Population-based linkage analysis is a new method for analysing genomewide single nucleotide polymorphism (SNP) genotype data in case-control samples, which does not assume a common disease, common variant model. The genome is scanned for extended segments that show increased identity-by-descent sharing within case-case pairs, relative to case-control or control-control pairs. The method is robust to allelic heterogeneity and is suited to mapping genes which contain multiple, rare susceptibility variants of relatively high penetrance. We analysed genomewide SNP datasets for two schizophrenia case-control cohorts, collected in Aberdeen (461 cases, 459 controls) and Munich (429 cases, 428 controls). Population-based linkage testing must be performed within homogeneous samples and it was therefore necessary to analyse the cohorts separately. Each cohort was first subjected to several procedures to improve genetic homogeneity, including identity-by-state outlier detection and multidimensional scaling analysis. When testing only cases who reported a positive family history of major psychiatric disease, consistent with a model of strongly penetrant susceptibility alleles, we saw a distinct peak on chromosome 19q in both cohorts that appeared in meta-analysis (P=0.000016) to surpass the traditional level for genomewide significance for complex trait linkage. The linkage signal was also present in a third case-control sample for familial bipolar disorder, such that meta-analysing all three datasets together yielded a linkage P=0.0000026. A model of rare but highly penetrant disease alleles may be more applicable to some instances of major psychiatric diseases than the common disease common variant model, and we therefore suggest that other genome scan datasets are analysed with this new, complementary method.