The impact of genetic relationship between training and validation populations on genomic prediction accuracy in Atlantic salmon

The potential of genomic selection to improve production traits has been widely demonstrated in many aquaculture species. Atlantic salmon breeding programmes typically consist of sibling testing schemes, where traits that cannot be measured on the selection candidates are measured on the candidates' siblings (such as disease resistance traits). While annual testing on close relatives is effective, it is expensive due to high genotyping and phenotyping costs. Therefore, accurate prediction of breeding values in distant relatives could significantly reduce the cost of genomic selection. The aims of this study were (i) to evaluate the impact of decreasing the genomic relationship between the training and validation populations on the accuracy of genomic prediction for two key target traits; body weight and resistance to sea lice; and (ii) to assess the interaction of genetic relationship with SNP density, a major determinant of genotyping costs. Phenotype and genotype data from two year classes of a commercial breeding population of Atlantic salmon were used. The accuracy of genomic predictions obtained within a year class was similar to that obtained combining the data from the two year classes for sea lice count (0.49 - 0.48) and body weight (0.63 - 0.61), but prediction accuracy was close to zero when the prediction was performed across year groups. Systematically reducing the relatedness between the training and validation populations within a year class resulted in decreasing accuracy of genomic prediction; when the training and validation populations were set up to contain no relatives with genomic relationships >0.3, the accuracies fell from 0.48 to 0.27 for sea lice count and from 0.63 to 0.29 for body weight. Lower relatedness between training and validation populations also tended to result in highly biased predictions. No clear interaction between decreasing SNP density and relatedness between training and validation population was found. These results confirm the importance of genetic relationships between training and selection candidate populations in salmon breeding programmes, and suggests that prediction across generations using existing approaches would severely compromise the efficacy of genomic selection.

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

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400800 ◽

2019 ◽

Vol 10 (2) ◽

pp. 581-590 ◽

Cited By ~ 4

Author(s):

Smaragda Tsairidou ◽

Alastair Hamilton ◽

Diego Robledo ◽

James E. Bron ◽

Ross D. Houston

Keyword(s):

Atlantic Salmon ◽

Genomic Selection ◽

Environmental Sustainability ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Imputation Accuracy ◽

Cost Effective ◽

High Density ◽

Genotype Imputation ◽

Breeding Programs

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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Optimizing genomic prediction of host resistance to koi herpesvirus disease in carp

10.1101/609784 ◽

2019 ◽

Author(s):

Christos Palaiokostas ◽

Tomas Vesely ◽

Martin Kocour ◽

Martin Prchal ◽

Dagmar Pokorova ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Genetic Relationships ◽

Animal Health ◽

Economic Losses ◽

The European Union ◽

Koi Herpesvirus ◽

Wide Range ◽

Close Relatives

AbstractGenomic selection (GS) is increasingly applied in breeding programmes of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organisation for Animal Health and the European Union, causing major economic losses to carp production. Genomic selection has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. Genomic selection (GS) was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using genomic selection was between 8 and 18 % higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives.

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PSXII-22 Genomic prediction accuracy for feed efficiency related traits using different pseudo-phenotypes, prediction and validation methods in Nellore cattle

Journal of Animal Science ◽

10.1093/jas/skaa278.446 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 245-246

Author(s):

Cláudio U Magnabosco ◽

Fernando Lopes ◽

Valentina Magnabosco ◽

Raysildo Lobo ◽

Leticia Pereira ◽

...

Keyword(s):

Body Weight ◽

Weight Gain ◽

Genomic Prediction ◽

Feed Efficiency ◽

Prediction Accuracy ◽

Body Weight Gain ◽

Prediction Methods ◽

Genomic Breeding ◽

Validation Population ◽

Nellore Cattle

Abstract The aim of the study was to evaluate prediction methods, validation approaches and pseudo-phenotypes for the prediction of the genomic breeding values of feed efficiency related traits in Nellore cattle. It used the phenotypic and genotypic information of 4,329 and 3,594 animals, respectively, which were tested for residual feed intake (RFI), dry matter intake (DMI), feed efficiency (FE), feed conversion ratio (FCR), residual body weight gain (RG), and residual intake and body weight gain (RIG). Six prediction methods were used: ssGBLUP, BayesA, BayesB, BayesCπ, BLASSO, and BayesR. Three validation approaches were used: 1) random: where the data was randomly divided into ten subsets and the validation was done in each subset at a time; 2) age: the division into the training (2010 to 2016) and validation population (2017) were based on the year of birth; 3) genetic breeding value (EBV) accuracy: the data was split in the training population being animals with accuracy above 0.45; and validation population those below 0.45. We checked the accuracy and bias of genomic value (GEBV). The results showed that the GEBV accuracy was the highest when the prediction is obtained with ssGBLUP (0.05 to 0.31) (Figure 1). The low heritability obtained, mainly for FE (0.07 ± 0.03) and FCR (0.09 ± 0.03), limited the GEBVs accuracy, which ranged from low to moderate. The regression coefficient estimates were close to 1, and similar between the prediction methods, validation approaches, and pseudo-phenotypes. The cross-validation presented the most accurate predictions ranging from 0.07 to 0.037. The prediction accuracy was higher for phenotype adjusted for fixed effects than for EBV and EBV deregressed (30.0 and 34.3%, respectively). Genomic prediction can provide a reliable estimate of genomic breeding values for RFI, DMI, RG and RGI, as to even say that those traits may have higher genetic gain than FE and FCR.

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Parasite induced mortality is context dependent in Atlantic salmon: insights from an individual-based model

Scientific Reports ◽

10.1038/s41598-019-53871-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Knut Wiik Vollset

Keyword(s):

Atlantic Salmon ◽

Environmental Gradients ◽

Population Level ◽

Relative Effect ◽

Host Population ◽

Sea Lice ◽

Fish Farms ◽

Individual Based Model ◽

Parasite Infestation ◽

The Impact

AbstractAn individual-based model was parameterized to explore the impact of a crustacean ectoparasite (sea louse, Lepeophtheirus salmonis & Caligus spp.) on migrating Atlantic salmon smolt. The model explores how environmental and intrinsic factors can modulate the effect of sea lice on survival, growth and maturation of Atlantic salmon at sea. Relative to other effects, the parasite infestation pressure from fish farms and the encounter process emerge as the most important parameters. Although small variations in parasite-induced mortality may be masked by variable environmental effects, episodes of high infestation pressure from fish farms should be observable in wild populations of Atlantic salmon if laboratory studies accurately reflect the physiological effects of sea lice. Increases in temperature in the model negatively influenced fish survival by affecting the development time of the parasite at a rate that was not compensated for by the growth of the host. Discharge from rivers was parameterized to increase migration speed and influenced parasite induced mortality by decreasing time spent in areas with increased infestation pressure. Initial size and growth of the host was inversely related to the impact of the parasite because of size-dependent parasite-induced mortality in the early phase of migration. Overall, the model illustrates how environmental factors modulate effects on the host population by impacting either the parasite load or the relative effect of the parasite. The results suggest that linking population-level effects to parasite infestation pressure across climatic and environmental gradients may be challenging without correctly accounting for these effects.

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Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

Plants ◽

10.3390/plants9060719 ◽

2020 ◽

Vol 9 (6) ◽

pp. 719

Author(s):

Mulusew Fikere ◽

Denise M. Barbulescu ◽

M. Michelle Malmberg ◽

Pankaj Maharjan ◽

Phillip A. Salisbury ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Seed Quality ◽

Agronomic Traits ◽

Genetic Correlations ◽

Quality Traits ◽

Blackleg Disease ◽

Genetic Progress ◽

Seed Quality Traits

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

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The control of sea lice in Atlantic salmon by selective breeding

Journal of The Royal Society Interface ◽

10.1098/rsif.2015.0574 ◽

2015 ◽

Vol 12 (110) ◽

pp. 20150574 ◽

Cited By ~ 26

Author(s):

Karim Gharbi ◽

Louise Matthews ◽

James Bron ◽

Ron Roberts ◽

Alan Tinch ◽

...

Keyword(s):

Atlantic Salmon ◽

Selective Breeding ◽

Time Window ◽

Fish Farming ◽

Sea Lice ◽

Important Species ◽

Susceptibility To Infection ◽

Chemical Treatments ◽

Farmed Atlantic Salmon ◽

The Impact

Sea lice threaten the welfare of farmed Atlantic salmon and the sustainability of fish farming across the world. Chemical treatments are the major method of control but drug resistance means that alternatives are urgently needed. Selective breeding can be a cheap and effective alternative. Here, we combine experimental trials and diagnostics to provide a practical protocol for quantifying resistance to sea lice. We then combined quantitative genetics with epidemiological modelling to make the first prediction of the response to selection, quantified in terms of reduced need for chemical treatments. We infected over 1400 young fish with Lepeophtheirus salmonis , the most important species in the Northern Hemisphere. Mechanisms of resistance were expressed early in infection. Consequently, the number of lice per fish and the ranking of families were very similar at 7 and 17 days post infection, providing a stable window for assessing susceptibility to infection. The heritability of lice numbers within this time window was moderately high at 0.3, confirming that selective breeding is viable. We combined an epidemiological model of sea lice infection and control on a salmon farm with genetic variation in susceptibility among individuals. We simulated 10 generations of selective breeding and examined the frequency of treatments needed to control infection. Our model predicted that substantially fewer chemical treatments are needed to control lice outbreaks in selected populations and chemical treatment could be unnecessary after 10 generations of selection. Selective breeding for sea lice resistance should reduce the impact of sea lice on fish health and thus substantially improve the sustainability of Atlantic salmon production.

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Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs

Genetics Selection Evolution ◽

10.1186/s12711-021-00661-y ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Cheng Bian ◽

Dzianis Prakapenka ◽

Cheng Tan ◽

Ruifei Yang ◽

Di Zhu ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Prediction Models ◽

Average Daily Gain ◽

Live Weight ◽

Feed Conversion ◽

Muscle Area ◽

Haplotype Blocks ◽

Low Coverage

Abstract Background Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. Methods We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Results Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Conclusions Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

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How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

Frontiers in Plant Science ◽

10.3389/fpls.2020.592977 ◽

2020 ◽

Vol 11 ◽

Author(s):

Christian R. Werner ◽

R. Chris Gaynor ◽

Gregor Gorjanc ◽

John M. Hickey ◽

Tobias Kox ◽

...

Keyword(s):

Family Structure ◽

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Cross Validation ◽

Careful Analysis ◽

Critical Approach ◽

Crop Species ◽

Breeding Programs ◽

Mendelian Sampling

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

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Genomic prediction of host resistance to sea lice in farmed Atlantic salmon populations

Genetics Selection Evolution ◽

10.1186/s12711-016-0226-9 ◽

2016 ◽

Vol 48 (1) ◽

Cited By ~ 88

Author(s):

Hsin-Yuan Tsai ◽

Alastair Hamilton ◽

Alan E. Tinch ◽

Derrick R. Guy ◽

James E. Bron ◽

...

Keyword(s):

Atlantic Salmon ◽

Host Resistance ◽

Genomic Prediction ◽

Sea Lice ◽

Farmed Atlantic Salmon

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A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars

PLoS ONE ◽

10.1371/journal.pone.0247824 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247824

Author(s):

Morteza Shabannejad ◽

Mohammad-Reza Bihamta ◽

Eslam Majidi-Hervan ◽

Hadi Alipour ◽

Asa Ebrahimi

Keyword(s):

Drought Stress ◽

Genomic Selection ◽

Bread Wheat ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Terminal Drought ◽

Genome Wide ◽

Best Linear Unbiased ◽

Terminal Drought Stress ◽

Trait Associations

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

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