The positional candidate gene approach accelerates the discovery of genes involved in disease. However, the properties of such disease genes are very diverse and the sample size of known disease genes is too small and does not warrant success by the use of a machine-learning approach. A user-defined scoring system may thus help to determine the priority of candidate genes. Spinocerebellar ataxia (SCA) is a good model to test this approach because most SCA subtypes are caused by an expansion of short tandem repeats (STRs). The SCA db is a candidate gene database for SCA, which collected 3185 genes for 17 types of SCA. Those SCA subtypes that have known disease genes can be used as positive controls to optimize the parameters. The users may browse the candidate genes of a given SCA subtype by using the default parameters. The known disease genes were found to be the top three candidates using the default parameters. Alternatively, the users may score the candidate genes by changing the weight or the scores on the basis of their own working hypothesis.