MIRA: Systems genomics of complex traits

项目概要 人类对疾病的绝大多数个体易感性是由复杂的因素产生的 多个基因位点与环境之间的相互作用，但我们仍然知之甚少 基因型和表型之间的图谱是如何构建的，或者这种结构如何影响对 环境和长期的进化变化。特别是，复杂的遗传网络应该产生 性状之间的多效性关系，其功能耦合可能使个体效应难以检查 使用传统的淘汰赛方法。尽管这些网络的结构众所周知 受到基因组背景的影响，无论是在其直接功能反应还是长期功能反应方面 尽管它们结构的演变，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。该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例，以线虫秀丽隐杆线虫及其近亲为对象 模型系统：（1）复杂的监管系统是如何构建的以及该结构如何决定 它们的进化动力学？, (2) 基因组超多样性的驱动因素是什么以及如何才能实现这种多样性 用作理解遗传系统进化的工具？以及（3）基因组景观如何 变异、重组和有性生殖会影响对选择的反应吗？这些问题将会 使用基因组学、单细胞分析、 微流体工程、高通量表型分析、遗传转化和计算生物学。这个 研究采用系统遗传学方法，将自然遗传变异的理解融入到 一个强大的功能假设检验框架来理解复杂的功能和演化 对人类健康具有重大影响的监管体系。

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.