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Depression gene search disappoints

Comprehensive effort to find DNA links to low mood comes up empty

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3:15pm, January 16, 2013
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A massive effort to uncover genes involved in depression has largely failed. By combing through the DNA of 34,549 volunteers, an international team of 86 scientists hoped to uncover genetic influences that affect a person’s vulnerability to depression. But the analysis turned up nothing.

The results are the latest in a string of large studies that have failed to pinpoint genetic culprits of depression. “I’m disappointed,” says study coauthor Henning Tiemeier of Erasmus Medical Center in Rotterdam, Netherlands. The negative finding, published online January 3 in Biological Psychiatry, “tells us that we have to be very modest,” he says. “Yet we think it’s doable to find some of the genes involved.”

Depression seems to run in families, leading scientists to think that certain genes are partially behind the disorder. But so far, studies on people diagnosed with depression have failed to reveal these genes.

Unlike earlier studies, the new study ignored depression diagnoses and instead focused solely on symptoms. Researchers combined the results of 17 studies that asked volunteers the same set of 20 questions about their emotional health at the time of the questionnaire. A person with many signs of depression scored high on the index (called CES-D), while a person with few signs scored low. The researchers hoped that capturing the continuum of symptoms — instead of a black-and-white depression diagnosis — would be a better way to ferret out the genes involved in depression.

No such luck. The initial results turned up zero genetic signatures associated with depressive symptoms. Adding more volunteers from studies that used different measures of depression didn’t help much either: After boosting the number of volunteers to 51,258, only one spot in the entire genome was associated with depressive symptoms, and that spot wasn’t close to any genes.

Some scientists say that the paltry findings are not surprising, given the study design. The researchers used several different measures of depression and combined samples of different ages, socioeconomic statuses and averages of symptom severity, making the results hard to parse. “There were lots of things thrown together in the same pot,” says psychiatrist and neuroscientist Elisabeth Binder of the Max Planck Institute of Psychiatry in Munich. “For me, it was hard to interpret.”

And a person’s depressive symptoms can fluctuate wildly over a lifetime, so symptoms at the time of the survey may not capture the whole story, says psychiatrist Douglas Levinson of Stanford University. What’s more, because the study measured symptoms and not diagnoses, the actual number of people with depression in the study is lower than in earlier genetic studies. “It’s not more powerful” than those earlier experiments, says Levinson. “It’s different.”

Despite these caveats, the results test an important question, says psychiatrist Steven Hamilton of Kaiser Permanente San Francisco Medical Center. “It was great that it was published,” because the study answered whether sliding scales of symptoms could be a useful way to study depression. “It was a very reasonable hypothesis, and people were interested in it,” says Hamilton, formerly of the University of California, San Francisco.

Although some disagree, many researchers believe that still larger experiments could uncover a genetic basis of depression. “My personal take on it all is that we need much larger samples … . The question is, how are we going to achieve that?” says Levinson.

But Tiemeier and his colleagues are undeterred, already planning a larger study that includes patients with depression diagnoses, he says. “To be negative and to give up is far too early.”
Citations

K. Hek et al. A genome-wide association study of depressive symptoms. Biological Psychiatry. doi: 10.1016/j.biopsych.2012.09.033. Available online: [Go to]
Further Reading

B. Bower. Gene makes kids more vulnerable to bullying's effects. Science News. Vol. 177, June 19, 2010, p. 13. Available online: [Go to]

T. Hesman Saey. Genetic dark matter. Science News. Vol. 178, December 18, 2010, p. 18. Available online: [Go to]

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