Methodology for the identification of shared genetic aetiology between epidemiologically linked disorders

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Many common health disorders are frequently observed to co-occur in individuals. Hundreds of genetic disease association analyses have been completed, resulting in the identification of numerous genetic variants that are associated with a specific disease, such as type 2 diabetes, cancer, and osteoarthritis. The primary aim of the proposed research is to develop methodology to detect genetic variants that have an influence on susceptibility to two disorders, which are suspected of having shared genetic causes. The majority of existing approaches have focused on identifying genetic casual elements for a single trait, and only few of them jointly analyse linked disorders. There are many pairs of health disorders that have been identified as either frequently existing together, or having an inverse relationship, where the presence of one disorder tends to reduce the risk of another disease. Disease co-occurrence parings include type 2 diabetes with Crohn's disease, cancer, and psychiatric disorders, as well as osteoarthritis with body mass index (BMI) and height. It has been established that there is an inverse relationship between prostate cancer and type 2 diabetes. Moreover, a treatment for prostate cancer was found to increase the risk of diabetes and cardiovascular disease. This may be due to the roles of particular shared genetic variants. Thus, the development of non-adverse treatments for either of the two diseases may be assisted by the identification of gentic variants with such inverse effects on two diseases. This highlights, among others, the importance of identifying common genetic causes between linked diseases.Shared genetic causes for most of these disease pairings have been identified using simple separate analyses of each disease. One method is to compare the individual results from each analysis, and to choose common criteria for the identification of genetic disease associations. The overlap of the two sets of results is then examined. A caveat of this approach is that each analysis has a different level of how well it can detect associations, so that although an association may exist with both traits, it may only be detectable within one of the studies, and thus not found in the overlap analysis. Also, as with many other approaches, it does not take advantage of any known genetic information. Alternatively, associations with one disease may be searched for within the genes that have been recognized as associated with the other disease. However, this greatly reduces the search area.In developing methods to jointly analyse traits, the level of how well associations could be detected may be increased by taking into account known pieces of genetic and biological information, such as previously identified genetic variants, and the biological functions ascribed to them. Statistical analysis methods will be developed and tested for performance by generating various plausible datasets under an assortment of assumptions. The method with the best performance will then be applied to real datasets, such as type 2 diabetes with schizophrenia, waist-hip ratio with BMI, osteoarthritis with BMI, and osteoarthritis with migraine.