AudioGene: predicting hearing loss genotypes from phenotypes to guide genetic screening

Kyle R Taylor; Adam P Deluca; A Eliot Shearer; Michael S Hildebrand; E Ann Black-Ziegelbein; V Nikhil Anand; Christina M Sloan; Robert W Eppsteiner; Todd E Scheetz; Patrick L M Huygen; Richard J H Smith; Terry A Braun; Thomas L Casavant

doi:10.1002/humu.22268

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AudioGene: predicting hearing loss genotypes from phenotypes to guide genetic screening

Journal article

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Peer reviewed

AudioGene: predicting hearing loss genotypes from phenotypes to guide genetic screening

Kyle R Taylor, Adam P Deluca, A Eliot Shearer, Michael S Hildebrand, E Ann Black-Ziegelbein, V Nikhil Anand, Christina M Sloan, Robert W Eppsteiner, Todd E Scheetz, Patrick L M Huygen, …

Human mutation, Vol.34(4), pp.539-545

04/2013

DOI: 10.1002/humu.22268

PMCID: PMC3753227

PMID: 23280582

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https://www.ncbi.nlm.nih.gov/pmc/articles/3753227View

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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.

Hearing Loss - genetics

Hearing Loss - diagnosis

Phenotype

Reproducibility of Results

Algorithms

Genetic Testing

Audiometry

Humans

Genotype

Software

Internet

Details

Title: Subtitle: AudioGene: predicting hearing loss genotypes from phenotypes to guide genetic screening
Creators: Kyle R Taylor - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
Adam P Deluca
A Eliot Shearer
Michael S Hildebrand
E Ann Black-Ziegelbein
V Nikhil Anand
Christina M Sloan
Robert W Eppsteiner
Todd E Scheetz
Patrick L M Huygen
Richard J H Smith
Terry A Braun
Thomas L Casavant
Resource Type: Journal article
Publication Details: Human mutation, Vol.34(4), pp.539-545
DOI: 10.1002/humu.22268
PMID: 23280582
PMCID: PMC3753227
NLM abbreviation: Hum Mutat
ISSN: 1059-7794
eISSN: 1098-1004
Publisher: United States
Grant note: R01 DC003544 / NIDCD NIH HHS R01 DC012049 / NIDCD NIH HHS T32 DC00040 / NIDCD NIH HHS DC012049 / NIDCD NIH HHS T32 DC000040 / NIDCD NIH HHS F30 DC011674 / NIDCD NIH HHS R01 DC002842 / NIDCD NIH HHS DC02842 / NIDCD NIH HHS
Language: English
Date published: 04/2013
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Molecular Physiology and Biophysics; Anatomy and Cell Biology; Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Center for Bioinformatics and Computational Biology; Otolaryngology; Internal Medicine; Ophthalmology and Visual Sciences
Record Identifier: 9983980060002771

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