Evidence map›Paper›PMID 41408151›Full record

SynthesisGenetics, selection, evolution : GSE2025

Multi-ancestry genome-wide association meta-analysis identifies candidate genes for computed tomography-based carcass composition traits in pigs.

He Han, Pengfei Yu, Zhenyang Zhang, Ran Wei, Wei Zhao, Xiaoliang Hou, Jianlan Wang, Yongqi He, Yan Fu, Zhen Wang and 3 more

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Genetics, selection, evolution : GSE, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

13 authors.

He HanZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Pengfei YuZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Zhenyang ZhangZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Ran WeiZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Wei ZhaoZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Xiaoliang HouSciGene Biotechnology Co., Ltd., Hefei, 230022, China.
Jianlan WangSciGene Biotechnology Co., Ltd., Hefei, 230022, China.
Yongqi HeSciGene Biotechnology Co., Ltd., Hefei, 230022, China.
Yan FuZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Zhen WangZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Yuchun PanZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Qishan WangZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China.
Zhe ZhangZhejiang Key Laboratory of nutrition and breeding for high-quality animal products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, China. zhe_zhang@zju.edu.cn.ORCID http://orcid.org/0000-0001-5320-3125

Funding

Key Research and Development Program of Zhejiang Province 2021C02068Key Technologies Research and Development Program 2023YFF1001100National Natural Science Foundation of China 32272832Natural Science Foundation of Zhejiang Province LZ23C170003
6 · The paper itself

Abstract

backgroundCarcass composition traits, such as lean meat percentage, bone percentage, and number of ribs, are critical factors determining meat production and profitability of pigs. Traditional slaughter measurements are time-consuming, labor-intensive and invasive and cannot be evaluated on selection candidates. However, computed tomography scanning, a non-invasive technique, enables in vivo measurement of these traits, facilitating rapid accumulation of extensive phenotypic data. Despite these advances, the genetic mechanisms underlying computed tomography-based carcass traits remain largely unexplored.

resultsIn this study, we performed a multi-ancestry genome-wide association meta-analysis (MA-GWAMA) using low-coverage whole-genome sequencing data from four breeds (1222 Duroc, 582 Landrace, 1018 Yorkshire, and 448 Piétrain). In total, we identified 11 independent genome-wide significant loci associated with carcass composition traits in the meta-analysis. Compared to standard genomic best linear unbiased prediction, weighting MA-GWAMA-significant SNPs increased genomic prediction accuracy in an independent population (N = 365, including 136 Duroc, 65 Landrace, 50 Piétrain, and 114 Yorkshire) by 16.3% for lean meat percentage, by 6.1% for bone percentage, and by 79.4% for number of ribs. Integrating MA-GWAMA results with public eQTL and single-cell data prioritized ALPK2 as a candidate gene for lean meat percentage, and ABCD4 and SLC8A3 as candidate genes for the number of ribs.

conclusionsOur study demonstrates the efficacy of computed tomography phenotyping coupled with multi-omics integration for dissecting the genetic architecture of porcine carcass composition traits. The prioritized variants and genes provide valuable targets for molecular breeding programs to enhance meat quality in pigs.

Indexed as

Body CompositionGenome-Wide Association StudyMeatSus scrofaAnimalsBreedingPhenotypePolymorphism, Single NucleotideQuantitative Trait LociSwineTomography, X-Ray Computed

Identifiers

PMID41408151
PMCPMC12784493

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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.