Estimation of the genetic correlation among human cancers and identification of pleiotropic cancer loci

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Over the last twenty-five years or so geneticists have been trying to identify those genes that when mutated increase the risk of developing disease. Some of these diseases are rare in the population and in many cases the main gene that increases risk has been identified. Other diseases are common in the population and scientists call them 'common diseases'. These include, among others, most cancers, diabetes and heart disease. Understanding the genetics of common diseases is tricky because they are likely to be caused by the aggregate effect of thousands of mutations in the genome, each increasing risk by very little. Technological developments after the completion of the Human Genome Project have allowed the possibility of trying to identify the regions of the genome that make some people more prone to develop common diseases than others. The strategy has worked to some extend and has identified large numbers of regions for different diseases. Despite that success we know that the identified loci cannot explain all the recurrent risk of disease that we observe within families. There are numerous reasons why the current approach has had limited success. One of them is that the statistical methods applied are not able to model the structure of the genome properly. For instance, there could be multiple mutations on the same region whose combined effect makes one more prone to disease. At the moment the statistical methods applied only investigate one mutation at a time. We will develop more complex statistical methods to solve this problem. Another limitation of the current strategy is that scientists study one disease at a time. This is quite unsatisfactory because we know that some people tend to have multiple diseases either simultaneously or at different times. For example, being overweight increases one risk of developing cancer and this could be because the same genes that increase susceptibility to being overweight increase the chances of having cancer. We believe that by taking a more general approach and modelling two or more diseases simultaneously we could be more successful in identifying those furtive genomic regions that current methods cannot locate. We propose to develop methodology that can do that.To make our research more useful we will also apply our methods to different cancers. Different cancers often develop within families and there is a good chance that this is because susceptibility genes are shared among cancers. However, we know very little about this. We wish to learn more because this could be used for predicting cancer risk more accurately or to find new uses for available drugs (i.e. use drugs developed to treat one cancer to treat another one).