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

Genome Medicine

Matthijs Verhage¹  Xin He²  Julien Bryois³  Patrick F. Sullivan³  Christina Hultman³  August B. Smit⁴  Douglas M. Ruderfer⁵  Hoang T. Nguyen⁶  Laura M. Huckins⁶  Pamela Sklar⁶  Dalila Pinto⁶  Amanda Dobbyn⁶  Shaun M. Purcell⁶  Eli A. Stahl⁶  Jens Hjerling-Leffler⁷  Sten Linnarsson⁷  Ana B. Munoz-Manchado⁷  Xinyi Xu⁸  Joseph D. Buxbaum⁸  Kasper Lage⁹  Giulio Genovese⁹  April Kim⁹  Menachem Fromer^1,10 

[1] Department of Functional Genomics, The Center for Neurogenomics and Cognitive Research, VU University and VU Medical Center;Department of Human Genetics, University of Chicago;Department of Medical Epidemiology and Biostatistics, Karolinska Institutet;Department of Molecular and Cellular Neurobiology, The Center for Neurogenomics and Cognitive Research, VU University;Division of Genetic Medicine, Departments of Medicine, Psychiatry and Biomedical Informatics, Vanderbilt Genetics Institute, Vanderbilt University Medical Center;Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai;Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet;Seaver Autism Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai;Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard;Verily Life Sciences;

关键词: De novo mutations;    Rare variants;    Schizophrenia;    Autism;    Developmental disorders;    Intellectual disability;   

DOI  :  10.1186/s13073-017-0497-y

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown