Network-based algorithms for target identification and drug repositioning from genetic associations

基于网络的算法，用于根据遗传关联进行目标识别和药物重新定位

• 批准号: 10447417
• 负责人: Casey S Greene
• 金额: $ 60.2万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447417
• 关键词: 3-Dimensional; Academia; Address; Affect; Algorithmic Software; Algorithms; American; Bioinformatics; Biological; Biological Assay; Cardiovascular system; Catalogs; Chromosome Mapping; Chromosome Structures; Complex; Computing Methodologies; Data; Databases; Diabetes Mellitus; Diagnosis; Disease; Drug Industry; Drug Targeting; Electronic Health Record; Funding; Genes; Genetic; Genetic Predisposition to Disease; Genetic study; Genome; Genomics; Genotype; Goals; Grant; Health system; Human; Hypertension; Individual; Lead; Link; Low-Density Lipoproteins; Measurement; Methods; Mission; Molecular; National Human Genome Research Institute; Network-based; Noise; Outcome; Pathogenesis; Pathway Analysis; Pathway interactions; Pharmaceutical Preparations; Phenocopy; Phenotype; Play; Precision Medicine Initiative; Proteins; Public Health; Research; Research Personnel; Role; Specificity; Structure; Testing; Therapeutic; Time; Tissues; Translating; United States National Institutes of Health; Untranslated RNA; Variant; Work; algorithm development; alternative treatment; base; biobank; causal variant; data resource; design; diverse data; drug candidate; drug development; drug discovery; drug metabolism; effective therapy; exome sequencing; experimental study; genetic association; genetic variant; genome wide association study; genomic data; human disease; innovation; insight; novel therapeutics; phenome; phenotypic data; prevent; primary outcome; trait; translational impact

In the field of genetics, genome-wide association studies of common variants (GWAS) and exome sequencing- based analyses are a common strategy to elucidate the relationship between genetic variants and a specific phenotype. While these approaches have strengths, they also have significant limitations such as their inability to identify complex biological interactions that lead to genetic predispositions, their inability to integrate distinct but related phenotypes, and their inability to separate genetic variants effects by tissue. If a phenotype is manifest only as a result of the complex interplay of multiple factors, it can be impossible to successfully isolate individual parts by investigating genotype-phenotype associations for only one outcome trait or disease alone. To affect a disease, drugs need to act on the right target and in the right tissue. Bioinformatics approaches that integrate multiple key layers of information to reveal effective drugs will address a critical unmet need because it is expected that a complex interplay of factors forms the basis for most human phenotypes and diseases. The overall objective of this proposal is the development of algorithms that integrate gene and phenome-wide association results with chromosome structure data and functional relationship networks to identify genes that give rise to complex phenotypes and drugs that modify them. These algorithms will provide a new and unique means to study the genetic etiology of complex traits and outcomes, increasing the interpretability of and ultimately the insights generated from high throughput association testing. The proposal's rationale is that robust tissue-specific methods will open the door for geneticists, researchers with biorepositories, and those with access to other extensive phenotyping data to effectively reposition drugs and identify new targets. Complementary algorithms to address distinct aspects of this challenge are proposed as specific aims: (AIM 1) Development of algorithms that integrate exome sequencing results with biological networks to identify genes and pathways associated with phenotypes in specific tissues; (AIM 2) Development of algorithms that integrate 3D genome structure with robust associations via biological networks to identify genes underlying phenotypes in specific tissues; (AIM 3) Development of algorithms that identify drugs that specifically alter regions of gene- gene networks associated with a complex phenotype. Methods will be applied to phenome-wide analysis of the Geisinger Health System MyCode® biorepository and a subset of candidates will be validated via molecular assays. The outcomes of this grant, namely algorithms for tissue-specific network analysis of genes and drugs, are expected to generate positive translational impact because such algorithms enable researchers to translate existing data resources into causal genes and effective drugs.

在遗传学领域，常见变异的全基因组关联研究（GWAS）和外显子组测序- 基础分析是阐明遗传变异与特定 表型虽然这些方法具有优势，但它们也有很大的局限性，例如它们无法 为了识别导致遗传倾向的复杂生物相互作用，它们无法整合不同的基因， 但是相关的表型，以及它们不能通过组织分离遗传变异效应。如果一个表型是 仅表现为多种因素复杂相互作用的结果，不可能成功地隔离 通过调查基因型-表型关联，仅对一个结果性状或疾病单独进行个体部分。 为了影响疾病，药物需要作用于正确的靶点和正确的组织。生物信息学方法， 整合多个关键信息层以揭示有效药物将解决关键的未满足需求，因为 预期多种因素的复杂相互作用形成大多数人类表型和疾病的基础。 这项提案的总体目标是开发整合基因和全表型的算法 将结果与染色体结构数据和功能关系网络相关联，以鉴定 产生复杂的表型和改变它们的药物。这些算法将提供一个新的和独特的 这意味着研究复杂性状和结果的遗传病因学，增加了遗传学的可解释性， 最终，从高通量关联测试产生的见解。该提案的理由是， 强大的组织特异性方法将为遗传学家，生物储存库的研究人员以及那些 通过访问其他广泛的表型数据来有效地重新定位药物并识别新的靶点。 补充算法，以解决这一挑战的不同方面提出了具体的目标：（目的1） 开发将外显子组测序结果与生物网络整合以识别基因的算法 和与特定组织中的表型相关的途径;（目的2）开发整合 3D基因组结构，通过生物网络进行强大的关联，以识别表型背后的基因 在特定组织中;（目的3）开发识别特异性改变基因区域的药物的算法， 与复杂表型相关的基因网络。方法将应用于全表型分析的 Geisinger Health System MyCode®生物储存库和一部分候选物将通过分子生物学方法进行验证。 分析。 这项资助的成果，即基因和药物的组织特异性网络分析算法，是 预计将产生积极的翻译影响，因为这样的算法使研究人员能够翻译 将现有的数据资源转化为致病基因和有效药物。