There is a rapid increase in world-wide burden of disease attributed to metabolic syndrome, as defined by co-occurrence of an array of phenotypes including abdominal obesity, dysglycemia, hypertrigylceridemia, low levels of high density lipoprotein (HDL) cholesterol and hypertension. Familial studies clearly indicate a genetic component to the disease and many linkage studies have identified a large number of linked loci. No disease-causing genes, however, have been conclusively identified, most likely because this is a multigenic disease for which effects of many causative genes may be small and combined with environmental effects. To assist empirical identification of metabolic syndrome associated genes, we present here a novel computational approach to prioritise candidate genes. We have used linkage studies and the clinical and population-specific presentation of the disease to select a final candidate gene list of nineteen most likely disease-causing genes. These are predominantly involved in chylomicron processing, transmembrane receptor activity and signal transduction pathways. We propose here that information about the clinical presentation of a complex trait can be used to effectively inform computational prioritisation of disease-causing genes for that trait.