Evaluation of the utility of genomic information to improve genetic evaluation of feed efficiency traits of the Pacific white shrimp Litopenaeus vannamei

Abstract

This study evaluated the utility of genomic information to improve genetic evaluation of feed efficiency traits of the Pacific white shrimp Litopenaeus vannamei. Three methods including the pedigree-based best linear unbiased prediction (BLUP), genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) were compared for the prediction accuracy of feed efficiency ratio (FER) and residual feed intake (RFI), as well as a growth trait, average daily weight gain (ADG). The dataset used for analysis consisted of 474 offspring from 34 full-sib families, with a pedigree of three generations, and all the offspring were genotyped on 35,869 SNPs. Two univariate animal models were used, one with the common environmental effect (Model 1) and one without (Model 2). The genomic and pedigree-genomic relationship matrices based heritability estimates of RFI, FER and ADG by Model 1 ranged from 0.187 ± 0.156 to 0.388 ± 0.167, with the common environmental effect in the range of 0.057 ± 0.080 to 0.283 ± 0.108, but it was unable to estimate both additive genetic and common environmental effects simultaneously using the pedigree-based relationship matrix. Across the three matrices, heritability estimates of RFI, FER and ADG by Model 2 ranged from 0.476 ± 0.101 to 0.796 ± 0.146, higher than the corresponding ones using Model 1. Cross-validation was used to evaluate the prediction accuracy of breeding values. For GBLUP and ssGBLUP, genomic prediction accuracy using Model 1 was significantly increased by 28.6–29.0% for RFI, 11.6–12.5% for FER and 62.2–69.4% for ADG, compared with using Model 2, indicative of the advantage of Model 1 over Model 2. When using Model 2, genomic predictions were more accurate than the pedigree-based predictions, with an increase of accuracy ranging from 8.9% to 13.3% across the three traits. Furthermore, accuracy of GBLUP was slightly higher than that of ssGBLUP. The results indicate that the genomic-based methods, especially GBLUP, combined with an animal model containing the common environmental effect, have obvious advantage in estimating variance components and predicting breeding values for feed efficiency traits as well as growth of L.vannamei compared with traditional BLUP.