Evidence map›Paper›PMID 30371787›Full record

ArticleJournal of animal science2019

Towards multi-breed genomic evaluations for female fertility of tropical beef cattle.

Ben J Hayes, Nicholas J Corbet, Jack M Allen, Alan R Laing, Geoffry Fordyce, Russel Lyons, Michael R McGowan, Brian M Burns

Open access · bronzeAbstract read
In one paragraph

Article in Journal of animal science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
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

23 citing papers in PubMed, 51 citations in OpenAlex.

  1. Article
  2. 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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  7. Application of Genomic Selection in Beef Cattle Disease Prevention.Animals : an open access journal from MDPI · 2025
    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

8 authors at 4 institutions in 1 country.

Ben J HayesQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
Nicholas J CorbetCentral Queensland University, School of Health, Medical and Applied Sciences, Rockhampton, QLD, Australia.
Jack M AllenAgricultural Business Research Institute, University of New England, Armidale, NSW, Australia.
Alan R LaingFormerly Department of Agriculture and Fisheries, Ayr, QLD, Australia.
Geoffry FordyceQueensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia.
Russel LyonsThe University of Queensland, School of Veterinary Science, St Lucia, QLD, Australia.
Michael R McGowanThe University of Queensland, School of Veterinary Science, St Lucia, QLD, Australia.
Brian M BurnsDepartment of Agriculture and Fisheries, Rockhampton, QLD, Australia.
University of Queensland · AUAgriculture and Food · AUCentral Queensland University · AUUniversity of New England · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing accurate genomic evaluations of fertility for tropical beef cattle must deal with at least two major challenges (i) recording cow fertility traits in extensive production systems on large numbers of cows and (ii) the genomic evaluations should work across the breeds, crossbreds, and composites used in tropical beef production. Here, we assess accuracy of genomic evaluations for a trait which can be collected on a large scale in extensive conditions, corpus luteum score (CLscore), which is 1 if ovarian scanning indicates a heifer has cycled by 600 d and 0 if not, in a multi-breed population. A total of 3,696 heifers, including 979 Brahmans, 914 Droughtmasters, and 1,803 Santa Gertrudis in seven herds across 3-yr cohorts with CLscores, were genotyped for 24,211 SNPs. Genotypes were imputed to 728,785 SNPs. GBLUP and BayesR were used to predict GEBV. Accuracy of GEBV was evaluated with two validation strategies. In the first strategy, the last year cohort of heifers from each herd was used for validation, such that every herd had heifers in both reference and validation populations. In the second validation strategy, each herd in turn was removed in its entirety from the reference population, and was used for validation. For both validation strategies, accuracy of GEBV for single breed and multi-breed reference populations was assessed. For the first validation strategy, accuracy of GEBV ranged from 0.2 for Brahmans to 0.4 for Droughtmasters. Increasing marker density from 24K SNPs to 728K SNPs resulted in a small increase in accuracy, and including multiple-breeds in the reference did not help improve accuracy. These results suggest that provided a herd has animals in the reference population, the accuracy of the GEBV is largely determined by within herd (linkage) information. The situation was very different when entire herds were predicted in the second validation. In this case accuracy of GEBV using only 24K SNPs and only a within breed reference was close to zero for all breeds. Accuracy increased substantially when 728K SNPs, BayesR, and a multi-breed reference were used, from 0.15 for Brahmans to 0.35 for Santa Gertrudis. Given the second validation strategy is more likely to reflect the situation for many herds in tropical beef production (no animals in the reference), genomic evaluations for fertility in tropical beef cattle should be based on high-density markers (728K SNPs) and should be multi-breed.

Indexed as

GenomicsAnimalsBreedingCattleFemaleFertilityGenomeGenotypePhenotypePolymorphism, Single Nucleotide

Identifiers

PMID30371787
PMCPMC6313145
OpenAlexW2898823745

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.