Literature DB >> 22915352

Dissecting the genetic heterogeneity of depression through age at onset.

Robert A Power1, Robert Keers, Mandy Y Ng, Amy W Butler, Rudolf Uher, Sarah Cohen-Woods, Marcus Ising, Nick Craddock, Michael J Owen, Ania Korszun, Lisa Jones, Ian Jones, Michael Gill, John P Rice, Joanna Hauser, Neven Henigsberg, Wolfgang Maier, Astrid Zobel, Ole Mors, Anna S Placentino, Marcella Rietschel, Daniel Souery, Dejan Kozel, Martin Preisig, Susanne Lucae, Elisabeth B Binder, Katherine J Aitchison, Federica Tozzi, Pierandrea Muglia, Gerome Breen, Ian W Craig, Anne E Farmer, Bertram Müller-Myhsok, Peter McGuffin, Cathryn M Lewis.   

Abstract

Genome-wide studies in major depression have identified few replicated associations, potentially due to heterogeneity within the disorder. Several studies have suggested that age at onset (AAO) can distinguish sub-types of depression with specific heritable components. This paper investigates the role of AAO in the genetic susceptibility for depression using genome-wide association data on 2,746 cases and 1,594 screened controls from the RADIANT studies, with replication performed in 1,471 cases and 1,403 controls from two Munich studies. Three methods were used to analyze AAO: First a time-to-event analysis with controls censored, secondly comparing controls to case-subsets defined using AAO cut-offs, and lastly analyzing AAO as a quantitative trait. In the time-to-event analysis three SNPs reached suggestive significance (P < 5E-06), overlapping with the original case-control analysis of this study. In a case-control analysis using AAO thresholds, SNPs in 10 genomic regions showed suggestive association though again none reached genome-wide significance. Lastly, case-only analysis of AAO as a quantitative trait resulted in 5 SNPs reaching suggestive significance. Sex specific analysis was performed as a secondary analysis, yielding one SNP reaching genome-wide significance in early-onset males. No SNPs achieved significance in the replication study after correction for multiple testing. Analysis of AAO as a quantitative trait did suggest that, across all SNPs, common genetic variants explained a large proportion of the variance (51%, P = 0.04). This study provides the first focussed analysis of the genetic contribution to AAO in depression, and establishes a statistical framework that can be applied to a quantitative trait underlying any disorder. 2012 Wiley Periodicals, Inc

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Year:  2012        PMID: 22915352     DOI: 10.1002/ajmg.b.32093

Source DB:  PubMed          Journal:  Am J Med Genet B Neuropsychiatr Genet        ISSN: 1552-4841            Impact factor:   3.568


  19 in total

1.  Age of onset and family history as indicators of polygenic risk for major depression.

Authors:  Anna R Docherty; Alexis C Edwards; Fuzhong Yang; Roseann E Peterson; Chelsea Sawyers; Daniel E Adkins; Ashlee A Moore; Bradley T Webb; Silviu A Bacanu; Jonathan Flint; Kenneth S Kendler
Journal:  Depress Anxiety       Date:  2017-02-02       Impact factor: 6.505

Review 2.  Genetic determinants of depression: recent findings and future directions.

Authors:  Erin C Dunn; Ruth C Brown; Yael Dai; Jonathan Rosand; Nicole R Nugent; Ananda B Amstadter; Jordan W Smoller
Journal:  Harv Rev Psychiatry       Date:  2015 Jan-Feb       Impact factor: 3.732

3.  A Preliminary Study of Genetic Variation in the Dopaminergic and Serotonergic Systems and Genome-wide Additive Genetic Effects on Depression Severity and Treatment Response.

Authors:  Rohan H C Palmer; Christopher G Beevers; John E McGeary; Leslie A Brick; Valerie S Knopik
Journal:  Clin Psychol Sci       Date:  2016-10-19

4.  Stability and change in reported age of onset of depression, back pain, and smoking over 29 years in a prospective cohort study.

Authors:  Diana Paksarian; Lihong Cui; Jules Angst; Vladeta Ajdacic-Gross; Wulf Rössler; Kathleen R Merikangas
Journal:  J Psychiatr Res       Date:  2017-01-10       Impact factor: 4.791

5.  Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors.

Authors:  E I Fried; R M Nesse; K Zivin; C Guille; S Sen
Journal:  Psychol Med       Date:  2013-12-02       Impact factor: 7.723

6.  Additive genetic contribution to symptom dimensions in major depressive disorder.

Authors:  Rahel Pearson; Rohan H C Palmer; Leslie A Brick; John E McGeary; Valerie S Knopik; Christopher G Beevers
Journal:  J Abnorm Psychol       Date:  2016-05

7.  Genetic analyses benefit from using less heterogeneous phenotypes: an illustration with the hospital anxiety and depression scale (HADS).

Authors:  Charles A Laurin; Jouke-Jan Hottenga; Gonneke Willemsen; Dorret I Boomsma; Gitta H Lubke
Journal:  Genet Epidemiol       Date:  2015-04-01       Impact factor: 2.135

8.  Familiality and SNP heritability of age at onset and episodicity in major depressive disorder.

Authors:  P Ferentinos; A Koukounari; R Power; M Rivera; R Uher; N Craddock; M J Owen; A Korszun; L Jones; I Jones; M Gill; J P Rice; M Ising; W Maier; O Mors; M Rietschel; M Preisig; E B Binder; K J Aitchison; J Mendlewicz; D Souery; J Hauser; N Henigsberg; G Breen; I W Craig; A E Farmer; B Müller-Myhsok; P McGuffin; C M Lewis
Journal:  Psychol Med       Date:  2015-02-20       Impact factor: 7.723

9.  Distinctive Clinical Correlates of Psychotic Major Depression: The CRESCEND Study.

Authors:  Seon-Cheol Park; Hwa-Young Lee; Jeong-Kyu Sakong; Tae-Youn Jun; Min-Soo Lee; Jae-Min Kim; Jung-Bum Kim; Hyeon-Woo Yim; Yong Chon Park
Journal:  Psychiatry Investig       Date:  2014-07-21       Impact factor: 2.505

10.  Epidemiology and Heritability of Major Depressive Disorder, Stratified by Age of Onset, Sex, and Illness Course in Generation Scotland: Scottish Family Health Study (GS:SFHS).

Authors:  Ana Maria Fernandez-Pujals; Mark James Adams; Pippa Thomson; Andrew G McKechanie; Douglas H R Blackwood; Blair H Smith; Anna F Dominiczak; Andrew D Morris; Keith Matthews; Archie Campbell; Pamela Linksted; Chris S Haley; Ian J Deary; David J Porteous; Donald J MacIntyre; Andrew M McIntosh
Journal:  PLoS One       Date:  2015-11-16       Impact factor: 3.240

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