Human-Precision Medicine Interaction: Public Perceptions of Polygenic Risk Score for Genetic Health Prediction

TL;DR

This study investigates public perceptions of Polygenic Risk Score (PRS) within a UK context, introducing Human-Precision Medicine Interaction (HPMI) to frame HCI approaches to personalized health data. Through a mixed-methods design (survey $n=254$ and interviews $n=11$), the authors identify a neutral but context-dependent stance toward PRS, reveal ten adoption barriers, and illuminate five interview themes around proactive health management, information complexity, inclusivity, psychological impact, and trust. The work argues that PRS operates within a complex socio-technical system, with non-linear, probabilistic risk outputs, delayed feedback, and governance/privacy challenges that require integrated design solutions, robust governance, and multi-stakeholder collaboration. The authors propose design implications across Pre-PRS, PRS, and Post-PRS phases to support responsible, equitable PRS use and advocate for HPMI as a guiding research direction for CHI to bridge PM technologies and public engagement. Overall, the paper contributes empirical insights and concrete recommendations to improve data diversity, interpretability, and ethical governance in PRS-enabled precision medicine.

Abstract

Precision Medicine (PM) transforms the traditional "one-drug-fits-all" paradigm by customising treatments based on individual characteristics, and is an emerging topic for HCI research on digital health. A key element of PM, the Polygenic Risk Score (PRS), uses genetic data to predict an individual's disease risk. Despite its potential, PRS faces barriers to adoption, such as data inclusivity, psychological impact, and public trust. We conducted a mixed-methods study to explore how people perceive PRS, formed of surveys (n=254) and interviews (n=11) with UK-based participants. The interviews were supplemented by interactive storyboards with the ContraVision technique to provoke deeper reflection and discussion. We identified ten key barriers and five themes to PRS adoption and proposed design implications for a responsible PRS framework. To address the complexities of PRS and enhance broader PM practices, we introduce the term Human-Precision Medicine Interaction (HPMI), which integrates, adapts, and extends HCI approaches to better meet these challenges.

Figures (7)

• Figure 1: Calculation and Interpretation of Polygenic Risk Score (PRS). The calculation of a PRS involves two main steps. First, genetic variants associated with increased or decreased risks are identified by comparing genotype data from cohorts with and without a specific disease, along with the magnitude of their impact. Second, individuals generate their personal genotype profiles through Direct-to-Consumer Genetic Testing (DTC-GT) services, which require saliva collection and DNA sequencing. PRS typically follow a normal distribution, with most individuals falling around the average risk and some at the tails, indicating either decreased or increased risks. Sometimes, PRS reports provide supplementary information to help individuals interpret the results.
• Figure 2: Overview of Study Design: Two Sub-Studies Conducted. A survey of 254 individuals explored public perception of PRS, followed by semi-structured interviews with 11 individuals for deeper insights. The findings from surveys guided the design and focus of interviews. A preliminary version of this work has also been reported in an extended abstract sun2024design.
• Figure 3: Responses to the Question: "To what extent do you agree the following institutions are being allowed to use personal DTC-GT & PRS results in a commercial context, on the condition that you consent?". "Net agree" calculated as the total number of agreed responses ("Agree" and "Strongly agree") minus the total number of disagreed responses ("Disgree" and "Strongly disagree").
• Figure 4: Reasons of Interested and Uninterested in Using PRS Services. Curiosity and desire for personal future health guidance as top motivators. Less structured reasons for not seeking PRS services, with the top reason being not introduced officially by professionals (e.g., GP).
• Figure 5: The 138-Interface Interactive Storyboard Prototype: Illustrated Case on the Topic of "Privacy" ($\oslash$1). The prototype is structured around story milestones and divided into three stages representing different contexts in the story. Stage 1: Participants visit the prototype site and select one of the ten story topics. Hovering over a button provides a further prompt question of the high-level topic. Upon selection, a background introduction to the story appears. Stage 2: The story begins by outlining the general topic and context. It then reaches the ContraVision point, where users must choose a scenario, leading to different story outcomes. Stage 3: The story continues based on the chosen scenario. At the end, participants have the option to return to the ContraVision point to select a different scenario and explore alternate story outcomes.