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

Yu Huang, Mohammad Ghouse Syed, Ruiye Chen, Cong Li, Xianwen Shang, Wei Wang, Xueli Zhang, Xiayin Zhang, Shulin Tang, Jing Liu, Shunming Liu, Sundar Srinivasan, Yijun Hu, Muthu Rama Krishnan Mookiah, Huan Wang, Emanuele Trucco, Honghua Yu, Colin Palmer, Zhuoting Zhu, Alexander S F Doney, Mingguang He

Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China. · pubmed

Paper

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 (h

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Research summary

The paper explores the genetic determinants of biological age as estimated through deep learning applied to retinal images, which is relevant to understanding aging processes. However, while it presents solid research and identifies specific loci associated with retinal age gap, the findings are more incremental in nature and do not fundamentally address the root causes of aging or propose mechanisms for lifespan extension. Thus, while it contributes to the field, its overall impact is limited.

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