Bioinformatics of genomic association mapping

In this thesis we present an overview of bioinformatics-based approaches for genomic association mapping, with emphasis on human quantitative traits and their contribution to complex diseases. We aim to provide a comprehensive walk-through of the classic steps of genomic association mapping illustrating the application and development of bioinformatics tools along the way. We start with a classic heritability study, continue with providing novel tools for genome-wide association studies (GWASs) of complex traits, and end with an integrated post-GWAS pipeline for translating GWAS findings of any human trait or disease to biological knowledge. Using this A-to-Z approach, we emphasize the importance of following the consecutive steps of genomic association mapping. To show how bioinformatics tools can facilitate and support analysis of high-throughput biological data, in Chapters 2, 3, 5, 6, and 7 we applied a number of already available tools, whereas in Chapters 3, 4, and 7 we developed novel bioinformatics tools supporting appropriate analysis of “big data” for genomic association mapping. Our in-house developed and extensively documented bioinformatics tools are freely available to the scientific community for further use. Furthermore, and as a running example of genomic association mapping of a typical human complex trait, we strictly adhered to serum levels of C-reactive protein (CRP). Using appropriate bioinformatics-based tools, either already available or our in-house developed ones, we succeeded to gain in knowledge of biological mechanisms controlling serum levels of CRP as well as its (causal) contribution to the pathophysiology of human diseases.

Dissertation: http://hdl.handle.net/11370/d325e2e8-d264-42d9-b718-b2db584b4553