A computational approach to understanding the molecular mechanisms of complex disease

With the discovery that sickle cell anaemia is the result of a structurally altered haemoglobin protein in the 1940s, it was established that genetic variation is the basis of disease. Thus the convention in studying genetic disease became explaining phenotypic variation through causal variations in the genome. Initially, single family-based clinical cases of a disease were investigated, focusing on a single disease-associated region, but this technique was inappropriate for more complex diseases were multiple genes and factors increased the disease risk. Rather, genome-wide and whole genome analyses are performed using thousands of disease cases within a population. These novel techniques in genotyping and genome sequencing are flooding the genetics community with huge amounts of data, but efficient analysis of this information is still lacking. Computational approaches to extract sensible information from genomic data are still in their infancy, with researchers relying on less scalable traditional methods.The aims of this project were to aid in the discovery of candidate disease genes from genome-wide association studies (GWAS) and thus potential molecular mechanisms behind complex diseases by analysing genomic information through data mining. Instead of relying solely on protein pathway analysis as most methods do, we looked at domain homology and regulatory control as tools for predicting likely candidates. Firstly, a method to help identify candidate disease genes and mechanisms in complex diseases that are assayed through GWAS was developed and benchmarked. The methodology was shown to be effective in identifying known disease and disease candidate genes from the GWAS data. Secondly, this method was applied to GWAS on coronary artery disease, hypertension, type II diabetes, bipolar disorder, Crohn's disease, rheumatoid arthritis and type I diabetes. Novel disease gene candidates that were overlooked in the initial analyses were predicted. At most, the number of predictions averaged at around 170 candidates for the average 400 disease-associated loci implicated. The top candidates were validated through literature searches and analyses. Finally, this method was made available to other researchers to use on their candidate disease gene studies from GWAS by upgrading a gene prediction webserver, Gentrepid