BackgroundPreviously, we have shown that bacterial cold water disease (BCWD) resistance in rainbow trout can be improved using traditional family-based selection, but progress has been limited to exploiting only between-family genetic variation. Genomic selection (GS) is a new alternative that enables exploitation of within-family genetic variation.MethodsWe compared three GS models [single-step genomic best linear unbiased prediction (ssGBLUP), weighted ssGBLUP (wssGBLUP), and BayesB] to predict genomic-enabled breeding values (GEBV) for BCWD resistance in a commercial rainbow trout population, and compared the accuracy of GEBV to traditional estimates of breeding values (EBV) from a pedigree-based BLUP (P-BLUP) model. We also assessed the impact of sampling design on the accuracy of GEBV predictions. For these comparisons, we used BCWD survival phenotypes recorded on 7893 fish from 102 families, of which 1473 fish from 50 families had genotypes [57 K single nucleotide polymorphism (SNP) array]. Naïve siblings of the training fish (n = 930 testing fish) were genotyped to predict their GEBV and mated to produce 138 progeny testing families. In the following generation, 9968 progeny were phenotyped to empirically assess the accuracy of GEBV predictions made on their non-phenotyped parents.ResultsThe accuracy of GEBV from all tested GS models were substantially higher than the P-BLUP model EBV. The highest increase in accuracy relative to the P-BLUP model was achieved with BayesB (97.2 to 108.8%), followed by wssGBLUP at iteration 2 (94.4 to 97.1%) and 3 (88.9 to 91.2%) and ssGBLUP (83.3 to 85.3%). Reducing the training sample size to n = ~1000 had no negative impact on the accuracy (0.67 to 0.72), but with n = ~500 the accuracy dropped to 0.53 to 0.61 if the training and testing fish were full-sibs, and even substantially lower, to 0.22 to 0.25, when they were not full-sibs.ConclusionsUsing progeny performance data, we showed that the accuracy of genomic predictions is substantially higher than estimates obtained from the traditional pedigree-based BLUP model for BCWD resistance. Overall, we found that using a much smaller training sample size compared to similar studies in livestock, GS can substantially improve the selection accuracy and genetic gains for this trait in a commercial rainbow trout breeding population.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0293-6) contains supplementary material, which is available to authorized users.

Previously accurate genomic predictions for Bacterial cold water disease (BCWD) resistance in rainbow trout were obtained using a medium-density single nucleotide polymorphism (SNP) array. Here, the impact of lower-density SNP panels on the accuracy of genomic predictions was investigated in a commercial rainbow trout breeding population. Using progeny performance data, the accuracy of genomic breeding values (GEBV) using 35K, 10K, 3K, 1K, 500, 300 and 200 SNP panels as well as a panel with 70 quantitative trait loci (QTL)-flanking SNP was compared. The GEBVs were estimated using the Bayesian method BayesB, single-step GBLUP (ssGBLUP) and weighted ssGBLUP (wssGBLUP). The accuracy of GEBVs remained high despite the sharp reductions in SNP density, and even with 500 SNP accuracy was higher than the pedigree-based prediction (0.50-0.56 versus 0.36). Furthermore, the prediction accuracy with the 70 QTL-flanking SNP (0.65-0.72) was similar to the panel with 35K SNP (0.65-0.71). Genomewide linkage disequilibrium (LD) analysis revealed strong LD (r ≥ 0.25) spanning on average over 1 Mb across the rainbow trout genome. This long-range LD likely contributed to the accurate genomic predictions with the low-density SNP panels. Population structure analysis supported the hypothesis that long-range LD in this population may be caused by admixture. Results suggest that lower-cost, low-density SNP panels can be used for implementing genomic selection for BCWD resistance in rainbow trout breeding programs.

Family growth response to fishmeal and plant-based diets shows genotype x diet interaction in rainbow trout (Oncorhynchus mykiss)Lindsey R. Pierce (Abstract)The ability of rainbow trout to efficiently utilize plant-based diets for growth and the genetic variation for that trait have not been thoroughly examined. In this study, growth of a pedigreed population from the commercial Kamloop strain was assessed while feeding plant-based or traditional fishmeal-based diets. Both fish oil (5.00%) and soybean oil (8.43%) were included in the plant-based diet, and only fish oil was used in the fishmeal diet (10.10%). Ninety-five full-sib families nested within 47 half-sib families were reared in a common environment. Parentage assignment was performed on approximately 1,000 fish fed each diet using eight microsatellite markers chosen for nonduplication, a minimum of five alleles with no known null alleles, at least 50% heterozygosity, and unambiguous scoring. Progeny were assigned to parental pairs using two allocation programs, PAPA and FAP, to increase accuracy and to test assignment efficiency. The fish fed the fish meal/oil diet were approximately 8% larger than the fish fed the plant-based diet (P < 0.05). A significant genotype x diet effect accounted for 5% of the random variation. The genetic correlation for growth on the two diets was 73%, with a heritability of 30% across the diets. With this, I conclude that substantial genetic variation for utilizing plant-based diets containing soybean meal and oil exists in this widely used commercial rainbow trout strain. The genetic variation can be explored to detect and select for genes involved in improved utilization of plant-based diets containing soybean meal and oil if growth on plant-based meals becomes a long-term breeding goal in rainbow trout production.iii Acknowledgements My ability to relocate, complete research, perform class assignments, work as a teaching assistant, and study would have been impossible without the support and encouragement of close family, faculty, and friends. I have made several lasting connections throughout my master's studies and will continue collaborating with these individuals.