Estimating the impact of genetic variants on the brain in space and time

描述（由申请人提供）：自人类基因组计划完成以来，我们读取基因组的能力有了巨大的提高。令人沮丧的是，我们解释和理解我们现在可以轻松阅读的东西的能力已经落后了。位于蛋白质编码区之外的遗传变异尤其具有挑战性，因为控制其调节功能的规则远不如蛋白质编码序列的原则为人所知。解决这一挑战尤为紧迫，因为全基因组测序成本的大幅下降将带来一波新发现的非编码和潜在因果变异。最近几个基因组规模项目的完成，以及从新的和正在进行的项目中发布的试点数据，使得开始建立模型成为可能，其目标是预测非编码遗传变异的功能。我们建议开发一个以大脑为中心的变异注释框架，该框架整合了来自现有数据集的时空表达信息、eQTL研究建立的调控关系以及ENCODE和其他研究发现的染色质状态信息，旨在为任何任意输入变异提供影响程度的估计，以及最有可能受变异影响的系统或组织。变异最有可能产生表型的发育阶段。模型将根据诊断和未诊断个体的全基因组测序研究的变体进行训练。该框架的发展将分三个阶段进行：1)上述基因组证据将与其他特征相结合，作为在诊断为神经精神疾病的个体中确定的变异富集的预测因子，产生一个表明变异表型塑造潜力的分数；2)整合时空基因表达矩阵，估计非编码查询位点最可能受变异影响的组织和时间点；3)通过结合阶段1和阶段2产生的估计，我们将创建一个单一加权上下文矩阵，该矩阵表示个体在空间（即脑组织/区域）和时间上的总调节变异负担。该框架将在自闭症和双相情感障碍中先前未发表的变异中得到证明。据我们所知，所提出的框架将是第一个关注对大脑影响的非编码变体注释系统，并能够指导用户何时何地产生潜在功能的影响

DESCRIPTION (provided by applicant): In the years since the completion of the human genome project, our ability to read the genome has improved tremendously. Frustratingly, our ability to interpret and comprehend what we can now easily read has lagged behind. Genetic variants that lie outside protein-coding regions are particularly challenging to interpret because the rules that govern their regulatory function are far less understood than the principles of protein- coding sequence. Addressing this challenge is particularly urgent because the drastic fall in whole genome sequencing costs will bring with it a wave of newly discovered non-coding and potentially causal variants. The recent completion of several genome-scale projects, and the release of pilot data from new and ongoing projects, have made it possible to begin building models whose goal is to predict the function of non-coding genetic variation. We propose the development of a brain-centric variant annotation framework that integrates temporal and spatial expression information from existing data sets, regulatory relationships established by eQTL studies, and chromatin state information uncovered by ENCODE and other studies, with the aim of providing, for any arbitrary input variant, an estimate of the magnitude of the effect, the systems or tissues most likely affected by the variant, and the stage of development at which the variant is most likely to produce a phenotype. Models will be trained on variants from whole genome sequencing studies of diagnosed and undiagnosed individuals. Development of this framework will proceed in three stages: 1) the above lines of genomic evidence will be combined with other features as predictors of enrichment for variants identified in individuals with diagnosed neuropsychiatric conditions, producing a score indicative of the variant's phenotype-shaping potential; 2) spatiotemporal gene expression matrices will be integrated to provide estimates of the tissues and time points most likely affected by variation at the non- coding query locus; 3) by combining the estimates produced in stages 1 and 2, we will create a single weighted context matrix that represents the individual's aggregate regulatory variant burden in space (i.e. brain tissue/region) and time. The framework will be demonstrated on previously unpublished variants in autism and bipolar disorder. The proposed framework would to our knowledge be the first non-coding variant annotation system that focuses on the effect on the brain, and is able to guide the user as to when and where the effects of potentially functional variants are likely to emerge in an individual. A further novel aspect of the proposed system is that it will provide an integrated estimate of the overall burden context for an individual in spac and time. The proposed project will provide a valuable resource for scientists performing research in the genomics of psychiatric and neurological conditions. Perhaps more importantly, the lessons learned in the course of this project will provide the foundation for developing tools that may one day make interpreting non-coding variation in the clinic a reality.