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

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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.