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AudioGene: Predicting Hearing Loss Genotypes from Phenotypes to Guide Genetic Screening

Authors

  • Kyle R. Taylor,

    1. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
    2. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
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  • Adam P. DeLuca,

    1. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
    2. Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
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  • A. Eliot Shearer,

    1. Department of Otolaryngology, Head and Neck Surgery, University of Iowa, Iowa City, Iowa
    2. Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, Iowa
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  • Michael S. Hildebrand,

    1. Department of Otolaryngology, Head and Neck Surgery, University of Iowa, Iowa City, Iowa
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  • E. Ann Black-Ziegelbein,

    1. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
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  • V. Nikhil Anand,

    1. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
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  • Christina M. Sloan,

    1. Department of Otolaryngology, Head and Neck Surgery, University of Iowa, Iowa City, Iowa
    2. Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, Iowa
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  • Robert W. Eppsteiner,

    1. Department of Otolaryngology, Head and Neck Surgery, University of Iowa, Iowa City, Iowa
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  • Todd E. Scheetz,

    1. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
    2. Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
    3. Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa
    4. Interdepartmental PhD Program in Genetics, University of Iowa, Iowa City, Iowa
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  • Patrick L. M. Huygen,

    1. Department of Otorhinolaryngology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
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  • Richard J. H. Smith,

    1. Department of Otolaryngology, Head and Neck Surgery, University of Iowa, Iowa City, Iowa
    2. Department of Molecular Physiology and Biophysics, University of Iowa Carver College of Medicine, Iowa City, Iowa
    3. Interdepartmental PhD Program in Genetics, University of Iowa, Iowa City, Iowa
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  • Terry A. Braun,

    1. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
    2. Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
    3. Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa
    4. Interdepartmental PhD Program in Genetics, University of Iowa, Iowa City, Iowa
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  • Thomas L. Casavant

    Corresponding author
    1. Center for Bioinformatics and Computational Biology, University of Iowa, Iowa City, Iowa
    2. Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
    3. Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa
    4. Interdepartmental PhD Program in Genetics, University of Iowa, Iowa City, Iowa
    • Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
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  • Contract grant sponsors: NIDCD (DC02842, DC012049, T32 DC00040, F30 DC011674); NHMRC Postdoctoral Training Fellowship (546493).

  • Communicated by: Rachel Karchin

Correspondence to: Thomas L. Casavant, Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242. E-mail: tomc@eng.uiowa.edu

ABSTRACT

Autosomal dominant nonsyndromic hearing loss (ADNSHL) is a common and often progressive sensory deficit. ADNSHL displays a high degree of genetic heterogeneity and varying rates of progression. Accurate, comprehensive, and cost-effective genetic testing facilitates genetic counseling and provides valuable prognostic information to affected individuals. In this article, we describe the algorithm underlying AudioGene, a software system employing machine-learning techniques that utilizes phenotypic information derived from audiograms to predict the genetic cause of hearing loss in persons segregating ADNSHL. Our data show that AudioGene has an accuracy of 68% in predicting the causative gene within its top three predictions, as compared with 44% for a majority classifier. We also show that AudioGene remains effective for audiograms with high levels of clinical measurement noise. We identify audiometric outliers for each genetic locus and hypothesize that outliers may reflect modifying genetic effects. As personalized genomic medicine becomes more common, AudioGene will be increasingly useful as a phenotypic filter to assess pathogenicity of variants identified by massively parallel sequencing.

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