A non-linear game for two: genetic parameters and prediction of fertilization success using Bayesian and machine learning frameworks

Fertility is an important but often cryptic and intrinsic characteristic of domesticated animals. Predicting reproductive potential is of great importance for the industry but assessment through indirect proxies is laborious and often impractical. Among other biological factors, genetic effects are expected to play a crucial role in shaping male and female fertility. In cases where heritable components are strong, polygenic merit could be a valuable tool for decision-making in breeding schemes. Here we estimate sex-specific variance components affecting fertilization success by analyzing outcomes of over 3000 controlled mating events in an Arctic charr breeding nucleus from Iceland. Furthermore, a machine learning framework using relationships-to-founders vectors as input and a two-tower neural network architecture is proposed and tested for prediction of fertilization success. Both approaches seem to capture a meaningful biological signal and offer alternative tools for ranking, selecting or even allocating matings between breeding candidates.