Evidence map›Paper›PMID 25880217›Full record

ArticleGenetics, selection, evolution : GSE2015

Non-additive genetic variation in growth, carcass and fertility traits of beef cattle.

Sunduimijid Bolormaa, Jennie E Pryce, Yuandan Zhang, Antonio Reverter, William Barendse, Ben J Hayes, Michael E Goddard

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

39 citing papers in PubMed, 91 citations in OpenAlex.

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  11. Genomic Analysis of Heterosis in an Angus × Hereford Cattle Population.Animals : an open access journal from MDPI · 2023
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  17. Genomic Selection for Milk Production Traits in Xinjiang Brown Cattle.Animals : an open access journal from MDPI · 2022
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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 at 3 institutions in 1 country.

Sunduimijid BolormaaVictorian Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, 3083, Australia. bolormaa.sunduimijid@ecodev.vic.gov.au.
Jennie E PryceVictorian Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, 3083, Australia. jennie.pryce@ecodev.vic.gov.au.
Yuandan ZhangAnimal Genetics and Breeding Unit, UNE, Armidale, NSW, 2351, Australia. yuandan.zhang@une.edu.au.
Antonio ReverterCSIRO Animal, Food and Health Sciences, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. toni.reverter-Gomez@csiro.au.
William BarendseCSIRO Animal, Food and Health Sciences, Queensland Bioscience Precinct, St. Lucia, QLD, 4067, Australia. bill.barendse@csiro.au.
Ben J HayesVictorian Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, 3083, Australia. ben.hayes@ecodev.vic.gov.au.
Michael E GoddardVictorian Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, 3083, Australia. mike.goddard@ecodev.vic.gov.au.
Department of Economic Development Jobs Transport and Resources · AUAnimal, Food and Health Sciences · AUUniversity of New England · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation.

methodsGenotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs.

resultsThe number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance.

conclusionsSignificant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.

Indexed as

Polymorphism, Single NucleotideAnimalsCattleEpistasis, GeneticFemaleFertilityGenes, DominantGenome-Wide Association StudyMaleMeatPhenotype

Identifiers

PMID25880217
PMCPMC4382858
OpenAlexW2010217885

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.