We consider large scale multiple testing for data that have locally clustered signals.With this structure,we apply techniques from change-point analysis and propose a boundary detection algorithm so that the clustering information can be utilised.Consequently the precision of the multiple testing procedure is substantially improved.We study tests with independent as well as dependent p-values.Monte Carlo simulations suggest that the methods perform well with realistic sample sizes and show improved detection ability compared with competing methods.Our procedure is applied to a genome-wide association dataset of blood lipids.