Evidence map›Paper›PMID 39587475›Full record

ArticleBMC genomics2024

Evaluation of genomic mating approach based on genetic algorithms for long-term selection in Huaxi cattle.

Yuanqing Wang, Bo Zhu, Jing Wang, Lupei Zhang, Lingyang Xu, Yan Chen, Zezhao Wang, Huijiang Gao, Junya Li, Xue Gao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. NADInternational journal of molecular sciences · 2025
    Article
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

10 authors.

Yuanqing Wang *Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Bo Zhu *Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Jing WangLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Lupei ZhangLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Lingyang XuLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Yan ChenLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Zezhao WangLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Huijiang GaoLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
Junya LiLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China. lijunya@caas.cn.
Xue GaoLaboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China. gaoxue@caas.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenomic mating (GM) can effectively control the growth rate of inbreeding in population and achieve long-term sustainable genetic progress. However, the design of GM method and assessment of its effects during long-term selection have not been fully explored in beef cattle breeding.

resultsIn this study, we constructed a simulated population based on the real genotypes of Huaxi cattle, where five generations of simulated breeding were carried out using the genomic optimal contribution selection (GOCS), genetic algorithms strategy and three traditional mating strategies. During the breeding process, genetic parameters including average genomic estimated breeding value (GEBV), genetic gain values (

conclusionsThese findings suggest that the GM method shows potential for achieving sustainable genetic gain and could be utilized during long-term selection in beef cattle breeding.

Indexed as

AlgorithmsGenomicsInbreedingSelection, GeneticAnimalsBreedingCattleFemaleGenomeGenotypeMaleModels, GeneticGenetic algorithmsGenetic gainGenomic matingHuaxi cattleSimulation

Identifiers

PMID39587475
PMCPMC11590262

What OpenQuestion holds

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Registered trials

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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.