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

Autor(es)

Yoshida, Grazyella M. ^{[1, 2]} ; Bangera, Rama ^[3] ; Carvalheiro, Roberto ^[1] ; Correa, Katharina ^[4] ; Figueroa, Rene ^[4] ; Lhorente, Jean P. ^[4] ; Yanez, Jose M. ^{[4, 2, 5]}

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ, Sch Agr & Veterinarian Sci, Dept Anim Sci, Campus Jaboticabal, BR-14884900 Jaboticabal - Brazil

^[2] Univ Chile, Fac Ciencias Vet & Pecuarias, Santiago 8820808 - Chile

^[3] Akvaforsk Genet, N-6600 Sunndalsora - Norway

^[4] Aquainnovo, Puerto Montt - Chile

^[5] Nucleo Milenio Salmonidos Invasores, Concepcion - Chile

• Tipo de documento: Artigo Científico
• Fonte: G3-GENES, GENOMES, GENETICS; v. 8, n. 2, p. 719-726, FEB 2018.
• Citações Web of Science: 28

Resumo

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 aims of this study were: (i) to compare the accuracy of estimated breeding values 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 PBLUP using different marker densities (0.5, 3, 10, 20, and 27 K) for resistance against P. salmonis in rainbow trout. Phenotypes were recorded as number of days to death (DD) and binary survival (BS) from 2416 fish challenged with P. salmonis. A total of 1934 fish were genotyped using a 57 K 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 27 K SNP. Between different genomic models, the highest relative increase in accuracy was obtained with Bayes C (approximate to 40%), where 3 K SNP was enough to achieve a similar accuracy to that of the 27 K 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 with 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. (AU)

• Processo FAPESP: 14/20626-4 - Seleção de acasalamento em espécies aquícolas
• Beneficiário: Grazyella Massako Yoshida
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 15/25232-7 - Seleção de acasalamentos em salmão coho
• Beneficiário: Grazyella Massako Yoshida
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado