Evidence map›Paper›PMID 28651536›Full record

ArticleGenetics, selection, evolution : GSE2017

Genomic selection for crossbred performance accounting for breed-specific effects.

Marcos S Lopes, Henk Bovenhuis, André M Hidalgo, Johan A M van Arendonk, Egbert F Knol, John W M Bastiaansen

Abstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  6. Genomic evaluation for two-way crossbred performance in cattle.Genetics, selection, evolution : GSE · 2023
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  8. Review
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  15. Next-generation biological control: the need for integrating genetics and genomics.Biological reviews of the Cambridge Philosophical Society · 2020
    Review
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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

6 authors.

Marcos S LopesTopigs Norsvin Research Center, P.O. Box 43, 6640 AA, Beuningen, The Netherlands. Marcos.Lopes@topigsnorsvin.com.
Henk BovenhuisAnimal Breeding and Genomics Centre, Wageningen University, 6708 PB, Wageningen, The Netherlands.
André M HidalgoAnimal Breeding and Genomics Centre, Wageningen University, 6708 PB, Wageningen, The Netherlands.
Johan A M van ArendonkAnimal Breeding and Genomics Centre, Wageningen University, 6708 PB, Wageningen, The Netherlands.
Egbert F KnolTopigs Norsvin Research Center, P.O. Box 43, 6640 AA, Beuningen, The Netherlands.
John W M BastiaansenAnimal Breeding and Genomics Centre, Wageningen University, 6708 PB, Wageningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreed-specific effects are observed when the same allele of a given genetic marker has a different effect depending on its breed origin, which results in different allele substitution effects across breeds. In such a case, single-breed breeding values may not be the most accurate predictors of crossbred performance. Our aim was to estimate the contribution of alleles from each parental breed to the genetic variance of traits that are measured in crossbred offspring, and to compare the prediction accuracies of estimated direct genomic values (DGV) from a traditional genomic selection model (GS) that are trained on purebred or crossbred data, with accuracies of DGV from a model that accounts for breed-specific effects (BS), trained on purebred or crossbred data. The final dataset was composed of 924 Large White, 924 Landrace and 924 two-way cross (F1) genotyped and phenotyped animals. The traits evaluated were litter size (LS) and gestation length (GL) in pigs.

resultsThe genetic correlation between purebred and crossbred performance was higher than 0.88 for both LS and GL. For both traits, the additive genetic variance was larger for alleles inherited from the Large White breed compared to alleles inherited from the Landrace breed (0.74 and 0.56 for LS, and 0.42 and 0.40 for GL, respectively). The highest prediction accuracies of crossbred performance were obtained when training was done on crossbred data. For LS, prediction accuracies were the same for GS and BS DGV (0.23), while for GL, prediction accuracy for BS DGV was similar to the accuracy of GS DGV (0.53 and 0.52, respectively).

conclusionsIn this study, training on crossbred data resulted in higher prediction accuracy than training on purebred data and evidence of breed-specific effects for LS and GL was demonstrated. However, when training was done on crossbred data, both GS and BS models resulted in similar prediction accuracies. In future studies, traits with a lower genetic correlation between purebred and crossbred performance should be included to further assess the value of the BS model in genomic predictions.

Indexed as

BreedingModels, GeneticAllelesAnimalsFemaleGenomeGenomicsGenotypePolymorphism, Single NucleotidePregnancyReproducibility of ResultsSelection, GeneticSwine

Identifiers

PMID28651536
PMCPMC5485705

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Registered trials

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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.