JOURNAL ARTICLE

Whole genome sequencing identifies genetic candidates for high-frequency hearing loss in canaries (serinus canaria).

  • Published In: Journal of the Acoustical Society of America, 2025, v. 157, n. 4. P. 2330 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Madison, Farrah N.; Conte, Matthew A.; Brown, Jane A.; Carleton, Karen L.; Dooling, Robert J. 3 of 3

Abstract

This article focuses on identifying genetic mutations associated with high-frequency sensorineural hearing loss in two domesticated canary strains selectively bred for low-pitched songs: the Belgian Waterslager (BWS) and American Singer (AS) canaries. Whole-genome sequencing revealed male-specific "high-impact" single nucleotide polymorphisms (SNPs) in BWS canaries within genes previously linked to mammalian hair cell abnormalities and hearing loss—pericentriolar material 1 (PCM1), p21 (RAC1) activated kinase 3 (PAK3-like), and protein tyrosine phosphatase receptor type K (PTPRK)—primarily located on the Z chromosome. In AS canaries, three male-specific high-impact SNPs were identified, including mutations in the RWD domain containing 2 A (RWDD2a) gene, also associated with hearing loss in mammals but not located on the Z chromosome. These findings suggest distinct genetic mechanisms underlie similar auditory deficits in these strains, offering valuable animal models for studying the relationship between hearing loss, hair cell pathology, and vocal development.

Additional Information

  • Source:Journal of the Acoustical Society of America. 2025/04, Vol. 157, Issue 4, p2330
  • Document Type:Article
  • Subject Area:Zoology
  • Publication Date:2025
  • ISSN:0001-4966
  • DOI:10.1121/10.0036218
  • Accession Number:184883947
  • Copyright Statement:Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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