Document Detail


AudioGene: predicting hearing loss genotypes from phenotypes to guide genetic screening.
MedLine Citation:
PMID:  23280582     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
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
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2013-02-19
Journal Detail:
Title:  Human mutation     Volume:  34     ISSN:  1098-1004     ISO Abbreviation:  Hum. Mutat.     Publication Date:  2013 Apr 
Date Detail:
Created Date:  2013-03-22     Completed Date:  2013-09-06     Revised Date:  2014-04-08    
Medline Journal Info:
Nlm Unique ID:  9215429     Medline TA:  Hum Mutat     Country:  United States    
Other Details:
Languages:  eng     Pagination:  539-45     Citation Subset:  IM    
Copyright Information:
© 2012 Wiley Periodicals, Inc.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Audiometry
Genetic Testing
Genotype
Hearing Loss / diagnosis*,  genetics*
Humans
Internet
Phenotype
Reproducibility of Results
Software*
Grant Support
ID/Acronym/Agency:
DC012049/DC/NIDCD NIH HHS; DC02842/DC/NIDCD NIH HHS; F30 DC011674/DC/NIDCD NIH HHS; R01 DC002842/DC/NIDCD NIH HHS; R01 DC003544/DC/NIDCD NIH HHS; R01 DC012049/DC/NIDCD NIH HHS; T32 DC00040/DC/NIDCD NIH HHS
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