Over the last few years, the number of genome-wide association studies GWAS has increased markedly and, in concert, these efforts have led to the identification of a large number of new susceptibility loci for common multi-factorial disorders [1]. The underlying technology is developing rapidly and is currently moving from the use of high density SNP arrays towards medical re-sequencing of large genomic regions. Given this development, the availability of thoroughly phenotyped patient and control samples is becoming even more important. Furthermore, due to the small effect sizes that characterize susceptibility genes for multi-factorial traits, potentially successful GWAS rely on large sample number, with additional pressure put on the quality of samples [2]. In reality, however, there will be only very few cohorts comprising 10,000 or even more samples (www.p3gconsortium.org). Exceptions include, for example, the DeCODE studies in Iceland (www.decode.com) and the EPIC (European Prospective Investigation into Cancer and Nutrition) cohort (http://epic.iarc.fr). Collaborations involving diverse sample collections are therefore essential and efforts in this field are promising, for example the establishment of the Biobanking and BioMolecular Resource Infrastructure (www.bbmri.eu). With cohorts
