Evidence map›Paper›PMID 41265485›Full record

ArticleAnimal bioscience2026

Estimation of genetic parameters for pre-weaning growth traits in Dorper sheep under local Chinese conditions.

Ruijun Wang, Xinle Wang, Lifei Zhang, Yue Shi, Baodong Liu, Jing Li, Dayong Chen, Yunhui Ma, Huijie He, Jie Liu and 2 more

Abstract read
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In one paragraph

Article in Animal bioscience, 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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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Ruijun WangCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Xinle WangCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Lifei ZhangCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Yue ShiCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Baodong LiuCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Jing LiCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Dayong ChenInner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab, China.
Yunhui MaInner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab, China.
Huijie HeInner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab, China.
Jie LiuInner Mongolia Sino Sheep Technology Co., Ltd., Ulanqab, China.
Yongbin LiuCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.
Yanjun ZhangCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, China.

Funding

China Agriculture Research System of Mutton Sheep CARS-38Fundamental Research Funds of Directly Subordinate Universities of Inner Mongolia Autonomous Region BR221108Fundamental Research Funds of Directly Subordinate Universities of Inner Mongolia Autonomous Region BR251201Inner Mongolia Autonomous Region 2022JBGS0024Inner Mongolia Autonomous Region Breeding Joint Research Project YZ2023011Inner Mongolia Education Department Special Research Project For First Class Disciplines YLXKZX-NND-007Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region NMGIRT2322Science and Technology Plan of Inner Mongolia Autonomous Region 2023KYPT0021
6 · The paper itself

Abstract

objectiveThis study aimed to estimate non-genetic factors, variance components, and genetic parameters, including heritability, genetic/phenotypic correlations for birth weight (BW), weaning weight (WW), average daily gain (ADG), and Kleiber ratio (KR) traits of Dorper sheep under localized Chinese conditions.

methodsData from 2,022 Dorper sheep lambs, collected between 2019 and 2021 at Inner Mongolia Sano Sheep Breeding Co., Ltd. were analyzed. Traits included BW, WW adjusted to 90 days, ADG, and KR. Generalized linear model (R 4.3.1) assessed non-genetic factors, including recipient dam age, sex, birth year, month, and herd. Six animal models were evaluated using ASReml's AIREML to determine the most suitable model for estimating genetic parameters while bivariate models were utilized to analyze genetic and phenotypic correlations.

resultsRecipient dam age, sex, birth year, month, and herd significantly affected all traits (p<0.05). Model 2, which incorporates direct additive genetic and maternal permanent environmental effects, was determined to be optimal. Heritability was low (BW: 0.0215; WW: 0.0287; ADG: 0.0391; KR: 0.0504). BW showed a negative genetic correlation with WW, ADG, and KR. In contrast, WW showed a strong positive genetic correlation with ADG (0.9952) and KR (0.9984), along with high phenotypic correlations with these traits (0.9829 and 0.8819, respectively).

conclusionThe low heritability limits direct selection for pre-weaning traits. Prioritizing WW enhances indirect genetic gains for ADG and KR, facilitating the optimization of Dorper sheep breeding strategies under Chinese intensive systems.

Indexed as

Dorper SheepGenetic CorrelationGenetic ParametersPhenotypic CorrelationPre-weaning Growth Traits

Identifiers

PMID41265485
PMCPMC13064997

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