The Evolution of Gene Regulation and Human Disease

基因调控的进化与人类疾病

• 批准号: 9904747
• 负责人: John Anthony Capra
• 金额: $ 8.64万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904747
• 关键词: Address; Binding; Biology; Clinical; Code; Complex; Computer Models; DNA Sequence Alteration; Disease; Elements; Epigenetic Process; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genome; Genotype; Goals; Human; Human Genome; Impairment; Lead; Machine Learning; Maps; Methods; Mission; Modeling; Modification; Mutation; Nucleic Acid Regulatory Sequences; Patients; Pattern; Phenotype; Pongidae; Population; Positioning Attribute; Preventive care; Regulator Genes; Risk; Specific qualifier value; Statistical Models; To specify; Untranslated RNA; Variant; Work; biobank; cellular development; combinatorial; disorder risk; genetic variant; human disease; mammalian genome; programs; species difference

PROJECT SUMMARY Genetic variants that disrupt the functionality of regulatory sequences, and thereby alter gene expression levels, are major contributors to both evolutionary divergence between species and differences in risk for complex disease among humans. However, due to the complexity of the gene regulatory programs encoded in mammalian genomes and their rapid turnover between species, evaluating the function of non-protein-coding mutations is challenging. This is a major roadblock to tracing the evolution of human-specific biology. In addition, since the majority of disease-associated variants are non-coding, it impairs our ability to map the genetics of complex disease. The long-term mission of my lab is to interpret the complex gene regulatory programs encoded in the human genome and accurately model the effects of genetic mutations to these elements on phenotypes relevant to disease and human evolution. We work toward these goals by integrating cutting-edge machine learning, statistical modeling of evolution, and the analysis of genotypes and phenotypes from large-scale clinical biobanks. In particular, my lab is uniquely well positioned to build on our previous work to address the following fundamental questions: 1. How have evolutionary transitions on the human-lineage modified the genome—in particular gene regulatory programs—to produce human-specific biology? And how do these modifications relate to human-specific disease risk? 2. What are the combinatorial rules underlying how TF binding patterns specify precise control of gene regulation? And how do these gene regulatory “programs” evolve between species? 3. How do genetic and epigenetic mechanisms interact to specify the dynamic gene regulatory programs that drive cellular development? And how are these programs perturbed in disease? 4. How can we interpret non-protein-coding mutations identified in patient genomes to inform treatment and preventative care? Our work will produce much-needed methods for understanding the effects of mutations to gene regulatory regions and identify mutations responsible for differences in disease risk between human populations.

项目总结 破坏调控序列的功能，从而改变基因表达的遗传变异 水平，是物种间进化差异和疾病风险差异的主要因素 人类之间的复杂疾病。然而，由于编码的基因调控程序的复杂性 哺乳动物基因组及其在物种间的快速转换，评估非蛋白质编码的功能 突变是具有挑战性的。这是追踪人类特有生物学进化的主要障碍。在……里面 此外，由于大多数与疾病相关的变异都是非编码的，这削弱了我们映射 复杂疾病的遗传学。 我的实验室的长期任务是解释编码在 并准确模拟这些元素的基因突变对表型的影响 与疾病和人类进化有关。我们通过集成最尖端的机器来实现这些目标 学习，进化的统计建模，以及来自大范围的基因型和表型的分析 临床生物库。特别是，我的实验室处于独特的有利地位，可以在我们之前的工作的基础上，解决 以下是基本问题： 1.人类谱系上的进化转变如何改变了基因组--特别是基因 监管计划--生产人类特有的生物学？这些修改与以下方面有何关系 人类特有的疾病风险？ 2.TF结合模式如何指定对基因的精确控制背后的组合规则是什么 监管？这些基因调控“程序”是如何在物种间进化的？ 3.遗传和表观遗传机制如何相互作用来指定动态的基因调控程序 来推动细胞的发展？这些项目是如何在疾病中受到干扰的？ 4.我们如何解释在患者基因组中发现的非蛋白质编码突变以指导治疗 和预防性护理呢？ 我们的工作将产生急需的方法来理解突变对基因调控的影响 并确定导致人类人群之间疾病风险差异的突变。