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Applying polygenic risk scores to postpartum depression

Overview of attention for article published in Archives of Women's Mental Health, July 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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Title
Applying polygenic risk scores to postpartum depression
Published in
Archives of Women's Mental Health, July 2014
DOI 10.1007/s00737-014-0428-5
Pubmed ID
Authors

Enda M. Byrne, Tania Carrillo-Roa, Brenda W. J. H. Penninx, Hannah M. Sallis, Alexander Viktorin, Brett Chapman, Anjali K. Henders, Psychiatric Genomic Consortium Major Depressive Disorder Working Group, Michele L. Pergadia, Andrew C. Heath, Pamela A. F. Madden, Patrick F. Sullivan, Lynn Boschloo, Gerard van Grootheest, George McMahon, Debbie A. Lawlor, Mikael Landén, Paul Lichtenstein, Patrik K. E. Magnusson, David M. Evans, Grant W. Montgomery, Dorret I. Boomsma, Nicholas G. Martin, Samantha Meltzer-Brody, Naomi R. Wray

Abstract

The etiology of major depressive disorder (MDD) is likely to be heterogeneous, but postpartum depression (PPD) is hypothesized to represent a more homogenous subset of MDD. We use genome-wide SNP data to explore this hypothesis. We assembled a total cohort of 1,420 self-report cases of PPD and 9,473 controls with genome-wide genotypes from Australia, The Netherlands, Sweden and the UK. We estimated the total variance attributable to genotyped variants. We used association results from the Psychiatric Genomics Consortia (PGC) of bipolar disorder (BPD) and MDD to create polygenic scores in PPD and related MDD data sets to estimate the genetic overlap between the disorders. We estimated that the percentage of variance on the liability scale explained by common genetic variants to be 0.22 with a standard error of 0.12, p = 0.02. The proportion of variance (R (2)) from a logistic regression of PPD case/control status in all four cohorts on a SNP profile score weighted by PGC-BPD association results was small (0.1 %) but significant (p = 0.004) indicating a genetic overlap between BPD and PPD. The results were highly significant in the Australian and Dutch cohorts (R (2) > 1.1 %, p < 0.008), where the majority of cases met criteria for MDD. The genetic overlap between BPD and MDD was not significant in larger Australian and Dutch MDD case/control cohorts after excluding PPD cases (R (2) = 0.06 %, p = 0.08), despite the larger MDD group affording more power. Our results suggest an empirical genetic evidence for a more important shared genetic etiology between BPD and PPD than between BPD and MDD.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 111 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 19%
Student > Ph. D. Student 15 13%
Student > Master 11 10%
Student > Bachelor 9 8%
Professor 8 7%
Other 20 18%
Unknown 28 25%
Readers by discipline Count As %
Medicine and Dentistry 23 21%
Psychology 16 14%
Biochemistry, Genetics and Molecular Biology 9 8%
Neuroscience 8 7%
Agricultural and Biological Sciences 7 6%
Other 17 15%
Unknown 32 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 06 July 2017.
All research outputs
#1,400,045
of 25,576,275 outputs
Outputs from Archives of Women's Mental Health
#97
of 1,034 outputs
Outputs of similar age
#13,665
of 240,399 outputs
Outputs of similar age from Archives of Women's Mental Health
#2
of 14 outputs
Altmetric has tracked 25,576,275 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,034 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.9. This one has done particularly well, scoring higher than 90% 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 240,399 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 94% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.