Evidence map›Paper›PMID 42036637›Full record

ArticleGenetics, selection, evolution : GSE2026

Integrating GWAS and eQTL analysis to decipher genetic mechanisms of feed efficiency and feeding behaviors in pigs.

Zhenyang Zhang, Yongqi He, Wei Zhao, Ran Wei, He Han, Quanjun Zhan, Pengfei Yu, Sisi Li, Xiaoliang Hou, Jianlan Wang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetics, selection, evolution : GSE, 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Zhenyang ZhangZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Yongqi HeSciGene Biotechnology Co., Ltd, 1111# Luzhou Avenue, Hefei, 230000, People's Republic of China.
Wei ZhaoZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Ran WeiZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
He HanZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Quanjun ZhanZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Pengfei YuZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Sisi LiSchool of Agriculture and Biology, Shanghai Jiao Tong University, 800# Dongchuan Road, Shanghai, 200240, People's Republic of China.
Xiaoliang HouSciGene Biotechnology Co., Ltd, 1111# Luzhou Avenue, Hefei, 230000, People's Republic of China.
Jianlan WangSciGene Biotechnology Co., Ltd, 1111# Luzhou Avenue, Hefei, 230000, People's Republic of China.
Qingbo ZhaoCollege of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, People's Republic of China.
Yan FuSciGene Biotechnology Co., Ltd, 1111# Luzhou Avenue, Hefei, 230000, People's Republic of China.
Zitao ChenZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Zhen WangZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Yuchun PanZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China.
Qishan WangZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China. wangqishan@zju.edu.cn.
Zhe ZhangZhejiang Key Laboratory of Nutrition and Breeding for High-Quality Animal Products, College of Animal Sciences, Zhejiang University, Hangzhou, 310058, People's Republic of China. zhe_zhang@zju.edu.cn.ORCID http://orcid.org/0000-0001-5320-3125

Funding

National Key Research and Development Program of China 2022YFF1000500National Key Research and Development Program of China 2023YFD1300404National Key Research and Development Program of China 2023YFF1001100National Natural Science Foundation of China 32172691National Natural Science Foundation of China 32272832National Natural Science Foundation of China 32272833Sanya Science and Technology Innovation Project 2022KJCX49Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province ZDYF2024XDNY186
6 · The paper itself

Abstract

backgroundFeed constitutes the largest cost in pig farming. However, the genetic mechanisms underlying feeding behavior (FB) and feed efficiency (FE), as well as their relationships, remain poorly understood. This study aims to: (1) identify genetic variants associated with FB, FE, and production traits, while improving genomic prediction (GP) accuracy; (2) investigate the relationships between FB, FE, and production traits; and (3) explore potential links between pig feeding-related traits and human health traits.

resultsA total of 358 lead SNPs associated with 28 feeding-related traits were identified through genome-wide association studies (GWAS) in 6938 genotyped pigs. In addition, GP was applied to an independent later-born population to validate the reliability of the GWAS summary statistics. By selection SNPs based on the P-values and linkage disequilibrium (LD), GP accuracy was improved by an average of 13.4%. Furthermore, a pleiotropic QTL located at SSC1:159,658,890–160,953,032 was identified for FB, FE, and production traits. Utilizing the cis-eQTL data from 34 tissues, seven trait-tissue-gene associations were identified. Additionally, mendelian randomization analysis indicated that increased feed intake per visit was genetically correlated with both FE and average daily gain, and eating more in the night might reduce lean meat percentage. Notably, heritability enrichment analysis revealed that GWAS signals for pig feeding behavior traits were significantly enriched in genomic regions associated with human fat intake–related traits.

conclusionThis study identified genetic variants associated with FE, FB, and production traits, and clarified their causal relationships. It represents the first elucidation of the connection between pig feeding traits and human traits. These insights are crucial for molecular breeding in pigs and for evaluating the suitability of pigs as model organisms in biomedical research.

Indexed as

Feeding BehaviorGenome-Wide Association StudyQuantitative Trait LociSus scrofaAnimal FeedAnimalsLinkage DisequilibriumPhenotypePolymorphism, Single NucleotideSwine

Identifiers

PMID42036637
PMCPMC13262426

What Socratic holds

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LicenceCC BY
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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 Socratic graph.