PT  - JOURNAL ARTICLE
AU  - Rajesh Joshi
AU  - Anders Skaarud
AU  - Mayet de Vera
AU  - Alejandro Tola Alvarez
AU  - Jørgen Ødegård
TI  - Genomic prediction for commercial traits using univariate and multivariate approaches in Nile tilapia (<em>Oreochromis niloticus</em>)
AID  - 10.1101/725143
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 725143
4099  - http://biorxiv.org/content/early/2019/08/05/725143.short
4100  - http://biorxiv.org/content/early/2019/08/05/725143.full
AB  - Background Over the past three decades, Nile tilapia industry has grown into a significant aquaculture industry spread over 120 tropical and sub-tropical countries around the world accounting for 7.4% of global aquaculture production in 2015. Across species, genomic selection has been shown to increase predictive ability and genetic gain, also extending into aquaculture. Hence, the aim of this paper is to compare the predictive abilities of pedigree- and genomic-based models in univariate and multivariate approaches, with the aim to utilize genomic selection in a Nile tilapia breeding program. A total of 1444 fish were genotyped (48,960 SNP loci) and phenotyped for body weight at harvest (BW), fillet weight (FW) and fillet yield (FY). The pedigree-based analysis utilized a deep pedigree, including 14 generations. Estimated breeding values (EBVs and GEBVs) were obtained with traditional pedigree-based (PBLUP) and genomic (GBLUP) models, using both univariate and multivariate approaches. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation with three different cross-validation approaches. Further, impact of these models and approaches on the genetic evaluation was assessed based on the ranking of the selection candidates.Results GBLUP univariate models were found to increase the prediction accuracy and reduce bias of prediction compared to other PBLUP and multivariate approaches. Relative to pedigree-based models, prediction accuracy increased by ∼20% for FY, >75% for FW and >43% for BW. GBLUP models caused major re-ranking of the selection candidates, with no significant difference in the ranking due to univariate or multivariate GBLUP approaches. The heritabilities using multivariate GBLUP models for BW, FW and FY were 0.19 ± 0.04, 0.17 ± 0.04 and 0.23 ± 0.04 respectively. BW showed very high genetic correlation with FW (0.96 ± 0.01) and a slightly negative genetic correlation with FY (−0.11 ± 0.15).Conclusion Predictive ability of genomic prediction models is substantially higher than for classical pedigree-based models. Genomic selection is therefore beneficial to the Nile tilapia breeding program, and it is recommended in routine genetic evaluations of commercial traits in the Nile tilapia breeding nucleus.AcronymFull FormBWBody Weight at HarvestFWFillet WeightFYFillet YieldGBLUPGenomic Best Linear Unbiased PredictionGSTGenoMar Supreme TilapiaG(EBVs)(Genomic) Estimated Breeding ValuesPBLUPPedigree Best Linear Unbiased Prediction