Genomic Prediction Accuracy for Resistance Against Piscirickettsia salmonis in Farmed Rainbow Trout

Salmonid Rickettsial Syndrome (SRS), caused by the intracellular bacterium Piscirickettsia salmonis, is one of the main diseases affecting rainbow trout (Oncorhynchus mykiss) farming. To accelerate genetic progress, genomic selection methods can be used as an effective approach to control the disease. The aim of this study were: "(i) to compare the accuracy of estimated breeding values (EBV) using pedigree-based best linear unbiased prediction (PBLUP) with genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayes C and Bayesian Lasso (LASSO), and (ii) to test the accuracy of genomic prediction and pedigree-based BLUP using different marker densities (0.5, 3, 10, 20 and 27K) for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD) and binary survival (BS) from 2,416 fish challenged with P. salmonis. A total of 1,934 fish were genotyped using 57K single nucleotide polymorphism (SNP) array. All genomic prediction methods achieved higher accuracies than PBLUP. The relative increase in accuracy for different genomic models ranged from 28 to 41% for both DD and BS at 27K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (~ 40%), where 3K SNP was enough to achieve a similar accuracy as the 27K SNP for both traits. For resistance against P. salmonis in rainbow trout we showed that genomic predictions using GBLUP, ssGBLUP, Bayes C and LASSO can increase accuracy compared to PBLUP. Moreover, it is possible to use relatively low-density SNP panels for genomic prediction without compromising accuracy predictions for resistance against P. salmonis in rainbow trout.

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