Multivariate genomic prediction for commercial traits of economic importance in Banana shrimp Fenneropenaeus merguiensis

Advantages of multi-trait machine and deep learning genomic prediction models for quantitative complex traits have not been documented or very limited in aquaculture species. Thus, the present study sought to understand effects of the multi-trait single-step genomic best linear unbiased prediction (ssGBLUP), Bayesian (BayesCpi), random forest (RF) and multilayer perceptron (MLP) models on genomic prediction accuracies for traits of commercial importance in banana white shrimp (Fenneropenaeus merguiensis). Our analyses were conducted in a breeding shrimp population comprising 562 individuals (offspring of 48 parental pairs) genotyped for 9472 single nucleotide polymorphisms (SNPs) and the animals had full phenotype records for five important traits (i. e., body weight, abdominal width, tail weight, raw colour of live shrimp and resistance to hepatopancreatic parvovirus). In both univariate and multi-trait analyses, machine (RF) and deep learning (MLP) models outperformed ssGBLUP for all traits studied. However, they had similar predictive performance to BayesCpi. The benefits of the multivariate relative to univariate models were trait- and method-specific. Multi-trait BayesCpi increased the prediction accuracies for growth (weight and width), carcass (tail weight) and HPV resistance by 9.3 to 17.8%. However, the multi-trait random forest models improved the predictive power for only abdominal width (14.3%) and disease resistance to hepatopancreatic parvovirus (10.0%). When the multi-trait MLP was used, the improvements in the prediction accuracies were observed for abdominal width and raw colour (4.9 and 6.0%, respectively). There were almost no differences in the predictive power between univariate and multi-trait ssGBLUP. Among the multi-trait models used, BayesCpi outperformed other methods (ssGBLUP, RF and MLP). It is concluded that either BayesCpi or machine and deep learning-based multi-trait genomic prediction models should be employed in large-scale genetic enhancement programs for banana shrimp. These approaches show enormous potential to enhance genetic progress made in this population of banana shrimp and potentially for other aquaculture species.

Automated summary

This study investigated the advantages of multi-trait machine and deep learning genomic prediction models for quantitative complex traits in aquaculture species. The results showed that machine learning and deep learning models outperformed traditional single-step genomic best linear unbiased prediction models for all the studied traits in banana white shrimp. The multi-trait Bayesian model significantly improved the prediction accuracies for growth, carcass, and disease resistance, while the multi-trait random forest model only showed improvements for abdominal width and disease resistance. The multi-trait multilayer perceptron model improved the prediction accuracies for abdominal width and raw colour. Among the multi-trait models, the Bayesian method performed the best.