Browse
Submit Data
Databases
API
Help

Dataset Information

14 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Targeted sequencing and integrative analysis to prioritize candidate genes in neurodevelopmental disorders.

ABSTRACT: Neurodevelopmental disorders (NDDs) are a group of diseases characterized by high heterogeneity and frequently co-occurring symptoms. The mutational spectrum in patients with NDDs is largely incomplete. Here, we sequenced 547 genes from 1102 patients with NDDs and validated 1271 potential functional variants, including 108 de novo variants (DNVs) in 78 autosomal genes and seven inherited hemizygous variants in six X chromosomal genes. Notably, 36 of these 78 genes are the first to be reported in Chinese patients with NDDs. By integrating our genetic data with public data, we prioritized 212 NDD candidate genes with FDR < 0.1, including 17 novel genes. The novel candidate genes interacted or were co-expressed with known candidate genes, forming a functional network involved in known pathways. We highlighted MSL2, which carried two de novo protein-truncating variants (p.L192Vfs*3 and p.S486Ifs*11) and was frequently connected with known candidate genes. This study provides the mutational spectrum of NDDs in China and prioritizes 212 NDD candidate genes for further functional validation and genetic counseling.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC8280036 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders.

Project description:Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case-control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E-06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E-07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype-genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.

| S-EPMC7530681 | biostudies-literature

Diagnostic yield and novel candidate genes for neurodevelopmental disorders by exome sequencing in an unselected cohort with microcephaly.

Project description:ObjectivesMicrocephaly is caused by reduced brain volume and most usually associated with a variety of neurodevelopmental disorders (NDDs). To provide an overview of the diagnostic yield of whole exome sequencing (WES) and promote novel candidates in genetically unsolved families, we studied the clinical and genetic landscape of an unselected Chinese cohort of patients with microcephaly.MethodsWe performed WES in an unselected cohort of 103 NDDs patients with microcephaly as one of the features. Full evaluation of potential novel candidate genes was applied in genetically undiagnosed families. Functional validations of selected variants were conducted in cultured cells. To augment the discovery of novel candidates, we queried our genomic sequencing data repository for additional likely disease-causing variants in the identified candidate genes.ResultsIn 65 families (63.1%), causative sequence variants (SVs) and clinically relevant copy number variants (CNVs) with a pathogenic or likely pathogenic (P/LP) level were identified. By incorporating coverage analysis to WES, a pathogenic or likely pathogenic CNV was detected in 15 families (16/103, 15.5%). In another eight families (8/103, 7.8%), we identified variants in newly reported gene (CCND2) and potential novel neurodevelopmental disorders /microcephaly candidate genes, which involved in cell cycle and division (PWP2, CCND2), CDC42/RAC signaling related actin cytoskeletal organization (DOCK9, RHOF), neurogenesis (ELAVL3, PPP1R9B, KCNH3) and transcription regulation (IRF2BP1). By looking into our data repository of 5066 families with NDDs, we identified additional two cases with variants in DOCK9 and PPP1R9B, respectively.ConclusionOur results expand the morbid genome of monogenic neurodevelopmental disorders and support the adoption of WES as a first-tier test for individuals with microcephaly.

| S-EPMC10373276 | biostudies-literature

Integrating human omics data to prioritize candidate genes.

Project description:BACKGROUND: The identification of genes involved in human complex diseases remains a great challenge in computational systems biology. Although methods have been developed to use disease phenotypic similarities with a protein-protein interaction network for the prioritization of candidate genes, other valuable omics data sources have been largely overlooked in these methods. METHODS: With this understanding, we proposed a method called BRIDGE to prioritize candidate genes by integrating disease phenotypic similarities with such omics data as protein-protein interactions, gene sequence similarities, gene expression patterns, gene ontology annotations, and gene pathway memberships. BRIDGE utilizes a multiple regression model with lasso penalty to automatically weight different data sources and is capable of discovering genes associated with diseases whose genetic bases are completely unknown. RESULTS: We conducted large-scale cross-validation experiments and demonstrated that more than 60% known disease genes can be ranked top one by BRIDGE in simulated linkage intervals, suggesting the superior performance of this method. We further performed two comprehensive case studies by applying BRIDGE to predict novel genes and transcriptional networks involved in obesity and type II diabetes. CONCLUSION: The proposed method provides an effective and scalable way for integrating multi omics data to infer disease genes. Further applications of BRIDGE will be benefit to providing novel disease genes and underlying mechanisms of human diseases.

| S-EPMC3878333 | biostudies-literature

Novel candidate genes and variants underlying autosomal recessive neurodevelopmental disorders with intellectual disability.

Project description:Identification of Mendelian genes for neurodevelopmental disorders using exome sequencing to study autosomal recessive (AR) consanguineous pedigrees has been highly successful. To identify causal variants for syndromic and non-syndromic intellectual disability (ID), exome sequencing was performed using DNA samples from 22 consanguineous Pakistani families with ARID, of which 21 have additional phenotypes including microcephaly. To aid in variant identification, homozygosity mapping and linkage analysis were performed. DNA samples from affected family member(s) from every pedigree underwent exome sequencing. Identified rare damaging exome variants were tested for co-segregation with ID using Sanger sequencing. For seven ARID families, variants were identified in genes not previously associated with ID, including: EI24, FXR1 and TET3 for which knockout mouse models have brain defects; and CACNG7 and TRAPPC10 where cell studies suggest roles in important neural pathways. For two families, the novel ARID genes CARNMT1 and GARNL3 lie within previously reported ID microdeletion regions. We also observed homozygous variants in two ID candidate genes, GRAMD1B and TBRG1, for which each has been previously reported in a single family. An additional 14 families have homozygous variants in established ID genes, of which 11 variants are novel. All ARID genes have increased expression in specific structures of the developing and adult human brain and 91% of the genes are differentially expressed in utero or during early childhood. The identification of novel ARID candidate genes and variants adds to the knowledge base that is required to further understand human brain function and development.

| S-EPMC6201268 | biostudies-literature

Using Integrative Analysis of DNA Methylation and Gene Expression Data in Multiple Tissue Types to Prioritize Candidate Genes for Drug Development in Obesity.

Project description:Obesity has become a major public health issue which is caused by a combination of genetic and environmental factors. Genome-wide DNA methylation studies have identified that DNA methylation at Cytosine-phosphate-Guanine (CpG) sites are associated with obesity. However, subsequent functional validation of the results from these studies has been challenging given the high number of reported associations. In this study, we applied an integrative analysis approach, aiming to prioritize the drug development candidate genes from many associated CpGs. Association data was collected from previous genome-wide DNA methylation studies and combined using a sample-size-weighted strategy. Gene expression data in adipose tissues and enriched pathways of the affiliated genes were overlapped, to shortlist the associated CpGs. The CpGs with the most overlapping evidence were indicated as the most appropriate CpGs for future studies. Our results revealed that 119 CpGs were associated with obesity (p ≤ 1.03 × 10-7). Of the affiliated genes, SOCS3 was the only gene involved in all enriched pathways and was differentially expressed in both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). In conclusion, our integrative analysis is an effective approach in highlighting the DNA methylation with the highest drug development relevance. SOCS3 may serve as a target for drug development of obesity and its complications.

| S-EPMC6305755 | biostudies-literature

Targeted next-generation sequencing in family with an occurrence of neurodevelopmental disorders and epilepsy

Project description:We performed a targeted NGS using the commercial gene panel design ClearSeq Inherited Disease (Agilent Technologies) to identify the pathogenic sequence variants in two boys with neurodevelopmental disorders and epilepsy and their unaffected parents

2019-12-31 | E-MTAB-8131 | biostudies-arrayexpress

Integrative analysis to select cancer candidate biomarkers to targeted validation.

Project description:Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

| S-EPMC4791256 | biostudies-literature

Tissue specific protein-protein associations prioritize candidate disease associated genes

Project description:Proteins that interact together participate in the same cellular process and influence the same organismal traits. Despite the progress in mapping protein interactions we lack knowledge of how these change across tissues. Due to coordinated (post)transcriptional control, protein complex members have highly correlated abundances that are predictive of functional association. Here, we have compiled 7840 proteomic samples measuring protein levels in 11 human tissues and use these to define an atlas of tissue specific protein associations. This method recapitulates known protein complexes and the larger structural organization of the cell. Stable protein complexes are well preserved across tissues with signaling and metabolic interactions showing larger variation. Less than 20% of interaction changes across tissues are estimated to be due to protein expression differences with cell-type specific cellular structures, such as synaptic vesicles, being a second significant driver of differences. We further supported the brain protein association network through co-fraction experiments in synaptosomes, curation of brain derived pull-down data and AlphaFold2 models and illustrate how this network can functionally prioritize candidate genes within loci linked to brain disorders

2024-05-17 | PXD049084 | Pride

Using whole-exome sequencing and protein interaction networks to prioritize candidate genes for germline cutaneous melanoma susceptibility.

Project description:Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein-protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC, and PROSER2. The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein-protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.

| S-EPMC7560829 | biostudies-literature

Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

Project description:We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

| S-EPMC4090166 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data