Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Exome Sequencing Of 75 Individuals From Multiply Affected Coeliac Families

ABSTRACT: Coeliac disease is a highly heritable common autoimmune disease involving chronic small intestinal inflammation in response to dietary wheat. The human leukocyte antigen (HLA) region, and 40 newer regions identified by genome wide association studies (GWAS) and dense fine mapping, account for ~40% of the disease heritability. We hypothesized that in pedigrees with multiple individuals with coeliac disease rare [minor allele frequency (MAF) <0.5%] mutations of larger effect size (odds ratios of ~ 2 – 5) might exist. We sequenced the exomes of 75 coeliac individuals of European ancestry from 55 multiply affected families. We selected interesting variants and genes for further follow up using a combination of: an assessment of shared variants between related subjects, a model-free linkage test, and gene burden tests for multiple, potentially causal, variants. We next performed highly multiplexed amplicon resequencing of all RefSeq exons from 24 candidate genes selected on the basis of the exome sequencing data in 2,248 unrelated coeliac cases and 2,230 controls. 1,335 variants with a 99.9% genotyping call rate were observed in 4,478 samples, of which 939 were present in coding regions of 24 genes (Ti/Tv 2.99). 91.7% of coding variants were rare (MAF <0.5%) and 60% were novel. Gene burden tests performed on rare functional variants identified no significant associations (P<1x10-3) in the resequenced candidate genes. Our strategy of sequencing multiply affected families with deep follow up of candidate genes has not identified any new coeliac disease risk mutations.

PROVIDER: EGAS00001001093 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

Project description:We investigated the cumulative contribution of rare, exonic genetic variants on the concentration of 1,487 metabolites and 53,714 metabolite ratios in urine by performing gene-based tests based on 226,233 variants from up to 4,864 participants of the German Chronic Kidney Disease (GCKD) study. There were 128 significant associations (53 metabolite-gene and 75 metabolite ratio-gene pairs) involving 30 unique genes, 16 of which are known to underlie recessively inherited inborn errors of metabolism (IEMs). Across the 30 genes, 47% of individuals carried at least one rare missense, stop or splice variant. The 30 genes were strongly enriched for shared high expression in liver and kidney (OR=65, p-FDR=3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of whole-exome sequencing data in the UK Biobank allowed for linking genes to diseases that could plausibly be explained by the identified metabolites. In silico constraint-based modeling of knockouts of the implicated genes in a virtual whole-body, organ-resolved metabolic human correctly predicted the observed direction of metabolite changes in urine and blood, highlighting the potential of linking population genetics to modeling to validate associations and to predict metabolic consequences of yet unknown IEMs. Our study extends the map of genes influencing urine metabolite concentrations, reveals metabolic processes and connected health outcomes, and implicates novel candidate variants and genes for IEMs. Further links;<a href='https://www.gckd.org/' rel='noopener noreferrer' target='_blank'>German Chronic Kidney Disease (GCKD)</a>

2021-03-09 | MTBLS284 | MetaboLights

Identification of Potentially Pathogenic Variants in the Posterior Polymorphous Corneal Dystrophy 1 Locus

Project description:Purpose: To identify the genetic basis of posterior polymorphous corneal dystrophy 1 (PPCD1). Methods: Next-generation sequencing was performed on DNA samples from 4 affected and 4 unaffected members of a previously reported family with PPCD1 linked to chromosome 20 between D20S182 and D20S195. Custom capture probes were utilized for targeted region capture of the linked interval. Single nucleotide variants (SNVs) and insertions/deletions (indels) were identified using two bioinformatics pipelines and two annotation databases. Candidate variants met the following criteria: quality score â¥20, read depth â¥5X, heterozygous, novel or rare (minor allele frequency (MAF) â¤ 0.05), present in each affected individual and absent in each unaffected individual. Structural variants were detected with two different microarray platforms to identify indels of varying sizes. Results: Sequencing reads aligned to the linked region on chromosome 20, and high coverage was obtained across the sequenced region. The majority of identified variants were detected with both pipelines and annotation databases, although unique variants were identified. Twelve SNVs in 10 genes (2 synonymous variants and 10 noncoding variants) and 9 indels in 7 genes met the filtering criteria and were considered candidate variants for PPCD1. Conclusions: Next-generation sequencing of the PPCD1 interval has identified 17 genes containing novel or rare SNVs and indels that segregate with the affected phenotype in an affected family previously mapped to the PPCD1 locus. We anticipate that screening of these candidate genes in other families previously mapped to the PPCD1 locus will result in the identification of the genetic basis of PPCD1. Four affected and 4 unaffected individuals from a single family were analyzed for copy number variation within the PPCD1 disease locus. Array design and analysis is based on genome build hg19.

2016-06-30 | E-GEOD-72617 | biostudies-arrayexpress

Identification of Potentially Pathogenic Variants in the Posterior Polymorphous Corneal Dystrophy 1 Locus

Project description:Purpose: To identify the genetic basis of posterior polymorphous corneal dystrophy 1 (PPCD1). Methods: Next-generation sequencing was performed on DNA samples from 4 affected and 4 unaffected members of a previously reported family with PPCD1 linked to chromosome 20 between D20S182 and D20S195. Custom capture probes were utilized for targeted region capture of the linked interval. Single nucleotide variants (SNVs) and insertions/deletions (indels) were identified using two bioinformatics pipelines and two annotation databases. Candidate variants met the following criteria: quality score ≥20, read depth ≥5X, heterozygous, novel or rare (minor allele frequency (MAF) ≤ 0.05), present in each affected individual and absent in each unaffected individual. Structural variants were detected with two different microarray platforms to identify indels of varying sizes. Results: Sequencing reads aligned to the linked region on chromosome 20, and high coverage was obtained across the sequenced region. The majority of identified variants were detected with both pipelines and annotation databases, although unique variants were identified. Twelve SNVs in 10 genes (2 synonymous variants and 10 noncoding variants) and 9 indels in 7 genes met the filtering criteria and were considered candidate variants for PPCD1. Conclusions: Next-generation sequencing of the PPCD1 interval has identified 17 genes containing novel or rare SNVs and indels that segregate with the affected phenotype in an affected family previously mapped to the PPCD1 locus. We anticipate that screening of these candidate genes in other families previously mapped to the PPCD1 locus will result in the identification of the genetic basis of PPCD1.

2016-06-30 | GSE72617 | GEO

Massively parallel identification of functionally consequential noncoding genetic variants in undiagnosed rare disease patients

Project description:Clinical whole genome sequencing has enabled the discovery of potentially pathogenic noncoding variants in the genomes of rare disease patients with a prior history of negative genetic testing. However, interpreting the functional consequences of noncoding variants and distinguishing those that contribute to disease etiology remains a challenge. Here we address this challenge by experimentally profiling the functional consequences of rare noncoding variants detected in a cohort of undiagnosed rare disease patients at scale using a massively parallel reporter assay. We demonstrate that this approach successfully identifies rare noncoding variants that alter the regulatory capacity of genomic sequences. In addition, we describe an integrative analysis that utilizes genomic features alongside patient clinical data to further prioritize candidate variants with an increased likelihood of pathogenicity. This works represents an important step towards establishing a framework for the clinical interpretation of noncoding variants.

2022-05-11 | GSE185795 | GEO

Coeliac_exome_dataset - samples

Project description:Exome bam files of 75 Individuals From Multiply Affected Coeliac Families

| EGAD00001001269 | EGA

Homo sapiens

Project description:Copy number variants in candidate genes are genetic modifiers of Hirschsprung disease

| PRJNA140231 | ENA

Sequencing in Over 50,000 Cases Identifies Coding and Structural Variation Underlying Atrial Fibrillation Risk

Project description:Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B We further identified associations between AF and rare structural variants due to deletions in CTNNA3 and duplications of GATA4 We broadly replicated our findings in independent samples from MyCode, deCODE and UK Biobank Finally, we found that CRISPR-knockout of KDM5B in stem cell derived atrial cardiomyocytes led to a shortening of the action potential duration and widespread transcriptomic dysregulation of genes relevant to atrial homeostasis and conduction Our results highlight the contribution of rare coding and structural variants to AF, the genetic link between AF and cardiomyopathies, and expand our understanding of the rare variant architecture for this common arrhythmia

2024-02-21 | GSE225290 | GEO

Transcriptome sequencing of a large human family identifies the impact of rare non-coding variants

Project description:We have combined high-quality genome sequencing and RNA-sequencing data within a 17-individual, three generation family. Using these data, we have contrasted cis-acting expression, allele-specific expression and splicing quantitative trait loci (collectively termed eQTLs) within the family to eQTLs discovered within a cell-type and ethnicity-matched population sample. We identified that eQTL that exhibit larger effects in the family compared to the population are enriched for rare regulatory and splicing variants and were more likely to influence essential genes. In addition, we identify several large effect-size eQTLs within the family for genes involved in complex disease. Through analysis of eQTLs in a large family we also report the utility of non-coding genome annotation to predicting the effect of rare non-coding variants. We find that a combination of distance to the transcription start site, evolutionary constraint and epigenetic annotation is considerably more informative for predicting the consequence of rare non-coding variants than for common variants. In summary, through transcriptome analyses within a large family we are able to identify the contribution of rare non-coding variants to expression phenotypes and further demonstrate the predictive potential of diverse non-coding genome annotation for interpretation of the impact of rare non-coding variants. RNA-Sequencing of CEPH/UTAH family 1463

2014-09-01 | E-GEOD-56961 | biostudies-arrayexpress

A complementary study pipeline unravels novel players in the pathoetiology of Hirschsprung's disease

Project description:Purpose: Hirschsprung’s disease (HSCR, OMIM 142623) represents one of the main causes of neonatal intestinal obstruction. It is caused by dysfunction of neural crest cells (NCCs) and their progeny during development of the enteric nervous system (ENS). HSCR is considered a multifactorial disorder, however, associated risk genes only account for a minority of cases. Consequently, defining disease-relevant variants is still a demanding task. Methods: To reduce the number of candidate genes identified by Whole Exome Sequencing (WES) and to examine their disease-causing relevance, we established a complementary study pipeline including transcriptome data of murine embryonic ENS-relevant tissues, literature and database searches, in silico network analyses as well as functional assays using genome-edited candidate-specific cell clones. Results: Applying this strategy on a pilot set of two HSCR patients and their non-affected parents led to the identification of four novel HSCR candidate genes: ATP7A, SREBF1, ABCD1 and PIAS2. This candidate gene selection was corroborated by the discovery of further rare variants in additional HSCR cases. Moreover, expression analyses revealed that all four genes are expressed in embryonic murine gastrointestinal tissues. Functional analyses using candidate gene-specific, neuronal-like CRISPR/Cas9-edited knockout cell clones demonstrated impaired differentiation, proliferation and/or cell survival capacity. Conclusions: Taken together, the presented study pipeline was proven to be suitable for the selection and validation of candidate genes as well as to gain insight into underlying pathomechanisms of HSCR.

2020-10-12 | GSE144442 | GEO

Rare and common genetic determinants of metabolic individuality and their effects on human health (EPIC-Norfolk study assays)

Project description:Garrod’s concept of “chemical individuality” has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P<1.25x10^-11) within 330 genomic regions, with rare variants (MAF≤1%) explaining 9.4% of associations. Jointly modelling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called GIMs (Genetically Influenced Metabotypes). We assigned causal genes for 62.4% of GIMs, providing new insights into fundamental metabolite physiology and their clinical relevance, including metabolite guided discovery of potential adverse drug effects (DPYD, SRD5A2). We show strong enrichment of Inborn Errors of Metabolism (IEM)-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of IEMs. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential aetiological relationships. EPIC-Norfolk study assays are reported in the current study MTBLS833INTERVAL study assays are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS834' rel='noopener noreferrer' target='_blank'>MTBLS834</a>

2022-09-09 | MTBLS833 | MetaboLights

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