This week, the PsychENCODE Consortium published a collection of 10 research papers in Science, Science Advances, and Science Translational Medicine presenting 3 years of work aimed at understanding the complex genetic systems involved in psychiatric disorders.
The consortium was founded in 2015 by the US National Institutes of Health, which has pumped a massive $50 million (around €44 million) into the project. Scientists from various institutions across the US joined forces with the goal of uncovering the genetic networks associated with mental illnesses ― mainly schizophrenia, autism, and bipolar. Samples of brain tissue were taken from thousands of cadavers and studied using multiple genomic-sequencing techniques. So far, the consortium has analysed more than 2000 brains in tissues banks to catalogue differences regulatory regions of the brain, in particular, the activities in different parts of the brain and at different stages of development in brains affected by the disorders.
Although a number have genetic variants have been associated with mental disorders, it is not clear modifications in these sequences alter gene function. Moreover, some gene variants do not even code for proteins. Scientists previously thought these particular genetic codes were unused but now believe they could regulate gene expression, including transcription factors and microRNAs, which can have an enormous influence an individuals risk of disease.
A huge amount of data has now been generated that will hopefully provide more insights into the brain and psychiatric disorders. The PsychENCODE data was generated by genotyping and various high-throughput sequencing techniques, including single-cell RNA sequencing. All data are now available on a central, publicly available resource. In addition, processed data from related projects, including ENCODE, the CommonMind Consortium (CMC), GTEx, and Roadmap, are accessible on the platform.
One study published on 14 December in Science uncovered new genomic elements and networks in the brain thereby providing insight into the molecular mechanisms underlying psychiatric disorders (1). Furthermore, the researchers were able to develop a more accurate deep learning model ― a type of machine learning ― to predict disease risk based on gene expression.
Another study, published on the same day in Science, explored how ‘epigenetic’ modifications ― changes in gene expression without changes to the underlying genetic sequence ― can alter gene expression in regulatory regions of the brain during development (2). The researchers found that the biggest variation in gene expression occurs during fetal development and adolescence, which are known to be crucial periods of brain development. During these important times, genes associated with neuropsychiatric disease risk appear to form networks in certain regions of the brain. This information could be used to form insights into when and how certain mental diseases develop.
Another paper slated for publication on 19 December in Science Translational Medicine suggests that insertions and deletions of large chunks of DNA sequences ― referred to as copy number variations (CNVs) ― can greatly increase the risk of schizophrenia (3). They identified a potential role for DGCR5 ― a non-encoding type of RNA found in one of these CNV-deletion regions ― in regulating certain schizophrenia-related genes.
Psychiatric diseases are known to be extremely heritable, however, the genetic reasons behind this heritability remain unknown. While scientists have a long way to go before deciphering the underlying mechanism of mental disorders, the findings could eventually lead to new treatments. The new data could be used to draw connections between specific genes and noncoding DNA variants previously linked to neuropsychiatric diseases. These studies do not yet answer how genetic changes contribute to brain diseases but are important steps towards better understanding the genetic factors involved in mental illness.
(1) Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science (2018). DOI: 10.1126/science.aat8464
(2) Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science (2018). DOI: 10.1126/science.aat7615
(3) Meng, Q. et al. The DGCR5 long noncoding RNA may regulate expression of several schizophrenia-related genes. Science Translational Medicine (2018). DOI: 10.1126/scitranslmed.aat6912