461 Machine learning and AI to improve genetic prediction in beef cattle: Potential uses and misuses

The current objective is to discuss if and where machine learning (ML) and artificial intelligence (AI) fit in the genetic prediction toolbox. Critical to the discussion is recognizing that, by definition, models currently employed for routine genetic prediction are part of the ML family, albeit linear as opposed to deep, and that ML fits under the more general umbrella of AI. Henderson’s Mixed Model Equations (MME) have served the animal breeding and genetic community well for several decades and have proven useful at predicting the additive genetic merit of animals. Moreover, extensions of the MME have clearly demonstrated the ability to fit non-additive genetic effects such as dominance and epistasis. The literature contains multiple examples of comparisons of genetic prediction between ML and mixed linear models, with a general consensus that ML does not outperform the MME when the trait(s) analyzed are largely additive in nature. With this in mind, the question of where ML fits is appropriate to ask. Given not all data are equally informative due to potential heterogeneity in the data generation process it is reasonable to employ ML to categorize data based on quality. Examples of using deep learning exist as a means of avoiding rules-based approaches to data editing and to categorize and weight data based on quality prior to inclusion in routine genetic evaluations. Additionally, the phenotypes used in genetic evaluations themselves could be predicted by ML. This might be the case in the use of animal tracking to garner data on social behavior, animal health and welfare, or metrics of libido and fertility; parasite loads predicted from camera data; or animal body weight gain or feed intake predicted from partial measurements. In all cases, the size of the raw data and lack of biological interpretation associated with it could necessitate the use of a predicted value. However, the error structure associated with the predicted values and the potential for compounding errors in genetic prediction need to be considered. Given the phenotype of an animal arises due to genetic and environmental effects and the complex interactions among and between these two broad sources of variation, the use of ML could be envisioned to predict phenotypic outcomes to aid in management and marketing decisions. To exploit this possibility, and outperform mixed linear models, the environmental data used would need to be sufficiently granular and high dimensional. There also exists the potential to employ ML to perform feature selection, as in the case of selecting variants from whole genome sequencing where feature subsets may be informed by multiple layers of external information. The use cases for AI also include decision support to help practitioners make use of enumerable genetic predictions given their unique objectives and circumstances.