Center for Critical Assessment of Genome Interpretation

基因组解释批判性评估中心

• 批准号: 9267171
• 负责人: Steven E Brenner
• 金额: $ 55.67万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-13 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9267171
• 关键词: Address; Basic Science; Blinded; Characteristics; Clinical; Clinical Research; Collaborations; Communication; Communities; Computing Methodologies; Data; Disease; Educational workshop; Ethics; Evaluation; Evolution; Foundations; Genetic; Genetic Variation; Genome; Goals; Health; Human; Human Genetics; International; Malignant Neoplasms; Methods; Mission; Online Systems; Participant; Pharmaceutical Preparations; Phenotype; Publications; Rare Diseases; Research; Research Infrastructure; Research Personnel; Resources; Testing; Work; clinical application; clinical practice; data acquisition; experimental study; genetic information; genetic variant; genomic data; genomic variation; hackathon; human disease; innovation; meetings; online resource; public health relevance; response; trait

 DESCRIPTION (provided by applicant): New genomic data hold the promise of revolutionizing our understanding and treatment of human disease, and hence of greatly influencing clinical practice. Multiple barriers stand between the acquisition of the data and realizing these and other benefits. In particular, powerful and well-characterized computational methods for deducing the disease relevant phenotypic impact of genomic variants are needed. Over fifty such methods already exist, but currently, even though some are already deployed in clinical practice, we do not know how well these perform on relevant genome interpretation tasks. Further, it is already clear that new and more sophisticated approaches must be developed to fully meet the new challenges. The Center for Critical Assessment of Genome Interpretation (C-CAGI) will address these needs, through ongoing objective evaluation of the state of the art in relating genetic information to phenotype, particularly the relationship between human genetic variation and health. These goals are embraced by three specific aims: 1. Assess the quality of current computational methods for interpreting genomic variation data, and highlight innovations & progress. Building on successful initial experiments in 2010, 2011 and 2013, C-CAGI will conduct community-wide experiments in which participants make bona fide blinded predictions of disease related phenotypes on the basis of genomic data. These are evaluated by independent assessors with access to the correct answers, to determine how well methods work both relatively and absolutely. These assessments will establish the state-of-the art and advance the field. 2. Guide future research efforts in computational genome interpretation and build a strong community for collaboration and interaction. C-CAGI aims to engage and expand the community of researchers interpreting the phenotypic impact of genomic variation with CAGI workshops, hackathons, tutorials, and other mechanisms. We hope to use CAGI to spur and recognize innovative approaches to the breadth of practical and clinical research. The C-CAGI will also encourage and commission experimental studies necessary for focused testing of the computational methods. C-CAGI operates on a robust ethical foundation in using human research participant data, supported by the CAGI Ethics Forum. 3. Broadly disseminate the results and conclusions from the CAGI experiments and analysis. C-CAGI aims to be the central resource for information on interpretation of genomic variation. This dissemination to the broader scientific and clinical community will be using its publications, best practice guides, and web resources, and through presentations, tutorials, and workshops at international meetings.

 描述（由申请人提供）：新的基因组数据有望彻底改变我们对人类疾病的理解和治疗，从而极大地影响临床实践。在获取数据与实现这些和其他效益之间存在着多重障碍。特别地，需要用于推断基因组变体的疾病相关表型影响的强大且充分表征的计算方法。已经存在超过50种这样的方法，但目前，即使一些已经在临床实践中部署，我们也不知道这些方法在相关基因组解释任务中的表现如何。此外，已经很清楚的是，必须制定新的和更复杂的办法，以充分迎接新的挑战。基因组判读关键评估中心（C-CAGI）将通过对遗传信息与表型相关的最新技术进行持续客观评估，特别是人类遗传变异与健康之间的关系，来满足这些需求。这些目标包括三个具体目标：1。评估当前用于解释基因组变异数据的计算方法的质量，并强调创新和进展。在2010年、2011年和2013年成功的初步实验的基础上，C-CAGI将进行社区范围的实验，参与者根据基因组数据对疾病相关表型进行真正的盲法预测。这些由独立的评估员进行评估，他们可以获得正确的答案，以确定方法的相对和绝对效果。这些评估将确立最新技术水平并推动该领域的发展。 2.指导未来在计算基因组解释方面的研究工作，并建立一个强大的协作和互动社区。C-CAGI旨在通过CAGI研讨会，黑客马拉松，教程和其他机制参与和扩大解释基因组变异表型影响的研究人员社区。我们希望利用CAGI来激励和认可实践和临床研究广度的创新方法。C-CAGI还将鼓励和委托进行必要的实验研究，以集中测试计算方法。C-CAGI在使用人类研究参与者数据方面建立了坚实的道德基础，并得到了CAGI伦理论坛的支持。 3.广泛传播CAGI实验和分析的结果和结论。 C-CAGI旨在成为解释基因组变异信息的中心资源。将利用其出版物、最佳实践指南和 网络资源，并通过在国际会议上的演示文稿，教程和研讨会。