Precision medicine research has seen growing efforts to increase participation of communities that have been historically underrepresented in biomedical research. Marginalized racial and ethnic communities have received particular attention, toward the goal of improving the generalizability of scientific knowledge and promoting health equity. Against this backdrop, research has highlighted three key issues that could impede the promise of precision medicine research: issues surrounding (dis)trust and representation, challenges in translational efforts to improve health outcomes, and the need for responsive community engagement. Existing efforts to address these challenges have predominantly centered on single-dimensional demographic criteria such as race, ethnicity, or sex, while overlooking how these and additional variables, such as disability, gender identity, and socioeconomic factors, can confound and jointly impact research participation. We argue that increasing cohort diversity and the responsiveness of precision medicine research studies to community needs requires an approach that transcends conventional boundaries and embraces a more nuanced, multi-layered, and intersectional framework for data collection, analyses, and implementation. We draw attention to gaps in existing work, highlight how overlapping layers of marginalization might shape and substantiate one another and affect the precision-medicine research cycle, and put forth strategies to facilitate equitable advantages from precision-medicine research to diverse participants and internally heterogeneous communities.

精准医学研究越来越多地努力增加历来在生物医学研究中代表性不足的社区的参与。为了提高科学知识的普遍性和促进健康公平，边缘化的种族和民族社区受到了特别关注。在此背景下，研究强调了可能阻碍精准医学研究前景的三个关键问题：围绕（不）信任和代表性的问题、改善健康结果的转化努力的挑战以及响应社区参与的需要。应对这些挑战的现有努力主要集中在单一维度的人口标准，例如种族、民族或性别，而忽视了这些变量以及其他变量，例如残疾、性别认同和社会经济因素，如何混淆并共同影响研究参与。我们认为，提高队列多样性和精准医学研究对社区需求的响应能力需要一种超越传统界限的方法，并采用更细致、多层次和交叉的数据收集、分析和实施框架。我们提请注意现有工作中的差距，强调边缘化的重叠层可能如何相互塑造和证实并影响精准医学研究周期，并提出促进精准医学研究向不同参与者和内部异质社区提供公平优势的战略。