Gene and Gene-Set Analysis for Genome-Wide Association Studies

Genome-wide association studies (GWAS) have identified hundreds of loci at very stringent levels of statistical significance across many different human traits. However, it is now clear that very large samples (n~104-105) are needed to find the majority of genetic variants underlying risk for most human diseases. Therefore, the field has engaged itself in a race to increase study sample sizes with some studies yielding very successful results but also studies which provide little or no new insights. This project started early on in this new wave of studies and I decided to use an alternative approach that uses prior biological knowledge to improve both interpretation and power of GWAS. The project aimed to a) implement and develop new gene-based methods to derive gene-level statistics to use GWAS in well established system biology tools; b) use of these gene-level statistics in networks and gene-set analyses of GWAS data; c) mine GWAS of neuropsychiatric disorders using gene, gene-sets and integrative biology analyses with gene-expression studies; and d) explore the ability of these methods to improve the analysis GWAS on disease sub-phenotypes which usually suffer of very small sample sizes. In this project, we focused on the analysis of GWAS on bipolar disorder, however, GWAS from other complex disorders have also been analysed and are used throughout to compare the performance of the methods developed across disorders that, very likely, have different genetic architecture. From the analysis of these datasets, it was possible to draw that conclusion. I found that from a computational perspective the application of gene and gene-set analyses to GWAS is feasible, even if calculation are performed on a common desktop computer. As a genetic mapping tool, gene-based association of GWAS provides a