easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies

Recent advances in sequencing technologies have made it possible for the first time to sequence and analyse the genomes of whole populations of individuals in both a cost-effective manner and in a reasonable amount of time. One of the primary applications of this data is to better understand and investigate the genetic basis of common traits or diseases. For this purpose, genome-wide association studies (GWASs) are often used to find loci that are associated with a phenotype of interest. However, conducting GWASs is a challenging endeavour: first, different types of hidden confounding factors, such as population structure, environmental or technical influences, could lead to spurious associations. Second, it has been shown in several studies that associated loci often fail to explain much of the phenotypic variability — a phenomenon referred to as the problem of missing heritability. Many tools have been developed to partly address these challenges. The large diversity of these tools, however, have led to a highly fragmented and confusing landscape of tools. In addition, most of these tools do not share a common data format and do not provide straightforward solutions to visualise and annotate their results. In this thesis, we aim to explain more of the missing heritability, while simultaneously simplifying the usage of different methods, by providing an integral solution for performing, visualising and annotating GWASs. Therefore, we develop easyGWASCore, an integrated framework for performing GWASs and meta-analyses. Our framework facilitates the use of popular association methods by providing a common data structure, an application programming interface and a Python command line interface. In addition, easyGWASCore offers an out-of-the-box visualisation and annotation pipeline. We compare the runtime of the easyGWASCore framework to other well-established tools and find that it is at least as efficient as the individual software tools. Next, we enrich the easyGWASCore annotation pipeline with pathogenicity prediction scores to prioritise associated loci for further biological investigation, as well as to narrow down potentially causal loci. However, a large variety of such pathogenicity prediction tools exists and it is not obvious which of these tools work best. We therefore investigate the question whether there are systematic differences in the quality of the predictive performance of pathogenicity prediction tools when evaluated on a large number of variant databases. We find that the evaluation is hindered by two types of circularity and that these types of circularity might lead to spurious biological interpretation. Hence, it is important that scientists are aware of these different types of circularity when pathogenicity prediction tools are used for further experiments or analyses. Increasing sample sizes and combining the results of several GWASs could help to explain parts of the missing heritability. For this purpose, we develop a cloud and web-service, called easyGWAS, to provide a platform to share and publish data and results of GWASs and meta-analyses in a straightforward manner. Simultaneously, easyGWAS facilitates the use of the easyGWASCore framework by providing an easy-to-