PercePRS: Public Perception of Polygenic Risk Scores

PhD project

PhD student:

Supervisors:

John Vines (Design Informatics), Albert Tenesa (Roslin Institute)

Outputs from this project

Sun, Y., Vines, J. & Tenesa, A. The future of polygenic risk scores in direct-to-consumer genomics. Nat Genet 57, 2077–2078 (2025). https://doi.org/10.1038/s41588-025-02313-z

Yuhao Sun, Albert Tenesa, and John Vines. 2025. Human-Precision Medicine Interaction: Public Perceptions of Polygenic Risk Score for Genetic Health Prediction. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, Article 522, 1–26. https://doi.org/10.1145/3706598.3713567

Yuhao Sun. 2024. Design for Debate: Exploring Public Perceptions of an Emerging Genetics Health Prediction Service ‘Polygenic Risk Score’ Through Design Methods. In Companion Publication of the 2024 ACM Designing Interactive Systems Conference (DIS ’24 Companion). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/3656156.3665137

I began my PhD project, PercePRS: Public Perception of Polygenic Risk Score (PRS), in November 2021 at the University of Edinburgh. PRS is an emerging genetic technology that estimates an individual’s likelihood of developing common diseases based on patterns across their genome. While it promises to reshape preventive and personalised medicine, its broader use in healthcare and consumer settings remains limited. Here, this hesitation comes not only from scientific uncertainty, but also from social, ethical, and design challenges around accuracy, privacy, and communication.

My research explores how people understand, interpret, and imagine PRS technologies. Using quantitative, qualitative and design methods, I examine how PRS might be integrated into future health services and how interaction design can mediate trust, understanding, and responsibility in this context. By combining perspectives from the fields of HCI and precision medicine, this project investigates what it means to design for genetic prediction technologies that are both scientifically rigorous and socially intelligible and responsible.

Through this work, I introduced the idea of Human-Precision Medicine Interaction (HPMI), a conceptual space that invites the communities to engage more deeply with the design and use of emerging medical and healthcare technologies. As one of the earliest interdisciplinary projects situated between the fields of HCI and precision medicine, this work has been shared across leading venues, including the ACM CHI Conference and Nature Genetics. For further details, please see the related publications below.

Funder: China Scholarship Council and the Genetics Society

Project dates: 2021 – 2025