Genomic prediction in an admixed population of Atlantic salmon (Salmo salar)

Abstract

Reliability of genomic selection (GS) models was tested in an admixed population of Atlantic salmon, originating from crossing of several wild subpopulations. The models included ordinary genomic BLUP models (GBLUP), using genome-wide SNP markers of varying densities (1–220 k), a genomic identity-by-descent model (IBD-GS), using linkage analysis of sparse genome-wide markers, as well as a classical pedigree-based model. Reliabilities of the models were compared through 5-fold cross-validation. The traits studied were salmon lice (Lepeophtheirus salmonis) resistance (LR), measured as (log) density on the skin and fillet color (FC), with respective estimated heritabilities of 0.14 and 0.43. All genomic models outperformed the classical pedigree-based model, for both traits and at all marker densities. However, the relative improvement differed considerably between traits, models and marker densities. For the highly heritable FC, the IBD-GS had similar reliability as GBLUP at high marker densities (>22 k). In contrast, for the lowly heritable LR, IBD-GS was clearly inferior to GBLUP, irrespective of marker density. Hence, GBLUP was robust to marker density for the lowly heritable LR, but sensitive to marker density for the highly heritable FC. We hypothesize that this phenomenon may be explained by historical admixture of different founder populations, expected to reduce short-range lice density (LD) and induce long-range LD. The relative importance of LD/relationship information is expected to decrease/increase with increasing heritability of the trait. Still, using the ordinary GBLUP, the typical long-range LD of an admixed population may be effectively captured by sparse markers, while efficient utilization of relationship information may require denser markers (e.g., 22 k or more).

Genome-Wide Association Study for Identifying Loci that Affect Fillet Yield, Carcass, and Body Weight Traits in Rainbow Trout (Oncorhynchus mykiss)

“…However, the accuracies of GEBVs were increased by ~100% for CAR and FW (from 0.28 and 0.25 to 0.55 and 0.50, respectively) and by ~420% for FY (from 0.13 to 0.55) compared traditional pedigree-based EBVs. For lethally-measured traits like FW, FY, and CAR, traditional selective breeding programs rely on sib-testing with limited reliability (Odegård et al, 2014). Therefore, methods that increase accuracy of predictions and expedite genetic progress by exploiting within-family genetic variation for economically-important traits in aquaculture species are important for continued development of the aquaculture industry (Yáñez et al, 2015).…”

“…Therefore, methods that increase accuracy of predictions and expedite genetic progress by exploiting within-family genetic variation for economically-important traits in aquaculture species are important for continued development of the aquaculture industry (Yáñez et al, 2015). Models that include GI from numerous SNP markers in addition to the phenotypic and pedigree information without previous knowledge of the underlying QTL outperformed models without GI (Nielsen et al, 2009; Odegård et al, 2014). This improved performance of GI models is expected based on the definition of the accuracy as a function of heritability and amount of information used (Chen et al, 2011).…”

“…This improved performance of GI models is expected based on the definition of the accuracy as a function of heritability and amount of information used (Chen et al, 2011). Examples of traits for which inclusion of GI resulted in an increase in accuracy include growth in broiler chickens (Wang et al, 2014); lice resistance and fillet color in Atlantic salmon (Odegård et al, 2014); and weight and length traits in Atlantic salmon (Tsai et al, 2015). …”

Genomic Prediction of Resistance to Pasteurellosis in Gilthead Sea Bream (Sparus aurata) Using 2b-RAD Sequencing

“…While marker-assisted selection for major disease resistance loci has been well documented in Atlantic salmon ( Salmo salar ) breeding programs (Houston et al 2008, 2010; Moen et al 2009), few successful examples exist for other farmed finfish species. Genomic prediction uses genome-wide markers to estimate breeding values, and can deliver significant improvements in selection accuracy compared to traditional pedigree-based approaches, even for traits with a polygenic architecture (Odegård et al 2014; Tsai et al 2015, 2016; Vallejo et al 2016). …”

Genomics in Aquaculture to Better Understand Species Biology and Accelerate Genetic Progress

“…However, the status of breeding programs and the level of technology used for aquatic species production are wide-ranging, from use of wild seed stocks through to family-based selection incorporating genomic tools. Family selection and genomic tools can be applied to improve traits that are expensive or difficult to measure on the selection candidates themselves including disease resistance (Yáñez et al, 2014;Ødegård et al, 2014), flesh color (Colihueque and Araneda, 2014;Ødegård et al, 2014) and other appearance traits such as body shape and skin pigmentation (Colihueque and Araneda, 2014) in finfish species. In contrast, despite the global importance of mollusc species for aquaculture, few selective breeding programmes exist and the state of genomic tools and knowledge for these species is typically lacking (Astorga, 2014).…”