Evidence map›Paper›PMID 34496773›Full record

ArticleGenetics, selection, evolution : GSE2021

A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens.

Christos Dadousis, Adriana Somavilla, Joanna J Ilska, Martin Johnsson, Lorena Batista, Richard J Mellanby, Denis Headon, Paolo Gottardo, Andrew Whalen, David Wilson and 4 more

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
5.4field-weighted citation impact, top 4% of its field
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

21 citing papers in PubMed, 39 citations in OpenAlex.

  1. Article
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  3. Review
  4. Veterinary world · 2025
    Review
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  8. Polymorphisms ofAnimals : an open access journal from MDPI · 2024
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  17. GoatAnimals : an open access journal from MDPI · 2023
    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

14 authors at 1 institution in 2 countries.

Christos DadousisThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Adriana SomavillaThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Joanna J IlskaThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Martin JohnssonThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Lorena BatistaThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Richard J MellanbyThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Denis HeadonThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Paolo GottardoItalian Brown Breeders Association, Loc. Ferlina 204, 37012, Bussolengo, Italy.
Andrew WhalenThe Roslin Institute, University of Edinburgh, Midlothian, UK.
David WilsonThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Ian C DunnThe Roslin Institute, University of Edinburgh, Midlothian, UK.
Gregor GorjancThe Roslin Institute, University of Edinburgh, Midlothian, UK.ORCID http://orcid.org/0000-0001-8008-2787
Andreas KranisThe Roslin Institute, University of Edinburgh, Midlothian, UK.
John M HickeyThe Roslin Institute, University of Edinburgh, Midlothian, UK. john.hickey@roslin.ed.ac.uk.
Roslin Institute · GB

Funding

Innovate UK 49521-361188
6 · The paper itself

Abstract

backgroundBody weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a "large" number of genes with "small" effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size.

methodsThe GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs.

resultsGWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 Gallus gallus autosomes (GGA1 to 4, 8, 10 to 15, 19 and 27), with the strongest associations on GGA4 at 65.67-66.31 Mb (Galgal4 assembly). The association of these regions points to several strong candidate genes including: (i) growth factors (GGA1, 4, 8, 13 and 14); (ii) leptin receptor overlapping transcript (LEPROT)/leptin receptor (LEPR) locus (GGA8), and the STAT3/STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene (TBX3/TBX5) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67-66.31 Mb).

conclusionsTo the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.

Indexed as

Genome-Wide Association StudyAnimalsBayes TheoremBody WeightChickensFemaleMultifactorial InheritanceTime Factors

Identifiers

PMID34496773
PMCPMC8424881
OpenAlexW3198137550

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.