Epigenetic Patterns May Help Identify Schizophrenia Early

A study published in Translational Psychiatry aims to take schizophrenia diagnostics one step forward. Using an epigenetics-based assay, researchers at Baylor College of Medicine and colleagues in the United Kingdom developed a blood test that can identify people with schizophrenia with about 85% accuracy.

Epigenetics involves a range of chemical modifications to DNA that help regulate gene activity. Many epigenetic changes arise from environmental exposures, and thus represent the interface of nature and nurture.

“Schizophrenia is intriguing from an epigenetic perspective because we know the disorder has a high hereditary component, but inheritance is not Mendelian [solely due to gene mutations],” said senior author Rob Waterland, Ph.D., a professor of pediatrics and molecular and human genetics at Baylor. Numerous historical studies have also shown that children born during harsh periods such as during a famine have elevated rates of schizophrenia, further highlighting the importance of environmental influence.

Using epigenetic modifications to predict schizophrenia is difficult, however, since most epigenetic patterns are unique to specific cell types; for instance, the epigenetic signatures in blood cells are different from those in brain cells.

In 2019, Waterland and his lab characterized a large set of epigenetic modifications known as correlated regions of systemic interindividual epigenetic variation (CoRSIVs). These epigenetic modifications show the same pattern in all cell types. They found that while CoRSIVs are found in less than 0.1% of the human genome, half are in regions that are associated with psychiatric illness.

In this follow-up study, Waterland and colleagues used a host of genetic data from schizophrenia patients and controls provided by U.K. biobanks; the samples included DNA taken from both blood and postmortem brain tissue. They first developed an algorithm that scanned CoRSIV signatures in one set of samples and identified patterns that distinguished schizophrenia patients from controls. They next tested the algorithm in a second set of samples and found their model was able to identify 85% of schizophrenia patients. By comparison, an algorithm using a current benchmark diagnostic—the patients genetic risk score—only identified 32% of schizophrenia patients using the same set of data.

There was no evidence to suggest that the differences between CoRSIV patterns in schizophrenia patients and controls might be due to antipsychotic medications or high rates of smoking among patients with schizophrenia (both substances alter epigenetic signatures).

Though the accuracy level is not yet high enough to be considered clinically applicable, Waterland told Psychiatric News that this current study only assessed a fraction of the available data. His team used publicly available epigenetic data sets, which include only about 10% of the CoRSIVs his team cataloged in the 2019 study. They have now assembled a more comprehensive array to study the entire panel of CoRSIVs and are validating its efficacy.

Waterland is hoping to test his algorithm in patients at high risk of psychosis to see if these CoRSIV differences already exist before symptoms emerge.

“All our available evidence from mouse models suggests these epigenetic changes are established very early in development, but we need to confirm this in prospective human studies,” he told Psychiatric News. “[I]t’s possible we may someday be able to assess psychosis risk in newborns using simple blood spot tests.”

This study was funded by grants from the National Institute of Diabetes and Digestive and Kidney Diseases, the Cancer Prevention and Research Institute of Texas, and the U.S. Department of Agriculture.■

“A Machine Learning Case–Control Classifier for Schizophrenia Based on DNA Methylation in Blood” is posted here.