Evidence map›Paper›PMID 41289952›Full record

ArticleAnimal bioscience2026

CT scanning technology to asses the genetic and phenotypic correlations, and selection potential of carcass traits in mutton sheep.

Yuan Zhao, Guoxing Jia, Xiaoxue Zhang, Huibin Tian, Deyin Zhang, Yukun Zhang, Xiaolong Li, Jiangbo Cheng, Liming Zhao, Quanzhong Xu and 5 more

Abstract read
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.

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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

15 authors.

Yuan ZhaoState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Guoxing JiaCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China.
Xiaoxue ZhangCollege of Animal Science and Technology, Gansu Agricultural University, Lanzhou, China.
Huibin TianState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Deyin ZhangState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Yukun ZhangState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Xiaolong LiState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Jiangbo ChengState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Liming ZhaoState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Quanzhong XuState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Xiaobin YangState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Zongwu MaState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Dan XuState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Fadi LiState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China.
Weimin WangState Key Laboratory of Herbage Improvement and Grassland Agro- Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, China. wangweimin@lzu.edu.cn.

Funding

Gansu Provincial Department of Education: 2025 Graduate Innovation Star Project 2025CXZX-065Gansu Provincial Science and Technology Plan Project 24CXGH007Gansu Provincial Science and Technology Plan Project 25CXGH008Gansu Provincial Science and Technology Plan Project 25YFNA017National Key Research and Development Program of China 2023YFF1001000
6 · The paper itself

Abstract

objectiveThis study aims to evaluate the variations in carcass fat percentage (CFP), carcass muscle percentage (CMP), carcass bone percentage (CBP), meat-to-bone ratio, carcass weight, loin eye area (REA), backfat thickness (BF), the total tissue depth of muscle and fat at the twelfth rib, 110 mm from the midline (GR), and dressing percentage in 574 mutton sheep using CT scanning technology. It also seeks to estimate the genetic and phenotypic correlations, as well as estimated genomic selection accuracy provides reference for breeding of mutton sheep carcasses.

methodsPhenotypic data from National Mutton Sheep Testing Station; uniform rearing for 155 days. CT scans were used to obtain body images of the sheep, and CT-Calc2012 software was employed to trait determination. Genomic data were sequenced using second-generation sequencing, and SNP calling was performed with GATK. A mixed linear model incorporating both genomic and pedigree data was used to estimate genetic parameters for various traits. Cross-validation through ten-fold was conducted to assess genomic selection accuracy, and both direct and correlated selection responses for carcass traits were analyzed.

resultsThe coefficient of variation for each trait ranging from 8.86% to 25.12%. All carcass composition traits demonstrated medium to high heritability (0.38-0.51). A strong genetic correlation was found between BF, REA, CFP, and CMP. The genomic selection accuracy for carcass traits ranged from 0.29 to 0.43, suggesting potential for genomic selection in mutton sheep breeding. The genetic progress of CMP and CFP after direct and indirect selection was tested, showing that indirect improvement of CMP and CFP was most significant when selecting for BF and REA.

conclusionThe study indicates that BF and REA are valuable traits for improving carcass composition in mutton sheep breeding, with genomic selection offering prospects for breeding carcass traits more effectively.

Indexed as

Carcass TraitsGenetic ParametersGenomic SelectionSheep

Identifiers

PMID41289952
PMCPMC13243941

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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.