A public resource facilitating clinical use of genomes
A public resource facilitating clinical use of genomes
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DOI：
10.1073/pnas.1201904109
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发表时间：
2012-07-24
影响因子：
11.1
通讯作者：
Church, George M.
中科院分区：
文献类型：
--
作者：
Ball, Madeleine P.;Thakuria, Joseph V.;Church, George M.
DNA测序的快速发展有望实现新的诊断和个性化治疗。然而，实现个性化医疗将需要对高度可重新识别的、整合的基因组和健康信息数据集进行广泛研究。为了帮助这一点，个人基因组计划的参与者选择放弃隐私，通过我们的机构审查委员会批准的"开放同意"过程。公共数据和样本的贡献有助于科学发现和方法的标准化。我们在招募了1，800多名参与者后展示了我们的研究结果，包括10个试点参与者基因组的全基因组测序（PGP-10）。我们介绍了基因组-环境-性状证据（GET-Evidence）系统。该工具自动处理基因组，并优先考虑已发表的和新的变体进行解释。在审查假定健康的PGP-10基因组的过程中，我们发现了许多暗示严重疾病的文献参考。虽然有时不可能排除迟发效应，但严格的证据要求可以解决偶然发现率高的问题。为此，我们开发了一个同行生产系统，用于根据标准证据指南记录和组织变体评估，创建一个公共论坛，以便就临床相关变体的解释达成共识。基因组分析变成了一个两步的过程：使用优先级列表来记录变异评估，然后使用这些注释自动对审查过的变异进行排序。基因组数据、健康和性状信息、参与者样本和变异解释都在公共领域共享-我们邀请其他人使用我们的参与者样本审查我们的结果并为我们的解释做出贡献。我们提供我们的公共资源和方法，以进一步个性化的医学研究。
Rapid advances in DNA sequencing promise to enable new diagnostics and individualized therapies. Achieving personalized medicine, however, will require extensive research on highly reidentifiable, integrated datasets of genomic and health information. To assist with this, participants in the Personal Genome Project choose to forgo privacy via our institutional review board-approved "open consent" process. The contribution of public data and samples facilitates both scientific discovery and standardization of methods. We present our findings after enrollment of more than 1,800 participants, including whole-genome sequencing of 10 pilot participant genomes (the PGP-10). We introduce the Genome-Environment-Trait Evidence (GET-Evidence) system. This tool automatically processes genomes and prioritizes both published and novel variants for interpretation. In the process of reviewing the presumed healthy PGP-10 genomes, we find numerous literature references implying serious disease. Although it is sometimes impossible to rule out a late-onset effect, stringent evidence requirements can address the high rate of incidental findings. To that end we develop a peer production system for recording and organizing variant evaluations according to standard evidence guidelines, creating a public forum for reaching consensus on interpretation of clinically relevant variants. Genome analysis becomes a two-step process: using a prioritized list to record variant evaluations, then automatically sorting reviewed variants using these annotations. Genome data, health and trait information, participant samples, and variant interpretations are all shared in the public domain-we invite others to review our results using our participant samples and contribute to our interpretations. We offer our public resource and methods to further personalized medical research.