Deep learning to obtain high-throughput morphological phenotypes and its genetic correlation with swimming performance in juvenile large yellow croaker

Breeding for swimming performance in fish may cause changes in morphological traits in offspring, which are not only related to industrial efficiency, but also affect animal welfare. However, the genetic correlation between swimming performance and morphological traits in juvenile large yellow croaker remains unclear, and traditional morphological traits collection is time-consuming and laborious. Deep learning offers new opportunities for high-throughput phenomics, especially for complex morphological traits. In this study, the automatic High-Resolution Network (HRNet) based on a deep learning approach was used for the first time to effectively detect the morphological traits of juvenile large yellow croaker. We assessed the swimming performance (Ucrit) of 1300 juvenile fish, and conducted a genome-wide association study (GWAS) and genetic parameters study on the morphological traits of 383 juvenile fish using a 55 K SNP array. The morphological traits data of 383 fish comes from the predicted results of the HRNet model. The results showed that: (1) All of the 10 morphological traits showed positively correlated with Ucrit, and mostly positive genetic correlated with Ucrit; (2) The heritability of all 10 morphological traits was low to moderate; (3) We identified 4 SNPs significantly associated with morphological traits, and identified candidate genes including sox8, setd6, and ctbp2, and enriched significant pathways including Notch signaling pathway. In conclusion, this study provides an efficient and multi-context adaptive HRNet based on deep learning for high-throughput morphological traits detection of juvenile large yellow croaker, and analyses the genetic basis between morphological traits and swimming performance for this species, which provides a new understanding of the genetic basis of morphological traits of large yellow croaker, and provides a basis for breeding programs.