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Takeda backs analysis of genomic data on brain diseases

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Major players in genomic research and mental health have teamed up to understand the complex mechanisms of neuropsychiatric diseases such as bipolar disorder and schizophrenia. The recently formed CommonMind Consortium will analyze large-scale genomic data and make the findings publicly available to researchers online.

Takeda Pharmaceutical, Japan's largest drugmaker, has agreed to provide seed funding for the private-public partnership. Other heavyweights that have signed on as contributors include Sage Bionetworks, Eric Schadt and his colleagues at Mt. Sinai School of Medicine, the NIH's National Institute of Mental Health and the University of Pennsylvania.

Mt. Sinai and UPenn are offering up their large brain-tissue banks for the consortium to use in gathering whole-genome scale RNA and DNA sequence data, according to the group. Sage Bionetworks, a Seattle nonprofit that promotes open access to disease models, plans to make the massive amounts of molecular data from the consortium available on its compute platform called Synapse, which enables integration of genetic pathways to study diseases.

The World Health Organization estimates that as many as 450 million people around the world suffer from neuropsychiatric disorders. Yet some Big Pharma companies such as Sanofi ($SNY) have fled from developing drugs for certain neuropsychiatric diseases such as depression. Why? One of the main reasons is the industry's poor understanding of the biology of the illnesses. Genomic analysis of brain tissue could shed light on the underpinnings of mental illnesses, providing pharma groups with new targets for drugs.

"The NIMH is particularly excited about this partnership that will leverage resources and expertise in both the public and the private sector to accelerate research into the causes and treatments of major mental illness," Dr. Thomas Lehner, the National Institutes of Mental Health's chief of genomics research, said in a statement.

- check out the release
- see GenomeWeb's article

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