Abstract
Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic breeding values (GEBVs) for BCWD resistance in 10 families from the first generation of the NCCCWA BCWD resistance breeding line, compared the predictive ability (PA) of GEBVs to pedigree-based estimated breeding values (EBVs), and compared the impact of two SNP genotyping methods on the accuracy of GEBV predictions. The BCWD phenotypes survival days (DAYS) and survival status (STATUS) had been recorded in training fish (n = 583) subjected to experimental BCWD challenge. Training fish, and their full sibs without phenotypic data that were used as parents of the subsequent generation, were genotyped using two methods: restriction-site associated DNA (RAD) sequencing and the Rainbow Trout Axiom® 57 K SNP array (Chip). Animal-specific GEBVs were estimated using four GS models: BayesB, BayesC, single-step GBLUP (ssGBLUP), and weighted ssGBLUP (wssGBLUP). Family-specific EBVs were estimated using pedigree and phenotype data in the training fish only. The PA of EBVs and GEBVs was assessed by correlating mean progeny phenotype (MPP) with mid-parent EBV (family-specific) or GEBV (animal-specific). The best GEBV predictions were similar to EBV with PA values of 0.49 and 0.46 vs. 0.50 and 0.41 for DAYS and STATUS, respectively. Among the GEBV prediction methods, ssGBLUP consistently had the highest PA. The RAD genotyping platform had GEBVs with similar PA to those of GEBVs from the Chip platform. The PA of ssGBLUP and wssGBLUP methods was higher with the Chip, but for BayesB and BayesC methods it was higher with the RAD platform. The overall GEBV accuracy in this study was low to moderate, likely due to the small training sample used. This study explored the potential of GS for improving resistance to BCWD in rainbow trout using, for the first time, progeny testing data to assess the accuracy of GEBVs, and it provides the basis for further investigation on the implementation of GS in commercial rainbow trout populations.
Highlights
Bacterial cold water disease (BCWD) causes significant mortality and economic losses in salmonid aquaculture, and methods to control outbreaks are limited (Nematollahi et al, 2003; Barnes and Brown, 2011)
While those loci can be evaluated for marker assisted selection (MAS) following fine-mapping, the complex genetic architecture of BCWD resistance and high genetic variation we discovered in past studies (Vallejo et al, 2014a) led us to hypothesize that a whole genome-enabled selection approach would be a more efficient strategy for improving rainbow trout genetic resistance against BCWD
In order to rule out potential errors in the single-step GBLUP (ssGBLUP) method and the used statistical model, we performed (1) genomic BLUP (GBLUP) analysis with the current statistical model to ensure that nothing was wrong with the ssGBLUP algorithm; and (2) ssGBLUP analysis with an alternative statistical model to assess the impact of the fixed effect tank in the accuracy of prediction with ssGBLUP
Summary
Bacterial cold water disease (BCWD) causes significant mortality and economic losses in salmonid aquaculture, and methods to control outbreaks are limited (Nematollahi et al, 2003; Barnes and Brown, 2011). The genetic architecture of resistance is complex (Vallejo et al, 2010) and we previously identified several major resistance QTL in the NCCCWA odd- and even-year rainbow trout selective-breeding populations (Wiens et al, 2013; Vallejo et al, 2014a; Liu et al, 2015b; Palti et al, 2015b). While those loci can be evaluated for marker assisted selection (MAS) following fine-mapping, the complex genetic architecture of BCWD resistance and high genetic variation we discovered in past studies (Vallejo et al, 2014a) led us to hypothesize that a whole genome-enabled selection approach would be a more efficient strategy for improving rainbow trout genetic resistance against BCWD
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