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.

项目概要 破坏调控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贡献者 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之间的快速更新，评估非蛋白质编码的功能 突变是追踪人类特异性生物学进化的主要障碍。 此外，由于大多数与疾病相关的变异都是非编码的，它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基因组中的复杂基因调控程序。 人类基因组并准确模拟这些元素的基因突变对表型的影响 我们通过整合尖端机器来实现这些目标。 学习、进化统计建模以及大规模基因型和表型分析 特别是，我的实验室具有独特的优势，可以在我们之前的工作基础上解决这个问题。 以下基本问题： 1. 人类谱系的进化转变如何改变了基因组——特别是基因 监管计划——产生人类特异性的生物学？这些修改与什么相关？ 人类特有疾病风险？ 2. TF结合模式如何指定基因的精确控制的组合规则是什么 这些基因调控“程序”如何在物种之间进化？ 3. 遗传和表观遗传机制如何相互作用来指定动态基因调控程序 这些程序如何在疾病中受到干扰？ 4. 我们如何解释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？ 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 区域并确定导致人群之间疾病风险差异的突变。