The Evolution of Gene Regulation and Human Disease

基因调控的进化与人类疾病

• 批准号: 10321189
• 负责人: John Anthony Capra
• 金额: $ 31.08万
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321189
• 关键词: Evolution; Gene; Regulation; Human; Disease

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 破坏调控序列功能从而改变基因表达的遗传变异 水平，是物种之间进化分歧和风险差异的主要贡献者， 人类的复杂疾病。然而，由于编码基因调控程序的复杂性， 哺乳动物基因组及其物种间的快速周转，评估非蛋白质编码的功能， 突变是具有挑战性的。这是追踪人类特定生物学进化的主要障碍。在 此外，由于大多数疾病相关的变异是非编码的，这削弱了我们绘制基因图谱的能力。 复杂疾病的遗传学 我实验室的长期使命是解释编码在细胞中的复杂基因调控程序。 人类基因组，并准确模拟基因突变对这些元素表型的影响 与疾病和人类进化有关。为了实现这些目标，我们将尖端的机器 学习，进化的统计建模，以及大规模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，可以在我们以前的工作基础上解决 以下基本问题： 1.人类谱系的进化转变是如何改变基因组的--特别是基因 调节程序-产生人类特有的生物学？这些修改与 人类特有的疾病风险？ 2. TF结合模式如何精确控制基因表达的组合规则是什么 监管？这些基因调控“程序”在物种之间是如何进化的？ 3.遗传机制和表观遗传机制如何相互作用以确定动态基因调控程序 驱动细胞发育的基因吗这些程序在疾病中是如何受到干扰的？ 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，为治疗提供信息 预防保健？ 我们的工作将产生急需的方法来了解突变对基因调控的影响， 区域，并确定突变负责不同人群之间的疾病风险。