Machine vision-assisted genomic prediction and genome-wide association of spleen-related traits in large yellow croaker infected with visceral white-nodules disease

Abstract

High-resolution and high-throughput genotype-to-phenotype studies in fish are rapidly advancing, driven by innovative technologies that aim to address the challenges of modern breeding models. In recent years, machine vision and deep learning techniques, particularly convolutional neural networks (CNNs), have achieved significant success in image recognition and segmentation. Moreover, qualitative and quantitative analysis of disease resistance has always been a crucial field of research in genetics. This motivation has led us to investigate the potential of large yellow croaker visceral white-nodules disease (VWND) in encoding information on disease resistance for the task of accession classification. In this study, we proposed an image segmentation framework for the feature extraction of the spleen after VWND infection based on machine vision. We utilized deep CNNs and threshold segmentation for automatic feature learning and object segmentation. This approach eliminates subjectivity and enhances work efficiency compared to using hand-crafted features. Additionally, we employed spleen-related traits to conduct genome-wide association analysis (GWAS), which led to the identification of 24 significant SNPs and 10 major quantitative trait loci. The results of function enrichment analysis on candidate genes also indicated potential relationships with immune regulation mechanisms. Furthermore, we explored the use of genomic selection (GS) technology for phenotype prediction of extreme individuals, which further supports the predictability of spleen-related phenotypes for VWND resistance in large yellow croakers. Our findings demonstrate that artificial intelligence (AI)-based phenotyping approaches can deliver state-of-the-art performance for genetics research. We hope this work will provide a paradigm for applying deep learning and machine vision to phenotyping in aquaculture species.