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By Anette Breindl and Omar Ford

Staff Writers

Looking at the prevalence data on depression is enough to set off depression in itself.

According to the Clarivate Analytics Incidence & Prevalence Database (IPD), the global prevalence of depression is a bit under 5 percent. Because depression is not a permanent condition, however, a person's lifetime risk of an episode of major depression is 15 percent. The adolescent depression rate is even higher.

Antidepressants are one effective way to treat depression. But they are not effective for everyone.

Roughly half of all patients do not respond to the first antidepressant that is prescribed to them, and the failure rates increase with each subsequent prescription.

While there are a few tests to predict such drug responses, "pharmacogenomics testing in psychiatry is largely limited to genes associated with drug metabolism, or a few obvious candidates like the serotonin transporter," depression researcher Cheong-He So told BioWorld MedTech.

In part, that is because the drugs themselves do not target anywhere near the breadth of mechanisms that cancer drugs do.

"All antidepressant drugs that are prescribed today essentially have the same mechanisms, with minor exceptions, to the ones that were discovered 60 years ago," Eric Nestler, director of the Friedman Brain Institute at the Mount Sinai School of Medicine, told BioWorld MedTech.

Genomewide association studies (GWAS) are the method of choice to identify genetic underpinnings of diseases that could make for new drug targets, which are a prerequisite for diagnostic tests.

Depression, though, has been notoriously resistant to attempts to identify genetic risk factors.

Identification of the first two single-nucleotide polymorphisms (SNPs) that predicted increased risk came only in 2015, and after researchers focused both on an ethnically homogenous population (Han Chinese women) and those with severe recurrent depression.

Depression risk SNPs are hard to find for several reasons. First, depression's estimated heritability of roughly 40 percent, while it certainly sounds high, is still lower than that of other psychiatric disorders such as schizophrenia where GWAS has successfully identified risk SNPs.

The lower heritability intersects with higher prevalence. About 15 percent of the population experience an episode of major depression in their lifetimes, as opposed to 1 percent who have a schizophrenic episode. And while cancer-causing mutations, on a cellular level, rapidly lead to changes in cellular behavior that can be seen almost immediately in cell line experiments – with the exception of BRCA mutations, those cancer genes that are being used to guide treatment are not germline variants – the effects of depression variants are much more subtle, and take much longer to show up as changes in function.

As a result, depression is common enough that there are likely to be participants with undiagnosed depression and with risk variants that have not yet led to clinical depression muddying the waters in any depression GWAS.

Depression is also particularly heterogeneous. Though there are nine core symptoms of major depression, a 2011 review reported that "almost 1,500 symptom combinations can fulfill the diagnostic criteria and . . . two patients with a diagnosis of [major depressive disorder] may not have a single symptom in common."

In the same vein, two patients with a diagnosis of major depressive disorder may not have any genetic risk factors in common, either. In fact, the behavioral symptoms of depression appear to result from different underlying biological processes, which likely include different genetic risk factors, in men and women.

A recent study has shown that gene expression signatures associated with depression differed between men and women.

Study lead Nestler noted that the study's sample sizes were small, and need to be replicated in larger cohorts. But the findings suggest that one possible way to improve the signal-to-noise ratio in GWAS of depression is to analyze men and women separately.

"Our data would suggest that that should certainly be looked at," Nestler said. "And it could be done in existing datasets."

Despite those challenges, researchers have begun to identify genetic variants that are associated with depression. Since the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE) consortium identified the first two risk loci in Han Chinese women in 2015, another consortium has identified two SNPs that were associated with depressive symptoms.

Genomics company 23andme identified 17 SNPs that were associated with risk of major depression in Caucasians.

Rather than honing in on genetically more homogeneous subtypes of depression, as the CONVERGE consortium did, 23andme traded less precise diagnosis for an increased sample size, and identified their SNPs by increasing their sample size to a whopping 300,000.

A conceptually similar approach allowed researchers from the Australian Queensland University of Technology to identify additional risk SNPs by combining patients with major and minor depression (MiDD). The Queensland team concluded that "broadening the case phenotype in GWA studies to include subthreshold definitions, such as MiDD, should facilitate the identification of additional genetic risk loci for depression."

Researchers from Hong Kong recently took what might be considered a perpendicular approach to utilizing GWAS results. Rather than using GWAS data to look for risk genes, they used a bioinformatics approach to derive gene expression profiles of individuals with psychiatric disorders. In a second step, they identified approved drugs that would target the genes whose expression was altered in psychiatric disorders.

Using that approach, corresponding author Cheong-He So and his colleagues were able to identify multiple existing drugs that might be repositioned for psychiatric disorders. Among the drugs identified were several that affected glutamatergic signaling. But the mechanisms appear to be varied, including phosphodiesterase inhibitor papaverine, which is used to treat smooth muscle spasms, the anti-nausea muscarinic inhibitor scopolamine, and the cortisol-lowering antifungal ketoconazole.

So Cheong-He So told BioWorld MedTech that one advantage of his team's approach is that "we only require the expression profile of the drugs to be specified; this method can therefore be applied to drugs with unknown targets or mechanisms, such as many Chinese medicinal products. Another advantage is by using GWAS-imputed transcriptome profiles, the disease expression data is not affected by prior use of medications which may confound the results."

Published  September 13, 2017

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