Evidence map›Paper›PMID 40660096›Full record

ArticleGenetics, selection, evolution : GSE2025

Estimating the genetic parameters of resilience toward known and unknown disturbances in sheep using wool fibre diameter and body weight variability.

Erin G Smith, Samuel F Walkom, Dominic L Waters, Sam A Clark

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Article in Genetics, selection, evolution : GSE, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 · What the graph read from it

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Erin G SmithSchool of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia. esmith76@myune.edu.au.ORCID http://orcid.org/0000-0002-0056-4645
Samuel F WalkomAnimal Genetics and Breeding Unit, University of New England, Armidale, NSW, 2351, Australia.
Dominic L WatersSchool of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.
Sam A ClarkSchool of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGeneral resilience in animals can be quantified by analysing the variability in longitudinal data. However, it is unclear whether resilience indicators derived from different longitudinal data series can predict resilience to known or unknown disturbances in sheep. This study aimed to use two sources of longitudinal data, wool fibre diameter and body weight, to develop potential indicators for resilience to the known stress of weaning and overall resilience to unknown disturbances. The genetic parameters of these traits were assessed, along with the genetic correlations between traits from different data series and different definitions of resilience. Additionally, correlations between resilience indicators, health and production traits were estimated to evaluate the suitability of including resilience indicators in breeding programs.

resultsFibre diameter and body weight records from approximately 6500 yearling Merino sheep were used to estimate four resilience indicators of resilience towards unknown disturbances: log-transformed variance (Lnvar), lag-1 Auto (Auto), skewness (Skewness) and absolute difference in the deviations (ABS) from these curves. Three other traits, rate of change in the response and recovery (ROC_response and ROC_recovery) and area between curves (ABC) during a known disturbance of weaning, were also estimated. Resilience indicators were found to be lowly heritable (0.03 ± 0.01 to 0.18 ± 0.04). Genetic correlations between the general resilience indicator and the indicator of resilience to weaning stress were generally moderate, particularly in the wool fibre diameter data, suggesting these may represent similar traits. Genetic correlations between resilience indicators derived from wool fibre diameter and body weight data were typically weak to moderate, which indicates that they possibly capture different aspects of resilience. The genetic correlations between resilience indicators and health traits were mostly low, except for body condition score. Correlations between resilience and production traits were low to moderate and favourable.

conclusionsResilience indicators based on deviations in wool fibre diameter and body weight can be used to potentially select animals that are less affected by environmental disturbances. The genetic correlations between resilience indicators and health and production traits suggest that these traits could be included in breeding programs to improve resilience without adversely affecting production traits.

Indexed as

Body WeightWoolWool FiberAnimalsBreedingMalePhenotypeQuantitative Trait, HeritableSheepWeaning

Identifiers

PMID40660096
PMCPMC12257680

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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.