EGGPT：an extensible and growing genomic prediction technology

Abstract

Genomic selection (GS) is an effective way to aid breeders in enhancing the precision and efficiency of plant and animal breeding through the use of genomic prediction (GP) methods. However, most of GP methods based on a single algorithm are poor robustness and lags behind the development of algorithms in the field of artificial intelligence (AI). To address these limitations, we present an extensible and growing genomic prediction technology (EGGPT). EGGPT is designed on engineering principles, that abstracts the GP process into a five-layer structure including data collection, processing, encoding, base and meta model construction. Using the ensemble learning, EGGPT overcomes the poor robustness. And the highly decoupled modular architecture enables rapid integration with new methods to achieve the best performance for all datasets. These datasets involve 84 various traits across 7 plant and 1 animal species, suggesting that EGGPT could emerge as a new paradigm in GP.