Systematic Evaluation of Genomic Prediction Algorithms for Genomic Prediction and Breeding of Aquatic Animals.

The extensive use of genomic selection (GS) in livestock and crops has led to a series of genomic-prediction (GP) algorithms despite the lack of a single algorithm that can suit all the species and traits. A systematic evaluation of available GP algorithms is thus necessary to identify the optimal GP algorithm for selective breeding in aquaculture species. In this study, a systematic comparison of ten GP algorithms, including both traditional and machine-learning algorithms, was conducted using publicly available genotype and phenotype data of eight traits, including weight and disease resistance traits, from five aquaculture species.

Genome-wide identification and characterization of toll-like receptor genes in spotted sea bass (Lateolabrax maculatus) and their involvement in the host immune response to Vibrio harveyi infection.

Toll-like receptor (TLR) genes are the earliest reported pathogen recognition receptors (PRRs) and have been extensively studied. These genes play pivotal roles in the innate immune defense against pathogen invasion. In this study, a total of 16 tlr genes were identified and characterized in spotted sea bass (Lateolabrax maculatus).