Genetics researchers and clinical professionals rely on diversity measures such as race, ethnicity, and ancestry (REA) to stratify study participants and patients for a variety of applications in research and precision medicine. However, there are no comprehensive, widely accepted standards or guidelines for collecting and using such data in clinical genetics practice. Two NIH-funded research consortia, the Clinical Genome Resource (ClinGen) and Clinical Sequencing Evidence-generating Research (CSER), have partnered to address this issue and report how REA are currently collected, conceptualized, and used. Surveying clinical genetics professionals and researchers (n = 448), we found heterogeneity in the way REA are perceived, defined, and measured, with variation in the perceived importance of REA in both clinical and research settings. The majority of respondents (>55%) felt that REA are at least somewhat important for clinical variant interpretation, ordering genetic tests, and communicating results to patients. However, there was no consensus on the relevance of REA, including how each of these measures should be used in different scenarios and what information they can convey in the context of human genetics. A lack of common definitions and applications of REA across the precision medicine pipeline may contribute to inconsistencies in data collection, missing or inaccurate classifications, and misleading or inconclusive results. Thus, our findings support the need for standardization and harmonization of REA data collection and use in clinical genetics and precision health research.

遗传学研究人员和临床专业人员依靠种族、族裔和血统 (REA) 等多样性指标对研究参与者和患者进行分层，以用于研究和精准医学的各种应用。然而，在临床遗传学实践中收集和使用此类数据还没有全面的、广泛接受的标准或指南。两个由 NIH 资助的研究联盟，即临床基因组资源 (ClinGen) 和临床测序证据生成研究 (CSER)，已合作解决这一问题，并报告目前 REA 的收集、概念化和使用情况。通过对临床遗传学专业人士和研究人员 (n = 448) 的调查，我们发现 REA 的感知、定义和测量方式存在异质性，临床和研究环境中对 REA 重要性的认知存在差异。大多数受访者 (>55%) 认为 REA 对于临床变异解释、安排基因检测以及向患者传达结果至少有一定程度的重要作用。然而，对于 REA 的相关性，包括如何在不同情况下使用这些措施以及它们在人类遗传学背景下可以传达哪些信息，尚未达成共识。在整个精准医疗管道中缺乏 REA 的通用定义和应用可能会导致数据收集不一致、分类缺失或不准确以及结果误导或不确定。因此，我们的研究结果支持在临床遗传学和精准健康研究中对 REA 数据收集和使用进行标准化和协调的需要。