The promise of genome‐wide SNP genotyping: from
population genetics to disease gene identification.
Doctoral thesis, UCL (University College London).
Advances in single nucleotide polymorphism (SNP) genotyping technologies have revolutionised our ability to scrutinise the human genome. My PhD research focuses on using these new technologies to catalogue genetic variability in a collection of diverse populations from around the globe, and to determine the role of genetic variants in neurological diseases. First, I present and discuss the analysis of genome‐wide SNP data in individuals from ethnically and geographically diverse human populations uncovering the diversity of genotype, haplotype and copy number variation in these populations. Second, I will describe an autozygosity mapping approach in three Brazilian dystoniaparkinsonism families which lead to the identification of a novel disease‐segregating mutation in the gene PRKRA. Third, I will report on a large genome‐wide association study in Parkinson’s disease, uncovering genetic variability at the SNCA and MAPT loci that are strongly associated with risk for developing disease. Forth, I provide compelling evidence that genetic variants at the SNCA locus are also significantly associated with risk for developing multiple system atrophy. This finding represents the first reproducible risk gene for this devastating disorder, and causally links this condition to the more common neurodegenerative disorder Parkinson’s disease. Finally, I present the results of a comprehensive mutational screening study investigating the frequency and spectrum of sequence and copy number mutations in the parkinsonism genes PRKN and PINK in individuals with early-onset Parkinson’s disease, in multiple system atrophy patients and in normal controls. In summary, the data presented in this thesis emphasise the critical role that genetic variability plays in the pathogenesis of neurological disorders.
|Title:||The promise of genome‐wide SNP genotyping: from population genetics to disease gene identification|
|Open access status:||An open access version is available from UCL Discovery|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Molecular Neuroscience|
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