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

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

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Title: Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders
Author: Nguyen, Hoang T.; Bryois, Julien; Kim, April; Dobbyn, Amanda; Huckins, Laura M.; Munoz-Manchado, Ana B.; Ruderfer, Douglas M.; Genovese, Giulio; Fromer, Menachem; Xu, Xinyi; Pinto, Dalila; Linnarsson, Sten; Verhage, Matthijs; Smit, August B.; Hjerling-Leffler, Jens; Buxbaum, Joseph D.; Hultman, Christina; Sklar, Pamela; Purcell, Shaun M.; Lage, Kasper; He, Xin; Sullivan, Patrick F.; Stahl, Eli A.

Note: Order does not necessarily reflect citation order of authors.

Citation: Nguyen, H. T., J. Bryois, A. Kim, A. Dobbyn, L. M. Huckins, A. B. Munoz-Manchado, D. M. Ruderfer, et al. 2017. “Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders.” Genome Medicine 9 (1): 114. doi:10.1186/s13073-017-0497-y. http://dx.doi.org/10.1186/s13073-017-0497-y.
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Abstract: Background: 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. Methods: 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). Results: 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. Conclusions: 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. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0497-y) contains supplementary material, which is available to authorized users.
Published Version: doi:10.1186/s13073-017-0497-y
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738153/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34651901
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