Bioinformatics Strategies for Genome Wide Association Studies

全基因组关联研究的生物信息学策略

• 批准号: 10654872
• 负责人: Jason H. Moore
• 金额: $ 34.89万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654872
• 关键词: Algorithms; Bioinformatics; Biology; Characteristics; ClinVar; Community Medicine; Computational algorithm; Computer software; Confusion; DNA; Data; Databases; Disease; Disease model; Etiology; Feedback; Frequencies; Genetic; Genetic study; Genome; Genomic medicine; Genomics; Genotype; Goals; Health; Human; Individual; Informatics; Information Resources; Knowledge; Machine Learning; Measures; Methodology; Methods; Modeling; Online Systems; Patients; Pattern; Pharmaceutical Preparations; Population; Population Analysis; Population Study; Process; PubMed; Pythons; Research Personnel; Risk; Risk Factors; Source; Technology; Time; Validation; data preservation; design; disorder risk; experimental study; genetic makeup; genetic variant; genome wide association study; human disease; individual patient; knowledge integration; machine learning algorithm; machine learning model; models and simulation; novel; open data; open source; phenotypic data; precision medicine; prevent; simulation; statistics; therapeutic target; virtual; virtual experiments; virtual intervention

The promise of precision medicine is to edit a patient’s DNA and/or administer therapeutics targeting etiologic molecules that prevent or reverse the disease process using a tailored design. All of this happens at the level of the individual and requires precision knowledge of that patient’s biology. In stark contrast, much of the knowledge we possess about genomic risk factors comes from statistical measures of association from human populations. The conceptual and practical disconnect between the populations we study and the individuals we want to treat is a major source of confusion about how to move forward in an era driven by genome technology. The primary goal of this proposal is to develop novel informatics methodology and software to facilitate precision medicine by connecting population and individual genomic phenomena. We propose here a Virtual Genomic Medicine (VGMed) workbench where clinicians can carry out thought experiments about the treatment of individual patients using models of disease risk derived from population-level studies. This will be accomplished by first developing a novel Genomics-guided Automated Machine Learning (GAML) algorithm for deriving risk models from real data that is accessible to clinicians (AIM 1). We will then develop a novel simulation approach that is able to generate artificial data that preserves the distribution of genetic effects observed in the real data while maintaining other characteristics such as genotype frequencies (AIM 2). This will generate open data allowing anyone to perform virtual interventions on patients derived from a population- level risk distribution. The workbench will allow editing of individual genotypes and simulate the administration of drugs by editing machine learning parameters in the simulation model (AIM 3). The change in risk and disease status for the specific patient will be tracked in real time. Finally, we provide a feature in the workbench that will allow the clinician to generate specific hypotheses about individual genetic variants that can then be validated using integrated knowledge sources that include databases such as PubMed and ClinVar thus giving the user immediate feedback (AIM 4). All methods and software will be provided as open-source (AIM 5).

精准医学的前景是编辑患者的 DNA 和/或针对病因进行治疗 使用定制设计来预防或逆转疾病过程的分子所有这一切都发生在水平上。 与此形成鲜明对比的是，许多人都需要对患者的生物学有精确的了解。 我们所掌握的关于基因组风险因素的知识来自于人类关联的统计测量 我们研究的人群和我们研究的个体之间概念上和实践上的脱节。 想要治疗是在基因组驱动的时代如何前进的一个主要困惑 该提案的主要目标是开发新颖的信息学方法和软件。 通过连接群体和个体基因组现象来促进精准医学。 虚拟基因组医学 (VGMed) 工作台，信徒可以在这里进行关于基因组医学的思想实验 使用源自人群水平研究的疾病风险模型对个体患者进行治疗。 首先开发一种新颖的基因组学引导的自动机器学习 (GAML) 算法来实现 从信徒可以获得的真实数据中推导出风险模型（AIM 1）然后我们将开发一种新颖的方法。 能够生成保留遗传效应分布的人工数据的模拟方法 在真实数据中观察到，同时保持其他特征，例如基因型频率（AIM 2）。 将生成开放数据，允许任何人对来自人群的患者进行虚拟干预- 工作台将允许编辑单个基因型并模拟管理。 通过编辑模拟模型中的机器学习参数来控制药物的风险和变化。 最后，我们在工作台中提供了一项功能。 这将使临床医生能够生成关于个体遗传变异的具体假设，然后可以将其 使用包括 PubMed 和 ClinVar 等数据库在内的综合知识源进行验证，从而给出 用户即时反馈（AIM 4）。所有方法和软件都将作为开源提供（AIM 5）。

项目成果

Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction.

• DOI: 10.1016/b978-0-12-380862-2.00005-9
• 发表时间: 2010
• 期刊: Advances in genetics
• 作者: J. Moore

Testing multiple hypotheses through IMP weighted FDR based on a genetic functional network with application to a new zebrafish transcriptome study.

• DOI: 10.1186/s13040-015-0050-8
• 发表时间: 2015
• 期刊: BioData mining
• 影响因子: 4.5
• 作者: Gui J;Greene CS;Sullivan C;Taylor W;Moore JH;Kim C
• 通讯作者: Kim C

Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene.

使用二分人类表型网络来揭示基因之外的多效性和上位性。

• 发表时间: 2014
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Darabos,Christian;Harmon,SamanthaH;Moore,JasonH
• 通讯作者: Moore,JasonH

STatistical Inference Relief (STIR) feature selection.

• DOI: 10.1093/bioinformatics/bty788
• 发表时间: 2019-04-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Le TT;Urbanowicz RJ;Moore JH;McKinney BA
• 通讯作者: McKinney BA

Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics.

• DOI: 10.1186/1752-0509-8-12
• 发表时间: 2014-02-05
• 期刊: BMC systems biology
• 作者: Penrod NM;Moore JH
• 通讯作者: Moore JH