Novel Method Reveals New Genetic Information on Depression

Researchers extend their analysis of the genomic regions of a patient to understand depression diagnosis and treatment.


In a modern twist on clinical data gathering, crowd-sourcing has helped researchers identify “weak genetic signals” of depression. By combining data from the genetic information website and previous genetic research, researchers identified for the first time 15 regions of the genome that may be associated with depression in people of European ancestry. Their findings should help “make clear that this is a brain disease,” said Roy Perlis, MD, MSc, a lead investigator and associate professor of psychiatry at Harvard Medical School, “which we hope will decrease the stigma still associated with these kinds of illnesses.”

Other studies have, of course, investigated genetic components of depression. But they may have been too small to uncover the subtle effects of the many genes influencing the risk of depression, these researchers say.

In their first analysis, using data from > 300,000 people of European ancestry who had purchased genetic profiles on (and who consented to share their information with researchers), the researchers identified 2 genomic regions significantly associated with depression risk, including 1 previously associated with epilepsy and intellectual disability.

They then combined that information with data from genomewide association studies of 9,200 people with a history of depression and 9,500 controls, along with another group of 151,800 people with and without depression. That analysis revealed 15 genomic regions, including 17 specific sites, significantly associated with a diagnosis of depression. Several of the sites are located in or near genes known to be involved in brain development.

“The neurotransmitter-based models we are currently using to treat depression are more than 40 years old,” says Dr. Perlis. “We really need new treatment targets. We hope that finding these genes will point us toward novel treatment strategies. “[T]he traditional way of doing genetic studies is not the only way that works. Using existing large datasets or biobanks may be far more efficient.”

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