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

Kjetså, Maria; Ødegård, Jørgen; Meuwissen, Theo H. E.

doi:10.1016/j.aquaculture.2020.735415

Cited by 19 publications

(12 citation statements)

References 29 publications

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“…In the following, we describe how the total genetic variance

σ_{u}^{2}

is partitioned over the fitted SNPs and the polygenic effect, following the method described in Kjetså et al (2020). For BayesGC, we need an assumption on the fraction of the variance that is explained by the individually fitted SNPs in the BayesC term of the model.…”

Section: Methodsmentioning

confidence: 99%

Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods

Kjetså

Gjuvsland

Nordbø

et al. 2022

J Animal Breeding Genetics

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The aim of this study was to compare three methods of genomic prediction: GBLUP, BayesC and BayesGC for genomic prediction of six maternal traits in Landrace sows using a panel of 660 K SNPs. The effects of different priors for the Bayesian methods were also investigated. GBLUP does not take the genetic architecture into account as all SNPs are assumed to have equally sized effects and relies heavily on the relationships between the animals for accurate predictions. Bayesian approaches rely on both fitting SNPs that describe relationships between animals in addition to fitting single SNP effects directly. Both the relationship between the animals and single SNP effects are important for accurate predictions. Maternal traits in sows are often more difficult to record and have lower heritabilities. BayesGC was generally the method with the higher accuracy, although its accuracy was for some traits matched by that of GBLUP and for others by that of BayesC. For piglet mortality within 3 weeks, BayesGC achieved up to 9.2% higher accuracy. For many of the traits, however, the methods did not show significant differences in accuracies.

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“…In the following, we describe how the total genetic variance

σ_{u}^{2}

Section: Methodsmentioning

confidence: 99%

Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods

Kjetså

Gjuvsland

Nordbø

et al. 2022

J Animal Breeding Genetics

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“…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%

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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“…Second, the imputation reduced the upward biases in the prediction accuracy for tagging weight when the c 2 estimates were omitted from our statistical models, mainly because the complete genotypes improved accuracy of estimated breeding values for tagging weight. The benefit of imputation on genomic prediction in aquaculture breeding has been reported in recent studies, such as for disease resistance to photobacteriosis in gilthead sea bream S. aurata ( Bargelloni et al, 2021 ), resistance to sea lice in Atlantic salmon S. salar ( Tsai et al, 2017 ; Kjetså et al, 2020 ), growth-related traits Yellowtail kingfish Seriola lalandi ( Nguyen et al, 2018a ) or with simulated data in rainbow trout O. mykiss ( Dufflocq et al, 2019 ). In selective breeding programs, imputation can help to reduce costs associated with sequencing.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have employed machine and deep learning and obtained higher accuracies for a range of traits than linear (GBLUP) and non-linear Bayesian methods ( Yin et al, 2020 ; Montesinos-López et al, 2021 ). Regardless of statistical methods used, imputation of missing genotypes or imputation from a low to high density SNP arrays or from commercial SNP arrays to whole genome sequence improved the prediction accuracy for complex traits ( Kjetså et al, 2020 ). Multivariate analysis also slightly increased the prediction accuracy for grain yield in wheat Triticum aestivum L. ( Sandhu et al, 2021 ) or cassava Manihot esculenta Crantz ( Okeke et al, 2017 ), although its benefits depend on statistical models used ( Montesinos-López et al, 2020 ) or characteristics of datasets and specifically genetic architecture of traits ( Gianola and Fernando, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfish Pangasianodon hypophthalmus

Phuc

Nguyen

et al. 2023

Front. Genet.

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Common full-sib families (c2) make up a substantial proportion of total phenotypic variation in traits of commercial importance in aquaculture species and omission or inclusion of the c2 resulted in possible changes in genetic parameter estimates and re-ranking of estimated breeding values. However, the impacts of common full-sib families on accuracy of genomic prediction for commercial traits of economic importance are not well known in many species, including aquatic animals. This research explored the impacts of common full-sib families on accuracy of genomic prediction for tagging weight in a population of striped catfish comprising 11,918 fish traced back to the base population (four generations), in which 560 individuals had genotype records of 14,154 SNPs. Our single step genomic best linear unbiased prediction (ssGLBUP) showed that the accuracy of genomic prediction for tagging weight was reduced by 96.5%–130.3% when the common full-sib families were included in statistical models. The reduction in the prediction accuracy was to a smaller extent in multivariate analysis than in univariate models. Imputation of missing genotypes somewhat reduced the upward biases in the prediction accuracy for tagging weight. It is therefore suggested that genomic evaluation models for traits recorded during the early phase of growth development should account for the common full-sib families to minimise possible biases in the accuracy of genomic prediction and hence, selection response.

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

Cited by 19 publications

References 29 publications

Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods

Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods

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

Effects of common full-sib families on accuracy of genomic prediction for tagging weight in striped catfish Pangasianodon hypophthalmus

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