Recent advances in genotyping technology coupled with an improved understanding of the architecture of linkage disequilibrium across the human genome have resulted in genome-wide association studies (GWAS) becoming a useful and widely applied tool for discovering common genetic variants associated with both quantitative traits and disease risk. After each GWAS was completed, it left behind a set of genotypes and phenotypes, often including anthropometric measures used as covariates. Genetic associations with anthropometric measures are not well characterized, perhaps due to lack of power to detect them in the sample sizes of individual studies. To improve power to detect variants associated with complex phenotypes such as anthropometric traits, data from multiple GWAS can be combined. This thesis describes the methods and results of several such analyses performed as part of the Genome-wide Investigation of ANThropemtric measures (GIANT) consortium, and compares various different methods that can be used to perform combined analyses of GWAS. In particular, the comparisons focus on comparing differences between meta-analysis methods, in which only summary statistics that result from within-study association testing are shared between studies, and mega-analysis methods in which individual-level genotype and phenotype data is analysed together. Finally, a brief discussion of technological means that have the potential to help overcome some of the challenges associated with performing mega-analyses is offered in order to suggest future work that could be undertaken in this area.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:588395 |
| Date | January 2012 |
| Creators | Randall, Joshua Charles |
| Contributors | Lindgren, Cecilia; Holmes, Chris |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:addfb69d-602c-43e3-ab18-6e6d3b269076 |
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