TY - JOUR
T1 - Predicting Growth Traits with Genomic Selection Methods in Zhikong Scallop (Chlamys farreri)
AU - Wang, Yangfan
AU - Sun, Guidong
AU - Zeng, Qifan
AU - Chen, Zhihui
AU - Hu, Xiaoli
AU - Li, Hengde
AU - Wang, Shi
AU - Bao, Zhenmin
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - Breeding
KW - Genomic selection
KW - Heritability
KW - Scallop
UR - http://www.scopus.com/inward/record.url?scp=85052126599&partnerID=8YFLogxK
U2 - 10.1007/s10126-018-9847-z
DO - 10.1007/s10126-018-9847-z
M3 - Article
C2 - 30116982
AN - SCOPUS:85052126599
VL - 20
SP - 769
EP - 779
JO - Marine Biotechnology
JF - Marine Biotechnology
SN - 1436-2228
IS - 6
ER -