AudioChip: A Deep Phenotyping Approach for Deconstructing and Quantifying Audiological Phenotypes of Self-Reported...

Area of Science:

• Audiology
• Genetics
• Neuroscience

Background:

• 15% of US adults report speech perception issues despite normal hearing tests.
• Genetic factors may influence speech perception difficulties.
• Deep phenotyping is needed to understand complex audiometric phenotypes.

Purpose of the Study:

• To describe AudioChipping, a deep phenotyping method.
• To deconstruct and quantify complex audiometric phenotypes.
• To explore genotype-phenotype relationships in hearing.

Main Methods:

• Evaluated 70 young females with normal audiograms using behavioral thresholds, otoacoustic emissions, and auditory brainstem responses (ABR).
• Assessed speech perception with the Speech, Spatial, and Quality of Hearing Scale (SSQ).
• Employed multiple linear regression and k-means clustering to develop the AudioChip phenotype.

Main Results:

• SSQ scores negatively correlated with noise exposure.
• ABR wave I amplitude, cABR offset latency, and loudness discomfort level predicted SSQ scores, explaining 18% of variance.
• K-means clustering identified subgroups, with 53% of participants showing low SSQ scores.

Conclusions:

• AudioChip effectively delineates audiometric heterogeneity in individuals with varying SSQ scores.
• Highlights the link between SSQ and auditory coding precision in the auditory brainstem.
• AudioChip can aid in genotype-phenotype analysis and longitudinal auditory studies.