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A Genome-Wide Association Study of Depressive Symptoms
Journal article   Open access   Peer reviewed

A Genome-Wide Association Study of Depressive Symptoms

Karin Hek, Ayse Demirkan, Jari Lahti, Antonio Terracciano, Alexander Teumer, Marilyn C. Cornelis, Najaf Amin, Erin Bakshis, Jens Baumert, Jingzhong Ding, …
Biological psychiatry (1969), v 73(7), pp 667-678
01 Apr 2013
PMID: 23290196
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.biopsych.2012.09.033View
Published, Version of Record (VoR)Open Access (License Unspecified) Open

Abstract

Center for Epidemiologic Studies Depression Scale CHARGE consortium depression depressive symptoms genetics genome-wide association study meta-analysis
Depression is a heritable trait that exists on a continuum of varying severity and duration. Yet, the search for genetic variants associated with depression has had few successes. We exploit the entire continuum of depression to find common variants for depressive symptoms. In this genome-wide association study, we combined the results of 17 population-based studies assessing depressive symptoms with the Center for Epidemiological Studies Depression Scale. Replication of the independent top hits (p<1×10−5) was performed in five studies assessing depressive symptoms with other instruments. In addition, we performed a combined meta-analysis of all 22 discovery and replication studies. The discovery sample comprised 34,549 individuals (mean age of 66.5) and no loci reached genome-wide significance (lowest p = 1.05×10−7). Seven independent single nucleotide polymorphisms were considered for replication. In the replication set (n = 16,709), we found suggestive association of one single nucleotide polymorphism with depressive symptoms (rs161645, 5q21, p = 9.19×10−3). This 5q21 region reached genome-wide significance (p = 4.78×10−8) in the overall meta-analysis combining discovery and replication studies (n = 51,258). The results suggest that only a large sample comprising more than 50,000 subjects may be sufficiently powered to detect genes for depressive symptoms.

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Collaboration types
Domestic collaboration
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Web of Science research areas
Neurosciences
Psychiatry
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