Cargando…

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

BACKGROUND: Body 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 associa...

Descripción completa

Detalles Bibliográficos
Autores principales: Dadousis, Christos, Somavilla, Adriana, Ilska, Joanna J., Johnsson, Martin, Batista, Lorena, Mellanby, Richard J., Headon, Denis, Gottardo, Paolo, Whalen, Andrew, Wilson, David, Dunn, Ian C., Gorjanc, Gregor, Kranis, Andreas, Hickey, John M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424881/
https://www.ncbi.nlm.nih.gov/pubmed/34496773
http://dx.doi.org/10.1186/s12711-021-00663-w
_version_ 1783749747768557568
author Dadousis, Christos
Somavilla, Adriana
Ilska, Joanna J.
Johnsson, Martin
Batista, Lorena
Mellanby, Richard J.
Headon, Denis
Gottardo, Paolo
Whalen, Andrew
Wilson, David
Dunn, Ian C.
Gorjanc, Gregor
Kranis, Andreas
Hickey, John M.
author_facet Dadousis, Christos
Somavilla, Adriana
Ilska, Joanna J.
Johnsson, Martin
Batista, Lorena
Mellanby, Richard J.
Headon, Denis
Gottardo, Paolo
Whalen, Andrew
Wilson, David
Dunn, Ian C.
Gorjanc, Gregor
Kranis, Andreas
Hickey, John M.
author_sort Dadousis, Christos
collection PubMed
description BACKGROUND: Body 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. METHODS: The 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. RESULTS: GWAS 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). CONCLUSIONS: To 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-021-00663-w.
format Online
Article
Text
id pubmed-8424881
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-84248812021-09-10 A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens Dadousis, Christos Somavilla, Adriana Ilska, Joanna J. Johnsson, Martin Batista, Lorena Mellanby, Richard J. Headon, Denis Gottardo, Paolo Whalen, Andrew Wilson, David Dunn, Ian C. Gorjanc, Gregor Kranis, Andreas Hickey, John M. Genet Sel Evol Research Article BACKGROUND: Body 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. METHODS: The 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. RESULTS: GWAS 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). CONCLUSIONS: To 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12711-021-00663-w. BioMed Central 2021-09-08 /pmc/articles/PMC8424881/ /pubmed/34496773 http://dx.doi.org/10.1186/s12711-021-00663-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Dadousis, Christos
Somavilla, Adriana
Ilska, Joanna J.
Johnsson, Martin
Batista, Lorena
Mellanby, Richard J.
Headon, Denis
Gottardo, Paolo
Whalen, Andrew
Wilson, David
Dunn, Ian C.
Gorjanc, Gregor
Kranis, Andreas
Hickey, John M.
A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
title A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
title_full A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
title_fullStr A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
title_full_unstemmed A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
title_short A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
title_sort genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424881/
https://www.ncbi.nlm.nih.gov/pubmed/34496773
http://dx.doi.org/10.1186/s12711-021-00663-w
work_keys_str_mv AT dadousischristos agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT somavillaadriana agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT ilskajoannaj agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT johnssonmartin agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT batistalorena agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT mellanbyrichardj agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT headondenis agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT gottardopaolo agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT whalenandrew agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT wilsondavid agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT dunnianc agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT gorjancgregor agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT kranisandreas agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT hickeyjohnm agenomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT dadousischristos genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT somavillaadriana genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT ilskajoannaj genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT johnssonmartin genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT batistalorena genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT mellanbyrichardj genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT headondenis genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT gottardopaolo genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT whalenandrew genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT wilsondavid genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT dunnianc genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT gorjancgregor genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT kranisandreas genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens
AT hickeyjohnm genomewideassociationanalysisforbodyweightat35daysmeasuredon137343broilerchickens