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.

The aim of this thesis is to obtain a better understanding of mutation rates within as well as between the genomes of humans and chimpanzees using data generated by high throughput sequencers. I will start with a review of the field and an overview of the technologies and protocols used to generate and analyse high throughput sequencing data. I apply some of the discussed techniques to show that there is evidence of a selective advantage of pathogenic de novo mutations in the Fibroblast Growth Factor Receptor 3 gene in the male germ line of humans. Furthermore, I use some of the methods to generate a map of genome-wide sequence variation in Western chimpanzees. Ever since Darwin [Darwin, 1871] and Huxley [Huxley, 1863] postulated more than a century ago that African great apes are our closest living evolutionary relatives, the study of chimpanzee individuals is of great scientific interest from an evolutionary point of view, as comparisons between the genomes of human and chimpanzee offer the potential to help to understand the molecular basis for similarities and differences between the two species. I use the generated data to explore the breadth of the nucleotide diversity in the chimpanzee genome in order to shed light on whether or not the local variation in mutation rate has been conserved since the divergence of the two species and to place human nucleotide diversity into perspective with an evolutionary closely related species. I explore the relationship of nucleotide diversity in chimpanzees with specific large-scale genome features to reveal a number of highly significant correlations which explain over 40% of the observed variation. I use data from the 1000 Genomes Project to examine the occurrence of ancestral polymorphisms shared between human and chimpanzee on a genome-wide scale. These ancestral polymorphisms do not only influence fine-scale divergence rates across the genome in very closely related species, they are also good candidates for regions under balancing selection and thus, they are a useful tool to study long-time population demographics and speciation. Using these variants, I postulate that long-term balancing selection may be more common than previously believed. I conclude with a discussion of the results contained in the body of the thesis and suggest a number of areas for future research.