Abstract
Abstract
Background
Quantitative traits are typically considered to be under additive genetic control. Although there are indications that non-additive factors have the potential to contribute to trait variation, experimental demonstration remains scarce. Here, we investigated the genetic basis of growth in Atlantic salmon by exploiting the high level of genetic diversity and trait expression among domesticated, hybrid and wild populations.
Results
After rearing fish in common-garden experiments under aquaculture conditions, we performed a variance component analysis in four mapping populations totaling ~ 7000 individuals from six wild, two domesticated and three F1 wild/domesticated hybrid strains. Across the four independent datasets, genome-wide significant quantitative trait loci (QTLs) associated with weight and length were detected on a total of 18 chromosomes, reflecting the polygenic nature of growth. Significant QTLs correlated with both length and weight were detected on chromosomes 2, 6 and 9 in multiple datasets. Significantly, epistatic QTLs were detected in all datasets.
Discussion
The observed interactions demonstrated that the phenotypic effect of inheriting an allele deviated between half-sib families. Gene-by-gene interactions were also suggested, where the combined effect of two loci resulted in a genetic effect upon phenotypic variance, while no genetic effect was detected when the two loci were considered separately. To our knowledge, this is the first documentation of epistasis in a quantitative trait in Atlantic salmon. These novel results are of relevance for breeding programs, and for predicting the evolutionary consequences of domestication-introgression in wild populations.
Funder
Research Council of Norway via the projects INTERACT
Research Council of Norway via the projects QUANTESCAPE
Publisher
Springer Science and Business Media LLC
Subject
Genetics(clinical),Genetics
Reference85 articles.
1. Diamond J. Evolution, consequences and future of plant and animal domestication. Nature. 2002;418(6898):700–7.
2. Goddard ME, Hayes BJ. Genomic selection. J Anim Breed Genet. 2007;124(6):323–30.
3. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29.
4. Powell JE, Kranis A, Floyd J, Dekkers JCM, Knott S, Haley CS. Optimal use of regression models in genome-wide association studies. Anim Genet. 2012;43(2):133–43.
5. Heffner EL, Jannink JL, Sorrells ME. Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome. 2011;4(1):65–75.
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