Scientists Create Genomic Resource to Explore the Biological Underpinnings of Brain Disorders
Published in Science, this research integrates a broad range of genomics data to help find molecular underpinnings of schizophrenia, autism, and other neuropsychiatric conditions. Hyejung Won, PhD, in the department of genetics and UNC Neuroscience Center was co-first author.
Media contact: Mark Derewicz, 984-974-1915, firstname.lastname@example.org
December 13, 2018
CHAPEL HILL, NC A team of researchers, including scientists from UNC School of Medicine, has developed a model of unprecedented sophistication that relates variations in DNA and gene activity to the risk of brain disorders.
The model, described in a paper published in Science, draws from prior studies of thousands of healthy people and people with brain disorders. Scientists can now use it as a tool to explore the biological mechanisms of disorders such as schizophrenia and autism, which have largely eluded a deep understanding and have no cure.
Its the most comprehensive functional genomic resource ever developed for understanding the brain, and it establishes a framework for integrating different kinds of genomics data to get deep insights into the biology of brain disorders, said co-first author Hyejung Won, PhD, assistant professor genetics at the UNC School of Medicine and member of the UNC Neuroscience Center.
Scientists in the last few decades have performed hundreds of studies that gather DNA-sequence and related data on large groups of people to identify DNA variations and other genome-related factors associated with diseases. These genomics studies have generated important clues to the biological causes of many illnesses. But for psychiatric disorders and many other common brain disorders, traditional genomics studies have been less useful. Schizophrenia, for example, has been linked to specific variations at more than 100 locations on the genome called risk loci but most of these loci do not contain genes, so it is unclear how they relate to the disease. Moreover, the many gene variants that have been linked to schizophrenia typically have only weak impacts on schizophrenia risk. This has suggested to scientists that schizophrenia, and probably many other brain disorders, are too complex to understand with traditional, one-dimensional genomics approaches.
In pursuit of a more sophisticated approach, a group of genomics researchers several years ago formed the PsychENCODE consortium. They began to pool data from their genomics studies and other publicly available studies to develop tools to find relationships between different kinds of data.
The new resource includes different kinds of genomics data on individuals who had schizophrenia, bipolar disorder, and autism spectrum disorder. The types of genomics data include DNA-sequences, data on gene expression from specific kinds of brain cells, maps of DNA regions called enhancers that promote gene expression, and other features of the genome known to affect gene activity.
Won contributed data from her own studies on chromosome conformation. This refers to the three-dimensional organization of looped DNA in the nuclei of cells, and in particular the points where different loops come close enough to influence each others gene expression. Won also developed a complex model of how gene expression in brain cells is regulated by chromosome conformation and other genomic factors.
The team used the gene regulation network model to evaluate 142 schizophrenia risk loci uncovered by prior genomics studies. These risk loci do not contain genes, but are suspected of contributing to schizophrenia risk by somehow influencing the expression of other genes. The model identified 321 genes, including some that are known schizophrenia risk genes, as the likely regulatory targets of these risk loci. Won and colleagues showed that these genes affect the functions of synapses, acetylcholine receptors, ion channels, and other pathways implicated in prior schizophrenia studies. The scientists also determined that schizophrenia is primarily a disorder of neurons, not of other brain cells.
The resource developed for the study includes an AI-powered deep-learning model that estimates the risk of psychiatric symptoms based on gene variant and gene expression data. The scientists compared the new model to a standard, much simpler model that predicts psychiatric illness based on an individuals genome.
The deep-learning model was much more accurate, and we think it will have a big impact in terms of risk assessment and diagnosis for patients, Won said.
She and her PsychENCODE colleagues now are continuing to develop their model by integrating more types of genomics data and extending their analyses beyond schizophrenia to other brain disorders.
The senior authors of the study were Daniel Geschwind, MD, PhD, of UCLAs Geffen School of Medicine, James Knowles, MD, PhD, of SUNY Downstate Medical Center College of Medicine, and Mark Gerstein, PhD, of Yale.
The National Institute of Mental Health funded this work.