The Drögemöller Lab is grateful to have received a CIHR CLSA Catalyst Grant
Together with co-PI, Galen Wright, we will be using these funds to explore neglected regions of the genome and their link to age-related hearing loss.
Age related hearing impairment (ARHI) affects approximately half of individuals older than 75, making it one of the most common sensory impairments in the older Canadian population. While clinical/demographic factors such as age, noise exposure and ototoxic drugs have been shown to increase the risk of experiencing hearing loss, genetics also plays a vital role in ARHI. Standard genome-wide association studies (GWAS) have uncovered genetic variants that are associated with ARHI. However, a large proportion of the observed heritability remains to be uncovered. This can largely be attributed to the following knowledge gaps:
Inaccurate ARHI phenotyping: Due to the extensive time and labour required to accurately assess hearing phenotypes using audiograms, large-scale GWAS have focused on using self-reported hearing phenotypes. Due to limitations in the accuracy of self-reported phenotypes, and the inability to classify individuals into different hearing loss phenotypes (e.g., metabolic and sensory phenotypes) using these data, there remain significant gaps in our understanding of ARHI.
Neglection of certain genomic regions: Previous GWAS of ARHI have focused on the association between common autosomal genetic variants, generally one base pair in length, and ARHI. However, as short tandem repeats (STRs) represent the largest source of variation in the genome and have been shown to play an important role in neurological and age-related phenotypes, the association between these variants and ARHI requires examination. Further, given the striking differences in ARHI between males and females, the lack of information regarding the association between genetic variants on the X-chromosome and ARHI requires addressing.
We aim to address these knowledge gaps through the following objectives:
- Objective 1: Automate the classification of hearing impairment using audiogram configurations
- Objective 2: Assess the contribution of imputed STRs to ARHI
- Objective 3: Use these unique data to perform a large-scale XWAS and GWAS of ARHI