Ziyi Xiong

TRIDENT is a framework for robust multi-trait GWAS that identifies 66 novel osteoarthritis signals by combining earlier and larger GWAS meta-analyses and applying translational annotation.

The paper presents a pipeline for analyzing image-based complex traits using AI phenotyping, combined-GWAS, and federated learning, applied to human facial data to identify genetic loci associated with facial shape.

This paper shows that knocking out TBX15 and PAX1 in mice alters facial and physical morphology, supporting their role in human facial development.

This study identifies 24 genetic loci associated with facial shape variation in humans through GWAS and confirms 13 of them in a multi-ethnic cohort.