Prioritisation of Positional Disease Gene Candidates

Although much work has been done in the linkage mapping of many common disorders with a genetic component such as diabetes, asthma, obesity and schizophrenia, few of the genes involved have been identified. These studies have often mapped disease susceptibility to regions of tens of megabases, potentially containing many hundreds of genes [4]. A method of prioritising such positional candidates for further study is therefore required. Typically this is done manually, by looking for genes whose characteristics (function/expression etc) fit with what is already known about the disease. Computational approaches have been developed to automate this process and produce a smaller list of candidate genes [1][5]. However, by definition such approaches would not be successful in identifying novel disease genes that do not fit with prior knowledge of the disease or where little is known about the underlying aetiology of the disease.