Evidence map›Paper›PMID 34233617›Full record

ArticleBMC genomics2021

Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size.

Akio Onogi, Toshio Watanabe, Atsushi Ogino, Kazuhito Kurogi, Kenji Togashi

Abstract read
In one paragraph

Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Artificial intelligence in animal breeding and genetics: applications, opportunities, and challenges.Animal frontiers : the review magazine of animal agriculture · 2026
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  4. Article
  5. Review
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  7. Article
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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

5 authors.

Akio OnogiDepartment of Plant Life Science, Faculty of Agriculture, Ryukoku University, 1-5, Yokotani, Seta, Oe-cho, Shiga, 520-2194, Otsu, Japan. onogiakio@gmail.com.ORCID http://orcid.org/0000-0003-1707-9539
Toshio WatanabeMaebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc, 371-0121, Maebashi, Japan.
Atsushi OginoMaebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc, 371-0121, Maebashi, Japan.
Kazuhito KurogiCattle Breeding Department, Livestock Improvement Association of Japan, Inc, 135-0041, Tokyo, Japan.
Kenji TogashiMaebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc, 371-0121, Maebashi, Japan.

Funding

Japan Society for the Promotion of Science 18K14567
6 · The paper itself

Abstract

backgroundGenomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes.

resultsRecords of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6-19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size.

conclusionsThe six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.

Indexed as

GenomeModels, GeneticAnimalsCattleGenomicsPhenotypePolymorphism, Single NucleotidePopulation DensityDominanceEpistasisGBLUPGenomic selectionMixed model

Identifiers

PMID34233617
PMCPMC8262069

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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.