The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.

To facilitate broad and convenient integrative visualization of and access to GWAS data, we have created the GWAS Central resource (http://www.gwascentral.org). This database seeks to provide a comprehensive collection of summary-level genetic association data, structured both for maximal utility and for safe open access (i.e., non-directional signals to fully preclude research subject identification). The resource emphasizes on advanced tools that allow comparison and discovery of relevant data sets from the perspective of genes, genome regions, phenotypes or traits. Tested markers and relevant genomic features can be visually interrogated across up to 16 multiple association data sets in a single view, starting at a chromosome-wide view and increasing in resolution down to individual bases. In addition, users can privately upload and view their own data as temporary files. Search and display utility is further enhanced by exploiting phenotype ontology annotations to allow genetic variants associated with phenotypes and traits of interest to be precisely identified, across all studies. Data submissions are accepted from individual researchers, groups and consortia, whereas we also actively gather data sets from various public sources. As a result, the resource now provides over 67 million P-values for over 1600 studies, making it the world's largest openly accessible online collection of summary-level GWAS association information.

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Genetic susceptibility to type 2 diabetes is primarily due to β cell dysfunction. However, a genetic study to directly interrogate β cell function ex vivo has never been previously performed. We isolated 233,447 islets from 483 Diversity Outbred (DO) mice maintained on a Western-style diet, and measured insulin secretion in response to a variety of secretagogues. Insulin secretion from DO islets ranged greater than 1000-fold even though none of the mice were diabetic. The insulin secretory response to each secretagogue had a unique genetic architecture; some of the loci were specific for one condition, whereas others overlapped. Human loci that are syntenic to many of the insulin secretion quantitative trait loci (QTL) from mice are associated with diabetes-related SNPs in human genome-wide association studies. We report on 3 genes, Ptpn18, Hunk, and Zfp148, where the phenotype predictions from the genetic screen were fulfilled in our studies of transgenic mouse models. These 3 genes encode a nonreceptor type protein tyrosine phosphatase, a serine/threonine protein kinase, and a Krϋppel-type zinc-finger transcription factor, respectively. Our results demonstrate that genetic variation in insulin secretion that can lead to type 2 diabetes is discoverable in nondiabetic individuals.