No abstract

Some cases of Alzheimer's disease are inherited as an autosomal dominant trait. Genetic linkage studies have mapped a locus (AD3) associated with susceptibility to a very aggressive form of Alzheimer's disease to chromosome 14q24.3. We have defined a minimal cosegregating region containing the AD3 gene, and isolated at least 19 different transcripts encoded within this region. One of these transcripts (S182) corresponds to a novel gene whose product is predicted to contain multiple transmembrane domains and resembles an integral membrane protein. Five different missense mutations have been found that cosegregate with early-onset familial Alzheimer's disease. Because these changes occurred in conserved domains of this gene, and are not present in normal controls, they are likely to be causative of AD3.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.