A new genomic study has identified for the first time 15 regions of the genome that appear to be associated with depression in individuals of European ancestry.
In the study, investigators used a novel method of enrolling participants. Rather than recruiting participants and then sequencing genes, researchers analyzed data already shared by people who had purchased their own genetic profiles via an online service and had elected to participate in its research option.
This made it possible to leverage the statistical power of a huge sample size to detect weak genetic signals associated with a diagnosis likely traceable to multiple underlying illness processes. This creative use of crowd-sourced data was confirmed with results from traditional genetics approaches in the study.
Results of the study appear in an advance online publication in Nature Genetics.
“Identifying genes that affect risk for a disease is a first step towards understanding the disease biology itself, which gives us targets to aim for in developing new treatments,” says Roy Perlis, M.D., M.Sc., at Massachusetts General Hospital, co-corresponding author of the report.
“More generally, finding genes associated with depression should help make clear that this is a brain disease, which we hope will decrease the stigma still associated with these kinds of illnesses.”
While it is well known that depression can run in families, most previous genetic studies have been unable to identify variants influencing the risk for depression.
One study did find two genomic regions that may contribute to disease risk in Chinese women, but those variants are extremely rare in other ethnic groups.
Perlis and his colleagues note that the many different forms in which depression appears and affects patients imply that, as with other psychiatric disorders, it is probably influenced by many genes with effects that could be too subtle to be found in previous, relatively small studies.
Unlike traditional methods of recruiting study participants — which involve seeking and enrolling prospective participants and then conducting comprehensive interviews before actually genotyping each individual — this study utilized data collected from customers of 23andMe, a direct-to-consumer genetic testing company, who consented to participate in research.
Participation includes responding to surveys and completing information about medical history, as well as physical and demographic information. 23andMe’s researcher platform uses aggregated non-identifying data.
For the current study, the investigators first analyzed common genetic variation using data from more than 300,000 individuals of European ancestry from the 23andMe database, more than 75,000 of whom reported having been diagnosed with or treated for depression and more than 230,000 with no reported history of depression.
That analysis identified two genomic regions — one containing a poorly understood gene known to be expressed in the brain and another containing a gene previously associated with epilepsy and intellectual disability — as significantly associated with depression risk.
The research team combined that information with data from a group of smaller genome-wide association studies enrolling around 9,200 individuals with a history of depression and 9,500 controls and then more closely analyzed sites of possible risk genes in samples from another group of 23andMe clients — almost 45,800 with depression and 106,000 controls.
The results identified 15 genomic regions, including 17 specific sites, as significantly associated with a diagnosis of depression. Several of these 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, and we really need new treatment targets. We hope that finding these genes will point us toward novel treatment strategies,” said Perlis, associate professor of psychiatry at Harvard Medical School.
“Another key takeaway from our study is that the traditional way of doing genetic studies is not the only way that works. Using existing large datasets or biobanks may be far more efficient and may be helpful for other psychiatric disorders, such as anxiety disorders, where traditional approaches also have not been successful.”
Source: Massachusetts General Hospital