New biomarker to beat the blues?

Depression is the most common mental health disorder worldwide, but why does it strike some people down after everyday challenges, whereas other people sail through traumatic events seemingly unscathed? Depression seems to be inherited, but researchers have yet to track down the key gene (or genes) behind the disorder. But what if medical professionals could screen for a biomarker that picks out the people who are at risk, and treat them preemptively? Texas Biomedical Research Institute is putting in the groundwork.

Using Texas Biomedical Research Institute's AT&T Genomics Computing Center (GCC), teams of researchers at the institute and Yale University looked at blood samples from 1,122 people (40 Mexican-American extended families from Texas) in the genetics of brain structure and function study.

The researchers looked at over 11,000 endophenotypes--biomarkers linking phenotypes and genotypes--and found that the strongest link with depression was with the gene RNF123, and four genes that appear to control its expression. The levels of the associated protein in the blood could show who is vulnerable to developing major depression.

"We might be able to know in advance that a person will be less able to respond to the normal challenges that come about in life," said John Blangero, director of the GCC. "Then doctors may be able to intervene earlier after a traumatic life event to remove some of the debilitation of depression."

According to the researchers, endophenotype-based research could also be used to find out more about the biology behind other mental illnesses.

- read the release
- see the abstract
- check out the article from KENS5.com

Suggested Articles

BD will begin working with Babson Diagnostics to help bring its lab-quality device for collecting blood from capillaries into retail pharmacies.

The former CEO of the molecular testing company Foundation Medicine, Troy Cox, has been named chairman of the Swiss big data firm Sophia Genetics.

Researchers at MIT used a machine-learning algorithm to uncover the potent antibiotic properties hiding within an old small-molecule candidate.