Abstract The goal of this proposal is to establish a novel artificial intelligence-based strategy of genome wide association studies (GWAS) to identify new genetic loci associated with common human disorders. Identification of the genetic factors underlying these diseases will provide not only mechanistic insights into the diseases but also form the basis for developing novel preventative, diagnostic, and targeted therapeutic methods. While GWAS has achieved great successes in the past 1.5 decades, only a small portion of common diseases' heritability can be currently explained by loci identified from traditional GWAS. Leveraging on the public genetics and clinical imaging data, we propose a novel approach, termed image based GWAS (iGWAS), where GWAS will be performed on endophenotypes derived from images using cutting edge deep learning (DL) algorithms. By creating a more objective and quantitative output from the images, with less information loss, many new disease-associated genetic loci are expected to be identified. To test the efficacy of this novel approach, the human visual system will be used as an example. A DL-phenotyper, Multi-modal multi-view Self-Supervised deep Learning Encoder for Retinal images (MuSSLER), will be developed to extract quantitative output from optical coherence tomography scans and fundus images from both normal eyes and patients with diabetic retinopathy (DR). GWAS will be performed on normal eye endophenotypes to elucidate genes relevant to retina development and physiology. Also, GWAS will be performed on DR-specific endophenotypes to identify DR associated genetic loci and candidate genes. If successful, our approach represents a general framework that can be readily extended to the study of other retinal diseases and other common diseases with imaging data. Furthermore, the phenotyping neural network architecture established in this project can be readily adopted to develop an automated grading tool for heterogeneous clinical data and applied to many other common diseases.