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

Animal breeding and genetic improvement are fundamental underpinnings of the large productivity gains that have been achieved by the livestock production sector around the world. For example, in Angus cattle between 1/1/199020, the genetic gain for the selection index that combines terminal traits increased by 80%, and the one for weaning traits increased by 400%. However, current selection approaches have some limitations, including the assumption that the ranking of the genetic value of animals is constant across environments, which in practice means that their progeny are expected to perform equally well irrespective of their environmental condition. This is, of course, not the case since the genetic merit of an animal varies based on its environmental conditions. More importantly, because of the interaction between genetics and environment, an animal with a high genetic merit for a favorable environment can perform below average in harsher environments, and vice-versa.This genotype by environment interaction (GEI) affects livestock, poultry, and aquaculture production, making it extremely important in the context of the highly diversified geographic and climate landscape of the United States. Unfortunately, there is currently no way to optimally allocate animals to maximizes their productivity across the broad range of different environments. We envision that the next breakthrough in livestock productivity will come from novel selection tools and methods that account for GEI. This will enable large production gains since animals can be correctly selected for the environment they will inhabit and perform in, will promote better animal welfare, and will help mitigate environmental impact from the production of underperforming animals that are ill-suited to particular environments. More forward-looking, the impact of heat stress on livestock is expected to continue worsening as global temperatures and humidity levels increase. Under heat stress, animals have reduced growth, milk production, and reproductive performance due to counteracting effects such as reduced feed intake. In the US, the annual economic losses caused by heat stress in the livestock industry are already up to $2.36 billion, $370 million in the beef cattle industry alone. Modern beef cattle have progressed considerably since the 1990s due to selection for growth performance but have difficulty adapting to high temperature and humidity, which results in reduced productive and reproductive performance. Understanding the relationship between genetics and environment, and selection for more resilient livestock will be crucial to secure animal protein for the growing world population in a climate-changing globe.In animal breeding, GEI is generally modeled either using multi-trait models to assess the genetic correlations between a finite set of discrete environments, or using reaction norm models (RNM) with a continuous description of the environment gradient. However, both methods are suboptimal, as quite often the environmental diversity in which livestock is raised cannot be split or classified into only a few discrete sets of environmental conditions, nor can they be summarized satisfactorily with a single linear environment gradient variable. Another shortcoming of the traditional mixed models used in animal breeding is that environmental factors, which contribute a significant component of the phenotypic variation are simply lumped together into contemporary groups (CG), defined as animals born and raised in similar conditions and generally expressed as the combination of farm, year, and management groups. Although the use of CG may satisfactorily correct for differences between environmental conditions, they do not allow for the detection of the specific environmental variables that significantly contribute to the phenotypic expression, nor the estimation of their effects. Because of that, such approaches do not provide information for accurate prediction of future cattle performance, nor for improving management decision making.An alternative to existing methods is to integrate breeding programs and genetic evaluations with comprehensive environmental data. This is now possible by leveraging geoprocessing technologies, such as geographic information systems (GIS) for Precision Breeding. The collection and processing of spatiotemporal data on weather, water, soil and yield variables are rapidly increasing due to the societal need for food security and technological advances. This thorough characterization of farm and environmental conditions has been termed enviromics and can provide valuable information for breeding and management decisions in livestock operations. Several types of environmental variables can be used in enviromics analyses, such as temporal climatic information and vegetation indices obtained from remote sensing-based canopies. Categorical indices can also be used, such as the climatic index of Köppen or soil classes. In this project, we postulate that enviromics data can be used in advanced modeling of GEI to breed better adapted and more resilient beef cattle, as well as better selection of livestock genetics for specific combinations of environmental conditions.Using GIS technology and integrating various sources of environmental information from publicly available databases and satellite imaging, we will generate a detailed description of the soil, climate, forage and weather conditions of US beef cattle farms. In addition, using survey data, farms will be comprehensively described in terms of their facilities and management practices. This enviromics data lake will then be used to expand traditional mixed model techniques. First, enviromics variables will be used to investigate the specific environment/management factors that significantly contribute to variation across contemporary groups (CG). Second, temporal-spatial enviromics data will be used to more efficiently model GEI. Lastly, the success of genomic selection for improved climatic performance depends on the availability of phenotypes that are heritable, can be measured on many animals, and that represent the behavioral and physiological mechanisms of heat stress response in beef cattle raised under extensive production systems. As such, defining these optimal phenotypes is a priority for the beef cattle industry to fully utilize genomic selection to breed more resilient cattle. Therefore, it is of utmost value to investigate the usefulness of phenotypes routinely measured in breeding and commercial beef cattle herds and define novel indicator traits that might better capture the genetic variability for heat tolerance and overall resilience in US beef cattle. These traits and statistical models need to be biologically validated through comprehensive (in-depth) phenotyping of animals with divergent genetic merit for the traits identified.Three main research-extension integrated Objectives are proposed in this project, which will leverage enviromics and genomics techniques to breed more resilient cattle: 1) Generation of data lake and data processing pipelines for comprehensive environmental characterization of US beef cattle production systems; 2) Comprehensive evaluation of genotype-by-environment interactions and future performance through an enviromics approach; and 3) Definition of novel indicators of animal resilience based on enviromics-derived breeding values and biological validation of the predictions through in-depth phenotyping of genetically divergent animals.