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

文献类型： 外文期刊

作者： Wang, Yangfan 1 ; Sun, Guidong 1 ; Zeng, Qifan 1 ; Chen, Zhihui 3 ; Hu, Xiaoli 1 ; Li, Hengde 5 ; Wang, Shi 1 ; Bao, Zhen 1 ;

作者机构： 1.Ocean Univ China, Coll Marine Sci, Minist Educ, Key Lab Marine Genet & Breeding, Qingdao 266003, Peoples R China

2.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Biol & Biotechnol, Qingdao 266237, Peoples R China

3.Univ Dundee, Div Cell & Dev Biol, Coll Life Sci, Dundee DD1 4HN, Scotland

4.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Fisheries Sci & Food Prod Proc, Qingdao 266237, Peoples R China

5.Chinese Acad Fishery Sci, CAFS Key Lab Aquat Genom, Key Lab Aquat Genom, Minist Agr, Beijing 100141, Peoples R China

6.Chinese Acad Fishery Sci, Ctr Appl Aquat Genom, Beijing Key Lab Fishery Biotechnol, Beijing 100141, Peoples R China

关键词： Genomic selection; Heritability; Breeding; Scallop

期刊名称：MARINE BIOTECHNOLOGY （ 影响因子：3.619； 五年影响因子：3.322 ）

ISSN： 1436-2228

年卷期： 2018 年 20 卷 6 期

页码：

收录情况： SCI

摘要： 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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