Evidence map›Paper›PMID 28250015›Full record

ArticleG3 (Bethesda, Md.)2017

Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon.

Hsin-Yuan Tsai, Oswald Matika, Stefan McKinnon Edwards, Roberto Antolín-Sánchez, Alastair Hamilton, Derrick R Guy, Alan E Tinch, Karim Gharbi, Michael J Stear, John B Taggart and 3 more

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

42 citing papers in PubMed.

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  16. Article
  17. Genomic Selection in Aquaculture Species.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
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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

13 authors.

Hsin-Yuan TsaiThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
Oswald MatikaThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
Stefan McKinnon EdwardsThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
Roberto Antolín-SánchezThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
Alastair HamiltonHendrix Genetics Aquaculture BV/ NetherlandsVilla 'de Körver', Spoorstraat 695831 CK BoxmeerThe Netherlands.
Derrick R GuyHendrix Genetics Aquaculture BV/ NetherlandsVilla 'de Körver', Spoorstraat 695831 CK BoxmeerThe Netherlands.
Alan E TinchHendrix Genetics Aquaculture BV/ NetherlandsVilla 'de Körver', Spoorstraat 695831 CK BoxmeerThe Netherlands.
Karim GharbiEdinburgh Genomics, Ashworth Laboratories, University of Edinburgh, EH9 3JT, United Kingdom.
Michael J StearInstitute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, G61 1QH, United Kingdom.
John B TaggartInstitute of Aquaculture, University of Stirling, FK9 4LA, United Kingdom.
James E BronInstitute of Aquaculture, University of Stirling, FK9 4LA, United Kingdom.
John M HickeyThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom.
Ross D HoustonThe Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, United Kingdom ross.houston@roslin.ed.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/H022007/1Biotechnology and Biological Sciences Research Council BB/J004235/1Biotechnology and Biological Sciences Research Council BB/J004243/1Biotechnology and Biological Sciences Research Council BB/J004324/1Biotechnology and Biological Sciences Research Council BB/M028321/1Biotechnology and Biological Sciences Research Council BB/N024044/1Biotechnology and Biological Sciences Research Council BBS/E/D/20211550Biotechnology and Biological Sciences Research Council BBS/E/D/20211551Biotechnology and Biological Sciences Research Council BBS/E/D/20211553Biotechnology and Biological Sciences Research Council BBS/E/D/20211554Medical Research Council MR/M000370/1
6 · The paper itself

Abstract

Genomic selection uses genome-wide marker information to predict breeding values for traits of economic interest, and is more accurate than pedigree-based methods. The development of high density SNP arrays for Atlantic salmon has enabled genomic selection in selective breeding programs, alongside high-resolution association mapping of the genetic basis of complex traits. However, in sibling testing schemes typical of salmon breeding programs, trait records are available on many thousands of fish with close relationships to the selection candidates. Therefore, routine high density SNP genotyping may be prohibitively expensive. One means to reducing genotyping cost is the use of genotype imputation, where selected key animals (

Indexed as

Cost-Benefit AnalysisAnimalsAquacultureBreedingGene FrequencyGenomicsGenotypePolymorphism, Single NucleotideSalmo salaraquaculturedisease resistanceGenomic SelectionGenPredimputationShared Data Resources

Identifiers

PMID28250015
PMCPMC5386885

What OpenQuestion holds

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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.