Development of a high-throughput pipeline to identify causal variants and its demonstration in pig muscle

该项目将通过开发一种识别因果基因组变异（控制育种者需要加强的性状的单个基因组元素）的方法，提高商业化牲畜育种计划的有效性。像肌肉这样的性状，我们在项目中使用的例子，是由成千上万的因果基因变异控制的，育种选择依赖于识别包含有益因果变异优势的基因组区域，而不是识别单个变异。如果育种者有一种识别因果基因组变异的方法，他们的选择将更加准确和精确，未来他们将能够使用基因组编辑来加速改进，同时保护遗传多样性。我们的方法将作为一个阶段框架，通过评估来自不同来源的信息来识别因果变量。第一阶段收集历史育种信息，识别出具有数百万个变异的基因组区域，这些变异对该性状有益的概率相等，有害的概率相等，但较低。框架的每个后续阶段都会引入一个新的信息源，并使用它来调整每个变体的两个概率。随着这些阶段的进行，变异数量的减少，对性状有因果关系和有益的可能性增加。框架的早期阶段使用已经可用或易于收集的信息，这样就可以拒绝大多数变体，而不必进入收集信息代价高昂的阶段。在项目中，我们提出开发框架，整合和测试培养肌肉细胞的基因编辑等四个阶段。将来，该框架可以扩展，以包括可用的新信息源。要想项目成功，需要解决三个问题：我们需要一个计算框架来整合来自不同来源的信息，并识别假定的因果变量。2. 我们需要通过对培养的肌肉细胞进行基因编辑来测试假定的因果变异。我们需要在一个真正的育种项目中评估这个框架。该项目将为育种计划开发一个“等位基因测试”框架，通过整合：-来自我们最近结束的一个项目的375,000头猪的序列数据和表型；-公开可用的功能基因组和表达数据，或我们在Roslin资助的泵启动项目中生成的数据，或将在本拟议项目中收集的数据；-拟在项目中收集的培养肌肉细胞基因编辑数据。本项目有以下三个目标：1。我们将开发基因组学产品线，整合；使用一套统计和生物信息学方法，GWAS，表达数量性状位点（eQTL）和功能注释到假定的因果变异的排名列表中。我们将使用基因编辑技术将假定的因果基因组变异引入猪体外细胞系统，以检测细胞表型。我们将通过预测一组验证猪的基因组育种值来验证“等位基因测试”框架，在“等位基因测试”框架中发现这些假定的因果基因组变异的信息，然后通过将两组基因组育种值与验证猪的后代测试记录相关联来比较两组基因组育种值的准确性。

This project will increase the effectiveness of commercial livestock breeding programmes by developing a method of identifying causal genomic variants, the individual genome elements that control the traits that breeders need to enhance. Traits like muscling, which is the example we use in the project, are controlled by thousands of causal genomic variants, and breeding selections depend on identifying genome regions that contain a preponderance of beneficial causal variants, without identifying individual variants. If breeders had a method of identifying causal genomic variants, their selections would be more accurate and more precise, and in the future they will be able to use genome editing to accelerate improvement while protecting genetic diversity.Our method will work as a framework of stages to identify causal variants by evaluating information from different sources. The first stage takes historical breeding information and identifies genome regions with millions of variants that have an equal probability of being beneficial to the trait and an equal, but lower, probability of being deleterious. Each subsequent stage of the framework brings in a new source of information and uses it to adjust the two probabilities for each variant. As the stages proceed, a reducing number of variants emerge with an increasing probability of being causal and beneficial for the trait. Early stages of the framework use information that is already available or easy to collect so that the majority of variants can be rejected without passing to stages where the information is expensive to collect. In the project we propose to develop the framework and integrate and test four stages including gene-editing of muscle cells in culture. In the future, the framework can be expanded to include new sources of information as they come available.To be successful the project needs to solve three problems:-1. We need a computational framework to integrate information from different sources and identify putative causal variants. 2. We need to test putative causal variants by gene-editing muscle cells in culture.3. We need to evaluate the framework in a real breeding program.The project will develop an "Allele Testing" framework for breeding programmes by integrating: - Sequence data and phenotypes on 375,000 pigs from a recently concluded project of ours; - Functional genomic and expression data that is publicly available, or which we have generated in a Roslin funded Pump Priming Project or will collect in this proposed project;- Data from gene-editing of cultured muscle cells to be collected in the proposed project.The project has three objectives, as follows:-1. We will develop a genomics pipeline that integrates; GWAS, expression quantitative trait loci (eQTL) and functional annotation into a ranked list of putative causal variants, using a suite of statistical and bioinformatic methods.2. We will use gene editing to introduce putative causal genomic variants into a pig in vitro cell system for detection of a cell phenotype.3. We will validate the "Allele Testing" framework by predicting genomic breeding values for a set of validation pigs, with and without the information on these putative causal genomic variants discovered by the "Allele Testing" framework, followed by comparing the accuracy of both sets of genomic breeding values by correlating them to progeny test records for the validation pigs.