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: 9116910
• 负责人: Stephen Montgomery
• 金额: $ 45.43万
• 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/9116910
• 关键词: Address; Affect; Algorithms; Alleles; Bayesian Method; Biological Assay; Biology; CRISPR/Cas technology; 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 Regulation; Genetic; Genetic Variation; Genome; Genome engineering; Genomic Segment; Genomics; Genotype-Tissue Expression Project; 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; 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; genomic data; human data; human disease; human genome sequencing; improved; innovation; insertion/deletion mutation; learning strategy; molecular phenotype; novel; prediction algorithm; trait; transcriptome; transcriptome sequencing; transcriptomics; whole genome

DESCRIPTION (provided by applicant): 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 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 a unique cohort from a founder population in Sardinia, with genome sequence and/or transcriptome data available from 3000 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 informative genomic features including epigenetic data, sequence motifs, and conservation information into a Bayesian approach jointly modeling multiple transcriptomic signals. We will optimize and apply these methods on genome and transcriptome data available for the Sardinia cohort 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 us 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 projet 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.

描述（由申请人提供）：为了表征人类疾病的分子和细胞原因，有必要阐明遗传变异的功能影响。然而，我们目前无法预测位于基因组非编码区域的大多数遗传变异的影响，而大多数复杂的疾病相关变异确实存在于该区域。此外，最近的证据表明，非编码基因组的很大一部分可能具有功能，通常在基因调控中发挥作用。因此，我们对非编码变异的有限理解是表征疾病遗传基础的关键障碍。该项目的目标是开发解释非编码遗传变异的方法：提供稳健且可扩展的贝叶斯方法来预测全基因组的因果变异，使用 CRISPR 技术识别和验证大量功能性非编码变异，并预测可能受每种变异影响的疾病相关性状。我们的项目将利用来自撒丁岛创始群体的独特队列，以及来自 3000 名个体的基因组序列和/或转录组数据，以及数百个性状的广泛表型分析。我们将先进的统计模型与基于基因组工程的实验验证相结合，以确定影响队列中生物医学特征的因果非编码变异，并预测这些变异最终扰乱细胞的功能机制。在目标 1 中，我们开发了用于预测全基因组因果非编码变异的计算方法，将包括表观遗传数据、序列基序和保守信息在内的信息基因组特征纳入贝叶斯方法，联合建模多个转录组信号。我们将优化这些方法并将其应用于撒丁岛队列可用的基因组和转录组数据，以识别大量预测会因果影响基因表达的变异。基于这些预测，在目标 2 中，我们将假定的因果变异与队列中测量的各种疾病相关性状联系起来，使用网络推理捕获从遗传变异到基因表达到疾病的级联。我们将使用目标 1 中的模型开发跨变体整合的方法，以确定与每个性状相关的常见因果机制。在目标 3 中，我们验证了预测影响高级性状的非编码变异的因果影响。我们将通过 CRISPR 进行基因组编辑，将个体遗传变异引入细胞系，并使用 qPCR 验证对基因表达的预测影响。最后，该提案的一个主要目标是为研究界提供方便的计算工具，用于预测个体基因组的因果非编码变异，并不断更新以整合最新的基因组注释和公共数据，以便在预测因果变异及其可能影响的特征方面提供尽可能高的准确性。我们的项目将极大地增进我们对非编码遗传变异、因果变异影响的具体机制以及对细胞和个体健康的下游影响的理解。