Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri)

Yangfan Wang, Guidong Sun, Qifan Zeng, Zhihui Chen, Xiaoli Hu, Hengde Li, Shi Wang, Zhenmin Bao

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
169 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)769-779
Number of pages11
JournalMarine Biotechnology
Issue number6
Early online date16 Aug 2018
Publication statusPublished - 1 Dec 2018


  • Breeding
  • Genomic selection
  • Heritability
  • Scallop


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