Novel strategies to explore The complex genetics of autoimmune diseases

Autoimmune diseases are dependent on both genetic and environmental factors in complex interplay. They arise from a faulty immune response against self-antigens, and causes great suffering in affected individuals. Despite large efforts and hundreds of thousands of research articles published, the disease etiologies remain largely unknown. Knowledge of which genes are involved and how they interact would increase the possibilities of understanding the diseases and to create specific treatments. The work in this thesis is focused on methods to identify genes involved in autoimmune diseases by using experimental animal models for the diseases, both on a large scale with different gene segregation-crosses and on a small scale with studies on specific candidate genes. The thesis is based on six papers with the aim to map genes in experimental mouse models for RA and MS, on a genome-wide level as well as a locus-based level. The commonly used F2 intercross is compared with alternative genome-wide strategies. The partial advanced intercross (PAI) strategy - a novel strategy for high-resolution mapping based on genetic interactions, is introduced as well as novel strategy for selection of candidate genes, the QTL-chip. These strategies were used to dissect the arthritis loci Cia5/Eae3 and Eae2 into three and four separate quantitative trait loci (QTLs) respectively, and to identify seven strong candidate-quantitative trait genes (QTGs) for Cia21 and Cia22. Genetic interactions were found to play a major role in collagen induced arthritis in mice, and we demonstrate that interactions can be used to increase the penetrance of a QTL with otherwise small effects. Moreover, we show that different genetic loci affect separate parts of the diseases, i.e. the onset, early phase, late phase or severity and that the sub-division of the phenotype could be a prerequisite for finding all underlying QTLs. The major conclusions are that the autoimmune experimental models are highly dependent on genetic, environmental and sex- specific interactions. The immune system is a complex network of interacting factors and there is no reason to believe that the genetics behind a disease dependent on it would be otherwise. This knowledge is important for future investigations regarding complex autoimmune diseases