Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates

SIMPLE SUMMARY: Aquaculture plays a key role in many emerging economies. Sources of fish for human consumption now exceed those from capture fisheries. However, the high cost of food to feed fish limits investment returns. Physiological inefficiencies in the way fish digest and assimilate nutrients and energy from food can be improved to lower the amount of feed required to farm fish whilst reducing waste and enhancing environmental sustainability. Being able to measure this ‘feed efficiency’ is crucial to develop strategies to improve it, yet we do not currently have a good means of doing this. With a focus on salmon, this research uses detailed physiological information involved in fish metabolic processes to better understand the mechanisms that can contribute towards better feed efficiency and growth, and at the same time enable development of models to accurately predict such measures. Our findings can be used to improve the efficiency of the salmon aquaculture industry and reduce the impact of farming practices on the environment. ABSTRACT: Fish aquaculture is a rapidly expanding global industry, set to support growing demands for sources of marine protein. Enhancing feed efficiency (FE) in farmed fish is required to reduce production costs and improve sector sustainability. Recognising that organisms are complex systems whose emerging phenotypes are the product of multiple interacting molecular processes, systems-based approaches are expected to deliver new biological insights into FE and growth performance. Here, we establish 14 diverse layers of multi-omics and clinical covariates to assess their capacities to predict FE and associated performance traits in a fish model (Oncorhynchus tshawytscha) and uncover the influential variables. Inter-omic relatedness between the different layers revealed several significant concordances, particularly between datasets originating from similar material/tissue and between blood indicators and some of the proteomic (liver), metabolomic (liver), and microbiomic layers. Single- and multi-layer random forest (RF) regression models showed that integration of all data layers provide greater FE prediction power than any single-layer model alone. Although FE was among the most challenging of the traits we attempted to predict, the mean accuracy of 40 different FE models in terms of root-mean square errors normalized to percentage was 30.4%, supporting RF as a feature selection tool and approach for complex trait prediction. Major contributions to the integrated FE models were derived from layers of proteomic and metabolomic data, with substantial influence also provided by the lipid composition layer. A correlation matrix of the top 27 variables in the models highlighted FE trait-associations with faecal bacteria (Serratia spp.), palmitic and nervonic acid moieties in whole body lipids, levels of free glycerol in muscle, and N-acetylglutamic acid content in liver. In summary, we identified subsets of molecular characteristics for the assessment of commercially relevant performance-based metrics in farmed Chinook salmon.