The Evolution of Gene Regulation and Human Disease

基因调控的进化与人类疾病

• 批准号: 10460911
• 负责人: John Anthony Capra
• 金额: $ 40.21万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10460911
• 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.

项目摘要 破坏调节序列功能的遗传变异，从而改变基因表达 水平，是物种之间进化差异和风险差异的主要因素 人类复杂的疾病。但是，由于基因调节程序的复杂性， 哺乳动物基因组及其在物种之间的快速转移，评估非蛋白质编码的功能 突变是挑战的。这是追踪人类特异性生物学发展的主要障碍。在 此外，由于大多数与疾病相关的变体是非编码的，因此它会损害我们绘制的能力 复杂疾病的遗传学。 我实验室的长期任务是解释编码的复杂基因调节程序 人类基因组并准确地模拟遗传突变对这些元素对表型的影响 与疾病和人类进化有关。我们通过整合尖端机器来实现这些目标 学习，进化的统计建模以及大规模的基因型和表型的分析 临床生物库。特别是，我的实验室在我们以前的工作以解决问题的基础上有一个独特的位置 以下基本问题： 1。在人段上的进化转变如何改变了基因组，尤其是基因 监管计划 - 生产人类特异性生物学？这些修改与 人类特异性疾病风险？ 2. TF结合模式如何指定基因的精确控制，是什么组合规则 规定？这些基因调节性“程序”如何在物种之间发展？ 3。遗传和表观遗传机制如何相互作用以指定动态基因调节程序 那驱动细胞发育？这些程序如何在疾病中干扰？ 4。我们如何解释在患者基因组中鉴定出的非蛋白质编码突变以告知治疗 和预防性护理？ 我们的工作将产生急需的方法来理解突变对基因调节的影响 区域并确定导致人口疾病风险差异的突变。