Performance of genome prediction for morphological and growth-related traits in Yellow River carp

Common Carp (Cyprinus carpio) is the earliest truly domesticated fish, and genetic improvement of its economic traits is important for satisfying human protein demand. However, very little is known about the performance of Genomic selection (GS) in common carp by used SNP array data. Here we estimated the genetic parameters and evaluated the performance of GS for 24 morphological and growth-related traits in Yellow River carp. The result showed that the heritability of these traits ranged from medium to high. The predictive ability of most traits based on family cross-validation was higher than that of random cross-validation. Compared with best linear unbiased prediction (BLUP) method, genomic BLUP (GBLUP) method has more advantage on predictive ability of low heritability traits, with a maximum increase ratio of 37.5%. Gene annotation information is not helpful to improving predictive ability, but GBLUP based on haplotype information obtained highest accuracy on 6 of 24 traits, and compared with GBLUP, the average ratio of prediction abilities increased by 2%. This study shows that it is necessary to assess the effect of family structure on reference group construction before calculated the breeding value. In additon, biological information has a certain potential for improving prediction ability. Low density array genotyping, as a kind of method can decrease the cost of genotyping and facilitate the implementation of genomic selection in aquatic breeding.