| Complex diseases constitute a class of disorders whose phenotypic variance is caused by the interplay of multiple genetic and environmental factors. Diabetes, hypertension, coronary artery disease, and Alzheimer's disease are a few examples of such diseases. Because complex disorders are of great public health importance and impose a lot of burden on patients and society, analyzing the complexity underlying their genetic architecture may help develop more efficient diagnostic tests and therapeutic protocols.;In recent years, genome-wide association study (GWAS) has become the method of choice in genetic dissection of complex diseases. However, despite the continuous advances in revealing the genetic basis of many of complex diseases using GWAS, a major proportion of their genetic variance has remained unexplained, in part because GWAS is unable to reliably detect small individual risk contributions and to capture the underlying genetic heterogeneity and the interactions existing among genetic factors.;In this study, I describe an innovative hypothesis-based method, called the Knowledge-based Association Studies (KBAS), to analyze the association between multiple genetic factors and a complex phenotype. Starting from sets of markers selected based on preexisting biological knowledge, the KBAS method generates multi-marker models relevant to the biological process underlying a complex trait for which genotype data is available. I also present the results obtained by testing the applicability of the KBAS method to large-scale genotyping datasets using the Wellcome Trust Case-Control Consortium (WTCCC) dataset. Analyzing a number of biological pathways, the method was able to identify several immune system related multi-SNP models significantly associated with Rheumatoid Arthritis (RA) and Crohn's disease (CD). RA-associated multi-SNP models were also replicated in an independent case-control dataset. Finally, I introduce a freely available software tool that implements the KBAS method.;The method I present provides a framework for performing genome-wide association analysis in situations in which combinations of genetic factors jointly contribute to the genetic variance of a trait of interest, as is often the case in complex diseases. In contrast to hypothesis-free approaches, the results obtained by employing the KBAS method can be given a direct biological interpretation. |
