GSK and MRC to Identify new Therapeutic Targets and Biomarkers from Genetic Association Studies
News Jan 25, 2008
GlaxoSmithKline (GSK) and The Medical Research Council (MRC) have announced the creation of a jointly funded programme seeking to identify and validate genes associated with common human diseases. A key aim of the programme will be to translate these observations into identifying new targets and biomarkers of disease.
The MRC and GSK will each invest £1 million in the programme over the next three years, which will pilot a new way of collaborative working between the two organizations. It will bring together academic and industrial expertise and resources in areas of mutual interest through joint funding and sharing from large databases and sample collections held by the partners. The collaboration will speed the translation of genetic insights into new concepts for therapy and other benefits for patients.
Projects funded through the programme will bring together MRC and GSK resources to study the role of genes in common human diseases through genetic association studies. This approach of combining data sets is increasingly recognized as necessary in order to increase the statistical robustness of studies and to enable the categorical identification of genes at the root of a disease.
As part of the collaboration, GSK will make extensive data acquired from large disease-related and population-based genetic studies available. The GSK collections have considerable potential to better link genetic traits to detailed variations in phenotypes.
The programme will be managed by a joint MRC-GSK Steering Group, which will oversee the funding and progress of projects. To mark the creation of this partnership two major awards are being announced.
The first will support research on depression where the genetic basis is complicated and not well understood at a molecular level. This study aims to identify genes involved in susceptibility to depression.
The second is focused on using large cohorts of patient and population data to identify new genetic variants associated with obesity and related metabolic disorders and to more precisely localize the causal variant underlying the genetic signal.
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