Increasingly, genetic studies contribute to our understanding of the pathogenesis of diabetes and its complications at the molecular level [1, 2]. With the advent of powerful new high-throughput analytical methods [3], there is every reason to expect further insights. Virtually all such genetic studies include some test of the association between genome sequence variation and the phenotype of interest, be that diabetes itself, the presence of a given complication, or measures of some diabetes-related intermediate trait. Association studies of this type have scored several recent successes in relation to both type 1 (CTLA4, INS and PTPN22 genes) [2, 4, 5] and type 2 diabetes (PPARG, KCNJ11) [6–8], identifying variants that are unequivocally associated with disease. Two further association studies, examining variation in the genes encoding the Kruppel-like transcription factor family and the receptors for adiponectin, are published in this issue of Diabetologia [9, 10]. Despite these advances, there are major concerns regarding the overall performance and robustness of genetic association studies. All too often, initial positive (or negative) association findings fail the test of replication, leaving the literature littered with the detritus of uncertain and poorly reproducible associations. As set out in a number of excellent review articles [11–15], the origins of such inconsistency lie in a series of common methodological failings. These include (but are not limited to) the use of sample sizes that are inadequately powered for the task in hand, incomplete assessments of sequence variation within the locus of interest, technical errors in genotyping, and the inappropriate interpretation of data when large number of statistical tests have been performed. Furthermore, discrepancies introduced by these failings are compounded by the rather low prior odds that any given variant contributes to susceptibility to a given trait and by the understandable bias of reviewers and journal editors towards the publication of novel, superficially interesting, positive associations (whilst otherwise well-performed association studies reporting no association are often dismissed as ‘negative’). In fact, where power is low and liberal thresholds for declaring significance are used, the vast majority of such ‘positive’ associations will be erroneous [16]. Of course, it is not all bad news. Susceptibility variants for diabetes and its complications do exist. However, with the exception of HLA in type 1 diabetes, the effect sizes are usually modest. Such effects can be reliably and reproducibly detected provided the studies are adequately powered and feature appropriately constructed sample sets, careful genotyping and appropriate analysis [2, 4–8]. For example, the variants P12A in PPARG and E23K in KCNJ11 have been detected repeatedly within large type 2 diabetes case-control samples, such that the overall evidence for association now exceeds any reasonable correction for genome-wide significance [17]. Other type 2 diabetessusceptibility effects—at CAPN10 for example—look inM. I. McCarthy (*) Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Headington, Oxford, OX3 7LJ, UK e-mail: mark.mccarthy@drl.ox.ac.uk Tel.: +44-1865-857298 Fax: +44-1865-857299