A research team led by Vanderbilt University scientists has identified a set of high-confidence risk genes for schizophrenia. The team’s results support the view that schizophrenia is a developmental disease, one which potentially can be detected and treated even before the onset of symptoms.
Wang et al predicted a set of high-confidence risk genes for schizophrenia; these genes account for a significantly enriched heritability; they are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for schizophrenia. Image credit: Gerd Altmann.
Schizophrenia is a chronic, severe mental disorder characterized by hallucinations and delusions, ‘flat’ emotional expression and cognitive difficulties.
Symptoms usually start between the ages of 16 and 30. Antipsychotic medications can relieve symptoms but there is no cure for the disease.
Genetics plays a major role. While schizophrenia occurs in 1% of the population, the risk rises sharply to 50% for a person whose identical twin has the disease.
Recent genome-wide association studies (GWAS) identified more than 100 loci, or fixed positions on different chromosomes, associated with schizophrenia.
That may not be where high-risk genes are located, however. The loci could be regulating the activity of the genes at a distance — nearby or very far away.
To solve this problem, the researchers developed a unique computational ‘framework.’
Named the integrative risk genes selector (iRIGS), the framework pulled the top genes from previously reported loci based on their cumulative supporting evidence from multi-dimensional genomics data, as well as gene networks.
The result was a list of 104 high-risk genes, some of which encode proteins targeted in other diseases by drugs already on the market. One gene is suspected in the development of autism spectrum disorder.
“Schizophrenia and autism have shared genetics,” said study co-author Dr. Rui Chen, a researcher in the Vanderbilt Genetics Institute and the Department of Molecular Physiology and Biophysics at Vanderbilt University.
“Much work remains to be done. But our framework can push GWAS a step forward to further identify genes. It also could be employed to help track down genetic suspects in other complex diseases.”
The study was published in the journal Nature Neuroscience.
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Quan Wang et al. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nature Neuroscience, published online April 15, 2019; doi: 10.1038/s41593-019-0382-7
