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

估计遗传变异对大脑在空间和时间上的影响

• 批准号: 8798957
• 负责人: Jacob James Michaelson
• 金额: $ 38.8万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2017-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8798957
• 关键词: Accounting; Address; Affect; Alleles; Autistic Disorder; Binding; Bioinformatics; Bipolar Disorder; Brain; Chromatin; Clinic; Clinical; Code; Computational algorithm; Computing Methodologies; Data; Data Set; Development; Diagnosis; Disease; Foundations; Gene Cluster; Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic Variation; Genome; Genomics; Goals; Human; Human Genome; Human Genome Project; Individual; Learning; Light; Measures; Medical; Methods; Modeling; Molecular; Mutation; Neurologic; Open Reading Frames; Other Genetics; Phenotype; Play; Potassium Hydroxide; Probability; Proteins; Reading; Reporting; Research; Resources; Risk; Role; Scientist; Shapes; Specificity; Staging; Sum; System; Time; Tissues; Training; Untranslated RNA; Variant; Weight; base; brain tissue; case control; cost; epigenomics; exome; experience; falls; genetic variant; genome sequencing; genome-wide; improved; insight; loss of function; model building; neuropsychiatry; novel; promoter; psychogenetics; public health relevance; research study; spatiotemporal; tool; trait; transcription factor

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.

描述（申请人提供）：在人类基因组计划完成后的几年里，我们阅读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能力已经落后了。位于蛋白质编码区之外的遗传变异特别难以解释，因为管理其调节功能的规则远不如蛋白质编码序列的原理那么容易理解。应对这一挑战尤为紧迫，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。最近完成了几个基因组规模的项目，并公布了新的和正在进行的项目的试验数据，这使得有可能开始建立模型，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，目的是为任何任意输入变异提供效应大小的估计，最有可能受变异影响的系统或组织，以及变异体最可能产生表型的发育阶段。模型将在来自诊断和未诊断个体的全基因组测序研究的变体上进行训练。该框架的开发将分三个阶段进行：1）上述基因组证据线将与其他特征相结合，作为在患有诊断的神经精神病症的个体中鉴定的变体的富集的预测因子，产生指示变体的表型形成潜力的评分; 2）将整合时空基因表达矩阵，以提供最有可能受到非组织变异影响的组织和时间点的估计。编码查询基因座; 3）通过组合在阶段1和2中产生的估计，我们将创建单个加权上下文矩阵，其表示个体在空间（即脑组织/区域）和时间上的聚集调节变体负担。该框架将在以前未发表的自闭症和双相情感障碍的变体上得到证明。据我们所知，所提出的框架将是第一个非编码变体注释系统，该系统关注对大脑的影响，并且能够指导用户何时何地使用潜在功能性变体的影响。 变异很可能出现在个体中。所提出的系统的另一个新颖方面是，它将提供个人在空间和时间上的总体负担背景的综合估计。拟议的项目将为从事精神和神经疾病基因组学研究的科学家提供宝贵的资源。也许更重要的是，在这个项目的过程中学到的经验教训将为开发工具提供基础，这些工具可能有一天会在临床上实现解释非编码变异。