Studying how changes in DNA affect organisms and gene functions, can give us an increased understanding of the genetic regulation of diseases and metabolic traits. Chickens and other farm animals have long been used as a tool to study human diseases and other complex traits since they provide an excellent opportunity to study these traits in a controlled environment. In this thesis I have applied new statistical methods to experimental data from two outbred chicken crosses to investigate the genetic architecture and regulation of autoimmune and metabolic traits. The first cross was generated from two chicken lines divergently selected for high and low body weight and was used to study how a four-QTL network affects metabolic traits (papers I-III). The second cross was generated to study the genetic regulation of autoimmune systemic sclerosis (paper IV).
Our explorations of the effects of the four-QTL network on other metabolic traits indicate that the major contribution of the network is to increase body size through an overall increase in growth of all organs rather than by changing the relative size of different components. To be able to study the networks affect on metabolic traits further, we first generated and analysed an advanced intercross line to narrow down the regions. Once the regions were fine mapped, we designed and generated a so called introgression line. Three of the fine mapped QTL were transferred (introgressed) from the low weight line into a high weight line background. This introgression line can be used to further investigate the QTL network. In the second cross, three suggestive autosomal susceptibility QTL were mapped in the chicken genome, with several orthologues of human systemic sclerosis candidate genes located within two of these loci. We also observed less disease in females with a W chromosome inherited from the diseased founder line