INTEGRATING ENVIROMICS, GENOMICS, AND MACHINE LEARNING FOR PRECISION BREEDING OF RESILIENT BEEF CATTLE

<b>Project Methods</b><br> The first objective will be the generation of an integrated enviromics data lake, including information on cattle performance, climatic condition, and farm management descriptors. Satellite images and vegetal indexes, together with detailed information regarding facilities and management of farms will be added to the current Aberdeen Angus Association (AAA) database to leverage big data in cattle breeding and production. First, based on the GPS locations and sizes of farms, spatial polygons of farms will be created to download soil, climate, and weather data from various public sources. In addition, based on the spatial polygons of the farms, long-term satellite imageries and indices, and satellite-derived products will be pre-processed and downloaded from various public sources. Lastly, an online survey will be distributed through the AAA to all 9K+ enrolled beef cattle producers for a detailed description of farms' facilities and management practices. All these data will be subjected to machine learning algorithms to select relevant variables across time and space associated with cattle performance and resilience, including growth and fertility traits. As part of the extension component of this first objective, a number of events will be held to receive beef producers' inputs and webinar/extension events on the intersection of climate change and beef cattle production, including mitigation alternatives.Objective 2 of the project will involve comprehensive evaluation of genotype-by-environment interactions (GEI) and future cattle performance through an enviromics approach. This component of the project will entail extensive data analysis, algorithm development, and simulations to model GEI based on enviromics data from Objective 1. It will involve leveraging the enviromics data to cluster farms into ecoregions so that traditional multi-trait models can be used to analyze the data. Phenotypes measured in each ecoregion will be considered as a different trait, and the genetic correlation between them will be used to assess GEI and to develop strategies for optimal selection within each ecoregion. Additional modelling techniques will include the use of enviromics data on their natural continuous scales using a multi-dimensional reaction norms approach. In addition, a spatial analysis methodology will be used to estimate breeding values specific for each farm or geographical location. Objective 2 will also involve the development of a predictive model of future cattle performance, and the investigation of genetic trends in the Angus cattle population. Importantly, it will involve various extension activities for an effective translational component of the research.The third objective will define novel indicators of animal resilience based on enviromics-derived breeding values and biologically validate the predictions through in-depth phenotyping of genetically divergent animals. Furthermore, routinely measured phenotypes including production, reproduction, longevity, health, and welfare traits will be assessed from the AAA database. These datasets generated will be used for validation of the genomic methods and traits proposed in Objectives 1 and 2, and will enable the identification of alternative traits to be measured cost-effectively at a large-scale in breeding programs aiming to improve animal resilience in US beef cattle.