4.7 Article

Genomic selection for improvement of growth traits in triangle sail mussel (Hyriopsis cumingii)

Journal

AQUACULTURE
Volume 561, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.aquaculture.2022.738692

Keywords

Genomic predictive ability; Growth trait; GBLUP; Selective breeding Hyriopsis cumingii

Funding

  1. National Key Research and Development Program of China [2018YFD0901406]
  2. Program of Shanghai Academic Research Leader [19XD1421500]
  3. earmarked fund for China Agriculture Research System [CARS -49]

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In this study, genomic selection was used to improve the quality of freshwater pearls by enhancing the growth performance of triangle sail mussels. The researchers identified single nucleotide polymorphisms (SNPs) related to growth traits and compared different SNP screening strategies and models for predicting breeding values. The results showed significant improvement in growth performance in the selected mussels, providing a cost-efficient genomic selection strategy for aquaculture species.
The triangle sail mussel, Hyriopsis cumingii, is the most important aquaculture species for freshwater pearl pro-duction in China. However, one major problem of freshwater pearl mussel industry is the low profit despite high pearl yield, and the focus of current research has now switched from yield to the improvement of pearl quality. It is essential to enhance the quality of pearls by improving the growth performance of mussels and, to this end, genomic selection (GS) has been proved as a useful method for selective breeding. In the present study, 764 mussels (496 for reference population, 268 for selection candidates) were genotyped by sequencing (GBS) and a total of 97,090 single nucleotide polymorphisms (SNPs) were identified. Based on genome-wide SNPs, the heritability was estimated to be moderate to high for the following five growth traits: shell length, shell height, shell width, shell weight, and body weight (0.36 +/- 0.11 to 0.51 & PLUSMN; 0.10). Then, the effects of five GS models (i.e., GBLUP, BayesA, BayesB, BayesC pi, and BayesLASSO) and different SNP numbers (selected using three SNP screening strategies) on the predictive ability (PA) of the genomic estimated breeding values (GEBVs) were compared. The GBLUP model containing 10,000 SNPs selected by GWAS strategy was established and was used to calculate GEBVs in selection candidates. The individuals with high GEBVs were then selected as broodstock for the fast-growth strain (FS), and the rest for control strains (CS). At last, encouraging results were detected that the FS performed significantly better than CS in terms of growth, with different traits increasing by 5.74-28.77% and 11.93%-54.58% at the WY and CM sites, respectively. These results may assist in the future large-scale commercial application of GS in H. cumingii and provide a cost-efficient GS strategy for other aquaculture spe-cies as well.

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