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

Abstract

BackgroundIntegrating 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.MethodsWe 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).ResultsFor 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.ConclusionsWe 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 materialThe online version of this article (doi:10.1186/s13073-017-0497-y) contains supplementary material, which is available to authorized users.

Meta-analysis of Scandinavian Schizophrenia Exomes

“…Three genes showed significant association at FDR 0.10, including SETD1A, TAF13 and MKI67 (Table 1). These results lend further support to SETD1A and, by adding evidence to previous reports 5, 23,24 , suggest TAF13 and MKI67 as genes conveying risk of schizophrenia if hit by dURVs. TAF13, encoding a transcription initiation factor, is involved with neuronal development and homozygous TAF13 missense mutations may cause a Mendelian recessive form of intellectual disability and microcephaly 25 .…”

“…To increase the power for gene discovery we jointly analyzed our case-control dataset together with de novo mutation data from 1,077 case-parents trios 23 , using an extended version of the Transmission And De novo Association (extTADA) test developed by H. Nguyen and colleagues 24 . As recommended by the authors, we clustered the data in different subsets, based on their covariates matrix in order to minimise the effect of covariates, which cannot be accounted for in the model itself (Online Methods 7.1).…”

“…The extTADA analysis has been carried out as described in Nguyen et al 2017, and for the de-novo counts and mutation rates we used the sample-adjusted ratios of mutation counts between 1,077 cases and 731 control presented in Nguyen work. Figure S8 shows the pairs plot of the extTADA analysis, showing the estimates for pi0 and hypergamma on each of the dataset clusters identified in the clustering analysis.…”

Using three-dimensional regulatory chromatin interactions from adult and fetal cortex to interpret genetic results for psychiatric disorders and cognitive traits

“…We evaluated the salience of Hi-C readouts for genes implicated in rare variant studies of intellectual disability (i.e., the lower tail of the cognitive ability distribution). We compiled a gene set from literature reviews, OMIM, and exome sequencing studies [58][59][60] . We could not analyze schizophrenia because too few genes have been identified.…”

Measuring “Intolerance to Mutation” in Human Genetics

“…The pLI measure has been broadly applied in human genetics to help identify genes in which a single disrupting mutation is likely of clinical significance 4,[38][39][40][41][42][43][44][45] . pLI is also increasingly used in clinical annotation and in databases of mouse models as indicative of haploinsufficiency and dosage sensitivity [46][47][48][49][50] .…”