Predicting and controlling polygenic health traits using probabilistic models and evolution-inspired gene editing

利用概率模型和进化激励基因预测和控制多基因健康性状 编辑 项目总结： 新的突变是适应性进化新颖性的来源之一，但也可能导致遗传病和癌症。 虽然我们现在可以使用CRISPR/Cas9技术来纠正有害的突变，但DNA修改可以 通过看似不可预测的上位论和环境相互作用产生的意想不到的后果，也可能 对于最近进入人类的CCR5中假定的艾滋病毒耐药突变就是这种情况。在更高的 真核生物、健康或健康特征，如适应性或疾病易感性，似乎受 许多突变共同作用--它们是所谓的多基因或复杂特征。这样的突变可能 甚至在一些环境中表现出有害而在另一些环境中有益，因此也称为对抗性 多效性。拟议工作的主要目标是使用多功能模式植物拟南芥来 增强对突变的多基因和拮抗适合性影响的可预测性和可控性。 该项目的结果将为加深我们对复杂的人类基因的理解提供普遍的原则 疾病，并通知安全纠正或避免有害的突变在未来。 具体地说，我将追求以下目标：1)预测多基因适合度效应 环境，2)通过控制有害和有益的突变来提高健康 多重基因组编辑和突变等位基因。拟南芥是一个理想的模型，梳理了 突变对工程突变的高延展性使其在复杂环境中的适应度效应，以及其 广泛的社区和资源。1001拟南芥基因组计划和全基因组敲除 (KO)收集允许量化数千个公开可用的自然和人工突变的适合度 跨环境。建立一个全球拟南芥研究人员网络，我们已经开始了一项实验 同样的自然菌株在45个地点，我将用它来量化与环境相关的突变影响。 将这一点与相关KO线的信息整合在一起，我将建立我之前的预测模型来理解 突变对环境中健康的影响，以及使它们有害的特征。这样的一个 对突变影响的深入了解最终将使我们能够以可预测的方式改变适应度。我要测试一下这个 有两种方式：首先，使用多路复用的CRISPR基本编辑，我将用有害的突变代替有益的突变。 其次，为了研究累积的突变如何影响健康，并学习如何纠正这一点，我将设计 具有已知诱变子和反诱变子等位基因的植物。这些等位基因与DNA修复机制有关 和癌症易感性，可以增加或降低拟南芥的突变率，帮助我们探索突变 在许多哺乳动物体内积累到致死水平。总体而言，我的研究将为 复杂健康性状的基因控制，最终为改善个性化基因组疾病铺平了道路 风险预测和安全地探索多基因疗法的局限性。

Predicting and controlling polygenic health traits using probabilistic models and evolution-inspired gene editing PROJECT SUMMARY: New mutations are a source of adaptive evolutionary novelty but can also cause genetic diseases and cancer. While we can now correct detrimental mutations using CRISPR/Cas9 technologies, DNA modifications can have unintended consequences through seemingly unpredictable epistatic and environmental interactions, as could well be the case for the presumed HIV-resistance mutations in CCR5 recently CRISPRed into humans. In higher eukaryotes, fitness or health traits such as adaptability or disease susceptibility appear to be controlled by numerous mutations acting in concert – they are so-called polygenic or complex traits. Such mutations might even manifest detrimental in some environments while beneficial in others, therefore also called antagonistic pleiotropic. The main goal of the proposed work is to use the versatile model plant Arabidopsis thaliana to enhance the predictability and control of the polygenic and antagonistic fitness effects of mutations. Results from this project will provide universal principles to deepen our understanding of complex human genetic disease and inform the safe correction or avoidance of harmful mutations in the future. Specifically, I will pursue the following aims: 1) predicting polygenic fitness effects across environments, 2) improving fitness by controlling deleterious and beneficial mutations using multiplexed genome editing and mutator alleles. Arabidopsis thaliana is an ideal model to tease apart the fitness effects of mutations in complex environments due to its high malleability to engineered mutations, and its extensive community and resources. The 1001 Arabidopsis Genome Project and a genome-wide Knock-Out (KO) collection allow for quantifying fitness of thousands of publicly available natural and artificial mutations across environments. Building a global network of Arabidopsis researchers, we have started an experiment with the same natural strains in 45 locations, which I will use to quantify environment-associated mutation effects. Integrating this with information of relevant KO lines, I will build on my previous predictive models to understand the effects of mutations on fitness across environments, and the features that make them deleterious. Such a deep understanding of mutation effects will ultimately allow us to alter fitness in predictable ways. I will test this in two ways: First, using multiplexed CRISPR base-edits, I will substitute detrimental for beneficial mutations. Second, to study how accumulating mutations impact fitness and to learn how to correct this, I will engineer plants with known mutator and anti-mutator alleles. These alleles, associated with the DNA repair machinery and cancer susceptibility, can increase or decrease the mutation rate in A. thaliana, helping us explore mutation accumulations up to lethal levels in many mammals. Overall, my research will provide fundamental insights into the genetic control of complex fitness traits, ultimately paving the way to improving personalized genomic disease risk predictions and safely probing the limits of poly-gene therapies.