CANSSI Ontario STAGE Welcomes PhD Trainee Yaqi Shi

Yaqi Shi Selected as a Trainee Under the CANSSI Ontario STAGE Program.

CANSSI Ontario STAGE is pleased to announce that Yaqi Shi has been selected as a CANSSI Ontario STAGE Trainee with her project, “Robust Mendelian Randomization in the Presence of Differential Measurement Error.”

Yaqi is currently a PhD student in the Department of Statistical Sciences at the University of Toronto, where she is conducting research under the supervision of Drs. Jessica Gronsbell, Linbo Wang, and Vanessa Goncalves.

Before embarking on her doctoral journey, she earned a Master of Science in Statistics from the University of Toronto. She is also a recipient of a CANSSI Graduate Student Enrichment Scholarship from the Canadian Statistical Sciences Institute (CANSSI) and a Doctoral Recruitment Award from the University of Toronto.

With a strong background in statistical methodology, Yaqi has previously contributed to the development of statistical techniques for electronic health records and the mitigation of unmeasured confounders in causal inference. Her current focus is on extending these methodologies to genetic data, ensuring both clinical applicability and methodological rigor.

Her CANSSI Ontario STAGE project aims to:

1. Develop a novel Mendelian Randomization (MR) method that accounts for differential measurement error in exposure variables, incorporating both theoretical advancements and comprehensive simulation studies.

2. Apply the proposed method to UK Biobank data to investigate the causal relationship between various lifestyle factors—such as alcohol consumption—and binary phenotypes, including cardiovascular disease.

By leveraging the extensive genetic and phenotypic data from the UK Biobank, Yaqi aims to offer new insights into how alcohol consumption influences cardiovascular disease risk while validating the robustness of her method against real-world biases inherent in self-reported data.

CANSSI Ontario STAGE is proud to support Yaqi in her research endeavors and looks forward to the contributions she will make in advancing statistical methodologies for genetic epidemiology.