Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Cáceres, Pablo; Barría, Agustín; Christensen, Kris A.; Correa, Katharina; Lhorente, Jean Paul; Yáñez, José M.

doi:10.1101/624031

Cited by 8 publications

(25 citation statements)

References 77 publications

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“…The genomic heritability using the HD SNP panel was found to be 0.19 (SE = 0.07) for sea lice resistance, and 0.57 (SE = 0.07) for body weight which was generally in agreement with findings from earlier studies, either based on the same (e.g., Tsai et al 2015;Tsai et al 2016) or different populations (Cáceres et al 2019).…”

Section: Genetic Parameters and Testing Of Genomic Prediction With Resupporting

confidence: 88%

Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

Tsairidou

Hamilton

Robledo

et al. 2020

G3 Genes|Genomes|Genetics

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Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

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Section: Genetic Parameters and Testing Of Genomic Prediction With Resupporting

confidence: 88%

Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

Tsairidou

Hamilton

Robledo

et al. 2020

G3 Genes|Genomes|Genetics

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“…Another lead SNP on the same chromosome is close to somatostatin receptor type 5-like at 72,684,148 bp. On ssa09, a lead SNP is nearby E3 ubiquitin-protein ligase ( trim 8), which was identified as a candidate gene for sea lice resistance in a previous study [19]. A lead SNP on ssa12 intercepted an exonic region of ral guanine nucleotide dissociation stimulator-like 1 at 15,628,486 bp and, at the same chromosome, a SNP is near to the filaggrin-2-like gene that participates in cell to cell adhesion and structure of epidermis [41].…”

Section: Resultsmentioning

confidence: 99%

“…to 200K [10,17,19,[42][43][44][45][46][47][48]. The use of imputed information increased heritability values for body weight traits as was observed in Nile tilapia [49,50] using the same population from medium to ultra high density and for sea lice trait [51], using low density to high density imputed genotypes.…”

Section: Discussionmentioning

confidence: 97%

“…Previous studies identified loci and candidate genes associated with sea lice resistance traits in Atlantic salmon [17, 19, 52], but the medium-density SNP panels and limited sample size used in these studies may have hindered the identification of genes truly associated to these traits. These GWAS were performed with moderate sample size using a maximum of ∼2,600 animals, and using only medium density SNP panels[10, 16, 18].…”

Section: Discussionmentioning

confidence: 99%

“…GWAS has been applied to provide insights into the genetic architecture of several important traits in Atlantic salmon, including sea lice load [16][17][18][19]. All these studies demonstrated a polygenic architecture of lice load, with no genome-wide significant association at any chromosome [16,19], except for a moderate quantitative trait locus (QTL) located in ssa03 which was reported to surpass the significance threshold in a recent study [17]. Previous studies have most likely failed in detecting significantly associated regions because they used a low to moderate number of animals with phenotypes and genotypes ,from 1,498 to 1,119 [18].…”

Section: Introductionmentioning

confidence: 99%

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Meta-analysis of GWAS for sea lice load in Atlantic salmon

Caceres

López²,

García

et al. 2022

Preprint

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Sea lice (Caligus rogercresseyi) is an ectoparasite that causes significant production losses in the salmon aquaculture industry of the southern hemisphere. Atlantic salmon (Salmo salar) is an important salmonid for the aquaculture industry and a species highly susceptible to sea lice infestation. Genetic variation for resistance to sea lice, defined as parasite load, has been found in Atlantic salmon. In addition, sea lice load has been shown to be a polygenic trait, controlled by several quantitative trait loci (QTL) which have small to medium effect, making them difficult to map with sufficient statistical power when sample sizes are limited. The use of medium density single nucleotide polymorphisms (SNP) can also adversely affect the success of identifying genetic variants significantly associated with sea lice load. In order to improve the ability to detect QTL significantly associated with sea lice load, we combined genotype imputation from medium- to high SNP-density and performed genome-wide association studies (GWAS) across different populations of Atlantic salmon. The imputation of genotypes of 6,144 fish challenged against sea lice from four year classes was performed to increase density from 70K SNPs to 600K SNPs. A meta-GWAS was then carried out for three different traits: lice count, lice density and log-lice density. Using this approach, we detected a genomic region highly associated with sea lice load on Atlantic salmon chromosomes (ssa) 3 and 12 pronounced peaks and several other regions surpassing the significance threshold across almost all other chromosomes. We also identified important genes within the QTL regions, many of these genes are involved in tissue reparation, such as Mucin-16-like isoform X2 and Filamentous growth regulator 23-like isoform X1. The QTL region on ssa03 also contained cytoskeletal-modifying and immune response related genes such as Coronin 1A and Claudin. Our results confirm the highly polygenic architecture of sea lice load, but they also show that high experimental power can lead to the identification of candidate genes and thus to increased insight into the biology of sea lice resistance in Atlantic salmon.

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Accuracy of genomic prediction of host resistance to salmon lice in Atlantic salmon (Salmo salar) using imputed high-density genotypes

2020

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Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout

Abstract: 8 4 1 share the same standing variation within the genomic regions and genes that regulate 4 2 resistance. Our results provide further knowledge and may help establish better control for 4 3 sea lice in fish populations. 4 4

Cited by 8 publications

References 77 publications

Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

Meta-analysis of GWAS for sea lice load in Atlantic salmon

Accuracy of genomic prediction of host resistance to salmon lice in Atlantic salmon (Salmo salar) using imputed high-density genotypes

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