Inside HGG Advances: A Chat with Jundong Liu

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 Jundong Liu to discuss his paper “A Mendelian randomization-based exploration of red blood cell distribution width and mean corpuscular volume with risk of hemorrhagic strokes”.

Left: Jundong Liu recently earned his PhD at City University of Hong Kong. Right: Dr. Chan
Left: Jundong Liu recently earned his PhD at City University of Hong Kong. Right: Dr. Kei Hang Katie Chan is an Assistant Professor in the Departments of Biomedical Sciences and Electrical Engineering at the City University of Hong Kong.

HGGA: What motivated you to start working on this project?  

JL: Numerous studies suggest red blood cell distribution width (RCDW) and mean corpuscular volume (MCV) are associated with known risk factors for hemorrhagic stroke. However, prior to our study, no causal link had been seen between these blood cell traits and hemorrhagic stroke. Therefore, we set out to conduct Mendelian randomization and mediation analyses to explore the mechanisms that underlie the relationship between MCV/RCDW and hemorrhagic stroke.

HGGA: What about this paper/project most excites you? 

JL:  It is exciting that we can use publicly available data and Mendelian randomization to explore evidence of causality at a low cost. Also, it was interesting to find evidence to support that a protective effect of increased MCV against hemorrhagic stroke mediated by systolic blood pressure.

HGGA: What do hope is the impact of this work for the human genetics community? 

JL: We hope our study will contribute to ongoing genetics and epidemiology studies related to stroke. Moreover, we also hope our findings will provide researchers with new insights regarding candidate the drug targets for hemorrhagic stroke.

HGGA: What are some of the biggest challenges you’ve faced as a young scientist? 

JL: It is always challenging to select an optimal method or strategy for modeling. Excluding the appropriate pleiotropic SNPs while maintaining the power to detect the causal effect requires multiple experiments. There are some strategies to follow, but which one is optimal can vary in different datasets. Deleting too many SNPs, for example by setting very strict thresholds, could result in non-significance and a null relationship.

HGGA:  And for fun, what is one of the most fascinating things in genetics you’ve learned about in the past year or so?

JL: It is fascinating that artificial intelligence can predict the shape of nearly every known protein. Machine learning has great potential in epidemiology, genetics, and bioinformatics. More and more powerful and promising tools are developed to facilitate and speed up the research into human diseases.

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