Evidence map›Paper›PMID 42638081›Full record

ArticleGenetics, selection, evolution : GSE2026

Heritability of behavioral traits and their genetic correlations with carcass traits in beef cattle.

Akio Onogi, Yuki Oya, Toshio Watanabe, Atsushi Ogino, Masakazu Shinomiya, Kazuhito Kurogi

Abstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

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

Funding

Japan Racing Association Livestock Promotion Fund Japan Racing Association Livestock Promotion Fund
6 · The paper itself

Abstract

backgroundFeeding and rumination behaviors in cattle are genetically associated with feed efficiency and growth traits. However, other behavioral traits such as lying, standing, and moving remain less studied. Moreover, the genetic relationships between behavioral traits and carcass traits are poorly understood in beef cattle. This study analyzed seven behavioral classes-feeding, lying, moving, standing, ruminating, ruminating while lying, and ruminating while standing-measured using wearable sensors in 1032 Japanese Black cattle and investigated their genetic correlations with carcass traits. In addition, the potential use of these behavioral traits as secondary traits to improve the prediction accuracy of carcass breeding values was examined using bi-trait mixed-effect models. Ratio-based behavioral traits, such as the ratio of daily total feeding duration to ruminating duration, were also analyzed.

resultsEstimated heritability were generally low, ranging from 0.02 to 0.37, accounting for 22.7%-67.5% of the repeatability. Genetic correlations between behavioral and carcass traits were also low in magnitude (- 0.28 to 0.29); nevertheless, significant correlations were detected for all carcass traits except rib thickness. These correlations were interpretable; for example, animals that fed and moved less frequently and for shorter durations, and that ruminated more frequently and for longer durations, exhibited greater carcass weight. Ratio-based behavioral traits were heritable and often showed significant genetic correlations with carcass traits, which aided in the interpretation of the results. Incorporating behavioral traits as secondary traits into bi-trait mixed-effect models did not improve the accuracy of carcass breeding value predictions, likely due to the low heritability and weak genetic correlations observed.

conclusionsAlthough heritability and genetic correlations were generally low, the findings provide novel insights into the genetic relationships between behavioral and carcass traits. These results offer fundamental information that may support the use of behavioral traits in breeding and management strategies for beef cattle.

Indexed as

Behavior, AnimalQuantitative Trait, HeritableRed MeatAnimalsBreedingCattleFeeding BehaviorFemaleMalePhenotype

Identifiers

PMID42638081
PMCPMC13501594

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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.