Unravelling the genetic architecture of common diseases is a continuing challenge in human genetics. While genome-wide association studies (GWAS) have proven to be successful in identifying many new disease susceptibility loci, the extension of these studies beyond single-SNP methods of analysis has been limited. The incorporation of multi-locus methods of analysis may, however, increase the power of GWAS to detect genes of smaller effect size, as well as genes that interact with each other and the environment. This investigation carried out large-scale simulations of four multi-locus model selection techniques; namely forward and backward selection, Bayesian model averaging (BMA) and least angle regression with a lasso modification (lasso), in order to compare the type I error rates and power of each method. At a type I error rate of ~5%, lasso showed the highest power across varied effect sizes, disease frequencies and genetic models. Lasso penalized regression was then used to perform three different types of analysis on GWAS data. Firstly, lasso was applied to the Wellcome Trust Case Control Consortium (WTCCC) data and identified many of the WTCCC SNPs that had a moderate-strong association (p<10-5) type 2 diabetes (T2D), as well as some of the moderate WTCCC associations (p<10-4) that have since been replicated in a large-scale meta-analysis. Secondly, lasso was used to fine-map the 17q21 childhood asthma risk locus and identified putative secondary signals in the 17q21 region, that may further contribute to childhood asthma risk. Finally, lasso identified three potential interaction effects potentially contributing towards coronary artery disease (CAD) risk. While the validity of these findings hinges on their replication in follow-up studies, the results suggest that lasso may provide scientists with exciting new methods of dissecting, and ultimately understanding, the complex genetic framework underlying common human diseases.

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Bisherige Methoden der Haplotyp-Block-Definition zielen entweder auf abwesende Rekombinationsereignisse oder eine effiziente Beschreibung genomischer Variation. Die vorliegende Arbeit definiert Blöcke von Single Nucleotide Polymorphisms (SNP) als Gebiete erhöhten Kopplungsungleichgewichtes (LD). Für dieses Ziel wird ein neues, entropie-basiertes Maß für LD zwischen multiplen Markern/Loci (Normalized Entropy Difference) entwickelt und als eine Multilocus-Erweiterung des paarweisen Maßes r2 charakterisiert. Ein zugehöriger Algorithmus für die Block-Definition wird vorgeschlagen. Seine Evaluierung an einem Datensatz des menschlichen Chromosoms 12 vom Internationalen Haplotype Map Projekt zeigt die Nützlichkeit der abgeleiteten Blöcke in Hinblick auf verschiedene Eigenschaften, einschließlich ihrer chromosomalen Coverage und der Anzahl sowie des Anteils der häufigen Block-Haplotypen. Der wesentliche Einfluß der SNP-Dichte auf die zu entdeckenden LD- und Blockstrukturen wird demonstriert. Der Erfolg von Assoziationsstudien in komplexen Erkrankungen mit Block-Haplotypen als multiallelischen Markern wird davon abhängen, ob die Common Variants/Common Diseases (CV/CD) Hypothese für solche Erkrankungen erfüllt ist. / Current approaches to haplotype block definition target either absent recombination events or the efficient description of genomic variation. This thesis aims to define blocks of single nucleotide polymorphisms (SNP) as areas of elevated linkage disequilibrium (LD). To this end, a new entropy-based measure for LD between multiple markers/loci, the Normalized Entropy Difference, is developed and is characterized as a multilocus extension of the pairwise measure r2. A corresponding algorithm for the block definition is proposed. Its evaluation on a data set of human chromosome 12 from the International Haplotype Map project proves the usefulness of the derived blocks with respect to several features, including their chromosomal coverage and the number and portion of common block haplotypes. The critical role of the SNP density for detectable LD and block structure is demonstrated. The success of association studies in common diseases with block haplotypes serving as multi-allelic markers will depend on whether the Common Variants/Common Diseases (CV/CD) hypothesis holds true for those diseases.