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Neuroticism Could Lead to Depression, Says Largest-ever Genetic Analysis

Neuroticism Could Lead to Depression, Says Largest-ever Genetic Analysis  content piece image
Chord diagram of genes significantly associated (P< 2.80 × 10−6) with depression and the second-level Anatomical Therapeutic Chemical classifications to which interacting drugs have been assigned. The width of each line is determined by the number of drugs known to interact with each gene. Credit: Howard et al.
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A new study has utilized genetic datasets to uncover genes linked to depression. The research, conducted at the University of Edinburgh, identified 269 genes associated with the condition, which affected 16.2 million people in the US alone in 2016. Scientists also deployed innovative statistical techniques to connect specific behaviors to depression. Their analysis provides evidence that neurotic behaviors could lead people to become depressed and suggests that leveraging genomic data can help us understand the origins of mood disorders.

The paper, published in Nature Neuroscience, included evidence from three large datasets held by the UK Biobank, the Psychiatry Genomics Consortium and the privately held genomics research company 23andMe.

Authors identified 87 significant genetic variants associated with depression. Previous studies had identified that underlying genetics accounts for 30-40% of depression’s variance in the population. The new study is the largest of its kind ever conducted, including a meta-analysis of over 800,000 genomic data donors, and over two million people in total. Senior author Professor Andrew McIntosh, of the University of Edinburgh Centre for Clinical Brain Science, commented on the study’s contribution, “These findings are further evidence that depression is partly down to our genetics. We hope the findings will help us understand why some people are more at risk of depression than others, and how we might help people living with depression more effectively in future.”

Statistical technique finds causality

Whilst the 87 variants identified provide only associative linkage (for example, a high level of triglycerides in the bloodstream of depressed people does not necessarily mean that high triglyceride levels cause depression) the researchers took the analysis of their data a step further, using a statistical approach called Mendelian randomization to investigate the results. This technique overcomes the confounding factors which often reduce the utility of association studies by using the certainty of genetic variants, which are unchanged from conception. This analysis, conducted on 33 putative influential factors, showed that neuroticism was the only trait which had a causative unidirectional effect on depression.

Neuroticism, a tendency to be worried or fearful, is a stable personality trait. It has previously been associated with poorer physical and mental health.

Serotonin genes a curious omission 

There is hope that research using genetic data can identify much-needed avenues for the treatment of depression. “This large study is an important advance in understanding how genetic variability might contribute to risk for depression. Given that current treatments work for only half of those who need them, the study provides some intriguing clues for future research to follow up,” said Raliza Stoyanova, Wellcome’s Senior Portfolio Developer for neuroscience and mental health.

One of the potential clues that Stoyanova referred to is that whilst the analysis revealed as-yet investigated druggable genes associated with depression, there was an absence of genes related to the serotonergic system. Modulating this system using the SSRI class of drugs, which includes citalopram (Celexa) and fluoxetine (Prozac), is usually the first-choice pharmacologic approach for treating depression, although these drugs fail to be effective for a large number of patients

This suggests that whilst serotonin-associated genes may be relevant for the treatment of depression, there may be another network of genes responsible for the onset and origin of depression. The authors conclude by highlighting the need to leverage multiple data sources to better explore the pharmacology of depression.