Tracing the cellular and genetic roots of neuropsychiatric disease

A new analysis has revealed detailed information about genetic variation in brain cells that could open new avenues for targeted treatment of diseases such as schizophrenia and Alzheimer’s disease.

The findings, reported May 23 in science, were the result of a multi-institutional collaboration known as PsychENCODE, founded in 2015 by the National Institutes of Health, which seeks new understandings of genomic influences on neuropsychiatric diseases. The study was published alongside related studies in science, Advances in scienceAND Translational Science Medicine.

Previous research has established a strong link between a person’s genetics and their likelihood of developing neuropsychiatric illness, says Mark Gerstein, the Albert L. Williams Professor of Biomedical Informatics at Yale School of Medicine and senior author of the new study.

“Correlations between genetics and your susceptibility to disease are much higher for brain disease than for cancer or heart disease,” Gerstein said. “If your parents have schizophrenia, you’re much more likely to get it than you are to get heart disease if your parents have it. There’s a very large heritability for these brain-related conditions.”

What is less clear, however, is how this genetic variation leads to disease.

“We want to understand the mechanism,” Gerstein said. “What is that gene variant doing in the brain?”

For the new study, the researchers sought to better understand the genetic variation between individual cell types in the brain. To do this, they performed several types of single-cell experiments on more than 2.8 million cells taken from the brains of 388 people, including healthy individuals and others with schizophrenia, bipolar disorder, autism spectrum disorder, stress disorder post-traumatic and Alzheimer’s. disease.

From that group of cells, the researchers identified 28 different cell types. Then they examined gene expression and regulation within those cell types.

In one analysis, the researchers were able to link gene expression to variants in “upstream” regulatory regions, parts of the genetic code located before the gene in question that can increase or decrease gene expression.

“This is useful because if you have a variant of interest, you can now link it to a gene,” Gerstein said. “And that’s really powerful because it helps you interpret the variants. It helps you understand what effect they have on the brain. And because we looked at all cell types, our data also allows you to link that variant with an individual cell type.”

The researchers also assessed how particular genes, such as those associated with neurotransmitters, varied between individuals and cell types, finding that variability was typically higher between cell types than between individuals. This pattern was even stronger for genes encoding proteins targeted for drug treatment.

“And that’s generally good for a drug,” Gerstein said. “That means those drugs are localized to specific types of cells and don’t affect your whole brain or body. It also means those drugs are more likely to be unaffected by genetic variants and work on many people.”

Using the data generated from the analysis, the researchers were able to map the type’s genetic regulatory networks within cells and communication networks between cells, and then connect those networks to a machine learning model. Then, using an individual’s genetic information, the model could predict whether they had a brain disease.

“Because these networks were encoded in the model, when the model made a prediction, we could see which parts of the network contributed to it,” Gerstein said. “So we could identify which genes and cell types were important for that prediction. And that could suggest candidate drug targets.”

In one example, the model predicted that an individual with a particular genetic variant might have bipolar disorder, and the researchers could see that the prediction was based on two genes in three cell types. In another, researchers identified six genes in six cell types that contributed to a prediction of schizophrenia.

The model also worked in the opposite direction. Researchers can introduce a genetic disturbance and see how it might affect an individual’s network and health. This, Gerstein says, is useful for drug design or to see how well drugs or drug combinations might work as treatments.

Together, the findings could help facilitate precision medicine approaches to neuropsychiatric diseases, the researchers said.

To further this work, the consortium has made its results and model available to other researchers.

“Our vision is that researchers interested in a particular gene or variant can use our resources to better understand what it’s doing in the brain or perhaps identify new candidate drug targets to investigate further,” said Gerstein.