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Integrative analysis of independent transcriptome data for rare diseases.

ABSTRACT: High-throughput technologies used to interrogate transcriptomes have been generating a great amount of publicly available gene expression data. For rare diseases that lack of clinical samples and research funding, there is a practical benefit to jointly analyze existing data sets commonly related to a specific rare disease. In this study, we collected a number of independently generated transcriptome data sets from four species: human, fly, mouse and worm. All data sets included samples with both normal and abnormal mitochondrial function. We reprocessed each data set to standardize format, scale and gene annotation and used HomoloGene database to map genes between species. Standardized procedure was also applied to compare gene expression profiles of normal and abnormal mitochondrial function to identify differentially expressed genes. We further used meta-analysis and other integrative analyses to recognize patterns across data sets and species. Novel insights related to mitochondrial dysfunction was revealed via these analyses, such as a group of genes consistently dysregulated by impaired mitochondrial function in multiple species. This study created a template for the study of rare diseases using genomic technologies and advanced statistical methods. All data and results generated by this study are freely available and stored at http://goo.gl/nOGWC2, to support further data mining.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC5014495 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Integrative analysis of independent transcriptome data for rare diseases.

Zhang Zhe Z Hailat Zeyad Z Falk Marni J MJ Chen Xue-wen XW

Methods (San Diego, Calif.) 20140627 3

High-throughput technologies used to interrogate transcriptomes have been generating a great amount of publicly available gene expression data. For rare diseases that lack of clinical samples and research funding, there is a practical benefit to jointly analyze existing data sets commonly related to a specific rare disease. In this study, we collected a number of independently generated transcriptome data sets from four species: human, fly, mouse and worm. All data sets included samples with bot ...[more]

PMID: 24981076

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Project description:BackgroundAccording to the International Rare Diseases Research Consortium (IRDiRC), a known rare disease (RD) should be diagnosable within a year. This study sought: firstly, to ascertain how long it takes to obtain the diagnosis of a RD in Spain, along with its associated time trend; and secondly, to identify and measure diagnostic delay (defined by the IRDiRC as any period exceeding a year) by reference to the characteristics of RDs and the persons affected by them.MethodsUsing data sourced from the Spanish Rare Diseases Patient Registry, we performed a descriptive analysis of the time elapsed between symptom onset and diagnosis of each RD, by sex, age and date of symptom onset, and type of RD. We analysed the time trend across the period 1960-2021 and possible change points, using a Joinpoint regression model and assuming a Poisson distribution. The multivariate analysis was completed with backward stepwise logistic regression.ResultsDetailed information was obtained on 3304 persons with RDs: 56.4% had experienced delay in diagnosis of their RDs, with the mean time taken being 6.18 years (median = 2; IQR 0.2-7.5). Both the percentage of patients with diagnostic delay and the average time to diagnosis underwent a significant reduction across the study period (p < 0.001). There was a higher percentage of diagnostic delays: in women (OR 1.25; 95% CI 1.07-1.45); in cases with symptom onset at age 30-44 years (OR 1.48; 95% CI 1.19-1.84): and when analysed by type of RD, in mental and behavioural disorders (OR 4.21; 95% CI 2.26-7.85), followed by RDs of the nervous system (OR 1.39; 95% CI 1.02-1.88).ConclusionsThis is the first study to quantify time to diagnosis of RDs in Spain, based on data from a national registry open to any RD. Since over half of all persons affected by RDs experience delay in diagnosis, new studies are needed to ascertain the factors associated with this delay and the implications this has on the lives of patients and their families.

Dataset Information

Integrative analysis of independent transcriptome data for rare diseases.

Publications

Integrative analysis of independent transcriptome data for rare diseases.

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