Zixuan (Eleanor) Zhang, PhD
Zixuan (Eleanor) Zhang, PhD
Zixuan (Eleanor) Zhang, PhD, is a postdoctoral researcher in the Department of Biostatistics, Epidemiology and Informatics whose work focuses on developing statistical and computational methods in statistical genetics to better understand the genetic architecture of complex human traits, with a particular emphasis on gene expression and single-cell data. Her research is driven by a desire to uncover biological mechanisms underlying disease and to translate methodological advances into meaningful biological insight.
In this Q&A, Zixuan shares what inspired her research path, how her work addresses real-world challenges in human genetics, what she values most about the DBEI community, and how she spends time outside of academia.
Can you tell us about your current research and what inspired you to pursue this area?
My research focuses on developing novel and scalable statistical and computational methods in statistical genetics to better understand the genetic architecture of complex human traits. Currently, I am working on applying causal inference methods to estimate genetic effects on gene expression (i.e., eQTLs) using single-cell data.
During my PhD at the University of Southern California, I developed statistical methods and computational tools for analyzing pleiotropic components using GWAS summary statistics and for mapping eQTLs in single-cell data. I find it extremely rewarding when the methods I develop uncover new biological insights that could ultimately inform disease diagnosis or treatment. I am also deeply appreciative of the supportive and collaborative nature of the statistical genetics community, which inspired me to continue my research journey as a postdoc.
How does your research intersect with real-world challenges?
A long-standing challenge in human genetics is to characterize the functional roles of genetic risk loci identified through genome-wide association studies (GWAS). Understanding the molecular impact of these risk loci is essential for improving therapeutic strategies. One promising approach is to study genetic variants that influence molecular traits such as gene expression (eQTLs) to help explain the mechanisms underlying GWAS findings.
With advances in single-cell RNA sequencing, we can now examine how genetic effects vary across different cell types and states, providing the necessary biological context for interpreting disease risk. My current work focuses on developing methods to estimate genetic effects across cells and leveraging this heterogeneity to better characterize disease-associated mechanisms.
What do you find most rewarding about working in the Department of Biostatistics, Epidemiology and Informatics?
I truly appreciate the collaborative environment and rich research ecosystem within DBEI. Since joining the department, I’ve had the opportunity to work with Dr. Brielin Brown, alongside Dr. Bogdan Pasaniuc and Dr. Michael Gandal at Penn. The department’s seminars and social events have allowed me to connect with faculty and students who are not only intellectually inspiring but also incredibly supportive. Their encouragement has been invaluable as I pursue my current research projects.
Can you share a significant recent project, publication, or professional recognition that you are particularly proud of contributing to or achieving?
I recently completed my PhD and began my postdoctoral work at Penn. One of my key projects during my PhD involved developing jaxQTL, a highly efficient tool for eQTL mapping using count-based models in single-cell data. We demonstrated that count-based models provide greater statistical power than traditional linear models for sparse count data. Since its release, jaxQTL has been adopted by multiple research groups working with single-cell data. I’m very excited to see it being used to uncover new insights into gene regulation and welcome further collaborations with researchers at Penn interested in applying it to their datasets.
What advice would you give to students or early-career professionals in your field?
Attend seminars and conferences—and don’t be afraid to introduce yourself! Talking with others about your work and learning about theirs can be both inspiring and fun. Some of the most valuable insights and collaborations I’ve had came from informal conversations at conferences. You may meet people working on entirely different topics, which can broaden your perspective, or others working on similar problems, opening the door to collaboration and new ideas.
What is an interest or pastime that you enjoy outside of academia?
Outside of work, I enjoy working out and playing tennis. I also like trying out new recipes — I recently bought an air fryer and have been experimenting with it! I love watching TV shows; one of my recent favorites is Severance.
Anything else you’d like to add?
I always enjoy meeting new people, whether it’s over coffee or a meal on campus. If you’re at Penn and would like to chat about research or anything else, please feel free to reach out!