Familial Exome Sequencing in Rare Pediatric Phenotypes
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ABSTRACT: To discover novel candidate genes associated with rare Mendelian phenotypes, we will conduct individual genomic and phenotypic characterization using genome-wide array, pedigree exome sequencing, candidate genotyping, and pertinent clinical testing to define phenotype. Pedigrees included in this submission will have a variety of clinical pathological phenotypes.
PROVIDER: phs000553.v1.p1 | EGA |
REPOSITORIES: EGA
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Project description:<p>To discover novel candidate genes associated with rare Mendelian phenotypes, we will conduct individual genomic and phenotypic characterization using genome-wide array, pedigree exome sequencing, candidate genotyping, and pertinent clinical testing to define phenotype. Pedigrees included in this submission will have a variety of clinical pathological phenotypes.</p>
| phs000553 | dbGaP
Project description:Familial exome sequencing in rare pediatric phenotypes
| PRJNA177191 | ENA
Project description:Background and Aims: Evidence for a genetic contribution to eosinophilic esophagitis (EoE) exists from family and genome-wide association studies. Extensive investigation into rare variants contributing to EoE has not been performed. The Aim is to evaluate families with multiple cases of EoE by genomic and transcriptomic sequencing to identify genes predisposing to EoE. Methods: We whole exome sequenced (WES) distant relative pairs (e.g., cousins) in extended EoE pedigrees and other affected relatives to identify rare, shared, potentially pathologic variants. Whole-transcriptome sequencing by RNA-Seq was performed in nuclear families with multiple EoE cases. We compared the overlap of genes from DNA and RNA sequencing for relevance to disease manifestations. Results: WES was performed in 50 familial cases in 21 EoE extended pedigrees. We observed 219 rare, candidate predisposition variants in 210 genes with complete sharing among all affected family members. Transcriptome sequencing was performed for 43 EoE cases in 18 nuclear kindreds, including 6 relatives without EoE. We observed 10,070 total differentially expressed genes compared to controls. We identified three genes (MUC16, ADGRE1, and TENM3) with evidence of rare variant sharing and differential gene expression among all affected family members. We identified 43 other genes with partial sharing of rare variants among affected family members and with differential gene expression. Several genes identified as prominent in EoE were also differentially expressed in unaffected relatives. Conclusions: Multiple genes related to immune response, barrier dysfunction, and cell adhesion were identified in familial EoE cases and unaffected family members supporting a genetic familial predisposition and a possible multi-hit background to disease pathophysiology.
2025-01-24 | GSE250595 | GEO
Project description:Male C57BL/6 mice were treated with ENU to generate single base substitutions, the variant genome sequences were breed to homozygosity in inbreeding pedigrees, and screened for antinuclear autoantibodies (ANA). The sanroque pedigree contained multiple progeny with ANA of mixed homogeneous nuclear and cytoplasmic immunofluorescence pattern by 12 weeks of age, due to an autosomal recessive gene variant. Comparison of the gene expression profile of CD4 cells from Sanroque to wild type was performed.
2008-06-12 | E-GEOD-2376 | biostudies-arrayexpress
Project description:Male C57BL/6 mice were treated with ENU to generate single base substitutions, the variant genome sequences were breed to homozygosity in inbreeding pedigrees, and screened for antinuclear autoantibodies (ANA). The sanroque pedigree contained multiple progeny with ANA of mixed homogeneous nuclear and cytoplasmic immunofluorescence pattern by 12 weeks of age, due to an autosomal recessive gene variant. Comparison of the gene expression profile of CD4 cells from Sanroque to wild type was performed. Keywords: repeat sample
2005-12-31 | GSE2376 | GEO
Project description:A mechanistic classification of clinical phenotypes in neuroblastoma
| PRJNA493612 | ENA
Project description:Molecular defects in some ultra-rare subtypes of familial lipodystrophies remain unidentified. We identified novel NOTCH3 heterozygous variants in familial partial lipodystrophy (FPL) pedigrees. All variants were clustered in the heterodimerization domain of the negative regulatory region of NOTCH3. Proteomics of skin fibroblasts revealed significantly higher RNA expression of NOTCH3 and activation of widespread senescence pathways in the FPL patients versus controls.
2024-10-28 | MSV000096235 | MassIVE
Project description:Background: Sepsis therapy is still limited to the treatment of the underlying infection and supportive measures. Therapeutic options that address the molecular changes of sepsis have not yet been identified. With the aim of a future individualized therapy, we used unsupervised machine learning (ML) to identify clinical phenotypes in a prospective multicenter cohort of patients with sepsis and characterized them using plasma proteomics. Methods: Routine clinical data and blood samples were collected from 384 sepsis patients. Clinical phenotypes were identified based on clinical routine measurements using the k-means algorithm. In addition, plasma samples from 276 patients were analyzed using liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). The obtained data were analyzed and interpreted in the context of the phenotypes and supervised ML classifiers were developed to prospectively allocate patients to the clusters and to identify the most important features for discrimination of the phenotypes. Results: Three clinical phenotypes were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure and a mortality rate of 92%. Cluster B, with a mortality rate of 45 %, also showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. The plasma proteome reflected the clinical features of the phenotypes and revealed the excessive consumption of complement and coagulation factors in severe sepsis. Supervised ML and feature importance analysis underlined these findings and highlighted specific clinical measures and proteins. Conclusions: ML identified clinical phenotypes showed different degrees of sepsis severity and could be translated to the plasma proteome. Plasma proteomics provided novel insights into the molecular processes of sepsis and allowed the characterization of the phenotypes. This approach represents a blueprint to identify molecular features of sepsis subgroups and may pave the way for future targeted sepsis therapy.
2025-08-28 | PXD062229 | Pride
Project description:Background: Sepsis therapy is still limited to the treatment of the underlying infection and supportive measures. Therapeutic options that address the molecular changes of sepsis have not yet been identified. With the aim of a future individualized therapy, we used unsupervised machine learning (ML) to identify clinical phenotypes in a prospective multicenter cohort of patients with sepsis and characterized them using plasma proteomics. Methods: Routine clinical data and blood samples were collected from 384 sepsis patients. Clinical phenotypes were identified based on clinical routine measurements using the k-means algorithm. In addition, plasma samples from 276 patients were analyzed using liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS). The obtained data were analyzed and interpreted in the context of the phenotypes and supervised ML classifiers were developed to prospectively allocate patients to the clusters and to identify the most important features for discrimination of the phenotypes. Results: Three clinical phenotypes were identified. Cluster C was characterized by the highest disease severity and multi-organ failure with leading liver failure and a mortality rate of 92%. Cluster B, with a mortality rate of 45 %, also showed relevant organ failure, with renal damage being particularly prominent in comparison to cluster A. The plasma proteome reflected the clinical features of the phenotypes and revealed the excessive consumption of complement and coagulation factors in severe sepsis. Supervised ML and feature importance analysis underlined these findings and highlighted specific clinical measures and proteins. Conclusions: ML identified clinical phenotypes showed different degrees of sepsis severity and could be translated to the plasma proteome. Plasma proteomics provided novel insights into the molecular processes of sepsis and allowed the characterization of the phenotypes. This approach represents a blueprint to identify molecular features of sepsis subgroups and may pave the way for future targeted sepsis therapy.
2025-12-08 | PXD058562 | Pride
Project description:Pedigreed primate ESCs display homogeneous and reliable expression profiles. These similarities to mouse ESCs suggest that heterogeneities found among hESCs result from their disparate origins rather than intrinsic biological limitations with primate embryonic stem cells. Keywords: different nhpESC lines RNA extraction was performed from 10 nhpESC and hybridization on Affymetrix microarrays. To determine whether primate ESCs have intrinsic biological limitations compared with mouse ESCs [mESCs], we examined expression profiles and pluripotency of newly established pedigreed non-human primate ESC (nhpESCs). Ten pedigreed nhpESCs, seven full-siblings (fraternal quadruplets and fraternal triplets) and nine half-siblings were derived from 41 rhesus embryos Information about pedigrees may be found in Pedigree_Attributes.xls.
2008-04-14 | E-GEOD-7534 | biostudies-arrayexpress