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

开发高通量管道来识别因果变异及其在猪肌肉中的演示

• 批准号: BB/T014067/1
• 负责人: Gregor Gorjanc
• 金额: $ 95.11万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT014067%2F1
• 关键词: Development; throughput; pipeline; identify; causal

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.

该项目将通过开发一种鉴定致病基因组变异的方法，提高商业牲畜育种方案的效力，致病基因组变异是控制育种者需要加强的性状的单个基因组元件。我们在项目中使用的肌肉等性状是由数千种致病基因组变异控制的，育种选择取决于识别含有大量有益致病变异的基因组区域，而不是识别单个变异。如果育种者有一种识别致病基因组变异的方法，他们的选择将更准确，更精确，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致病变异。第一阶段获取历史育种信息，并识别具有数百万个变异的基因组区域，这些变异对性状有利的概率相等，但有害的概率较低。框架的每个后续阶段都会引入新的信息源，并使用它来调整每个变量的两个概率。随着阶段的进行，出现的变异数量减少，而成为因果关系和对性状有益的变异的概率增加。该框架的早期阶段使用已经可用或易于收集的信息，以便大多数变体可以被拒绝，而不会进入信息收集昂贵的阶段。在该项目中，我们建议开发框架并整合和测试四个阶段，包括培养中肌肉细胞的基因编辑。将来，这个框架可以扩展到包括新的信息来源，因为它们是可用的。要成功的项目需要解决三个问题：我们需要一个计算框架来整合来自不同来源的信息，并确定推定的因果变异。2.我们需要通过对培养的肌肉细胞进行基因编辑来测试推定的因果变异。我们需要在一个真实的育种计划中评估该框架。该项目将通过整合以下内容为育种计划开发一个“等位基因测试”框架：-来自我们最近完成的项目的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，或我们在罗斯林资助的泵启动项目中产生的或将在这个拟议的项目中收集的数据;- 拟进行的研究计划将收集肌肉细胞基因编辑的数据。研究计划有以下三个目的：我们将开发一个基因组学管道，该管道使用一套统计和生物信息学方法，将GWAS、表达数量性状基因座（eQTL）和功能注释整合到推定因果变异的排名列表中。2.我们将使用基因编辑将推定的致病基因组变体引入猪体外细胞系统中以检测细胞表型。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，使用和不使用“等位基因测试”框架发现的这些推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。

项目成果

Genetic architecture and major genes for backfat thickness in pig lines of diverse genetic backgrounds.

• DOI: 10.1186/s12711-021-00671-w
• 发表时间: 2021-09-22
• 期刊: Genetics, selection, evolution : GSE
• 作者: Gozalo-Marcilla M;Buntjer J;Johnsson M;Batista L;Diez F;Werner CR;Chen CY;Gorjanc G;Mellanby RJ;Hickey JM;Ros-Freixedes R
• 通讯作者: Ros-Freixedes R

Genetic variation in recombination rate in the pig.

猪重组率的遗传变异。

• DOI: 10.1186/s12711-021-00643-0
• 发表时间: 2021-06-25
• 期刊: Genetics, selection, evolution : GSE
• 作者: Johnsson M;Whalen A;Ros-Freixedes R;Gorjanc G;Chen CY;Herring WO;de Koning DJ;Hickey JM
• 通讯作者: Hickey JM

Rare and population-specific functional variation across pig lines.

• DOI: 10.1186/s12711-022-00732-8
• 发表时间: 2022-06-03
• 期刊: GENETICS SELECTION EVOLUTION
• 影响因子: 4.1
• 作者: Ros-Freixedes, Roger;Valente, Bruno D.;Chen, Ching-Yi;Herring, William O.;Gorjanc, Gregor;Hickey, John M.;Johnsson, Martin
• 通讯作者: Johnsson, Martin

Evidence for and localization of proposed causative variants in cattle and pig genomes.

• DOI: 10.1186/s12711-021-00662-x
• 发表时间: 2021-08-30
• 期刊: Genetics, selection, evolution : GSE
• 作者: Johnsson M;Jungnickel MK
• 通讯作者: Jungnickel MK

Optimisation of the core subset for the APY approximation of genomic relationships.

• DOI: 10.1186/s12711-022-00767-x
• 发表时间: 2022-11-22
• 期刊: GENETICS SELECTION EVOLUTION
• 影响因子: 4.1
• 作者: Pocrnic, Ivan;Lindgren, Finn;Tolhurst, Daniel;Herring, William O.;Gorjanc, Gregor
• 通讯作者: Gorjanc, Gregor