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Developmental Changes Within the Genetic Architecture of Social Communication Behavior: A Multivariate Study of Genetic Variance in Unrelated Individuals

Overview of attention for article published in Biological Psychiatry, April 2018
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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 (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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17 tweeters
facebook
1 Facebook page

Citations

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2 Dimensions

Readers on

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40 Mendeley
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Title
Developmental Changes Within the Genetic Architecture of Social Communication Behavior: A Multivariate Study of Genetic Variance in Unrelated Individuals
Published in
Biological Psychiatry, April 2018
DOI 10.1016/j.biopsych.2017.09.020
Pubmed ID
Authors

Beate St Pourcain, Lindon J. Eaves, Susan M. Ring, Simon E. Fisher, Sarah Medland, David M. Evans, George Davey Smith

Abstract

Recent analyses of trait-disorder overlap suggest that psychiatric dimensions may relate to distinct sets of genes that exert maximum influence during different periods of development. This includes analyses of social communication difficulties that share, depending on their developmental stage, stronger genetic links with either autism spectrum disorder or schizophrenia. We developed a multivariate analysis framework in unrelated individuals to model directly the developmental profile of genetic influences contributing to complex traits, such as social communication difficulties, during an approximately 10-year period spanning childhood and adolescence. Longitudinally assessed quantitative social communication problems (N ≤ 5551) were studied in participants from a United Kingdom birth cohort (Avon Longitudinal Study of Parents and Children; age range, 8-17 years). Using standardized measures, genetic architectures were investigated with novel multivariate genetic-relationship-matrix structural equation models incorporating whole-genome genotyping information. Analogous to twin research, genetic-relationship-matrix structural equation models included Cholesky decomposition, common pathway, and independent pathway models. A two-factor Cholesky decomposition model described the data best. One genetic factor was common to Social Communication Disorder Checklist measures across development; the other accounted for independent variation at 11 years and later, consistent with distinct developmental profiles in trait-disorder overlap. Importantly, genetic factors operating at 8 years explained only approximately 50% of genetic variation at 17 years. Using latent factor models, we identified developmental changes in the genetic architecture of social communication difficulties that enhance the understanding of autism spectrum disorder- and schizophrenia-related dimensions. More generally, genetic-relationship-matrix structural equation models present a framework for modeling shared genetic etiologies between phenotypes and can provide prior information with respect to patterns and continuity of trait-disorder overlap.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Unspecified 8 20%
Researcher 8 20%
Student > Bachelor 4 10%
Student > Master 4 10%
Other 5 13%
Readers by discipline Count As %
Neuroscience 9 23%
Unspecified 8 20%
Psychology 6 15%
Medicine and Dentistry 5 13%
Social Sciences 3 8%
Other 9 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 14 May 2018.
All research outputs
#1,340,603
of 12,272,106 outputs
Outputs from Biological Psychiatry
#1,055
of 4,360 outputs
Outputs of similar age
#48,012
of 273,414 outputs
Outputs of similar age from Biological Psychiatry
#30
of 75 outputs
Altmetric has tracked 12,272,106 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,360 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. This one has done well, scoring higher than 82% 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 273,414 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 75 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.