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GWAS analyses reveal QTL in egg layers that differ in response to diet differences

BACKGROUND: The genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identifie...

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Autores principales: Romé, Hélène, Varenne, Amandine, Hérault, Frédéric, Chapuis, Hervé, Alleno, Christophe, Dehais, Patrice, Vignal, Alain, Burlot, Thierry, Le Roy, Pascale
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617898/
https://www.ncbi.nlm.nih.gov/pubmed/26482360
http://dx.doi.org/10.1186/s12711-015-0160-2
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author Romé, Hélène
Varenne, Amandine
Hérault, Frédéric
Chapuis, Hervé
Alleno, Christophe
Dehais, Patrice
Vignal, Alain
Burlot, Thierry
Le Roy, Pascale
author_facet Romé, Hélène
Varenne, Amandine
Hérault, Frédéric
Chapuis, Hervé
Alleno, Christophe
Dehais, Patrice
Vignal, Alain
Burlot, Thierry
Le Roy, Pascale
author_sort Romé, Hélène
collection PubMed
description BACKGROUND: The genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identified. The availability of a high-density SNP (single nucleotide polymorphism) chicken array developed by Affymetrix, i.e. the 600K Affymetrix(®) Axiom(®) HD genotyping array offers the possibility to narrow down the localization of previously detected QTL and to detect new QTL. This high-density array is also anticipated to take research beyond the classical hypothesis of additivity of QTL effects or of QTL and environmental effects. The aim of our study was to search for QTL that influence laying traits using the 600K SNP chip and to investigate whether the effects of these QTL differed between diets and age at egg collection. RESULTS: One hundred and thirty-one QTL were detected for 16 laying traits and were spread across all marked chromosomes, except chromosomes 16 and 25. The percentage of variance explained by a QTL varied from 2 to 10 % for the various traits, depending on diet and age at egg collection. Chromosomes 3, 9, 10 and Z were overrepresented, with more than eight QTL on each one. Among the 131 QTL, 60 had a significantly different effect, depending on diet or age at egg collection. For egg production traits, when the QTL × environment interaction was significant, numerous inversions of sign of the SNP effects were observed, whereas for egg quality traits, the QTL × environment interaction was mostly due to a difference of magnitude of the SNP effects. CONCLUSIONS: Our results show that numerous QTL influence egg production and egg quality traits and that the genomic regions, which are involved in shaping the ability of layer chickens to adapt to their environment for egg production, vary depending on the environmental conditions. The next question will be to address what the impact of these genotype × environment interactions is on selection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-015-0160-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-46178982015-10-25 GWAS analyses reveal QTL in egg layers that differ in response to diet differences Romé, Hélène Varenne, Amandine Hérault, Frédéric Chapuis, Hervé Alleno, Christophe Dehais, Patrice Vignal, Alain Burlot, Thierry Le Roy, Pascale Genet Sel Evol Research Article BACKGROUND: The genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identified. The availability of a high-density SNP (single nucleotide polymorphism) chicken array developed by Affymetrix, i.e. the 600K Affymetrix(®) Axiom(®) HD genotyping array offers the possibility to narrow down the localization of previously detected QTL and to detect new QTL. This high-density array is also anticipated to take research beyond the classical hypothesis of additivity of QTL effects or of QTL and environmental effects. The aim of our study was to search for QTL that influence laying traits using the 600K SNP chip and to investigate whether the effects of these QTL differed between diets and age at egg collection. RESULTS: One hundred and thirty-one QTL were detected for 16 laying traits and were spread across all marked chromosomes, except chromosomes 16 and 25. The percentage of variance explained by a QTL varied from 2 to 10 % for the various traits, depending on diet and age at egg collection. Chromosomes 3, 9, 10 and Z were overrepresented, with more than eight QTL on each one. Among the 131 QTL, 60 had a significantly different effect, depending on diet or age at egg collection. For egg production traits, when the QTL × environment interaction was significant, numerous inversions of sign of the SNP effects were observed, whereas for egg quality traits, the QTL × environment interaction was mostly due to a difference of magnitude of the SNP effects. CONCLUSIONS: Our results show that numerous QTL influence egg production and egg quality traits and that the genomic regions, which are involved in shaping the ability of layer chickens to adapt to their environment for egg production, vary depending on the environmental conditions. The next question will be to address what the impact of these genotype × environment interactions is on selection. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-015-0160-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-19 /pmc/articles/PMC4617898/ /pubmed/26482360 http://dx.doi.org/10.1186/s12711-015-0160-2 Text en © Romé et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Romé, Hélène
Varenne, Amandine
Hérault, Frédéric
Chapuis, Hervé
Alleno, Christophe
Dehais, Patrice
Vignal, Alain
Burlot, Thierry
Le Roy, Pascale
GWAS analyses reveal QTL in egg layers that differ in response to diet differences
title GWAS analyses reveal QTL in egg layers that differ in response to diet differences
title_full GWAS analyses reveal QTL in egg layers that differ in response to diet differences
title_fullStr GWAS analyses reveal QTL in egg layers that differ in response to diet differences
title_full_unstemmed GWAS analyses reveal QTL in egg layers that differ in response to diet differences
title_short GWAS analyses reveal QTL in egg layers that differ in response to diet differences
title_sort gwas analyses reveal qtl in egg layers that differ in response to diet differences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617898/
https://www.ncbi.nlm.nih.gov/pubmed/26482360
http://dx.doi.org/10.1186/s12711-015-0160-2
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