Genomic determinants of biological age estimated by deep learning applied to retinal images

Abstract

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysis (GWAS) on the RAG using the 31,271 UK Biobank participants and replicated our findings in 8034 GoDARTS participants. The genetic correlation between RAGs predicted from the two cohorts was 0.67 (P = 0.021). After meta-analysis, we found 13 RAG loci which might be related to retinal vessel density and other aging processes. The SNP-wide heritability (h2) of RAG was 0.15. Meanwhile, by performing Mendelian randomization analysis, we found that glycated hemoglobin, inflammation hemocytes, and anemia might be associated with accelerated retinal aging. Our study explored the biological implications and molecular-level mechanism of RAG, which might enable causal inference of the aging process as well as provide potential pharmaceutical intervention targets for further treatment.