Billions of dollars have been invested annually in research aimed at trying to determine the genetic contribution to complex traits where both genetics and the environment impact the severity of the phenotype. This major investment was built around the promise of using an individual's unique genetic code in order to make more precise management or medical decisions. However, despite decades of research and extensive collaborative projects focused on connecting genetics to complex traits, the implementation of precision management and medicine have not yet come to fruition. The recent USDA Blueprint identified that the continued development of genome tools and resources is necessary to advance genome to phenome discovery. Major steps towards this movement are integration of datasets that incorporate multiple levels of 'omics data (i.e. genetics, metabolomics, and gene transcription and regulation), as well as clearly and consistently describe complex traits across species.The first objective of this project is to create a streamlined workflow for researchers to be able to integrate multi-omics datasets across species. This objective will be accomplished by leveraging data collected from the equine metabolic syndrome (EMS) model as proof of concept. Data and statistical analyses will be performed to assess correlations between (1) the genome, metabolome and phenome by performing a genome-wide association analysis and co-mapping regions of the genome, (2) genome, transcriptome and phenome by performing differential gene expression and gene set enrichment analysis, and (3) genome, transcriptome and metabolome through co-expression network analysis. This will enable development of workflows and procedure documentation that can be broadly applied to other animal systems. The second objective of this project is to develop ontology-based tools that facilitate genotype to phenotype discovery through consistent phenotype descriptors. This objective will be accomplished by curating important traits to the USDA agricultural community from phenotype ontology databases, adding key missing descriptors, and creating a gene to phenotype database using similarities across species. This work will be developed into an open-source phenotype-based ontology tools for use in agricultural species.