Dataset Information

Prioritization of retinal disease genes: an integrative approach.

ABSTRACT: The discovery of novel disease-associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine-learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA-seq, and ChIP-seq datasets of biological relevance to retinal disease, to observe patterns in gene expression specificity among tissues of the body and the eye, in addition to photoreceptor-specific signals by the CRX transcription factor. Using these features, we describe a novel algorithm, positive and unlabeled learning for prioritization (PULP). This article compares several popular supervised learning techniques as the regression function for PULP. The results demonstrate a highly significant enrichment for previously characterized disease genes using a logistic regression method. Finally, a comparison of PULP with the popular gene prioritization tool ENDEAVOUR shows superior prioritization of retinal disease genes from previous studies. The java source code, compiled binary, assembled feature vectors, and instructions are available online at https://github.com/ahwagner/PULP.

SUBMITTER: Wagner AH

PROVIDER: S-EPMC4509594 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Prioritization of retinal disease genes: an integrative approach.

Wagner Alex H AH Taylor Kyle R KR DeLuca Adam P AP Casavant Thomas L TL Mullins Robert F RF Stone Edwin M EM Scheetz Todd E TE Braun Terry A TA

Human mutation 20130412 6

The discovery of novel disease-associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine-learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA-seq, and ChIP-seq datasets of biological relevance to retina ...[more]

PMID: 23508994

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Project description:BackgroundHundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets.MethodsWe aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task).ResultsWe identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks.ConclusionsWe identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

Project description:Microarray-based profiling represents an effective method to analyze cellular or tissue-specific gene expression on the genome-level. However, in comparative analyses between control and mutant samples, microarrays often identify a large number of differentially expressed genes, in turn making it challenging to isolate the select "high-priority candidates" that are most relevant to an observed mutant phenotype. Here, we describe an integrative approach for mouse mutant lens microarray gene expression analysis using publically accessible systems-level information such as wild-type mouse lens expression data in iSyTE (integrated Systems Tool for Eye gene discovery), protein-protein interaction data in public databases, gene ontology enrichment data, and transcription factor binding profile data. This strategy, when applied to small Maf Mafg-/-:Mafk+/- mouse lens microarray datasets (deposited in NCBI Gene Expression Omnibus database with accession number GSE65500) in Agrawal et al. 2015 [1], led to the effective prioritization of candidate genes linked to lens defects in these mutants. Indeed, from the original list of genes that are differentially expressed at ±1.5-fold and p<0.05 in Mafg-/-:Mafk+/- mutant lenses, this analysis led to the identification of thirty-six high-priority candidates, in turn reducing the number of genes for further study by approximately 1/3rd of the total. Moreover, eight of these genes are linked to mammalian cataract in the published literature, validating the efficacy of this approach. Additionally, these high-priority candidates contribute valuable information for the assembly of a gene regulatory network in the lens. In sum, the pipeline outlined in this report represents an effective approach for initial as well as downstream microarray expression data analysis to identify genes important for lens biology and cataracts. We anticipate that this integrative strategy can be extended to prioritize phenotypically relevant candidate genes from microarray data in other cells and tissues.

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Prioritization of retinal disease genes: an integrative approach.

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Prioritization of retinal disease genes: an integrative approach.

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