644 cases and 8695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP effect concordance analysis (SECA) was used to examine genetic pleiotropy—the same SNP affecting two traits—and concordance—the agreement in single nucelotide polymorphism (SNP) effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage disequilibrium score regression (LDSR) was used as validation of the SECA method. SECA revealed significant pleiotropy between TS and putamen (p = 2 × 10−4) and caudate (p = 4 × 10−4) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p = 1 × 10−3). LDSR lent additional support for the association between TS and thalamus volume (p = 5.85 × 10−2). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamus volume (p = 1 × 10−3), as well as concordance between symmetry behaviour and greater putamen volume (p = 7 × 10−4). Conditional FDR analysis further revealed novel variants significantly associated with TS (p < 8 × 10−7) when conditioning on intracranial (rs2708146, q = 0.046; and rs72853320, q = 0.035) and hippocampal (rs1922786, q = 0.001) volumes, respectively. These data indicate concordance for genetic variation involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.
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    Determining ancestry proportions in complex admixture scenarios in South Africa using a novel proxy ancestry selection method
    (PLoS, 2013-09) Chimusa, Emile R.; Daya, Michelle; Möller, Marlo; Ramesar, Raj; Henn, Brenna M.; Van Helden, Paul D.; Mulder, Nicola J.; Hoal, Eileen G.
    Admixed populations can make an important contribution to the discovery of disease susceptibility genes if the parental populations exhibit substantial variation in susceptibility. Admixture mapping has been used successfully, but is not designed to cope with populations that have more than two or three ancestral populations. The inference of admixture proportions and local ancestry and the imputation of missing genotypes in admixed populations are crucial in both understanding variation in disease and identifying novel disease loci. These inferences make use of reference populations, and accuracy depends on the choice of ancestral populations. Using an insufficient or inaccurate ancestral panel can result in erroneously inferred ancestry and affect the detection power of GWAS and meta-analysis when using imputation. Current algorithms are inadequate for multi-way admixed populations. To address these challenges we developed PROXYANC, an approach to select the best proxy ancestral populations. From the simulation of a multi-way admixed population we demonstrate the capability and accuracy of PROXYANC and illustrate the importance of the choice of ancestry in both estimating admixture proportions and imputing missing genotypes. We applied this approach to a complex, uniquely admixed South African population. Using genome-wide SNP data from over 764 individuals, we accurately estimate the genetic contributions from the best ancestral populations: isiXhosa (33%±0:226), {Khomani SAN (31%±0:195), European (16%±0:118), Indian (13%±0:094), and Chinese (7%±0:0488). We also demonstrate that the ancestral allele frequency differences correlate with increased linkage disequilibrium in the South African population, which originates from admixture events rather than population bottlenecks.
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    Host and microbiome genome-wide association studies : current state and challenges
    (Frontiers Media, 2019) Awany, Denis; Allali, Imane; Dalvie, Shareefa; Hemmings, Sian M. J.; Mwaikono, Kilaza S.; Thomford, Nicholas E.; Gomez, Andres; Mulder, Nicola; Chimusa, Emile R.
    The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host–microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.
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    A panel of ancestry informative markers for the complex five-way admixed South African Coloured population
    (PLoS, 2013-12) Daya, Michelle; Van der Merwe, Lize; Ushma Galal; Möller, Marlo; Salie, Muneeb; Chimusa, Emile R.; Galanter, Joshua M.; Van Helden, Paul D.; Henn, Brenna M.; Gignoux, Chris R.; Hoal, Eileen
    Admixture is a well known confounder in genetic association studies. If genome-wide data is not available, as would be the case for candidate gene studies, ancestry informative markers (AIMs) are required in order to adjust for admixture. The predominant population group in the Western Cape, South Africa, is the admixed group known as the South African Coloured (SAC). A small set of AIMs that is optimized to distinguish between the five source populations of this population (African San, African non-San, European, South Asian, and East Asian) will enable researchers to cost-effectively reduce falsepositive findings resulting from ignoring admixture in genetic association studies of the population. Using genome-wide data to find SNPs with large allele frequency differences between the source populations of the SAC, as quantified by Rosenberg et. al’s In-statistic, we developed a panel of AIMs by experimenting with various selection strategies. Subsets of different sizes were evaluated by measuring the correlation between ancestry proportions estimated by each AIM subset with ancestry proportions estimated using genome-wide data. We show that a panel of 96 AIMs can be used to assess ancestry proportions and to adjust for the confounding effect of the complex five-way admixture that occurred in the South African Coloured population.
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    Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans
    (Nature Research (part of Springer Nature), 2017) Choudhury, Ananyo; Ramsay, Michele; Hazelhurst, Scott; Aron, Shaun; Bardien, Soraya; Botha, Gerrit; Chimusa, Emile R.; Christoffels, Alan; Gamieldien, Junaid; Sefid-Dashti, Mahjoubeh J.; Joubert, Fourie; Meintjes, Ayton; Mulder, Nicola; Ramesar, Raj; Rees, Jasper; Scholtz, Kathrine; Sengupta, Dhriti; Soodyall, Himla; Venter, Philip; Warnich, Louise; Pepper, Michael S.
    ENGLISH ABSTRACT: The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p < 10−6) differentiation, and FST analysis identifies regions with high divergence. The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease.