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Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders

Overview of attention for article published in Genome Medicine, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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25 tweeters
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2 Facebook pages

Citations

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67 Dimensions

Readers on

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138 Mendeley
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Title
Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders
Published in
Genome Medicine, December 2017
DOI 10.1186/s13073-017-0497-y
Pubmed ID
Authors

Hoang T. Nguyen, Julien Bryois, April Kim, Amanda Dobbyn, Laura M. Huckins, Ana B. Munoz-Manchado, Douglas M. Ruderfer, Giulio Genovese, Menachem Fromer, Xinyi Xu, Dalila Pinto, Sten Linnarsson, Matthijs Verhage, August B. Smit, Jens Hjerling-Leffler, Joseph D. Buxbaum, Christina Hultman, Pamela Sklar, Shaun M. Purcell, Kasper Lage, Xin He, Patrick F. Sullivan, Eli A. Stahl

Abstract

Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified. We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls). For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq. We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

Twitter Demographics

The data shown below were collected from the profiles of 25 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 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 138 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 18%
Researcher 20 14%
Student > Bachelor 17 12%
Student > Master 14 10%
Student > Doctoral Student 6 4%
Other 23 17%
Unknown 33 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 17%
Medicine and Dentistry 15 11%
Neuroscience 14 10%
Agricultural and Biological Sciences 12 9%
Psychology 8 6%
Other 25 18%
Unknown 40 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 28 October 2019.
All research outputs
#2,450,698
of 20,649,055 outputs
Outputs from Genome Medicine
#567
of 1,335 outputs
Outputs of similar age
#68,874
of 436,199 outputs
Outputs of similar age from Genome Medicine
#58
of 100 outputs
Altmetric has tracked 20,649,055 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,335 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.5. This one has gotten more attention than average, scoring higher than 57% 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 436,199 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 84% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.