ArticleGenetics, selection, evolution : GSE2017
The use of genomic information increases the accuracy of breeding value predictions for sea louse (Caligus rogercresseyi) resistance in Atlantic salmon (Salmo salar).
Article in Genetics, selection, evolution : GSE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 115 citations in OpenAlex.
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- Why sequence the genome of every species? A view from evolutionary biology.Journal of the Marine Biological Association of the United Kingdom. Marine Biological Association of the United Kingdom · 2025Article
- Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024Review
- Keratinocytes drive the epithelial hyperplasia key to sea lice resistance in coho salmon.BMC biology · 2024Article
- Genomic selection for resistance to one pathogenic strain ofFrontiers in genetics · 2024Article
- Genome-wide association identifies genomic regions influencing fillet color in Northwest Atlantic salmon (Frontiers in genetics · 2024Article
- Heritability of Immunity Traits and Resistance of Atlantic Salmon against the Sea LouseBiology · 2023Article
- Increased accuracy of genomic predictions for growth under chronic thermal stress in rainbow trout by prioritizing variants from GWAS using imputed sequence data.Evolutionary applications · 2022Article
- Genome-Wide Association and Genomic Prediction of Growth Traits in the European Flat Oyster (Frontiers in genetics · 2022Article
- Genomic Selection in Aquaculture Species.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Tomato genomic prediction for good performance under high-temperature and identification of loci involved in thermotolerance response.Horticulture research · 2021Article
- Impact of genotypic errors with equal and unequal family contribution on accuracy of genomic prediction in aquaculture using simulation.Scientific reports · 2021Article
- Genome-scale comparative analysis for host resistance against sea lice between Atlantic salmon and rainbow trout.Scientific reports · 2021Article
- Investigating mechanisms underlying genetic resistance to Salmon Rickettsial Syndrome in Atlantic salmon using RNA sequencing.BMC genomics · 2021Article
- Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels.BMC genomics · 2021Article
- Development of a SNP linkage map and genome-wide association study for resistance to Aeromonas hydrophila in pacu (Piaractus mesopotamicus).BMC genomics · 2020Article
- Prediction of genomic breeding values based on pre-selected SNPs using ssGBLUP, WssGBLUP and BayesB for Edwardsiellosis resistance in Japanese flounder.Genetics, selection, evolution : GSE · 2020Article
- Harnessing genomics to fast-track genetic improvement in aquaculture.Nature reviews. Genetics · 2020Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sea lice infestations caused by Caligus rogercresseyi are a main concern to the salmon farming industry due to associated economic losses. Resistance to this parasite was shown to have low to moderate genetic variation and its genetic architecture was suggested to be polygenic. The aim of this study was to compare accuracies of breeding value predictions obtained with pedigree-based best linear unbiased prediction (P-BLUP) methodology against different genomic prediction approaches: genomic BLUP (G-BLUP), Bayesian Lasso, and Bayes C. To achieve this, 2404 individuals from 118 families were measured for C. rogercresseyi count after a challenge and genotyped using 37 K single nucleotide polymorphisms. Accuracies were assessed using fivefold cross-validation and SNP densities of 0.5, 1, 5, 10, 25 and 37 K. Accuracy of genomic predictions increased with increasing SNP density and was higher than pedigree-based BLUP predictions by up to 22%. Both Bayesian and G-BLUP methods can predict breeding values with higher accuracies than pedigree-based BLUP, however, G-BLUP may be the preferred method because of reduced computation time and ease of implementation. A relatively low marker density (i.e. 10 K) is sufficient for maximal increase in accuracy when using G-BLUP or Bayesian methods for genomic prediction of C. rogercresseyi resistance in Atlantic salmon.
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