The United States' National Institutes of Health (NIH) has announced the increased funding of a global initiative to pool data about the human brain, which will include grant funding for Université de Montréal and CHU Sainte-Justine. The ENIGMA project – a large, multi-site, data-pooling initiative focused on genetics and the brain that has analyzed tens of thousands of study participants at more than 100 labs in over 30 countries – will receive an $11 million increase in Federal funding.
A piece of this funding (and the only Canadian-funded site) will allow Université de Montréal Professor of Psychiatry and CHU Sainte-Justine Researcher, Patricia Conrod, Ph.D., a co-developer of the ENIGMA Addiction Working Group, to direct the meta-analyses of over 9,000 genetic-neuroimaging datasets in an effort to understand biological causes and consequences of addiction.
The economic and personal cost of diseases such as schizophrenia, Alzheimer's and addiction are very substantial but their underlying causes remain unknown. The ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) project aims to identify new sources of disease risk and develop better diagnostic tools by screening brain scans and genetic tests collected across 33 countries of the world. Currently, computing facilities worldwide analyze ENIGMA's data around the clock, in order to detect effects of treatments or risk factors that may vary and even trend worldwide.
Named after an allied code-breaking initiative in World War II, ENIGMA unites brain researchers to discover factors that affect the brain, either by helping or harming it. Started in 2009 by medical researchers in the U.S. (Paul Thompson, USC), Europe and Australia, the research alliance studies medical scans of the brain and DNA collected from 30,000 people at over 185 sites globally.
Conrod's team will focus on addiction by coordinating the standardized analyses of addiction brain phenotypes and GWAS (genome-wide association studies) at the participating sites and will take the lead on harmonizing addiction phenotypes in the combined meta-analyses.
“This coordinated research consortium will allow us to address two major barriers in identifying genetic risk factors for addictions: low power to detect polygenic effects (effects of multiple genes) on brain and behavior; and lack of power to appropriately model the high rate of comorbidity in addictions” says Conrod. “We currently have pooled brain-gene data from almost 10,000 participants, allowing us to compare large samples with specific clinical presentations to identify genetic factors and brain phenotypes that might be associated with different stages of addiction, different comorbidity patterns or specific drug use vulnerabilities.”
The Addiction Working Group will analyze data relevant to addiction-related genetic characteristics, including case-control comparisons across a variety of abused substances. In addition, Conrod and her team and colleagues at the University of Vermont and Yale University will examine the influence of co-occurring chronic conditions, gender and stages of dependence. Using aggregate data from case-control and developmental cohorts, the researchers will examine the relative contribution of various genetic and brain correlates on risk for early onset substance misuse, transition to regular use, susceptibility to dependence, and individual differences in relapse vulnerability.
The announcement of funding for ENIGMA comes as part of a $100 million Federal program to support 11 national Centers of Excellence, part of the Big Data to Knowledge Initiative announced in 2013, to discover patterns in large scale collections of medical data. The efforts targeting large scale biomedical data promise to discover better diagnostic tools for dementia, schizophrenia and developmental disorders such as autism, which have been challenging to treat as their root causes are unknown.