Genetic Insights of Image-Based Traits: Analysis Pipeline for AI-based Phenotyping, Combined-GWAS, and Federated Learning with Application to the Human Face

Abstract

Biological images capture rich morphological information and understanding their genetic basis is crucial for elucidating underlying molecular mechanisms, relevant in biomedical, evolutionary, and forensic research and applications. However, progress in understanding the genetic basis of image-based complex traits is hindered by key limitations in the current methodology, regarding both, the degree of which the large image complexity is captured with the phenotyping methods and how the analysis methods deal with the underlying large genetic complexity. Here, we present a robust, scalable, privacy-preserving computer pipeline for analysing image-based complex traits and unveiling their genetic basis by integrating (i) AI-based phenotyping for large-scale extraction of image-based traits across cohorts in a federated learning framework, without sharing individual images; (ii) Combined-GWAS (C-GWAS) for identifying genetic variants underlying the numerous AI-derived image-based traits; and (iii) Explainable AI (XAI)-based image visualization of the identified genetic association effects. In its first application to the example of the human face, we analysed 3D facial images and genomic data from two European cohorts (N=7,309), extracted 200 AI-derived facial traits, identified 43 significantly face associated genetic loci, including 12 novel ones, and replicated 70% of them in an independent European dataset (N=8,246). XAI-based visualization of the identified genetic effects shows the involvement of many of these loci in different parts of the face. Our study provides a privacy-aware and extensible pipeline for investigating the genetic basis of image-based complex traits implemented in a computationally efficient python package available at GitLab and its first application yielded new insights into the genetic architecture of facial shape variation.