Zixuan Wen

Identifying Imaging Genetic Associations via Regional Morphometricity Estimation

Thumbnail of Poster PDF
Click to View


Default Presenter Image

My name is Zixuan Wen and I am a research assistant in Shen Lab. I received my MA degree in Applied Mathematics and Computational Science program from University of Pennsylvania. My research interests include applied mathematics, machine learning, and biomedical data science.


Z Wen1, J Bao1, M Kim1, A Saykin2, M Thompson3, Y Zhao4, L Shen1

  1. University of Pennsylvania Perelman School of Medicine
  2. Indiana University School of Medicine
  3. University of Southern California School of Medicine
  4. Yale University School of Public Health


Brain imaging genetics is an emerging research field aiming to reveal the genetic basis of brain traits captured by imaging data. Inspired by heritability analysis, the concept of morphometricity was recently introduced to assess trait association with whole brain morphology. In this study, we extend the concept of morphometricity from its original definition at the whole brain level to a more focal level based on a region of interest (ROI). We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP). We perform an empirical study on the structural MRI and genotyping data from a landmark Alzheimer’s disease (AD) biobank; and yield promising results. Our findings indicate that the AD-related SNPs have higher overall regional morphometricity estimates than the SNPs not yet related to AD. This observation suggests that the variance of AD SNPs can be explained more by regional morphometric features than non-AD SNPs, supporting the value of imaging traits as targets in studying AD genetics. Also, we identified 11 ROIs, where the AD/non-AD SNPs and significant/insignificant morphometricity estimation of the corresponding SNPs in these ROIs show strong dependency. Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) are enriched by these ROIs. Our results also demonstrate that using all the detailed voxel-level measures within the ROI to incorporate morphometric information outperforms using only a single average ROI measure, and thus provides improved power to detect imaging genetic associations.


Brain imaging genetics; Regional morphometricity; Alzheimer’s Disease


Super exciting work! I’m wondering if you have a method of linking these morphological regions of interest to potential disease etiology or mechanisms or if this remains a challenge. Great work!

About Us

To understand health and disease today, we need new thinking and novel science —the kind  we create when multiple disciplines work together from the ground up. That is why this department has put forward a bold vision in population-health science: a single academic home for biostatistics, epidemiology and informatics. 

© 2023 Trustees of the University of Pennsylvania. All rights reserved.. | Disclaimer

Follow Us