scholarly journals Rare variants in neuronal excitability genes influence risk for bipolar disorder

2015 ◽  
Vol 112 (11) ◽  
pp. 3576-3581 ◽  
Author(s):  
Seth A. Ament ◽  
Szabolcs Szelinger ◽  
Gustavo Glusman ◽  
Justin Ashworth ◽  
Liping Hou ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Candidate Genes ◽  
Neuronal Excitability ◽  
Rare Variants ◽  
Association Studies ◽  
Missense Variant ◽  
Genome Wide Association Studies ◽  
Regulatory Variants ◽  
Genes Encoding ◽  
Coding Variants

We sequenced the genomes of 200 individuals from 41 families multiply affected with bipolar disorder (BD) to identify contributions of rare variants to genetic risk. We initially focused on 3,087 candidate genes with known synaptic functions or prior evidence from genome-wide association studies. BD pedigrees had an increased burden of rare variants in genes encoding neuronal ion channels, including subunits of GABAA receptors and voltage-gated calcium channels. Four uncommon coding and regulatory variants also showed significant association, including a missense variant in GABRA6. Targeted sequencing of 26 of these candidate genes in an additional 3,014 cases and 1,717 controls confirmed rare variant associations in ANK3, CACNA1B, CACNA1C, CACNA1D, CACNG2, CAMK2A, and NGF. Variants in promoters and 5′ and 3′ UTRs contributed more strongly than coding variants to risk for BD, both in pedigrees and in the case-control cohort. The genes and pathways identified in this study regulate diverse aspects of neuronal excitability. We conclude that rare variants in neuronal excitability genes contribute to risk for BD.

Candidate Genes for Nonsyndromic Cleft Palate Detected by Exome Sequencing

2017 ◽  
Vol 96 (11) ◽  
pp. 1314-1321 ◽  
Author(s):  
A.K. Hoebel ◽  
D. Drichel ◽  
M. van de Vorst ◽  
A.C. Böhmer ◽  
S. Sivalingam ◽  
...  
Keyword(s):  
Cleft Palate ◽  
Exome Sequencing ◽  
Candidate Genes ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Missense Variant ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Novel Variant

Nonsyndromic cleft palate only (nsCPO) is a facial malformation that has a livebirth prevalence of 1 in 2,500. Research suggests that the etiology of nsCPO is multifactorial, with a clear genetic component. To date, genome-wide association studies have identified only 1 conclusive common variant for nsCPO, that is, a missense variant in the gene grainyhead-like-3 ( GRHL3). Thus, the underlying genetic causes of nsCPO remain largely unknown. The present study aimed at identifying rare variants that might contribute to nsCPO risk, via whole-exome sequencing (WES), in multiply affected Central European nsCPO pedigrees. WES was performed in 2 affected first-degree relatives from each family. Variants shared between both individuals were analyzed for their potential deleterious nature and a low frequency in the general population. Genes carrying promising variants were annotated for 1) reported associations with facial development, 2) multiple occurrence of variants, and 3) expression in mouse embryonic palatal shelves. This strategy resulted in the identification of a set of 26 candidate genes that were resequenced in 132 independent nsCPO cases and 623 independent controls of 2 different ethnicities, using molecular inversion probes. No rare loss-of-function mutation was identified in either WES or resequencing step. However, we identified 2 or more missense variants predicted to be deleterious in each of 3 genes ( ACACB, PTPRS, MIB1) in individuals from independent families. In addition, the analyses identified a novel variant in GRHL3 in 1 patient and a variant in CREBBP in 2 siblings. Both genes underlie different syndromic forms of CPO. A plausible hypothesis is that the apparently nonsyndromic clefts in these 3 patients might represent hypomorphic forms of the respective syndromes. In summary, the present study identified rare variants that might contribute to nsCPO risk and suggests candidate genes for further investigation.

scholarly journals Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

2021 ◽  
Author(s):  
Abhishek Nag ◽  
Lawrence Middleton ◽  
Ryan S Dhindsa ◽  
Dimitrios Vitsios ◽  
Eleanor M Wigmore ◽  
...  
Keyword(s):  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Protein Coding ◽  
The Uk ◽  
Metabolic Biomarkers ◽  
Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

scholarly journals Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohns disease susceptibility

2021 ◽  
Author(s):  
Aleksejs Sazonovs ◽  
Christine R Stevens ◽  
Guhan R Venkataraman ◽  
Kai Yuan ◽  
Brandon Avila ◽  
...  
Keyword(s):  
Rare Variants ◽  
Disease Risk ◽  
Sequence Data ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Crohns Disease ◽  
Biological Targets ◽  
Genome Wide ◽  
Coding Variants ◽  
First Time

Genome-wide association studies (GWAS) have identified hundreds of loci associated with Crohns disease (CD); however, as with all complex diseases, deriving pathogenic mechanisms from these non-coding GWAS discoveries has been challenging. To complement GWAS and better define actionable biological targets, we analysed sequence data from more than 30,000 CD cases and 80,000 population controls. We observe rare coding variants in established CD susceptibility genes as well as ten genes where coding variation directly implicates the gene in disease risk for the first time.

scholarly journals Integrating comprehensive functional annotations to boost power and accuracy in gene-based association analysis

PLoS Genetics ◽  
2020 ◽  
Vol 16 (12) ◽  
pp. e1009060
Author(s):  
Corbin Quick ◽  
Xiaoquan Wen ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
Hyun Min Kang
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
Early Gene ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Regulatory Variants ◽  
Causal Genes ◽  
And Performance ◽  
The Uk ◽  
Coding Variants

Gene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

scholarly journals Predicting target genes of non-coding regulatory variants with IRT

2020 ◽  
Vol 36 (16) ◽  
pp. 4440-4448 ◽  
Author(s):  
Zhenqin Wu ◽  
Nilah M Ioannidis ◽  
James Zou
Keyword(s):  
Target Genes ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Association Studies ◽  
Characteristic Curve ◽  
Gc Content ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Regulatory Variants ◽  
Coding Variants

Abstract Summary Interpreting genetic variants of unknown significance (VUS) is essential in clinical applications of genome sequencing for diagnosis and personalized care. Non-coding variants remain particularly difficult to interpret, despite making up a large majority of trait associations identified in genome-wide association studies (GWAS) analyses. Predicting the regulatory effects of non-coding variants on candidate genes is a key step in evaluating their clinical significance. Here, we develop a machine-learning algorithm, Inference of Connected expression quantitative trait loci (eQTLs) (IRT), to predict the regulatory targets of non-coding variants identified in studies of eQTLs. We assemble datasets using eQTL results from the Genotype-Tissue Expression (GTEx) project and learn to separate positive and negative pairs based on annotations characterizing the variant, gene and the intermediate sequence. IRT achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.799 using random cross-validation, and 0.700 for a more stringent position-based cross-validation. Further evaluation on rare variants and experimentally validated regulatory variants shows a significant enrichment in IRT identifying the true target genes versus negative controls. In gene-ranking experiments, IRT achieves a top-1 accuracy of 50% and top-3 accuracy of 90%. Salient features, including GC-content, histone modifications and Hi-C interactions are further analyzed and visualized to illustrate their influences on predictions. IRT can be applied to any VUS of interest and each candidate nearby gene to output a score reflecting the likelihood of regulatory effect on the expression level. These scores can be used to prioritize variants and genes to assist in patient diagnosis and GWAS follow-up studies. Availability and implementation Codes and data used in this work are available at https://github.com/miaecle/eQTL_Trees. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Low tolerance for transcriptional variation at cohesin genes is accompanied by functional links to disease-relevant pathways

2020 ◽  
pp. jmedgenet-2020-107095
Author(s):  
William Schierding ◽  
Julia A Horsfield ◽  
Justin M O'Sullivan
Keyword(s):  
Association Studies ◽  
Genome Wide Association Studies ◽  
Cohesin Complex ◽  
Loss Of Function ◽  
Regulatory Variants ◽  
Genome Wide ◽  
Genes Encoding ◽  
Full Complement ◽  
Transcriptional Changes ◽  
Robust Regulation

Background: The cohesin complex plays an essential role in genome organisation and cell division. A full complement of the cohesin complex and its regulators is important for normal development, since heterozygous mutations in genes encoding these components can be sufficient to produce a disease phenotype. The implication that genes encoding the cohesin subunits or cohesin regulators must be tightly controlled and resistant to variability in expression has not yet been formally tested.Methods: Here, we identify spatial-regulatory connections with potential to regulate expression of cohesin loci (Mitotic: SMC1A, SMC3, STAG1, STAG2, RAD21/RAD21-AS; Meiotic: SMC1B, STAG3, REC8, RAD21L1), cohesin-ring support genes (NIPBL, MAU2, WAPL, PDS5A, PDS5B) and CTCF, including linking their expression to that of other genes. We searched the genome-wide association studies (GWAS) catalogue for SNPs mapped or attributed to cohesin genes by GWAS (GWAS-attributed) and the GTEx catalogue for SNPs mapped to cohesin genes by cis-regulatory variants in one or more of 44 tissues across the human body (expression quantitative trail locus-attributed).Results: Connections that centre on the cohesin ring subunits provide evidence of coordinated regulation that has little tolerance for perturbation. We used the CoDeS3D SNP-gene attribution methodology to identify transcriptional changes across a set of genes coregulated with the cohesin loci that include biological pathways such as extracellular matrix production and proteasome-mediated protein degradation. Remarkably, many of the genes that are coregulated with cohesin loci are themselves intolerant to loss-of-function.Conclusions: The results highlight the importance of robust regulation of cohesin genes and implicate novel pathways that may be important in the human cohesinopathy disorders.

scholarly journals Exome sequencing in families with severe mental illness identifies novel and rare variants in genes implicated in Mendelian neuropsychiatric syndromes

2018 ◽  
Author(s):  
Suhas Ganesh ◽  
Ahmed P Husayn ◽  
Ravi Kumar Nadella ◽  
Ravi Prabhakar More ◽  
Manasa Sheshadri ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Exome Sequencing ◽  
Rare Variants ◽  
Association Studies ◽  
Mental Illnesses ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Genome Wide Association Studies ◽  
Complex Disorders ◽  
Family Based

AbstractIntroductionSevere Mental Illnesses (SMI), such as bipolar disorder and schizophrenia, are highly heritable, and have a complex pattern of inheritance. Genome wide association studies detect a part of the heritability, which can be attributed to common genetic variation. Examination of rare variants with Next Generation Sequencing (NGS) may add to the understanding of genetic architecture of SMIs.MethodsWe analyzed 32 ill subjects (with diagnosis of Bipolar Disorder, n=26; schizophrenia, n=4; schizoaffective disorder, n=1 schizophrenia like psychosis, n=1) from 8 multiplex families; and 33 healthy individuals by whole exome sequencing. Prioritized variants were selected by a 4-step filtering process, which included deleteriousness by 5 in silico algorithms; sharing within families, absence in the controls and rarity in South Asian sample of Exome Aggregation Consortium.ResultsWe identified a total of 42 unique rare, non-synonymous deleterious variants in this study with an average of 5 variants per family. None of the variants were shared across families, indicating a ‘private’ mutational profile. Twenty (47.6%) of the variant harboring genes identified in this sample have been previously reported to contribute to the risk of neuropsychiatric syndromes. These include genes which are related to neurodevelopmental processes, or have been implicated in different monogenic syndromes with a severe neurodevelopmental phenotype.ConclusionNGS approaches in family based studies are useful to identify novel and rare variants in genes for complex disorders like SMI. The study further validates the phenotypic burden of rare variants in Mendelian disease genes, indicating pleiotropic effects in the etiology of severe mental illnesses.

scholarly journals Integrating Comprehensive Functional Annotations to Boost Power and Accuracy in Gene-Based Association Analysis

2019 ◽  
Author(s):  
Corbin Quick ◽  
Xiaoquan Wen ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
Hyun Min Kang
Keyword(s):  
Association Analysis ◽  
Association Studies ◽  
Early Gene ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Regulatory Variants ◽  
Causal Genes ◽  
And Performance ◽  
The Uk ◽  
Coding Variants

AbstractGene-based association tests aggregate genotypes across multiple variants for each gene, providing an interpretable gene-level analysis framework for genome-wide association studies (GWAS). Early gene-based test applications often focused on rare coding variants; a more recent wave of gene-based methods, e.g. TWAS, use eQTLs to interrogate regulatory associations. Regulatory variants are expected to be particularly valuable for gene-based analysis, since most GWAS associations to date are non-coding. However, identifying causal genes from regulatory associations remains challenging and contentious. Here, we present a statistical framework and computational tool to integrate heterogeneous annotations with GWAS summary statistics for gene-based analysis, applied with comprehensive coding and tissue-specific regulatory annotations. We compare power and accuracy identifying causal genes across single-annotation, omnibus, and annotation-agnostic gene-based tests in simulation studies and an analysis of 128 traits from the UK Biobank, and find that incorporating heterogeneous annotations in gene-based association analysis increases power and performance identifying causal genes.

scholarly journals Genetics of bipolar disorder

2008 ◽  
Vol 10 (2) ◽  
pp. 141-152
Keyword(s):  
Bipolar Disorder ◽  
Candidate Genes ◽  
Current Knowledge ◽  
Association Studies ◽  
Genetic Maps ◽  
Behavioral Disorder ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Positional Candidate ◽  
Replication Studies

Bipolar disorder especially the most severe type (type I), has a strong genetic component. Family studies suggest that a small number of genes of modest effect are involved in this disorder. Family-based studies have identified a number of chromosomal regions linked to bipolar disorder, and progress is currently being made in identifying positional candidate genes within those regions. A number of candidate genes have also shown evidence of association with bipolar disorder, and genome-wide association studies are now under way, using dense genetic maps. Replication studies in larger or combined datasets are needed to definitively assign a role for specific genes in this disorder. This review covers our current knowledge of the genetics of bipolar disorder, and provides a commentary on current approaches used to identify the genes involved in this complex behavioral disorder.

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