Predicting causal non-coding variants in a founder population

预测创始人群体中的因果非编码变异

• 批准号: 8792751
• 负责人: Stephen Montgomery
• 金额: $ 47.76万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8792751
• 关键词: Address; Affect; Algorithms; Alleles; Bayesian Method; Biological Assay; Biology; Cataloging; Catalogs; Categories; Cell Line; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Complex; Computing Methodologies; Data; Data Set; Databases; Development; Disease; Epigenetic Process; Family; Founder Generation; Frequencies; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genetic; Genetic Variation; Genome; Genome engineering; Genomic Segment; Genomics; Goals; Health; Human Genetics; Human Genome; Individual; Inherited; Link; Linkage Disequilibrium; Machine Learning; Maps; Measures; Methods; Modeling; Molecular; Mutation; Nucleotides; Open Reading Frames; Pathogenesis; Phenotype; Play; Population; Property; RNA Splicing; Research; Resolution; Resources; Role; Sampling; Sardinia; Signal Transduction; Statistical Models; System; Techniques; Technology; Testing; Transcript; Untranslated RNA; Update; Validation; Variant; Widespread Disease; base; cohort; computerized tools; data modeling; density; disease phenotype; disorder risk; functional genomics; genetic linkage analysis; genetic variant; genome annotation; genome editing; genome sequencing; genome-wide; human data; human disease; human genome sequencing; improved; innovation; insertion/deletion mutation; molecular phenotype; novel; public health relevance; trait; transcriptome sequencing; transcriptomics

Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. In order to characterize the molecular and cellular causes of human disease, it will be essential to unravel the functional impact of genetic variation. However, we are currently unable to predict the impact of the majority of genetic variants that lie in non-coding regions of the genome, where indeed most complex disease-associated variants are found. Additionally, recent evidence suggests that a significant fraction of the non-coding genome is likely to be functional, often playing a role in gene regulation. Therefore, our limited understanding of non- coding variation is a critical hurdle to characterizing the genetic basis of disease. The goal of this project is to develop methods for interpreting non-coding genetic variation: to provide a robust and extensible Bayesian method for predicting causal variants from full genomes, to identify and validate a large set of functional non- coding variants using CRISPR technology, and to predict disease-relevant traits likely to be affected by each variant. Our project will leverage the increasing availability of cohorts such as UK10K, GTEx, CARTaGENE and SardiNIA, with genome sequence and transcriptome data available from thousands of individuals, along with extensive phenotyping for hundreds of traits. We will combine advanced statistical modeling with experimental validation based on genome engineering to identify causal non-coding variants affecting biomedical traits in the cohort, along with predicting functional mechanisms through which these variants ultimately perturb the cell. In Aim 1, we develop computational methods for predicting causal non-coding variation from full genomes, incorporating multiple informative genomic features into a Bayesian approach. We will optimize and apply these methods on genome and transcriptome data available for well-studied and broadly-accessible cohorts to identify a large set of variants predicted to causally affect gene expression. Based on these predictions, in Aim 2, we connect putative causal variants with the diverse set of disease- relevant traits measured in the cohort, using network inference to capture the cascade from genetic variation to gene expression to disease. We will develop methods to integrate across variants, using the models in Aim 1, to identify the common causal mechanisms related to each trait. In Aim 3, we validate the causal impact of non-coding variants predicted to affect high-level traits. We will use genome editing through CRISPR to introduce individual genetic variants into cell lines and use qPCR to validate the predicted effects on gene expression. Finally, a major goal throughout this proposal will be to provide the research community with convenient computational tools for the prediction of causal non-coding variants from individual genomes, updated on an ongoing basis to integrate the most recent genomic annotations and public data in order to provide the best possible accuracy in predicting causal variants and the traits they are likely to affect. Our project will greatly advance our understanding of non-coding genetic variation, the specific mechanisms affected by causal variants, and the downstream consequences to the cell and individual health. 2.

在此处输入文本，这是您应用程序的新摘要信息。本节必须不再 超过30行文字。 为了表征人类疾病的分子和细胞原因，揭开 遗传变异的功能影响。但是，我们目前无法预测大多数的影响 基因组的非编码区域中的遗传变异，确实是最复杂的疾病相关的 找到了变体。此外，最近的证据表明，非编码基因组的很大一部分 可能是功能性的，通常在基因调节中起作用。因此，我们对非 - 编码变化是表征疾病遗传基础的关键障碍。这个项目的目标是 开发解释非编码遗传变异的方法：提供可靠且可扩展的贝叶斯 预测全基因组的因果变异的方法，以识别和验证大量功能性非 - 使用CRISPR技术编码变体，并预测可能受到每个特征 变体。我们的项目将利用诸如UK10K，GTEX，Cartagene之类的同类群体的可用性增加 和撒丁岛，具有数千个个体的基因组序列和转录组数据， 具有数百个特征的广泛表型。我们将将高级统计建模与 基于基因组工程的实验验证，以识别影响因果的非编码变体 队列中的生物医学特征，以及预测这些变体的功能机制 最终扰动细胞。在AIM 1中，我们开发用于预测因果非编码的计算方法 完整基因组的变化，将多个信息性基因组特征纳入贝叶斯方法。我们 将优化并将这些方法应用于可用于良好研究和的基因组和转录组数据上 广泛可访问的人群，以识别一套预测的因果关系影响基因表达的变体。 基于这些预测，在AIM 2中，我们将推定的因果变体与各种疾病的种类联系起来 - 在队列中测量的相关性状，使用网络推断将级联从遗传变异捕获到 基因表达疾病。我们将使用AIM 1中的模型来开发跨变体集成的方法 确定与每个性状相关的常见因果机制。在AIM 3中，我们验证了 预测的非编码变体会影响高级特征。我们将通过CRISPR使用基因组编辑到 将单个遗传变异引入细胞系，并使用QPCR验证对基因的预测影响 表达。最后，整个建议的主要目标是为研究社区提供 方便的计算工具，用于预测各个基因组的因果非编码变体， 持续更新以整合最新的基因组注释和公共数据，以便 在预测因果变体及其可能影响的特征方面提供最佳的准确性。我们的 项目将大大提高我们对非编码遗传变异的理解，这是特定机制 受因果变异的影响，以及对细胞和个人健康的下游后果。 2。