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Transcription Imputation Spots Hundreds of Schizophrenia-Associated Genes

April 04, 2019

By David Douglas

NEW YORK—Transcriptomic-imputation association analyses of large-scale genotype data, a novel machine learning approach, has given new insight into schizophrenia risk, according to researchers.

"Our study has major implications in terms of methodology, and relevance to schizophrenia," said Dr. Laura Huckins of the Icahn School of Medicine at Mount Sinai, in New York City.

"First, we are able to predict gene expression in the brain from genotype, using the largest set of post-mortem brains to date," she told Reuters Health by email. "Our models are freely available, and can be used to immediately translate any genome-wide association study data into predicted dorso-lateral prefrontal cortex (DLPFC) gene expression - adding tissue and directional resolution to genome-wide association study (GWAS) loci."

Dr. Huckins added, "By applying these models to a large GWAS of schizophrenia, we are able to probe gene expression in schizophrenia cases and controls at unprecedented scale. We identified 413 genes associated with schizophrenia, across 13 different brain regions."

Dr. Huckins and colleagues examined data on more than 40,000 people with schizophrenia and more than 65,000 matched controls. The DLPFC gene-expression prediction models were constructed using CommonMind Consortium genotype and gene-expression data.

The researchers compared transcriptomic imputation in European and African Americans, "and found that our models were applicable to either ancestry with only a small decrease in accuracy," they write in Nature Genetics, online March 25.

They also identified "four clusters of genes, with expression in four distinct spatiotemporal regions, ranging from early prenatal to strictly postnatal expression." This, they say, "should spur interest in extending transcriptomic imputation data and/or methods to early development."

As Dr. Huckins pointed out, "Understanding the genetic aetiology of schizophrenia will require us to probe disease risk throughout development, and understand how expression of schizophrenia-associated genes changes throughout pregnancy and early life."

"The genes we identify," she added, "seem to fall into four groups, with distinct patterns of expression throughout development. This is important for a few reasons - first, expression of schizophrenia-associated genes in early pregnancy might explain some of the childhood symptoms seen in schizophrenia (for example, early onset of cognitive decline), as well as overlap with neurodevelopmental disorders."

"The genes expressed specifically in adolescence and adulthood," she concluded, "provide potential targets for research into biomarkers and therapeutics for schizophrenia."

SOURCE: https://bit.ly/2VhflxK

Nat Genet 2019.

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