MIRA: Systems genomics of complex traits

MIRA：复杂性状的系统基因组学

• 批准号: 10447114
• 负责人: Patrick C. Phillips
• 金额: $ 36.62万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-03 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447114
• 关键词: Address; Biological Models; Caenorhabditis elegans; Complex; Computational Biology; Coupling; Disease; Engineering; Environment; Evolution; Gene Expression Profile; Genes; Genetic; Genetic Recombination; Genetic Transformation; Genetic Variation; Genomics; Genotype; Goals; Health; Human; Individual; Knock-out; Maps; Microfluidics; Nematoda; Organism; Phenotype; Population; Predisposition; Problem Solving; Regulatory Element; Research; Sexual Reproduction; Structure; System; Testing; Variant; genetic approach; genetic evolution; genomic locus; human disease; innovation; response; single cell analysis; success; tool; trait

PROJECT SUMMARY The vast majority of individual susceptibility to sickness and disease in humans is generated by complex interactions between multiple genetic loci and the environment, yet we still have very little understanding of how the map between genotype and phenotype is structured or how this structure influences the response to the environment and long-term evolutionary change. In particular, complex genetic networks should generate pleiotropic relationships among traits whose functional coupling can make individual effects difficult to examine using traditional knockout approaches. Although the structure of these networks is known to be strongly influenced by genomic context, both in terms of their immediate functional response and in the long-term evolution of their structure, we still have little understanding of how all of these factors interact within complex living systems. The goal of this project is to address these questions as part of a broad research framework using three specific experimental paradigms with the nematode Caenorhabditis elegans and its relatives as model systems: (1) How are complex regulatory systems structured and how does this structure determine their evolutionary dynamics?, (2) What are the drivers of genomic hyperdiversity and how can this diversity be used as a tool to understand the evolution of genetic systems?, and (3) How does the genomic landscape of variation, recombination and sexual reproduction influence the response to selection? These questions will be addressed using an innovative integration of approaches drawn from genomics, single-cell analysis, microfluidic engineering, high-throughput phenotyping, genetic transformation, and computational biology. This research uses a systems-genetics approach that integrates an understanding of natural genetic variation within a strong functional hypothesis-testing framework to understand the function and evolution of complex regulatory systems with critical implications for human health.

项目总结 人类对疾病和疾病的绝大多数个体易感性是由复合体产生的 多个基因座与环境之间的相互作用，但我们对此仍知之甚少 基因型和表型之间的映射是如何构成的，或者这种结构是如何影响对 环境和长期的进化变化。特别是，复杂的遗传网络应该产生 功能耦合可能使个体效应难以检验的性状之间的多效性关系 使用传统的击倒方法。尽管已知这些网络的结构强烈地 受到基因组环境的影响，无论是在即时功能反应方面，还是在长期反应方面 它们的结构的演变，我们仍然对所有这些因素如何在复杂的内部相互作用知之甚少 生命系统。这个项目的目标是解决这些问题，作为广泛研究框架的一部分。 使用三种特定的实验范式对秀丽线虫及其近缘线虫进行研究 模型系统：(1)复杂的监管系统是如何构建的，这种结构是如何确定的 它们的进化动态？(2)基因组超多样性的驱动因素是什么？这种多样性是如何形成的 作为一种工具来理解遗传系统的进化？以及(3)基因组图谱是如何 变异、重组和有性繁殖影响对选择的反应？这些问题将是 使用来自基因组学、单细胞分析、 微流控工程、高通量表型、遗传转化和计算生物学。这 研究使用系统遗传学的方法，将对自然遗传变异的理解整合到 一个强大的功能假设检验框架，用于理解复合体的功能和演化 对人类健康具有关键影响的监管制度。