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Title: Fine-mapping, gene prioritisation and colocalisation with gene expression for CVD associated genetic markers
Author: Giambartolomei, C. T. M.
ISNI:       0000 0004 8502 2507
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2015
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Genetic association studies, in particular the Genome-Wide Association Study (GWAS) design, have revealed a large number of reliable links between a genomic region and complex traits and cardiovascular disease outcomes, most of them mediated by non-coding regulatory variants. However, gaps in knowledge remain regarding the genes and tissues that mediate these associations. The integration of datasets, in particular Expression Quantitative Trait Locus (eQTL) with GWAS results, can contribute towards understanding the molecular basis of these associa- tions. To enable this process, we developed a novel statistical methodology integrating single variant summary statistics and testing whether two association signals are consistent with a shared causal variant. After a summary of our current understanding of GWAS and eQTL sig- nals (chapter 1), the colocalisation methodology is introduced (chapter 2) and the datasets used in this thesis are described (chapter 3). The value of the approach is demonstrated by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis including >100,000 individuals of European ancestry (chapter 4). A further application involving the systematic review of overlaps be- tween 73 biomarker associations collected on over 20,000 individuals in the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium, and cis eQTLs on multiple tissues and cell states is described (chapter 5). Im- plications of the results of this analysis for the design of drugs to target disease pathways are illustrated for two cases (i.e. ABCA1 and SORT1). Additionally, important known genes involved in cardiovascular disease pathways are analysed in light of our colocalisation findings (e.g. APOE, HMGCR). Finally, the limitations of the method, possible improvements, and future applications are discussed (chapter 6). As more data becomes available, this approach can be extended to RNA-Seq data, at the gene and isoform levels, to increase our understanding of how regulatory vari- ants influence complex diseases.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available