Extensive study provides new insights into genomic influences on 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, established in 2015 by the National Institutes of Health, which seeks new understandings of genomic influences on neuropsychiatric disease. The study was published alongside related studies in Science, Science Advances and Science Translational Medicine.

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

The correlations between genetics and your susceptibility to disease are much higher for brain disease than for cancer or heart disease. If your parents have schizophrenia, you are much more likely to develop it than to develop heart disease if your parents have the disease. There’s a lot of heritability for these brain-related conditions.”

Mark Gerstein, Albert L. Williams Professor of Biomedical Informatics, Yale School of Medicine

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

“We want to understand the mechanism,” Gerstein said. “What does this gene variant do to the brain?”

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

From this pool of cells, the researchers identified 28 different cell types. They then looked at gene expression and regulation in these cell types.

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

“This is useful because if you have a variant you’re interested in, you can now link it to a gene,” Gerstein said. “And that’s very powerful because it helps you interpret the variants. It helps you understand what effects they have in the brain. And because we looked at cell types, our data also allows you to link that variant to an individual cell type’s action.”

The researchers also assessed how specific genes, such as those related to neurotransmitters, differed 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 therapy.

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

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

“Because these networks were hard-coded into the model, when the model made a prediction, we could see which parts of the network contributed to it,” Gerstein said. “So we could determine which genes and cell types were important for this prediction. And that might suggest candidate drug targets.”

In one example, the model predicted that a person with a certain 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 schizophrenia prediction.

The model also worked in the opposite direction. Researchers could introduce a genetic disorder and see how it might affect a person’s network and health. This, Gerstein says, is useful for designing drugs or previewing how well drugs or combinations of drugs might work as treatments.

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

To advance 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 does in the brain, or perhaps identify new candidate drug targets to investigate further,” Gerstein said.