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Genome-wide Association for Major Depression Through Age at Onset Stratification: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

Overview of attention for article published in Biological Psychiatry, May 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
twitter
97 X users
patent
2 patents
facebook
2 Facebook pages
wikipedia
1 Wikipedia page

Citations

dimensions_citation
176 Dimensions

Readers on

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354 Mendeley
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Title
Genome-wide Association for Major Depression Through Age at Onset Stratification: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
Published in
Biological Psychiatry, May 2016
DOI 10.1016/j.biopsych.2016.05.010
Pubmed ID
Authors

Robert A. Power, Katherine E. Tansey, Henriette Nørmølle Buttenschøn, Sarah Cohen-Woods, Tim Bigdeli, Lynsey S. Hall, Zoltán Kutalik, S. Hong Lee, Stephan Ripke, Stacy Steinberg, Alexander Teumer, Alexander Viktorin, Naomi R. Wray, Volker Arolt, Bernard T. Baune, Dorret I. Boomsma, Anders D. Børglum, Enda M. Byrne, Enrique Castelao, Nick Craddock, Ian W. Craig, Udo Dannlowski, Ian J. Deary, Franziska Degenhardt, Andreas J. Forstner, Scott D. Gordon, Hans J. Grabe, Jakob Grove, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, Lynne J. Hocking, Georg Homuth, Jouke J. Hottenga, Stefan Kloiber, Jesper Krogh, Mikael Landén, Maren Lang, Douglas F. Levinson, Paul Lichtenstein, Susanne Lucae, Donald J. MacIntyre, Pamela Madden, Patrik K.E. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Christel M. Middeldorp, Yuri Milaneschi, Grant W. Montgomery, Ole Mors, Bertram Müller-Myhsok, Dale R. Nyholt, Hogni Oskarsson, Michael J. Owen, Sandosh Padmanabhan, Brenda W.J.H. Penninx, Michele L. Pergadia, David J. Porteous, James B. Potash, Martin Preisig, Margarita Rivera, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurdsson, Johannes H. Smit, Blair H. Smith, Hreinn Stefansson, Kari Stefansson, Jana Strohmaier, Patrick F. Sullivan, Pippa Thomson, Thorgeir E. Thorgeirsson, Sandra Van der Auwera, Myrna M. Weissman, CARDIoGRAM Consortium CONVERGE Consortium, Gerome Breen, Cathryn M. Lewis

Abstract

Major depressive disorder (MDD) is a disabling mood disorder, and despite a known heritable component, a large meta-analysis of genome-wide association studies revealed no replicable genetic risk variants. Given prior evidence of heterogeneity by age at onset in MDD, we tested whether genome-wide significant risk variants for MDD could be identified in cases subdivided by age at onset. Discovery case-control genome-wide association studies were performed where cases were stratified using increasing/decreasing age-at-onset cutoffs; significant single nucleotide polymorphisms were tested in nine independent replication samples, giving a total sample of 22,158 cases and 133,749 control subjects for subsetting. Polygenic score analysis was used to examine whether differences in shared genetic risk exists between earlier and adult-onset MDD with commonly comorbid disorders of schizophrenia, bipolar disorder, Alzheimer's disease, and coronary artery disease. We identified one replicated genome-wide significant locus associated with adult-onset (>27 years) MDD (rs7647854, odds ratio: 1.16, 95% confidence interval: 1.11-1.21, p = 5.2 × 10(-11)). Using polygenic score analyses, we show that earlier-onset MDD is genetically more similar to schizophrenia and bipolar disorder than adult-onset MDD. We demonstrate that using additional phenotype data previously collected by genetic studies to tackle phenotypic heterogeneity in MDD can successfully lead to the discovery of genetic risk factor despite reduced sample size. Furthermore, our results suggest that the genetic susceptibility to MDD differs between adult- and earlier-onset MDD, with earlier-onset cases having a greater genetic overlap with schizophrenia and bipolar disorder.

X Demographics

X Demographics

The data shown below were collected from the profiles of 97 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 354 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 <1%
Italy 1 <1%
United Kingdom 1 <1%
China 1 <1%
Russia 1 <1%
Unknown 348 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 57 16%
Student > Ph. D. Student 53 15%
Researcher 44 12%
Student > Bachelor 30 8%
Student > Doctoral Student 25 7%
Other 71 20%
Unknown 74 21%
Readers by discipline Count As %
Medicine and Dentistry 65 18%
Psychology 51 14%
Biochemistry, Genetics and Molecular Biology 38 11%
Neuroscience 28 8%
Agricultural and Biological Sciences 23 6%
Other 54 15%
Unknown 95 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 66. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 June 2023.
All research outputs
#657,380
of 25,711,998 outputs
Outputs from Biological Psychiatry
#436
of 6,626 outputs
Outputs of similar age
#12,448
of 349,854 outputs
Outputs of similar age from Biological Psychiatry
#8
of 62 outputs
Altmetric has tracked 25,711,998 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,626 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.5. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 349,854 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.