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Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools Mix P and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop(Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction(GBLUP) method which has been applied widely. Our results showed that both Mix P and gsbay could accurately estimate single-nucleotide polymorphism(SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values(GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by Mix P; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by Mix P and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with Mix P the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by Mix P and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.
The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different gen data selection tools Mix P and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop (Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction (GBLUP) method which has been applied widely. Mix P and gsbay could have been estimated for single-nucleotide polymorphism (SNP) marker effects, and therefore could be applied for the analysis of genomic estimated breeding values (GEBV). In simulated data from different scenarios, the accuracy of GEBV was was ranged from 0.20 to 0.78 by Mix P; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by Mix P and gsbay wer e expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with Mix P the computation is much faster, especially in dealing with large-scale data. These results suggest both both algorithms implemented by Mix P and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.