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，揭示基因变异对功能的影响将是至关重要的。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，实际上，在这些区域发现了最复杂的与疾病相关的变异。此外，最近的证据表明，很大一部分非编码基因组可能具有功能，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描述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：提供一种稳健和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，并预测可能受到每个变异影响的疾病相关特征。我们的项目将利用撒丁岛创始人群体的独特队列，拥有3000个人的基因组序列和/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，我们开发了从全基因组预测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结合到联合建模多个转录信号的贝叶斯方法中。我们将在撒丁岛队列的基因组和转录组数据上优化和应用这些方法，以识别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，在目标2中，我们将假定的因果变异与队列中测量的各种与疾病相关的特征集联系起来，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，以确定与每种特征相关的共同因果机制。在目标3中，我们验证了预测会影响高水平特征的非编码变量的因果影响。我们将通过CRISPR进行基因组编辑，将单个基因变异引入细胞系，并使用qPCR来验证预测的基因表达效果。最后，整个提案的一个主要目标将是为研究界提供便利的计算工具，用于预测单个基因组的因果非编码变体，并在不断更新的基础上进行更新，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尽可能好的准确性。我们的项目将极大地促进我们对非编码遗传变异、因果变异影响的特定机制以及对细胞和个人健康的下游后果的理解。

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.