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An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation

Overview of attention for article published in Genome Biology (Online Edition), August 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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Citations

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250 Dimensions

Readers on

mendeley
343 Mendeley
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5 CiteULike
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Title
An integrated genetic-epigenetic analysis of schizophrenia: evidence for co-localization of genetic associations and differential DNA methylation
Published in
Genome Biology (Online Edition), August 2016
DOI 10.1186/s13059-016-1041-x
Pubmed ID
Authors

Eilis Hannon, Emma Dempster, Joana Viana, Joe Burrage, Adam R. Smith, Ruby Macdonald, David St Clair, Colette Mustard, Gerome Breen, Sebastian Therman, Jaakko Kaprio, Timothea Toulopoulou, Hilleke E. Hulshoff Pol, Marc M. Bohlken, Rene S. Kahn, Igor Nenadic, Christina M. Hultman, Robin M. Murray, David A. Collier, Nick Bass, Hugh Gurling, Andrew McQuillin, Leonard Schalkwyk, Jonathan Mill

Abstract

Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease. This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

Twitter Demographics

The data shown below were collected from the profiles of 69 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 343 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Finland 1 <1%
Unknown 340 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 18%
Researcher 59 17%
Student > Postgraduate 39 11%
Student > Bachelor 36 10%
Student > Master 29 8%
Other 57 17%
Unknown 61 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 89 26%
Agricultural and Biological Sciences 60 17%
Medicine and Dentistry 33 10%
Neuroscience 28 8%
Psychology 20 6%
Other 32 9%
Unknown 81 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 121. 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 13 September 2017.
All research outputs
#289,449
of 22,790,780 outputs
Outputs from Genome Biology (Online Edition)
#144
of 4,114 outputs
Outputs of similar age
#6,471
of 336,745 outputs
Outputs of similar age from Genome Biology (Online Edition)
#7
of 52 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,114 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 96% 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 336,745 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 98% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.