The genetic roots of psychiatric diseases are notoriously difficult to unravel. They each involve hundreds of genes. And many of the genetic variations at play are found in the non-coding parts of DNA, which makes it even trickier to draw connections to disease.

But a sweeping set of studies published Thursday make a dent in that mystery, and shows the potential of big data and teamwork among many labs to unlock valuable clues.

The 10 papers — appearing in Science, Science Translational Medicine, and Science Advances — are part of a unique, nationwide collaboration among more than a dozen research sites known as the PsychENCODE Consortium.

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Funded by the National Institute of Mental Health, the project targeted the role that non-coding parts of the genome — those that don’t contain instructions for making proteins — play in the development of psychiatric disorders. The first phase of the consortium received roughly $35 million in funding and the second phase, which just got underway, got about $15 million.

The foundation of that work: roughly 2,000 human brains from both healthy people and individuals with psychiatric diseases, including autism, bipolar disorder, and schizophrenia. The researchers don’t just have access to data from the DNA of those individuals — they also have phenotypic information, including their physical characteristics, symptoms, and other medical information.

The studies span a wide range of topics, from a look at neuronal changes specific to schizophrenia to a way to predict the risk of autism and bipolar disorder.

“They all had similar goals: mapping, characterizing, looking at the role of genes,” said said Geetha Senthil, who works at the National Institute of Mental Health and previously served as the PsychENCODE program director. All of the papers offer new insights for researchers to follow up on how psychiatric diseases develop, and how they affect the brain.

It will be years before the work yields new therapies, but more immediately, experts said the research is a testament to the value of collaboration — and of the power of vast data sets.

“What was really striking to me was how much more refined our picture is now with this larger sample set,” said said Dr. Daniel Geschwind, a neurogenetics researcher at the University of California, Los Angeles, and a member of the consortium. “It gives you a much more complete view.”

Geschwind and other scientists in the consortium said that the large quantity of data made it easier to pinpoint specific patterns that might be easy to miss in smaller data sets. It also gave them more certainty about the validity of new discoveries.

“You can be more confident that you’re not just looking at a biased picture of what the disorder is,” Geschwind said.

The data from the consortium are being made available to researchers everywhere, so that they can run their own studies on other questions the consortium hasn’t addressed.

“It’s a huge resource for the community,” said Lilia Iakoucheva, a professor at the University of California, San Diego, and a consortium member.

The consortium is hopeful that sharing the data will speed up the pace of research and one day, potentially lead to new therapeutic discoveries.

“This is a first step,” Geschwind said. “It’s definitely not the final step.”

Correction: A previous version of this story misstated the number of brains in the consortium database. 

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  • These types of studies potentially stem from the belief that there is a gene-environment interaction effect for psychiatric conditions. However, according to the following recent meta-analysis, there is NO evidence to support this widely held belief:

    Culverhouse, R. C., et al. (2018). Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression. Molecular Psychiatry, 23(1), 133.

  • This article states that the NIMH/NIH granted $70mill into this research. How then can it be possible, for a non-institution associated person to read any of these 10 papers, which they paid for, w/o shelling out lots of money again?

  • Inspirational content, have achieved a good knowledge from the above content on training useful for all the aspirants of Data Science training.

  • https//justpaste.it/777YEHOVAH
    This link proves schizophrenia is a lie just like the theory of evolution.

    http://tinyurl.com/7tnv3

    This link proves these drugs shorten lifespan and do lots of harm. By the way any brain differences in so called schizophrenics is undoughtly the result of medicines not some inborn or acquired defect.

  • Does the study include exposome data as well as genetic data? Interaction and epigenetics are key, along with life experiences.

  • I would guess these researchers are motivated by profit-driven big pharma, rather than being motivated by actual research evidence. Numerous studies have repeatedly demonstrated that our brains are constantly changing AS A RESULT OF our mental experiences (via neuroplastic and epigenetic mechanisms). For example, psychological stresses (e.g. prolonged worry as a result of losing a job, etc.) can bring about structural changes in the brain and these changes are reversible when these stresses are addressed (e.g. getting a job again, or when life circumstances change).

    In other words, there are fundamentally incorrect assumptions that go with the idea that the organ brain or various genes have to be somehow studied and then ‘manipulated’ or ‘treated’ with medicines if someone has mental issues. Many studies conducted so far have demonstrated that such interventions merely result in ‘emotional blunting’ along with terrible long-term side effects.

    • Thank you for stating so clearly what I know to be true. Starting with an assumption and then trying to find supporting evidence (no matter how tangential) doesn’t seem to be a very good methodology.

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