Evidence map›Paper›PMID 40000888›Full record

ArticleCommunications biology2025

Improving multi-trait genomic prediction by incorporating local genetic correlations.

Jun Teng, Tingting Zhai, Xinyi Zhang, Changheng Zhao, Wenwen Wang, Hui Tang, Chao Ning, Yingli Shang, Dan Wang, Qin Zhang

Abstract read
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Indirect Genomic Predictions for Indicine Cattle Breeds With SNP Effects From a Multi-Breed Genomic Evaluation.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
    Article
  2. 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.

Jun TengShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China.
Tingting ZhaiNational Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural University, Tai'an, China.
Xinyi ZhangShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China.
Changheng ZhaoShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China.
Wenwen WangShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China.
Hui TangShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China.
Chao NingShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China.
Yingli ShangCollege of Veterinary Medicine, Shandong Agricultural University, Tai'an, China.ORCID http://orcid.org/0000-0001-5052-8346
Dan WangShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China. wangd_18@163.com.ORCID http://orcid.org/0000-0001-7789-6747
Qin ZhangShandong Provincial Key Laboratory for Livestock Germplasm Innovation & Utilization, College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China. qzhang@sdau.edu.cn.ORCID http://orcid.org/0000-0002-7551-5020

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32002172National Natural Science Foundation of China (National Science Foundation of China) 32370675Natural Science Foundation of Shandong Province (Shandong Provincial Natural Science Foundation) ZR2020QC175Natural Science Foundation of Shandong Province (Shandong Provincial Natural Science Foundation) ZR2020QC176
6 · The paper itself

Abstract

Genomic prediction holds significant potential for advancing precision medicine in humans, as well as accelerating genetic improvement in animals and plants. For multi-trait prediction, the conventional multi-trait models are primarily based on global genetic correlations between traits. With the development of local genetic correlation (LGC) estimation methods, it is now possible to analyze LGCs confined to specific genomic regions and it is expected that incorporating LGCs into multi-trait prediction model would enhance the prediction ability. Here, we proposed three models to address this issue and evaluated their performances using simulated data and three real datasets from human, cow, and pig populations. Our results demonstrate that LGCs are heterogeneous across the genome and incorporating LGCs in multi-trait prediction would increase the prediction accuracy by an average of 12.76% ± 2.07% compared to conventional multi-trait genomic prediction method (MTGBLUP) in the real datasets. Our findings highlight the importance of considering LGCs in improving multi-trait genomic prediction.

Indexed as

GenomicsModels, GeneticQuantitative Trait LociAnimalsCattleComputer SimulationHumansSwine

Identifiers

PMID40000888
PMCPMC11861333

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.