Viral nervous necrosis resistance in gilthead sea bream (Sparus aurata) at the larval stage: heritability and accuracy of genomic prediction with different training and testing settings Article Swipe
Sara Faggion
,
Paolo Carnier
,
Rafaella Franch
,
Massimiliano Babbucci
,
Francesco Pascoli
,
Giulia Dalla Rovere
,
Massimo Caggiano
,
Hervé Chavanne
,
Anna Toffan
,
Luca Bargelloni
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s12711-023-00796-0
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1186/s12711-023-00796-0
Background The gilthead sea bream ( Sparus aurata ) has long been considered resistant to viral nervous necrosis (VNN), until recently, when significant mortalities caused by a reassortant nervous necrosis virus (NNV) strain were reported. Selective breeding to enhance resistance against NNV might be a preventive action. In this study, 972 sea bream larvae were subjected to a NNV challenge test and the symptomatology was recorded. All the experimental fish and their parents were genotyped using a genome-wide single nucleotide polymorphism (SNP) array consisting of over 26,000 markers. Results Estimates of pedigree-based and genomic heritabilities of VNN symptomatology were consistent with each other (0.21, highest posterior density interval at 95% (HPD95%): 0.1–0.4; 0.19, HPD95%: 0.1–0.3, respectively). The genome-wide association study suggested one genomic region, i.e., in linkage group (LG) 23 that might be involved in sea bream VNN resistance, although it was far from the genome-wide significance threshold. The accuracies ( r ) of the predicted estimated breeding values (EBV) provided by three Bayesian genomic regression models (Bayes B, Bayes C, and Ridge Regression) were consistent and on average were equal to 0.90 when assessed in a set of cross-validation (CV) procedures. When genomic relationships between training and testing sets were minimized, accuracy decreased greatly ( r = 0.53 for a validation based on genomic clustering, r = 0.12 for a validation based on a leave-one-family-out approach focused on the parents of the challenged fish). Classification of the phenotype using the genomic predictions of the phenotype or using the genomic predictions of the pedigree-based, all data included, EBV as classifiers was moderately accurate (area under the ROC curve 0.60 and 0.66, respectively). Conclusions The estimate of the heritability for VNN symptomatology indicates that it is feasible to implement selective breeding programs for increased resistance to VNN of sea bream larvae/juveniles. Exploiting genomic information offers the opportunity of developing prediction tools for VNN resistance, and genomic models can be trained on EBV using all data or phenotypes, with minimal differences in classification performance of the trait phenotype. In a long-term view, the weakening of the genomic ties between animals in the training and test sets leads to decreased genomic prediction accuracies, thus periodical update of the reference population with new data is mandatory.
Related Topics
Concepts
Biology
Heritability
Genetics
Genome
Single-nucleotide polymorphism
Aquaculture
Evolutionary biology
Fishery
Gene
Genotype
Fish <Actinopterygii>
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12711-023-00796-0
- https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00796-0
- OA Status
- gold
- Cited By
- 8
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362522613
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362522613Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12711-023-00796-0Digital Object Identifier
- Title
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Viral nervous necrosis resistance in gilthead sea bream (Sparus aurata) at the larval stage: heritability and accuracy of genomic prediction with different training and testing settingsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-03Full publication date if available
- Authors
-
Sara Faggion, Paolo Carnier, Rafaella Franch, Massimiliano Babbucci, Francesco Pascoli, Giulia Dalla Rovere, Massimo Caggiano, Hervé Chavanne, Anna Toffan, Luca BargelloniList of authors in order
- Landing page
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https://doi.org/10.1186/s12711-023-00796-0Publisher landing page
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https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00796-0Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://gsejournal.biomedcentral.com/counter/pdf/10.1186/s12711-023-00796-0Direct OA link when available
- Concepts
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Biology, Heritability, Genetics, Genome, Single-nucleotide polymorphism, Aquaculture, Evolutionary biology, Fishery, Gene, Genotype, FishTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.aurata | 8 |
| abstract_inverted_index.caused | 25 |
| abstract_inverted_index.fish). | 235 |
| abstract_inverted_index.larvae | 54 |
| abstract_inverted_index.models | 167, 316 |
| abstract_inverted_index.offers | 304 |
| abstract_inverted_index.single | 79 |
| abstract_inverted_index.strain | 33 |
| abstract_inverted_index.study, | 50 |
| abstract_inverted_index.update | 363 |
| abstract_inverted_index.values | 159 |
| abstract_inverted_index.HPD95%: | 114 |
| abstract_inverted_index.Results | 89 |
| abstract_inverted_index.action. | 47 |
| abstract_inverted_index.against | 41 |
| abstract_inverted_index.animals | 348 |
| abstract_inverted_index.average | 179 |
| abstract_inverted_index.between | 196, 347 |
| abstract_inverted_index.density | 107 |
| abstract_inverted_index.enhance | 39 |
| abstract_inverted_index.focused | 228 |
| abstract_inverted_index.genomic | 94, 123, 165, 194, 215, 242, 250, 302, 315, 345, 358 |
| abstract_inverted_index.greatly | 205 |
| abstract_inverted_index.highest | 105 |
| abstract_inverted_index.linkage | 127 |
| abstract_inverted_index.minimal | 328 |
| abstract_inverted_index.nervous | 17, 29 |
| abstract_inverted_index.parents | 73, 231 |
| abstract_inverted_index.region, | 124 |
| abstract_inverted_index.testing | 199 |
| abstract_inverted_index.trained | 319 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Bayesian | 164 |
| abstract_inverted_index.accuracy | 203 |
| abstract_inverted_index.accurate | 263 |
| abstract_inverted_index.although | 140 |
| abstract_inverted_index.approach | 227 |
| abstract_inverted_index.assessed | 185 |
| abstract_inverted_index.breeding | 37, 158, 290 |
| abstract_inverted_index.estimate | 275 |
| abstract_inverted_index.feasible | 286 |
| abstract_inverted_index.gilthead | 3 |
| abstract_inverted_index.interval | 108 |
| abstract_inverted_index.involved | 134 |
| abstract_inverted_index.markers. | 88 |
| abstract_inverted_index.necrosis | 18, 30 |
| abstract_inverted_index.programs | 291 |
| abstract_inverted_index.provided | 161 |
| abstract_inverted_index.training | 197, 351 |
| abstract_inverted_index.(HPD95%): | 111 |
| abstract_inverted_index.Estimates | 90 |
| abstract_inverted_index.Selective | 36 |
| abstract_inverted_index.challenge | 60 |
| abstract_inverted_index.decreased | 204, 357 |
| abstract_inverted_index.estimated | 157 |
| abstract_inverted_index.genotyped | 75 |
| abstract_inverted_index.implement | 288 |
| abstract_inverted_index.included, | 257 |
| abstract_inverted_index.increased | 293 |
| abstract_inverted_index.indicates | 282 |
| abstract_inverted_index.long-term | 339 |
| abstract_inverted_index.phenotype | 239, 246 |
| abstract_inverted_index.posterior | 106 |
| abstract_inverted_index.predicted | 156 |
| abstract_inverted_index.recently, | 21 |
| abstract_inverted_index.recorded. | 66 |
| abstract_inverted_index.reference | 366 |
| abstract_inverted_index.reported. | 35 |
| abstract_inverted_index.resistant | 14 |
| abstract_inverted_index.selective | 289 |
| abstract_inverted_index.subjected | 56 |
| abstract_inverted_index.suggested | 121 |
| abstract_inverted_index.weakening | 342 |
| abstract_inverted_index.0.1–0.3, | 115 |
| abstract_inverted_index.0.1–0.4; | 112 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Exploiting | 301 |
| abstract_inverted_index.accuracies | 150 |
| abstract_inverted_index.challenged | 234 |
| abstract_inverted_index.considered | 13 |
| abstract_inverted_index.consistent | 100, 176 |
| abstract_inverted_index.consisting | 84 |
| abstract_inverted_index.developing | 308 |
| abstract_inverted_index.mandatory. | 372 |
| abstract_inverted_index.minimized, | 202 |
| abstract_inverted_index.moderately | 262 |
| abstract_inverted_index.nucleotide | 80 |
| abstract_inverted_index.periodical | 362 |
| abstract_inverted_index.phenotype. | 336 |
| abstract_inverted_index.population | 367 |
| abstract_inverted_index.prediction | 309, 359 |
| abstract_inverted_index.preventive | 46 |
| abstract_inverted_index.regression | 166 |
| abstract_inverted_index.resistance | 40, 294 |
| abstract_inverted_index.threshold. | 148 |
| abstract_inverted_index.validation | 212, 222 |
| abstract_inverted_index.Conclusions | 273 |
| abstract_inverted_index.Regression) | 174 |
| abstract_inverted_index.accuracies, | 360 |
| abstract_inverted_index.association | 119 |
| abstract_inverted_index.classifiers | 260 |
| abstract_inverted_index.clustering, | 216 |
| abstract_inverted_index.differences | 329 |
| abstract_inverted_index.genome-wide | 78, 118, 146 |
| abstract_inverted_index.information | 303 |
| abstract_inverted_index.mortalities | 24 |
| abstract_inverted_index.opportunity | 306 |
| abstract_inverted_index.performance | 332 |
| abstract_inverted_index.phenotypes, | 326 |
| abstract_inverted_index.predictions | 243, 251 |
| abstract_inverted_index.procedures. | 192 |
| abstract_inverted_index.reassortant | 28 |
| abstract_inverted_index.resistance, | 139, 313 |
| abstract_inverted_index.significant | 23 |
| abstract_inverted_index.experimental | 69 |
| abstract_inverted_index.heritability | 278 |
| abstract_inverted_index.polymorphism | 81 |
| abstract_inverted_index.significance | 147 |
| abstract_inverted_index.relationships | 195 |
| abstract_inverted_index.Classification | 236 |
| abstract_inverted_index.classification | 331 |
| abstract_inverted_index.heritabilities | 95 |
| abstract_inverted_index.pedigree-based | 92 |
| abstract_inverted_index.respectively). | 116, 272 |
| abstract_inverted_index.symptomatology | 64, 98, 281 |
| abstract_inverted_index.pedigree-based, | 254 |
| abstract_inverted_index.cross-validation | 190 |
| abstract_inverted_index.larvae/juveniles. | 300 |
| abstract_inverted_index.leave-one-family-out | 226 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5067083946 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I138689650 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.9086584 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |