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:Schizophrenia is a common mental disorder with high heritability and strong genetic heterogeneity. Common disease-common variants hypothesis predicts that schizophrenia is attributable in part to common genetic variants. However, recent studies have clearly demonstrated that copy number variations (CNVs) also play pivotal roles in schizophrenia susceptibility and explain a proportion of missing heritability. Though numerous CNVs have been identified, many of the regions affected by CNVs show poor overlapping among different studies, and it is not known whether the genes disrupted by CNVs contribute to the risk of schizophrenia. By using cumulative scoring, we systematically prioritized the genes affected by CNVs in schizophrenia. We identified 8 top genes that are frequently disrupted by CNVs, including NRXN1, CHRNA7, BCL9, CYFIP1, GJA8, NDE1, SNAP29, and GJA5. Integration of genes affected by CNVs with known schizophrenia susceptibility genes (from previous genetic linkage and association studies) reveals that many genes disrupted by CNVs are also associated with schizophrenia. Further protein-protein interaction (PPI) analysis indicates that protein products of genes affected by CNVs frequently interact with known schizophrenia-associated proteins. Finally, systematic integration of CNVs prioritization data with genetic association and PPI data identifies key schizophrenia candidate genes. Our results provide a global overview of genes impacted by CNVs in schizophrenia and reveal a densely interconnected molecular network of de novo CNVs in schizophrenia. Though the prioritized top genes represent promising schizophrenia risk genes, further work with different prioritization methods and independent samples is needed to confirm these findings. Nevertheless, the identified key candidate genes may have important roles in the pathogenesis of schizophrenia, and further functional characterization of these genes may provide pivotal targets for future therapeutics and diagnostics.

Dataset Information

Prioritization of retinal disease genes: an integrative approach.

Publications

Prioritization of retinal disease genes: an integrative approach.

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