Evidence map›Paper›PMID 39482597›Full record

ArticleGenetics, selection, evolution : GSE2024

Investigating genotype by environment interaction for beef cattle fertility traits in commercial herds in northern Australia with multi-trait analysis.

James P Copley, Benjamin J Hayes, Elizabeth M Ross, Shannon Speight, Geoffry Fordyce, Benjamin J Wood, Bailey N Engle

Abstract read
In one paragraph

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Better Multi-Breed Genomic Predictions for Tropical Bull Fertility Using a Breed-Adjusted Genomic Relationship Matrix.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
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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

7 authors.

James P CopleyCentre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, 4072, Australia. james.copley@uq.edu.au.ORCID http://orcid.org/0000-0003-4931-891X
Benjamin J HayesCentre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, 4072, Australia.
Elizabeth M RossCentre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, 4072, Australia.
Shannon SpeightCentre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, 4072, Australia.
Geoffry FordyceCentre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, 4072, Australia.
Benjamin J WoodSchool of Veterinary Science, University of Queensland, Gatton, QLD, 4343, Australia.
Bailey N EngleCentre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, St Lucia, QLD, 4072, Australia.

Funding

Meat and Livestock Australia P.PSH.P0833
6 · The paper itself

Abstract

backgroundGenotype by environment interactions (GxE) affect a range of production traits in beef cattle. Quantifying the effect of GxE in commercial and multi-breed herds is challenging due to unknown genetic linkage between animals across environment levels. The primary aim of this study was to use multi-trait models to investigate GxE for three heifer fertility traits, corpus luteum (CL) presence, first pregnancy and second pregnancy, in a large tropical beef multibreed dataset (n = 21,037). Environmental levels were defined by two different descriptors, burden of heat load (temperature humidity index, THI) and nutritional availability (based on mean average daily gain for the herd, ADWG). To separate the effects of genetic linkage and real GxE across the environments, 1000 replicates of a simulated phenotype were generated by simulating QTL effects with no GxE onto real marker genotypes from the population, to determine the genetic correlations that could be expected across environments due to the existing genetic linkage only. Correlations from the real phenotypes were then compared to the empirical distribution under the null hypothesis from the simulated data. By adopting this approach, this study attempted to establish if low genetic correlations between environmental levels were due to GxE or insufficient genetic linkage between animals in each environmental level.

resultsThe correlations (being less than <0.8) for the real phenotypes were indicative of GxE for CL presence between ADWG environmental levels and in pregnancy traits. However, none of the correlations for CL presence or first pregnancy between ADWG levels were below the 5th percentile value for the empirical distribution under the null hypothesis from the simulated data. Only one statistically significant (P < 0.05) indication of GxE for first pregnancy was found between THI environmental levels, where r

conclusionsOnly one case of statistically significant GxE for fertility traits was detected for first pregnancy between THI environmental levels 2 and 3. Other initial indications of GxE that were observed from the real phenotypes did not prove significant when compared to an empirical null distribution from simulated phenotypes. The lack of compelling evidence of GxE indicates that direct selection for fertility traits can be made accurately, using a single evaluation, regardless of environment.

Indexed as

FertilityGene-Environment InteractionGenotypeAnimalsAustraliaCattleFemaleModels, GeneticPhenotypePregnancyQuantitative Trait Loci

Identifiers

PMID39482597
PMCPMC11526658

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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.