Evidence map›Paper›PMID 31826882›Full record

ArticleG3 (Bethesda, Md.)2020

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

Smaragda Tsairidou, Alastair Hamilton, Diego Robledo, James E Bron, Ross D Houston

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Deep learning for genomic selection of aquatic animals.Marine life science & technology · 2024
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. What Can Genetics Do for the Control of Infectious Diseases in Aquaculture?Animals : an open access journal from MDPI · 2022
    Review
  13. Article
  14. Article
  15. Article
  16. Genomic Selection in Aquaculture Species.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Smaragda TsairidouThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, United Kingdom, Smaragda.Tsairidou@roslin.ed.ac.uk.ORCID 0000-0003-0792-9421
Alastair HamiltonHendrix Genetics Aquaculture BV/ Netherlands Villa 'de Körver', Spoorstraat 695831 CK Boxmeer, The Netherlands, and.
Diego RobledoThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, United Kingdom.ORCID 0000-0002-9616-5912
James E BronInstitute of Aquaculture, University of Stirling, FK9 4LA, United Kingdom.
Ross D HoustonThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, EH25 9RG, United Kingdom.ORCID 0000-0003-1805-0762

Funding

Biotechnology and Biological Sciences Research Council BB/P013732/1Biotechnology and Biological Sciences Research Council BB/P013740/1Biotechnology and Biological Sciences Research Council BB/P013759/1
6 · The paper itself

Abstract

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,

Indexed as

Genetic MarkersGenomicsGenotypeGenotyping TechniquesSelection, GeneticAlgorithmsAnimalsGenetic TestingGenome-Wide Association StudyModels, GeneticPhenotypePolymorphism, Single NucleotideReproducibility of ResultsSalmo salarGenetic Markersaquaculturegenomic predictionGenomic Predictiongenotype imputationGenPredSalmon breedingsea lice resistanceShared Data Resources

Identifiers

PMID31826882
PMCPMC7003102

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.