Browsing by Author "Step, Kathryn"

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    Analysis of a neurochip array dataset to study Parkinson’s disease in a South African study collection
    (Stellenbosch : Stellenbosch University,, 2023-02) Step, Kathryn; Bardien, Soraya; Vorster, Alvera; Müller-Nedebock, Amica; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences. Molecular Biology and Human Genetics.
    ENGLISH ABSTRACT: Parkinson’s disease (PD) is an incurable, and complex neurodegenerative disease. Both genetic and environmental factors likely contribute to disease onset. Notably, while several pathogenic variants and susceptibility factors have been described in populations of Asian and European ancestry, such variants have seldom been identified in individuals from sub-Saharan Africa (SSA). This could be due to the limited number of studies investigating the genetic etiology of PD in SSA. To address this knowledge gap, the present study undertook the largest, to date, PD-focused genomewide association study (GWAS), and pathogenic variant screening study in SSA to identify possible susceptibility variants and pathogenic variants in South African PD cases. For this, we used raw genotyping data generated from a large collaborative project known as COmprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson’s Disease (Courage-PD), whose goal was to identify PD-associated variants. The NeuroChip array, used to genotype the study participants, contained a total of 306,670 tagging variants and 179,467 custom content variants, including 349 associated with PD. The South African dataset genotyped on the array comprised 452 cases and 280 controls. We hypothesised that these individuals would harbour susceptibility and pathogenic variants. To test this hypothesis, the NeuroChip genotyping data was analysed using various bioinformatic approaches. The quality control (QC) and association analysis were completed using PLINK, and the results were visualized using R software. After excluding 15 individuals during the QC stage, population stratification analysis identified two ‘broad’ ancestral groups, designated as ‘European’ (n=497) and ‘non-European’ (n=220). For the GWAS, no variants reached the genome-wide significance threshold of 5x10-8 , however, variants were found that met the ‘suggestive significance’ criteria (1x10-5 ). A total of 17 variants of interest were identified in the European ancestral group (in the KHDRBS2, FGF14, and PDXK genes) and 2 variants of interest were identified in the non-European ancestral group (in the SYNPR and PDE10A genes). These variants highlighted possible new PD genes that are plausible candidates, but that will need to be confirmed in future, much larger GWAS. Thereafter, a Polygenic Risk Score (PRS) analysis was performed, using PRSice software, on the European ancestral group where the most predictive PRS explained 4.5% of the phenotypic variation (the phenotype being PD). Furthermore, use of the NeuroChip data as a method of pathogenic variant screening, revealed that all 12 variants detected by our group previously were also detected by the array. Moreover, an additional 16 variants in 14 individuals were prioritized as being potentially pathogenic, and warrant further study. Finally, screening of p.G2019S in the LRRK2 gene, arguably the most prevalent PD pathogenic variant, using high-resolution melt analysis, revealed a relatively low frequency of 1.2% (n= 8/689) in our entire PD study collection. Notably, this variant has not been identified in any PD individuals of African ancestry, to date. Collectively, this study highlights the importance of screening and studying underrepresented populations to uncover additional genetic-related risks for PD development. However, future largescale whole-genome sequencing and association studies, including all South African ancestral groups, will likely be needed to identify the remaining, potentially novel genetic factors contributing to PD in our local populations.