Efficient Translation of Genetics Research for Clinical Decision Support

遗传学研究有效转化为临床决策支持

• 批准号: 10227350
• 负责人: Olivia J Veatch
• 金额: $ 18.03万
• 依托单位: UNIVERSITY OF KANSAS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10227350
• 关键词: Area; Basic Science; Biological Process; Brain; Clinic; Clinical; Clinical Research; Complex; Data; Databases; Disease; Drug Targeting; Environment; Etiology; Genes; Genetic; Genetic Research; Genetic Translation; Genetic Variation; Genetic study; Genomics; Goals; Healthcare; Individual; K-Series Research Career Programs; Knowledge; Link; Maps; Mendelian disorder; Methods; Modeling; Ontology; Output; Pathogenicity; Patients; Pharmaceutical Preparations; Pharmacogenetics; Pharmacogenomics; Pharmacologic Substance; Precision Medicine Initiative; Preventive Intervention; Primary Health Care; Process; Proteins; Research; Resources; Risk; Running; Rural; Sea; Source; Technology; Time; Tissue Sample; Tissues; Training; Translating; Translations; Work; algorithm development; autism spectrum disorder; base; bioinformatics pipeline; biomedical informatics; clinical decision support; clinical decision-making; clinical practice; clinical translation; clinically actionable; clinically relevant; design; genetic architecture; genetic information; genetic predictors; genetic testing; genetic variant; human data; human disease; individual patient; individual response; innovation; interest; knowledge integration; mobile application; multiple data sources; personalized care; precision medicine; programs; protein function; relational database; search engine; skills; tool; translational medicine; treatment optimization; treatment planning; treatment response

PROJECT SUMMARY Recent advances in genomics technologies are rapidly generating data related to the underlying genetic architecture contributing to complex human diseases. Indeed, there are currently thousands of genetic variants implicated in risk for complex human disease and the list is continually growing. In order to understand how genetic information can be useful to informing treatment, it is important to identify efficient ways to sort through the sea of association study results to determine clinically actionable genes. There are a number of excellent tools available that allow for identifying clinically useful ways to interpret information from genetic studies. Unfortunately, there is limited opportunity for many individuals who have the most opportunity to enact precision medicine approaches to healthcare (e.g., clinicians) to spend time training in the skill sets necessary to use these tools. This represents a pressing issue given the current push for clinicians to begin using evidence related to clinically actionable genetic variation to guide preventative interventions and clinical decision making. The study proposed within this K-Award is intended to be a first step in a larger program of research that will serve to build bridges between basic and clinical research by developing and applying innovative biomedical informatics methods to inform translational medicine for complex human diseases. Important databases that incorporate evidence from multiple sources will be used to automate a bioinformatics pipeline to: 1) identify genes expressed in tissues relevant to the disease of interest with evidence for convergent biological function related to the disease, 2) identify genes with evidence for functional consequences relevant to the disease, 3) identify genes with evidence of pathogenic genetic variants or predict potential genes via evidence for direct interactions with the protein products of known pathogenic genes, 4) identify genetic mechanisms targeted by approved pharmaceutical compounds with evidence for known genetic effects influencing individual treatment response. This career development award is designed to provide critical training in the fundamentals of biomedical informatics that are necessary for developing a tool to help translate results from genetic studies into clinically useful information. The primary goal is to develop a method for prioritizing results from genetic studies to help inform clinicians as to whether or not genetic testing will be beneficial toward optimizing treatment for a patient with a complex disease. Additionally, an easy-to-use tool (i.e., a mobile application) will be built to rapidly present this information in a manner that is conducive to clinical translation. The proposed project will build an important tool to help provide information that will be useful to the eventual goal of using genetics to tailor precision care for each individual patient.

项目摘要 基因组学技术的最新进展正在迅速产生与潜在遗传相关的数据。 导致复杂的人类疾病。事实上，目前有数千种基因变异 与复杂的人类疾病风险有关，而且名单还在不断增加。为了理解如何 遗传信息可以用于通知治疗，重要的是要确定有效的方法来分类， 关联研究结果的海洋，以确定临床上可操作的基因。有许多优秀的 现有的工具，允许确定临床上有用的方法来解释遗传研究的信息。 不幸的是，对于许多最有机会制定法律的人来说， 医疗保健的精确医学方法（例如，临床医生）花时间进行必要的技能培训 使用这些工具。鉴于目前临床医生开始使用 与临床上可操作的遗传变异相关的证据，以指导预防性干预和临床 决策。在这个K奖中提出的研究旨在成为一个更大的计划的第一步， 通过开发和应用基础研究和临床研究之间的桥梁 创新的生物医学信息学方法，为复杂的人类疾病提供转化医学信息。 包含来自多个来源的证据的重要数据库将用于自动化生物信息学 1）鉴定与感兴趣的疾病相关的组织中表达的基因， 与疾病相关的趋同生物学功能，2）鉴定具有功能性证据的基因 与疾病相关的后果，3）鉴定具有致病性遗传变异证据的基因或预测 通过与已知致病基因的蛋白质产物直接相互作用的证据确定潜在基因，4） 确定已批准药物化合物靶向的遗传机制， 影响个体治疗反应的遗传效应。这个职业发展奖旨在 提供开发工具所必需的生物医学信息学基础知识的关键培训 帮助将基因研究的结果转化为临床有用的信息。主要目标是开发一个 对遗传研究结果进行优先排序以帮助告知临床医生是否需要进行遗传检测的方法 将有利于优化对患有复杂疾病的患者的治疗。此外，易于使用的 工具（即，移动的应用程序）将被构建为以有利于 临床翻译拟议的项目将建立一个重要的工具，以帮助提供信息， 这有助于最终实现利用遗传学为每个病人量身定制精确护理的目标。