Predicting causal non-coding variants in a founder population

描述（由申请人提供）：为了描述人类疾病的分子和细胞原因，有必要揭示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，而实际上大多数复杂的疾病相关变异都是在基因组非编码区域发现的。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解释非编码遗传变异的方法：提供一种鲁棒性和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。我们的项目将利用撒丁岛一个创始人群体的独特队列，从3000个个体中获得基因组序列和/或转录组数据，以及数百个特征的广泛表型。我们将结合先进的统计建模和基于基因组工程的实验验证，以确定影响队列生物医学性状的因果非编码变异，并预测这些变异最终扰乱细胞的功能机制。在Aim 1中，我们开发了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性基因组特征纳入贝叶斯方法，共同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，以确定预测会对基因表达产生因果影响的大量变异。基于这些预测，在Aim 2中，我们将假定的因果变异与在队列中测量的各种疾病相关特征联系起来，使用网络推理来捕捉从遗传变异到基因表达再到疾病的级联。我们将使用目标1中的模型开发跨变量集成的方法，以确定与每个特征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，将单个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，本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时提供尽可能高的准确性。我们的项目将极大地促进我们对非编码遗传变异的理解，受因果变异影响的具体机制，以及对细胞和个体健康的下游后果。

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.