ArticleMarine biotechnology (New York, N.Y.)2018
Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri).
Article in Marine biotechnology (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Genomic selection improves survival time under high ammonia nitrogen stress in Litopenaeus vannamei.Genetics, selection, evolution : GSE · 2026Article
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- Genomic Prediction for Growth-Related Traits in Golden Pompano (Evolutionary applications · 2025Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Advances in biotechnology and breeding innovations in China's marine aquaculture.Advanced biotechnology · 2024Review
- Development and validation of a 66K SNP array for the hard clam (Mercenaria mercenaria).BMC genomics · 2024Article
- Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits inFrontiers in plant science · 2023Article
- First genomic prediction and genome-wide association for complex growth-related traits in Rock Bream (Evolutionary applications · 2022Article
- Mutations in Growth-Related Genes Induced by EMS Treatment in Scallops.Frontiers in genetics · 2022Article
- Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation.Scientific reports · 2021Article
- Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Genes · 2021Article
- Genome-Wide Association Study RevealsFrontiers in genetics · 2021Article
- Harnessing genomics to fast-track genetic improvement in aquaculture.Nature reviews. Genetics · 2020Review
- QTL Mapping for Red Blotches in Malaysia Red Tilapia (Oreochromis spp.).Marine biotechnology (New York, N.Y.) · 2019Article
- Systematic identification and validation of the reference genes from 60 RNA-Seq libraries in the scallop Mizuhopecten yessoensis.BMC genomics · 2019Article
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8 authors.
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Abstract
Selective breeding is a common and effective approach for genetic improvement of aquaculture stocks with parental selection as the key factor. Genomic selection (GS) has been proposed as a promising tool to facilitate selective breeding. Here, we evaluated the predictability of four GS methods in Zhikong scallop (Chlamys farreri) through real dataset analyses of four economical traits (e.g., shell length, shell height, shell width, and whole weight). Our analysis revealed that different GS models exhibited variable performance in prediction accuracy depending on genetic and statistical factors, but non-parametric method, including reproducing kernel Hilbert spaces regression (RKHS) and sparse neural networks (SNN), generally outperformed parametric linear method, such as genomic best linear unbiased prediction (GBLUP) and BayesB. Furthermore, we demonstrated that the predictability relied mainly on the heritability regardless of GS methods. The size of training population and marker density also had considerable effects on the predictive performance. In practice, increasing the training population size could better improve the genomic prediction than raising the marker density. This study is the first to apply non-linear model and neural networks for GS in scallop and should be valuable to help develop strategies for aquaculture breeding programs.
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