Design, prediction, and prioritization of systematic perturbations of the human genome

人类基因组系统扰动的设计、预测和优先级排序

• 批准号: 10295506
• 负责人: ANDREW S ALLEN
• 金额: $ 35.37万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10295506
• 关键词: Address; Biological Assay; Catalogs; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Computational Biology; Coupled; Coupling; Data; Diagnostic; Disease; End Point Assay; Epigenetic Process; Evolution; Experimental Designs; Explosion; Gene Expression; Gene Expression Regulation; Genetic; Genetic Code; Genetic Determinism; Genetic Variation; Genome; Genomic Segment; Goals; Health; Human; Human Genome; Incidence; Link; Machine Learning; Measures; Methods; Modeling; Mutagenesis; Neural Network Simulation; Outcome; Output; Pathogenicity; Patients; Phenotype; Population; Population Genetics; Prevention; Recommendation; Regulatory Element; Risk; Sample Size; Site; Software Tools; Statistical Models; Testing; Untranslated RNA; Update; Variant; base; cell type; combinatorial; deep neural network; design; exhaustion; experience; experimental study; flexibility; gene discovery; genetic variant; genome sequencing; human disease; improved; individual patient; machine learning method; member; models and simulation; novel; predictive modeling; statistical and machine learning; success; tool; whole genome

ABSTRACT Noncoding genetic variation that alters gene regulation is of paramount importance for health, disease, and evolution. Diseases ranging in incidence from the most common to the most rare all have substantial risk associated with regulatory variation; and most of the genetic differences between closely related species are noncoding. Whole genome sequencing can directly identify that variation but to realize its potential to elucidate the genetic determinants of health and disease, will require accurate annotation of this noncoding variation for functionality. In coding sequence, the genetic code allows variants to be annotated to a rough hierarchy of likely functional effects and pathogenicity. In noncoding sequence such annotation is less clear. Perturbation assays, i.e., assays that modify genetic or epigenetic states and measure the effect of those perturbations on regulatory endpoints, offer a possible path to annotating noncoding variation. However, to fully leverage this data, novel and sophisticated statistical and machine learning approaches are required to extract useful information from those assays, to integrate that information across regulatory endpoints, and to extrapolate findings so that annotation of previously unobserved (unperturbed) variation in diverse cell types is possible. The goal of the Duke Prediction Center is to develop the analytic approaches and tools that will allow for the routine annotation of noncoding variation for functionality and ultimately pathogenicity. Aim 1 is to establish best practices in perturbation assay design and analysis. This will allow IGVF characterization centers design their experiments so that, when coupled with optimized analyses, the data produced will be maximally informative for subsequent predictive modeling. Aim 2 is to develop novel mechanistic machine learning approaches for predicting the functional effect of noncoding variation on function in diverse cell-types. Aim 3 is to identify noncoding genomic regions that are subject to functional constraint which will be leveraged in prioritizing variants for pathogenicity. The expected outcomes of this project will be (i) robust estimates of optimal experimental design parameters and recommendations for analysis tools and best practices for the various assays used within the IGVF consortium, (ii) predicted functional effects of observed variation to be shared through the IGVF variant/phenotype catalog as well as a state-of-the-art machine learning method (and associated tools) that can identify previously-unknown interactions among genomic variants, both observed and novel, and predict their functional impact in diverse cell types, and (iii) a list of regulatory elements subject to functional constraint shared through the IGVF variant/phenotype catalog and a principled prioritization framework (and associated tools) for interpreting variation within patient genomes for pathogenicity. Due to the considerable success of genetics, there are thousands of unknown regulatory causes of disease. Each of those causes is an opportunity to improve treatment, diagnostics, or prevention. This project will be a major advance towards unlocking that potential.

摘要 改变基因调控的非编码遗传变异对健康、疾病和 进化发病率从最常见到最罕见的疾病都有很大的风险 与调控变异有关;密切相关的物种之间的大多数遗传差异是 非编码。全基因组测序可以直接识别这种变异，但要实现其阐明 健康和疾病的遗传决定因素，将需要准确注释这种非编码变异， 功能.在编码序列中，遗传密码允许变异被注释到可能的粗略层次结构， 功能效应和致病性。在非编码序列中，这种注释不太清楚。扰动测定， 也就是说，修饰遗传或表观遗传状态并测量这些扰动对调节性细胞因子的影响的测定法， 端点，为注释非编码变体提供了一条可能的途径。然而，为了充分利用这些数据， 需要复杂的统计和机器学习方法来提取有用的信息 这些检测，整合监管终点的信息，并推断发现， 可以注释不同细胞类型中先前未观察到的（未扰动的）变化。的目标 杜克预测中心将开发分析方法和工具， 功能性和最终致病性的非编码变异的注释。目标1：建立最佳 扰动分析设计和分析的实践。这将允许IGVF表征中心设计其 实验，以便当与优化分析相结合时，产生的数据将最大限度地提供信息， 随后的预测建模。目标2是开发新的机械机器学习方法， 预测非编码变异对不同细胞类型功能的功能影响。目标3：识别 受功能约束的非编码基因组区域，其将在优先化变体中被利用 致病性。该项目的预期成果将是（i）对最佳实验的稳健估计 分析工具的设计参数和建议，以及内部使用的各种测定的最佳实践 IGVF联盟，（ii）预测通过IGVF共享的观察到的变化的功能效应 变体/表型目录以及最先进的机器学习方法（和相关工具）， 识别先前未知的基因组变异之间的相互作用，包括观察到的和新的，并预测其 在不同细胞类型中的功能影响，和（iii）受共享的功能约束的调节元件的列表 通过IGVF变体/表型目录和原则性优先级框架（和相关工具）， 解释患者基因组内的致病性变异。由于遗传学的巨大成功， 有数以千计的未知的疾病原因。其中每一个原因都是改进的机会 治疗、诊断或预防。该项目将是释放这一潜力的一个重大进展。