School of Engineering Design and Innovation, Pennsylvania State University, University Park, PA 16802, USA.
Institute for Computational and Data Science, Pennsylvania State University, University Park, PA 16802, USA.
HGG Adv. 2023 Jan 13;4(2):100178. doi: 10.1016/j.xhgg.2023.100178. eCollection 2023 Apr 13.
The use of genetic and genomic technology to infer ancestry is commonplace in a variety of contexts, particularly in biomedical research and for direct-to-consumer genetic testing. In 2013 and 2015, two roundtables engaged a diverse group of stakeholders toward the development of guidelines for inferring genetic ancestry in academia and industry. This report shares the stakeholder groups' work and provides an analysis of, commentary on, and views from the groundbreaking and sustained dialogue. We describe the engagement processes and the stakeholder groups' resulting statements and proposed guidelines. The guidelines focus on five key areas: application of genetic ancestry inference, assumptions and confidence/laboratory and statistical methods, terminology and population identifiers, impact on individuals and groups, and communication or translation of genetic ancestry inferences. We delineate the terms and limitations of the guidelines and discuss their critical role in advancing the development and implementation of best practices for inferring genetic ancestry and reporting the results. These efforts should inform both governmental regulation and self-regulation.
在各种情况下,包括在生物医学研究和直接面向消费者的基因检测中,使用遗传和基因组技术来推断祖源是很常见的。在 2013 年和 2015 年,两个小组组织了一次由不同利益相关者参与的圆桌会议,以制定学术界和工业界推断遗传祖源的准则。本报告分享了利益相关者群体的工作,并对具有开创性和持续对话的分析、评论和观点进行了分析。我们描述了参与过程以及利益相关者群体的陈述和拟议准则。该准则侧重于五个关键领域:遗传祖源推断的应用、假设和置信度/实验室和统计方法、术语和人群标识符、对个人和群体的影响,以及遗传祖源推断的沟通或翻译。我们阐述了准则的术语和局限性,并讨论了它们在推进遗传祖源推断和报告结果的最佳实践的制定和实施方面的关键作用。这些努力应该为政府监管和自我监管提供信息。