Genomic selection applications can improve the environmental performance of aquatics: A case study on the heat tolerance of abalone

• Published in: Evolutionary Applications
• Main Authors: Liu, Junyu, Peng, Wenzhu, Yu, Feng, Shen, Yawei, Yu, Wenchao, Lu, Yisha, Lin, Weihong, Zhou, Muzhi, Huang, Zekun, Luo, Xuan, You, Weiwei, Ke, Caihuan
• Other Authors: National Key Research and Development Program of China, National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Pacific; Ocean acidification
• Online Access: http://dx.doi.org/10.1111/eva.13388; https://onlinelibrary.wiley.com/doi/pdf/10.1111/eva.13388; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/eva.13388

Abstract Aquaculture is one of the world's fastest‐growing and most traded food industries, but it is under the threat of climate‐related risks represented by global warming, marine heatwave (MHW) events, ocean acidification, and deoxygenation. For the sustainable development of aquaculture, selective breeding may be a viable method to obtain aquatic economic species with greater tolerance to environmental stressors. In this study, we estimated the heritability of heat tolerance trait of Pacific abalone Haliotis discus hannai , performed genome‐wide association studies (GWAS) analysis for heat tolerance to detect single nucleotide polymorphisms (SNPs) and candidate genes, and assessed the potential of genomic selection (GS) in the breeding of abalone industry. A total of 1120 individuals were phenotyped for their heat tolerance and genotyped with 64,788 quality‐controlled SNPs. The heritability of heat tolerance was moderate (0.35–0.42) and the predictive accuracy estimated using BayesB (0.55 ± 0.05) was higher than that using GBLUP (0.40 ± 0.01). A total of 11 genome‐wide significant SNPs and 2 suggestive SNPs were associated with heat tolerance of abalone, and 13 candidate genes were identified, including got2 , znfx1 , l(2)efl , and lrp5 . Based on GWAS results, the prediction accuracy using the top 5K SNPs was higher than that using randomly selected SNPs and higher than that using all SNPs. These results suggest that GS is an efficient approach for improving the heat tolerance of abalone and pave the way for abalone selecting breeding programs in rapidly changing oceans.