Evaluation of machine learning method in genomic selection for growth traits of Pacific white shrimp

The Pacific white shrimp is one of the most important species in the aquaculture industry worldwide, and the growth is regarded as primary trait for selective breeding programmes. In this study, the heritability and genetic correlation of two growth traits, including body length (BL) and the ratio of abdomen length to cephalothorax length (AL/CL) were analyzed, and the genomic prediction based on different genomic selection models including machine learning method were evaluated. The heritabilities of BL and AL/CL were 0.25 +/- 0.04 and 0.07 +/- 0.03, respectively. The two phenotypes showed moderate negative correlations (-0.70 +/- 0.14). Com-parison of the different prediction models showed that NeuralNet had the highest prediction accuracy. The prediction accuracy of NeuralNet increased by about 10% compared to GBLUP. Furthermore, NeuralNet pre-sented the highest prediction accuracy under different marker densities, and the prediction accuracy using 1000 SNPs was similar to that estimated by total SNPs. When comparing multi-trait models (MTM) and single-trait models (STM), NeuralNet outperformed the other methods, which increased prediction accuracy by around 30%. Overall, the NeuralNet model may have better application prospects for genomic selection breeding in shrimp. These results provide a strong basis for accelerating the application of genomic selection breeding in shrimp improvement programmes.

Automated summary

This study analyzed the heritability and genetic correlation of two growth traits in Pacific white shrimp and evaluated the genomic prediction using different genomic selection models. The results showed that the NeuralNet model had the highest prediction accuracy and better prospects for predicting shrimp growth traits.