Dataset Information

Atlantic salmon populations invaded by farmed escapees: quantifying genetic introgression with a Bayesian approach and SNPs.

ABSTRACT:

Background

Many native Atlantic salmon populations have been invaded by domesticated escapees for three decades or longer. However, thus far, the cumulative level of gene-flow that has occurred from farmed to wild salmon has not been reported for any native Atlantic salmon population. The aim of the present study was to investigate temporal genetic stability in native populations, and, quantify gene-flow from farmed salmon that caused genetic changes where they were observed. This was achieved by genotyping historical and contemporary samples from 20 populations covering all of Norway with recently identified single nucleotide polymorphism markers that are collectively diagnostic for farmed and wild salmon. These analyses were combined with analysis of farmed salmon and implementation of Approximate Bayesian computation based simulations.

Results

Five of the populations displayed statistically significant temporal genetic changes. All five of these populations became more similar to a pool of farmed fish with time, strongly suggesting introgression of farmed fish as the primary cause. The remaining 15 populations displayed weak or non-significant temporal genetic changes. Estimated introgression of farmed fish ranged from 2-47% per population using approximate Bayesian computation. Thus, some populations exhibited high degrees of farmed salmon introgression while others were more or less unaffected. The observed frequency of escapees in each population was moderately correlated with estimated introgression per population R² = 0.47 P < 0.001. Genetic isolation by distance existed within the historical and contemporary data sets, however, the among-population level of divergence decreased with time.

Conclusions

This is the first study to quantify cumulative introgression of farmed salmon in any native Atlantic salmon population. The estimations demonstrate that the level of introgression has been population-specific, and that the level of introgression is not solely predicted by the frequency of escapees observed in the population. However, some populations have been strongly admixed with farmed salmon, and these data provide policy makers with unique information to address this situation.

SUBMITTER: Glover KA

PROVIDER: S-EPMC3765417 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Publications

Atlantic salmon populations invaded by farmed escapees: quantifying genetic introgression with a Bayesian approach and SNPs.

Glover Kevin Alan KA Pertoldi Cino C Besnier Francois F Wennevik Vidar V Kent Matthew M Skaala Øystein Ø

BMC genetics 20130823

<h4>Background</h4>Many native Atlantic salmon populations have been invaded by domesticated escapees for three decades or longer. However, thus far, the cumulative level of gene-flow that has occurred from farmed to wild salmon has not been reported for any native Atlantic salmon population. The aim of the present study was to investigate temporal genetic stability in native populations, and, quantify gene-flow from farmed salmon that caused genetic changes where they were observed. This was ac ...[more]

PMID: 23968202

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Project description:BackgroundSea lice have significant negative economic and welfare impacts on marine Atlantic salmon farming. Since host resistance to sea lice has a substantial genetic component, selective breeding can contribute to control of lice. Genomic selection uses genome-wide marker information to predict breeding values, and can achieve markedly higher accuracy than pedigree-based methods. Our aim was to assess the genetic architecture of host resistance to sea lice, and test the utility of genomic prediction of breeding values. Individual lice counts were measured in challenge experiments using two large Atlantic salmon post-smolt populations from a commercial breeding programme, which had genotypes for ~33 K single nucleotide polymorphisms (SNPs). The specific objectives were to: (i) estimate the heritability of host resistance; (ii) assess its genetic architecture by performing a genome-wide association study (GWAS); (iii) assess the accuracy of predicted breeding values using varying SNP densities (0.5 to 33 K) and compare it to that of pedigree-based prediction; and (iv) evaluate the accuracy of prediction in closely and distantly related animals.ResultsHeritability of host resistance was significant (0.22 to 0.33) in both populations using either pedigree or genomic relationship matrices. The GWAS suggested that lice resistance is a polygenic trait, and no genome-wide significant quantitative trait loci were identified. Based on cross-validation analysis, genomic predictions were more accurate than pedigree-based predictions for both populations. Although prediction accuracies were highest when closely-related animals were used in the training and validation sets, the benefit of having genomic-versus pedigree-based predictions within a population increased as the relationships between training and validation sets decreased. Prediction accuracy reached an asymptote with a SNP density of ~5 K within populations, although higher SNP density was advantageous for cross-population prediction.ConclusionsHost resistance to sea lice in farmed Atlantic salmon has a significant genetic component. Phenotypes relating to host resistance can be predicted with moderate to high accuracy within populations, with a major advantage of genomic over pedigree-based methods, even at relatively sparse SNP densities. Prediction accuracies across populations were low, but improved with higher marker densities. Genomic selection can contribute to lice control in salmon farming.

Project description:Increased knowledge of heritable traits in Atlantic Salmon (Salmo salar) is important to overcome bottlenecks in salmonid aquaculture. Atlantic salmonid populations, both landlocked and anadromous, represent an interesting model to gain insight into anadromy related traits, most notably, the probability to smoltify. While a previous study has identified several genomic regions diverging between anadromous and landlocked populations across the species range, the present study explores these data further with the aim to uncover if some of these genomic regions are linked to beneficial genetic traits associated with smoltification. In this study 17 of these loci were monitored in 669 anadromous salmon originating from 36 full-sibling families that had been reared under common garden conditions. The Smolt Index was calculated, using multiple visual markers, and provided a means of assessing smoltification stage. One SNP, located in Ssa04, showed a significant association with probability to smoltify, where individuals homozygous for the landlocked variant (LL) displayed a decrease in probability of smoltifying after one winter when compared with the homozygous for the anadromous variant (AA). This effect was independent of individual fish size. A separate common garden study comprising 200 individuals from either anadromous or landlocked strains showed that expression levels of ncor1, a thyroid mediator hormone located on the same chromosomal region (Ssa04), were significantly reduced in landlocked individuals post smoltification but remained constant in their anadromous counterparts. This study therefore suggests that while size is still the most important trigger for the induction of smoltification, there may also be an additional genetic component or trigger that has been 'lost' during the years deprived of SW transfer. In conclusion, the LL genotype identified here could potentially be used by the industry to delay smoltification and may also represent one of the first clues to the genetic regulation of smoltification in Atlantic salmon.

Dataset Information

Atlantic salmon populations invaded by farmed escapees: quantifying genetic introgression with a Bayesian approach and SNPs.

Background

Results

Conclusions

Publications

Atlantic salmon populations invaded by farmed escapees: quantifying genetic introgression with a Bayesian approach and SNPs.

Similar Datasets

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