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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

基本信息

批准号：
9306895
负责人：
Stephen Montgomery
金额：
$ 45.43万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-08-01 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9306895
关键词：
Address Affect Algorithmic Analysis 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 Dimensions 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 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 Supervision 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 predictors genetic variant genome annotation genome editing genome sequencing genomic data genomic variation human data human disease human genome sequencing improved innovation insertion/deletion mutation learning strategy molecular phenotype novel prediction algorithm public health relevance trait transcriptome transcriptome sequencing transcriptomics whole genome

项目摘要

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，必须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的大多数遗传变异的影响，事实上，在那里发现了最复杂的疾病相关变异。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用于解释非编码遗传变异的方法：提供一种稳健且可扩展的贝叶斯方法，用于从全基因组预测因果变异，使用CRISPR技术识别和验证大量功能性非编码变异，并预测可能受每个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人群体的独特队列，从3000个个体获得基因组序列和/或转录组数据，沿着数百种性状的广泛表型。我们将结合联合收割机先进的统计建模与实验验证的基础上基因组工程，以确定因果非编码变异影响生物医学性状的队列，沿着预测功能机制，通过这些变异最终扰乱细胞。在目标1中，我们开发了用于预测全基因组的因果非编码变异的计算方法，将信息丰富的基因组特征，包括表观遗传数据，序列基序和保守信息纳入贝叶斯方法，共同建模多个转录组信号。我们将优化并应用这些方法对撒丁岛队列的基因组和转录组数据，以确定预测会因果影响基因表达的大量变体。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，使用网络推理来捕获从遗传变异到基因表达再到疾病的级联反应。我们将开发跨变异整合的方法，使用目标1中的模型，以确定与每个性状相关的共同因果机制。在目标3中，我们验证了预测影响高水平性状的非编码变体的因果影响。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，整个提案的一个主要目标是为研究界提供方便的计算工具，用于预测来自个体基因组的因果非编码变体，并不断更新，以整合最新的基因组注释和公共数据，以便在预测因果变体及其可能影响的性状方面提供最佳的准确性。我们的项目将极大地推进我们对非编码遗传变异的理解，受因果变异影响的特定机制，以及对细胞和个体健康的下游后果。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PML nuclear bodies contribute to the basal expression of the mTOR inhibitor DDIT4.

DOI：
10.1038/srep45038
发表时间：
2017-03-23
期刊：
Scientific reports
影响因子：
4.6
作者：
Salsman J;Stathakis A;Parker E;Chung D;Anthes LE;Koskowich KL;Lahsaee S;Gaston D;Kukurba KR;Smith KS;Chute IC;Léger D;Frost LD;Montgomery SB;Lewis SM;Eskiw C;Dellaire G
通讯作者：
Dellaire G

RNA Sequencing and Analysis.