Polygenic Risk Score Grant Winners Announced: Advancing Genomic Medicine Through Innovative Research

Four University of Toronto research teams awarded the DSI-McLaughlin Centre Polygenic Risk Score Grant, highlighting their groundbreaking contributions to the advancement of genomic medicine.

by Sara Elhawash

The Data Sciences Institute (DSI) is pleased to announce the recipients of the DSI-McLaughlin Centre Polygenic Risk Score Grant competition. This grant, created in partnership with the University of Toronto’s McLaughlin Centre and the Dalla Lana School of Public Health, aims to support emerging research and build capacity in the field of polygenic risk score studies. Polygenic risk scores enable researchers to use multiple genetic factors to estimate an individual’s genetic risk for complex diseases, providing important information for predicting, preventing and treating diseases. 

Professor France Gagnon, Chair of the adjudication committee and Associate Dean Research at the Dalla Lana School of Public Health, expressed enthusiasm for the wide range of proposals received from researchers across the University and partner institutions. These proposals demonstrate the potential for innovative methodologies in polygenic risk scores to impact a wide range of fields. “We are thrilled to support this cutting-edge research and look forward to seeing its impact on the field of precision population health and medicine,” said Gagnon. 

Two of the grant recipients are Professors Frank Wendt and Esteban Parra, Department of Anthropology at the University of Toronto Mississauga. They are taking a new approach to the study of major depressive disorder (MDD) and hippocampus volume. Their research aims to improve the accuracy of polygenic risk score predictions for this disorder and expand our understanding of its biology. Wendt and Parra said, “By taking a tandem repeat aware approach to risk scores, we hope to uncover new insights into the biology of major depressive disorder, improve prediction accuracy, and develop scores that better translate across population groups. We are thrilled to contribute to this important area of research that takes an interdisciplinary approach to pressing matters in genomic medicine.” 

Grant recipients Lei Sun and Ziang Zhang from the Department of Statistical Sciences, Faculty of Arts & Science, are collaborating with Dr. Andrew Paterson from The Hospital for Sick Children on a project to develop polygenic risk scores for binary traits, which are traits that can only take on two possible outcomes, such as the presence or absence of a particular disease. Their research aims to investigate how the estimated effects of different genetic factors can be biased and propose a new way to adjust for this bias to improve the accuracy of the polygenic risk scores. “Because of DSI’s emphasis on interdisciplinary research, all team members with complementary expertise worked closely to define and develop a research project with statistical rigor and practical impact. This grant also provides graduate students in Statistical Sciences a unique opportunity to lead a grant application, which is rare in our discipline,” said Sun. These projects have the potential to improve our understanding of complex diseases and advance the fields of precision medicine and population health. 

Congratulations to all the DSI – McLaughlin Centre Polygenic Risk Score Grant collaborative research teams! 

A Multimodal AI Solution for Improved Outcome Prediction using Polygenic Scores and EHR  

  • Zahra Shakeri (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, U of T); Kuan Liu (Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, U of T) 

Addressing non-collapsibility in logistic regression when constructing polygenic risk scores for binary traits 

  • Lei Sun (Department of Statistical Sciences, Faculty of Arts & Science, U of T), Andrew Paterson (Genetics and Genome Biology, The Hospital for Sick Children), and Ziang Zhang (Department of Statistical Sciences, Faculty of Arts & Science, U of T) 

Inclusive Trans-ancestry Polygenic Genetic Risk Scores (iPRS) via Robust Transfer Learning 

  • Jessica Gronsbell (Department of Statistical Sciences, Faculty of Arts & Science, U of T); Jianhui Gao (Department of Statistical Sciences, Faculty of Arts & Science, U of T)

Tandem repeat aware risk scores linking major depression and hippocampus volume 

  • Frank Wendt (Department of Anthropology, University of Toronto Mississauga); Esteban Parra (Department of Anthropology, University of Toronto Mississauga) 

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