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Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders.

ABSTRACT: Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated significant but modest associations between elevated expression and phenotype for most tissues (maximum area under ROC curve = 0.69). At extreme elevation, associations were marked. Upregulation of disease genes in affected tissues was pronounced for genes associated with autosomal dominant over recessive disorders. Pathways enriched for genes expressed and associated with phenotypes highlighted tissue functionality, including lipid metabolism in spleen and DNA repair in adipose tissue. These results suggest features useful for evaluating the likelihood of particular tissue manifestations in genetic disorders. The web address of an interactive platform integrating these data is provided.

SUBMITTER: Feiglin A

PROVIDER: S-EPMC5928498 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders.

Feiglin Ariel A Allen Bryce K BK Kohane Isaac S IS Kong Sek Won SW

Cell systems 20170816 2

Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated signif ...[more]

PMID: 28822752

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Project description:The ability to discover genetic variants in a patient runs far ahead of the ability to interpret them. Databases with accurate descriptions of the causal relationship between the variants and the phenotype are valuable since these are critical tools in clinical genetic diagnostics. Here, we introduce a comprehensive and global genotype-phenotype database focusing on rare diseases.This database (CentoMD ®) is a browser-based tool that enables access to a comprehensive, independently curated system utilizing stringent high-quality criteria and a quickly growing repository of genetic and human phenotype ontology (HPO)-based clinical information. Its main goals are to aid the evaluation of genetic variants, to enhance the validity of the genetic analytical workflow, to increase the quality of genetic diagnoses, and to improve evaluation of treatment options for patients with hereditary diseases. The database software correlates clinical information from consented patients and probands of different geographical backgrounds with a large dataset of genetic variants and, when available, biomarker information. An automated follow-up tool is incorporated that informs all users whenever a variant classification has changed. These unique features fully embedded in a CLIA/CAP-accredited quality management system allow appropriate data quality and enhanced patient safety.More than 100,000 genetically screened individuals are documented in the database, resulting in more than 470 million variant detections. Approximately, 57% of the clinically relevant and uncertain variants in the database are novel. Notably, 3% of the genetic variants identified and previously reported in the literature as being associated with a particular rare disease were reclassified, based on internal evidence, as clinically irrelevant.The database offers a comprehensive summary of the clinical validity and causality of detected gene variants with their associated phenotypes, and is a valuable tool for identifying new disease genes through the correlation of novel genetic variants with specific, well-defined phenotypes.

Project description:BackgroundEstimation of incidence in rare diseases is often challenging due to unspecific and incomplete coding and recording systems. Patient- and health care provider-driven data collections are held with different organizations behind firewalls to protect the privacy of patients. They tend to be fragmented, incomplete and their aggregation leads to further inaccuracies, as the duplicated records cannot easily be identified. We here report about a novel approach to evaluate the incidences of Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA) in Germany.MethodsWe performed a retrospective epidemiological study collecting data from patients with dystrophinopathies (DMD and Becker muscular dystrophy) and SMA born between 1995 and 2018. We invited all neuromuscular centers, genetic institutes and the patient registries for DMD and SMA in Germany to participate in the data collection. A novel web-based application for data entry was developed converting patient identifying information into a hash code. Duplicate entries were reliably allocated to the distinct patient.ResultsWe collected 5409 data entries in our web-based database representing 1955 distinct patients with dystrophinopathies and 1287 patients with SMA. 55.0% of distinct patients were found in one of the 3 data sources only, while 32.0% were found in 2, and 13.0% in all 3 data sources. The highest number of SMA patients was reported by genetic testing laboratories, while for DMD the highest number was reported by the clinical specialist centers. After the removal of duplicate records, the highest yearly incidence for DMD was calculated as 2.57:10,000 in 2001 and the highest incidence for SMA as 1.36:10,000 in 2014.ConclusionWith our novel approach (compliant with data protection regulations), we were able to identify unique patient records and estimate the incidence of DMD and SMA in Germany combining and de-duplicating data from patient registries, genetic institutes, and clinical care centers. Although we combined three different data sources, an unknown number of patients might not have been reported by any of these sources. Therefore, our results reflect the minimal incidence of these diseases.

Dataset Information

Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders.

Publications

Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders.

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