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Integrated Analysis Defines Potentially Predictive Schizophrenia Risk Genes

NEW YORK (GenomeWeb News) – A genetic risk score based on nominally associated variants in a few-dozen schizophrenia-related genes can help define groups of individuals at elevated risk of schizophrenia, according to a study online today in Molecular Psychiatry.

"[T]hese genetic variants are present throughout the population," corresponding author Alexander Niculescu, an Indiana University psychiatry researcher and director of the neurophenomics lab at the IU Institute of Psychiatric Research, said in a statement. "If you have too many of them, in the wrong combination, in an environment where you are exposed to stress, alcohol and drugs, and so on, that can lead to the development of the clinical illness."

Niculescu and his colleagues from the US and the UK brought together data from past genome-wide association, linkage, copy number, gene expression, and animal model studies of schizophrenia using what they call translational convergent functional genomics to get an integrated view of genes and pathways affected in the psychiatric condition.

The analysis pointed to candidate genes involved in brain development, connectivity, and signaling-related pathways, though authors of the study emphasized that environmental factors are also believed to have key roles in the condition.

"At its core, schizophrenia is a disease of decreased cellular connectivity in the brain, precipitated by environmental stress during brain development, among those with genetic vulnerability," Niculescu said.

Using a set of nominally associated SNPs found in and around the most promising candidate genes, the team developed a genetic risk prediction score that accurately classified individuals from additional cohorts into higher or lower schizophrenia risk groups roughly two-thirds of the time.

Several human and animal studies have looked at genetic, biological, and environmental factors involved in schizophrenia, the study's authors explained, but a complete understanding of the processes involved remains elusive.

They decided to bring together as much of this functional and genetic data as possible via translational convergent functional genomics in the hopes that an integrated view of the disease might eventually help in its diagnosis, treatment, and prevention.

"Animal model data provide sensitivity of detection, and human data provide specificity for the illness," they wrote. "Together, they help to identify and prioritize candidate genes for the illness, using a polyevidence [convergent functional genomics] score, resulting in essence in a de facto field-wide integration putting together the best available evidence to date."

"Once that is done," researchers added, "biological pathway analyses can be conducted and mechanical models can be constructed."

For their convergent functional genomics analysis, researchers relied on International Schizophrenia Consortium GWAS data on 3,322 individuals with and 3,587 individuals without the condition.

They also used gene expression data on human post-mortem samples, cell lines, or blood samples, and findings from other genetic and animal model studies that had been pulled together for a psychiatric disorder database maintained by Indiana University School of Medicine's neurophenomics lab.

Based on their analysis, as well as validation testing using data from other GWAS, the researchers saw an over-representation of genes involved in neuronal developmental and connectivity genes, as well as genes from glutamate receptor, G-protein receptor, and cyclic AMP related signaling pathways.

In addition, the genes implicated in schizophrenia became more consistent and reproducible from one GWAS to the next when the team focused on higher-level processes — for instance, biological pathways or genes — rather than specific SNPs.

"Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability," the study authors wrote.

To look at whether variants in the genes might be useful for predicting schizophrenia risk, the researchers put together a genetic risk test based on all of the nominally associated variants falling in each of the top 42 candidate genes from their convergent functional genomic analysis.

In four cohorts comprised of European American or African American cases and controls, the risk score could distinguish between individuals with or without the disease the majority of the time. The risk score tended to be higher in individuals who developed schizophrenia between the ages of 15 and 30 years old than it was in those who got the disease earlier or later than usual, researchers noted, hinting at differences in the risk factors involved.

"[T]he genetic risk component identified by us seems to be stronger for classic age of onset schizophrenia than for early or late-onset illness," they wrote, "suggesting that the latter two may be more environmentally driven or have somewhat different genetic architecture."