Whole-genome mapping of quantitative trait loci and accuracy of genomic predictions for resistance to columnaris disease in two rainbow trout breeding populations

Abstract

Background Columnaris disease (CD) is an emerging problem for the rainbow trout aquaculture industry in the US. The objectives of this study were to: (1) identify common genomic regions that explain a large proportion of the additive genetic variance for resistance to CD in two rainbow trout ( Oncorhynchus mykiss ) populations; and (2) estimate the gains in prediction accuracy when genomic information is used to evaluate the genetic potential of survival to columnaris infection in each population. Methods Two aquaculture populations were investigated: the National Center for Cool and Cold Water Aquaculture (NCCCWA) odd-year line and the Troutlodge, Inc., May odd-year (TLUM) nucleus breeding population. Fish that survived to 21 days post-immersion challenge were recorded as resistant. Single nucleotide polymorphism (SNP) genotypes were available for 1185 and 1137 fish from NCCCWA and TLUM, respectively. SNP effects and variances were estimated using the weighted single-step genomic best linear unbiased prediction (BLUP) for genome-wide association. Genomic regions that explained more than 1% of the additive genetic variance were considered to be associated with resistance to CD. Predictive ability was calculated in a fivefold cross-validation scheme and using a linear regression method. Results Validation on adjusted phenotypes provided a prediction accuracy close to zero, due to the binary nature of the trait. Using breeding values computed from the complete data as benchmark improved prediction accuracy of genomic models by about 40% compared to the pedigree-based BLUP. Fourteen windows located on six chromosomes were associated with resistance to CD in the NCCCWA population, of which two windows on chromosome Omy 17 jointly explained more than 10% of the additive genetic variance. Twenty-six windows located on 13 chromosomes were associated with resistance to CD in the TLUM population. Only four associated genomic regions overlapped with quantitative trait loci (QTL) between both populations. Conclusions Our results suggest that genome-wide selection for resistance to CD in rainbow trout has greater potential than selection for a few target genomic regions that were found to be associated to resistance to CD due to the polygenic architecture of this trait, and because the QTL associated with resistance to CD are not sufficiently informative for selection decisions across populations. Electronic supplementary material The online version of this article (10.1186/s12711-019-0484-4) contains supplementary material, which is available to authorized users.

Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

“…The use of genomic information has been shown to be effective in increasing the gains in accuracy of GEBV for multiple traits in aquaculture species, including Atlantic salmon [ 22 , 23 ], catfish [ 24 ], tilapia [ 20 , 25 ], and rainbow trout [ 12 , 26 – 29 ]. For rainbow trout, the accuracy of GEBV was assessed for body weight, carcass weight, fillet weight, fillet yield [ 12 ], and resistance to diseases such as bacterial cold water disease (BCWD) [ 26 , 27 ], columnaris disease [ 28 ], and infectious pancreatic necrosis virus [ 29 ]. GS will allow within-family selection and hence increase the accuracy of genomic predictions and selection response, especially for lethally measured traits where within-family selection relies on traits measured in sibs of breeding candidates [ 25 ].…”

A Major QTL for Resistance to Vibrio anguillarum in Rainbow Trout

“…This mid-density genotyping array has proven to be of great value for investigating the genetic basis of phenotypic variation and host-pathogen interactions. The applicability of the 57 K SNP array (Affymetrix Axiom ® ) was confirmed for detection of QTL associated with rainbow trout resistance against infectious hematopoietic necrosis virus (IHNV) ( Vallejo et al, 2019 ), columnaris disease ( Silva et al, 2019 ), BCWD ( Vallejo et al, 2017 ; Liu et al, 2018 ), enteric red mouth disease (ERM) ( Zuo et al, 2020 ) and white spot disease (WSD) ( Jaafar et al, 2020 ). Correspondingly, an array developed for Atlantic salmon ( Salmo salar ) was applied to detect QTL associated with resistance in this host species against salmonid alphavirus (SAV) ( Hillestad et al, 2020 ).…”

Genomic Predictions for Muscle Yield and Fillet Firmness in Rainbow Trout using Reduced-Density SNP Panels

“…The predictive ability of the genomic model was evaluated using a ve-fold cross-validation scheme. Using ve replicates helps to minimize errors that could be generated due to a single-fold sampling [26]. Table 2 shows the average predictive ability for muscle yield and rmness under the ve-fold crossvalidation strategy.…”

“…To evaluate predictive abilities of both traditional pedigree-based and genomic evaluations, as well as the impact of different SNP densities, ve-fold cross-validation was conducted [22,26,60]. The genotyped animals with phenotypes (muscle yield N = 775; shear force N = 772) were randomly split into ve mutually exclusive folds/groups.…”

“…Besides, the gains in accuracy of GEBV with the implementation of GS were assessed for harvest and carcass weight, and growth and llet yield in cat sh [22], and tilapia [23], respectively. For rainbow trout, the accuracy of GEBV was assessed for body weight, carcass weight, llet weight, llet yield [12], and resistance to diseases such as bacterial cold water disease (BCWD) [24,25], columnaris disease [26], and infectious pancreatic necrosis virus [27]. GS will allow within-family selection and hence increase the accuracy of genomic predictions and selection response, especially for lethally measured traits [23].…”