Posted By: HGG Advances
Each month, the editors of Human Genetics and Genomics Advances interview an early-career researcher who has published work in the journal. This month we check in with Zhaotong Lin, PhD, to discuss her paper, “A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization.”
HGGA: What motivated you to start working on this project?
ZL: Mendelian randomization (MR) is a powerful and increasingly popular tool for understanding the causal relationships between complex traits and diseases, especially with the growing availability of GWAS summary statistics. However, MR consistently faces the challenge of horizontal pleiotropy. While there are many robust MR methods designed to address pleiotropy, they rely on different alternative assumptions on the horizontal pleiotropy, which are often untestable. It would be great to have a framework that can fundamentally reduce the degree of horizontal pleiotropy, which motivates this project.
HGGA: What about this paper/project most excites you?
ZL: In general, horizontal pleiotropy in MR can be classified into LD-induced pleiotropy and biological pleiotropy. What excited me most is that by using a simple yet effective conditional analysis, the proposed CMR framework can effectively remove LD-induced pleiotropy. Furthermore, it is also compatible with many other existing robust MR methods, which can further account for biological horizontal pleiotropy. This framework can greatly improve the performance of existing methods.
HGGA: What do you hope is the impact of this work for the human genetics community?
ZL: We hope this work will offer researchers new insights into modeling in MR, and the proposed CMR framework can be integrated in the standard practice for performing MR using GWAS summary statistics, reducing the likelihood of false positives or misleading results due to horizontal pleiotropy.
HGGA: What are some of the biggest challenges you’ve faced as a young scientist?
ZL: One of the biggest challenges I’ve faced is navigating the constant need to balance innovation with practicality. In a field like human genetics, where data is vast and methods evolve rapidly, it can be challenging to keep up with the latest advances while staying focused on the core questions of your research.
HGGA: And for fun, what is one of the most fascinating things in genetics you’ve learned about in the past year or so?
ZL: One of the most fascinating things I’ve learned about is the growing potential of single-cell data in uncovering the complexities of gene regulation and disease. Single-cell RNA sequencing allows us to study how genetic variation influences specific cell types differently. This fine-grained resolution has been eye-opening, especially in understanding how certain genetic effects might be masked in bulk tissue analysis but become clear at the single-cell level.
Zhaotong Lin, PhD is an assistant professor in the Department of Statistics at Florida State University.