Prioritizing causal disease genes using unbiased genomic features View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Rahul C Deo, Gabriel Musso, Murat Tasan, Paul Tang, Annie Poon, Christiana Yuan, Janine F Felix, Ramachandran S Vasan, Rameen Beroukhim, Teresa De Marco, Pui-Yan Kwok, Calum A MacRae, Frederick P Roth

ABSTRACT

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits. RESULTS: To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM. CONCLUSION: Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature. More... »

PAGES

534

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-014-0534-8

    DOI

    http://dx.doi.org/10.1186/s13059-014-0534-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010160022

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25633252


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