Evidence map›Paper›PMID 36971291›Full record

ArticleGigaScience2023

Resequencing of a Pekin duck breeding population provides insights into the genomic response to short-term artificial selection.

Simeng Yu, Zihua Liu, Ming Li, Dongke Zhou, Ping Hua, Hong Cheng, Wenlei Fan, Yaxi Xu, Dapeng Liu, Suyun Liang and 6 more

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In one paragraph

Article in GigaScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Unveiling the Genomic Landscape of Yan Goose (Animals : an open access journal from MDPI · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
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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

16 authors.

Simeng YuState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0003-1093-546X
Zihua LiuKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.
Ming LiKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.
Dongke ZhouKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.
Ping HuaKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.
Hong ChengKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.
Wenlei FanState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0003-0092-0512
Yaxi XuState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0001-9879-6147
Dapeng LiuState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0001-5865-4937
Suyun LiangState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Yunsheng ZhangState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Ming XieState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Jing TangState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0001-7653-8438
Yu JiangKey Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China.ORCID 0000-0003-4821-3585
Shuisheng HouState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Zhengkui ZhouState Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.ORCID 0000-0002-5192-2465

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundShort-term, intense artificial selection drives fast phenotypic changes in domestic animals and leaves imprints on their genomes. However, the genetic basis of this selection response is poorly understood. To better address this, we employed the Pekin duck Z2 pure line, in which the breast muscle weight was increased nearly 3-fold after 10 generations of breeding. We denovo assembled a high-quality reference genome of a female Pekin duck of this line (GCA_003850225.1) and identified 8.60 million genetic variants in 119 individuals among 10 generations of the breeding population.

resultsWe identified 53 selected regions between the first and tenth generations, and 93.8% of the identified variations were enriched in regulatory and noncoding regions. Integrating the selection signatures and genome-wide association approach, we found that 2 regions covering 0.36 Mb containing UTP25 and FBRSL1 were most likely to contribute to breast muscle weight improvement. The major allele frequencies of these 2 loci increased gradually with each generation following the same trend. Additionally, we found that a copy number variation region containing the entire EXOC4 gene could explain 1.9% of the variance in breast muscle weight, indicating that the nervous system may play a role in economic trait improvement.

conclusionsOur study not only provides insights into genomic dynamics under intense artificial selection but also provides resources for genomics-enabled improvements in duck breeding.

Indexed as

DNA Copy Number VariationsDucksAnimalsFemaleGenome-Wide Association StudyGenomicsPlant BreedingPolymorphism, Single NucleotideSelection, Geneticartificial selectionbreast muscle weightduckgenomeselection signatures

Identifiers

PMID36971291
PMCPMC10041536

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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 OpenQuestion graph.