Evidence map›Paper›PMID 39294578›Full record

ArticleGenetics, selection, evolution : GSE2024

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs.

Md Sharif-Islam, Julius H J van der Werf, Mark Henryon, Thinh Tuan Chu, Benjamin J Wood, Susanne Hermesch

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Article in Genetics, selection, evolution : GSE, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Md Sharif-IslamAGBU, a joint venture of NSW Department of Primary Industries and University of New England, Armidale, NSW, 2351, Australia. mislam40@myune.edu.au.ORCID http://orcid.org/0000-0003-2442-6869
Julius H J van der WerfSchool of Environmental and Rural Science, University of New England, Armidale, 2351, Australia.
Mark HenryonDanish Pig Research Center, Danish Agriculture and Food Council, Axeltorv 3, 1609, Copenhagen V, Denmark.
Thinh Tuan ChuCenter for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark.
Benjamin J WoodSchool of Veterinary Science, University of Queensland, Brisbane, QLD, 4072, Australia.
Susanne HermeschAGBU, a joint venture of NSW Department of Primary Industries and University of New England, Armidale, NSW, 2351, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).

methodsStochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates.

resultsBased on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies.

conclusionsGenotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Indexed as

GenotypeWeaningAnimalsBreedingFemaleGenotyping TechniquesInbreedingLitter SizeMaleModels, GeneticPedigreePhenotypeSelection, GeneticSwine

Identifiers

PMID39294578
PMCPMC11409791

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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.