scholarly journals Erratum: Genes with de novo mutations are shared by four neuropsychiatric disorders discovered from NPdenovo database

2015 ◽  
Vol 21 (2) ◽  
pp. 298-298 ◽  
Author(s):  
Jinchen Li ◽  
Tao Cai ◽  
Yi Jiang ◽  
Huiqian Chen ◽  
Xin He ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
De Novo Mutations

scholarly journals mTADA: a framework for analyzing de novo mutations in multiple traits

2018 ◽  
Author(s):  
Hoang T. Nguyen ◽  
Amanda Dobbyn ◽  
Joseph Buxbaum ◽  
Dalila Pinto ◽  
Shaun M Purcell ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Joint Analysis ◽  
Data Sets ◽  
Analysis Method ◽  
De Novo Mutations ◽  
Multiple Traits ◽  
Risk Genes ◽  
Trait Analysis ◽  
Chd Risk

AbstractJoint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. In addition, approaches that consider multiple traits can aid in the characterization of shared genetic etiology among those traits. In recent years, parent-offspring trio studies have reported an enrichment of de novo mutations (DNMs) in neuropsychiatric disorders. The analysis of DNM data in the context of neuropsychiatric disorders has implicated multiple putatively causal genes, and a number of reported genes are shared across disorders. However, a joint analysis method designed to integrate de novo mutation data from multiple studies has yet to be implemented. We here introduce multi pi e-trait TAD A (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. mTADA uses two single-trait analysis data sets to estimate the proportion of overlapping risk genes, and reports genes shared between and specific to the relevant disorders. We applied mTADA to >13,000 trios for six disorders: schizophrenia (SCZ), autism spectrum disorder (ASD), developmental disorders (DD), intellectual disability (ID), epilepsy (EPI), and congenital heart disease (CHD). We report the proportion of overlapping risk genes and the specific risk genes shared for each pair of disorders. A total of 153 genes were found to be shared in at least one pair of disorders. The largest percentages of shared risk genes were observed for pairs of DD, ID, ASD, and CHD (>20%) whereas SCZ, CHD, and EPI did not show strong overlaps In risk gene set between them. Furthermore, mTADA identified additional SCZ, EPI and CHD risk genes through integration with DD de novo mutation data. For CHD, using DD information, 31 risk genes with posterior probabilities > 0.8 were identified, and 20 of these 31 genes were not in the list of known CHD genes. We find evidence that most significant CHD risk genes are strongly expressed in prenatal stages of the human genes. Finally, we validated our findings for CHD and EPI in independent cohorts comprising 1241 CHD trios, 226 CHD singletons and 197 EPI trios. Multiple novel risk genes identified by mTADA also had de novo mutations in these independent data sets. The joint analysis method introduced here, mTADA, is able to identify risk genes shared by two traits as well as additional risk genes not found through single-trait analysis only. A number of risk genes reported by mTADA are identified only through joint analysis, specifically when ASD, DD, or ID are one of the two traits examined. This suggests that novel genes for the trait or a new trait might converge to a core gene list of the three traits.

scholarly journals Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders

2017 ◽  
Author(s):  
Hon-Cheong So ◽  
Yui-Hang Wong
Keyword(s):  
Drug Discovery ◽  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Lipid Lowering ◽  
De Novo Mutations ◽  
Histone Deacetylase Inhibition ◽  
Gene Set ◽  
Drug Candidates ◽  
Drug Classes ◽  
Sequencing Studies

AbstractRecent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored.In this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations. Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.

scholarly journals Cross-Disorder Analysis of De Novo Mutations in Neuropsychiatric Disorders

2021 ◽  
Author(s):  
Kuokuo Li ◽  
Zhenghuan Fang ◽  
Guihu Zhao ◽  
Bin Li ◽  
Chao Chen ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Epileptic Encephalopathy ◽  
De Novo Mutations ◽  
Specific Expression ◽  
Functional Pathway ◽  
Specific Expression Pattern ◽  
Phenotype Similarity ◽  
Shared Genetic

AbstractThe clinical similarity among different neuropsychiatric disorders (NPDs) suggested a shared genetic basis. We catalogued 23,109 coding de novo mutations (DNMs) from 6511 patients with autism spectrum disorder (ASD), 4,293 undiagnosed developmental disorder (UDD), 933 epileptic encephalopathy (EE), 1022 intellectual disability (ID), 1094 schizophrenia (SCZ), and 3391 controls. We evaluated that putative functional DNMs contribute to 38.11%, 34.40%, 33.31%, 10.98% and 6.91% of patients with ID, EE, UDD, ASD and SCZ, respectively. Consistent with phenotype similarity and heterogeneity in different NPDs, they show different degree of genetic association. Cross-disorder analysis of DNMs prioritized 321 candidate genes (FDR < 0.05) and showed that genes shared in more disorders were more likely to exhibited specific expression pattern, functional pathway, genetic convergence, and genetic intolerance.

scholarly journals Genes with de novo mutations are shared by four neuropsychiatric disorders discovered from NPdenovo database

2015 ◽  
Vol 21 (2) ◽  
pp. 290-297 ◽  
Author(s):  
Jinchen Li ◽  
Tao Cai ◽  
Yi Jiang ◽  
Huiqian Chen ◽  
Xin He ◽  
...  
Keyword(s):  
De Novo ◽  
Neuropsychiatric Disorders ◽  
De Novo Mutations

scholarly journals Post-transcriptionally impaired de novo mutations contribute to the genetic etiology of four neuropsychiatric disorders

2017 ◽  
Author(s):  
Fengbiao Mao ◽  
Lu Wang ◽  
Xiaolu Zhao ◽  
Zhongshan Li ◽  
Luoyuan Xiao ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Genetic Disorders ◽  
Interaction Network ◽  
Neuropsychiatric Disorders ◽  
Protein Protein Interactions ◽  
De Novo Mutations ◽  
Coding Region

AbstractWhile deleterious de novo mutations (DNMs) in coding region conferring risk in neuropsychiatric disorders have been revealed by next-generation sequencing, the role of DNMs involved in post-transcriptional regulation in pathogenesis of these disorders remains to be elucidated. Here, we identified 1,736 post-transcriptionally impaired DNMs (piDNMs), and prioritized 1,482 candidate genes in four neuropsychiatric disorders from 7,748 families. Our results revealed higher prevalence of piDNMs in the probands than in controls (P = 8.19×10−17), and piDNM-harboring genes were enriched for epigenetic modifications and neuronal or synaptic functions. Moreover, we identified 86 piDNM-containing genes forming convergent co-expression modules and intensive protein-protein interactions in at least two neuropsychiatric disorders. These cross-disorder genes carrying piDNMs could form interaction network centered on RNA binding proteins, suggesting a shared post-transcriptional etiology underlying these disorders. Our findings illustrate the significant contribution of piDNMs to four neuropsychiatric disorders, and lay emphasis on combining functional and network-based evidences to identify regulatory causes of genetic disorders.

Sign in / Sign up

Export Citation Format

Share Document