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 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.
INRA, UMR1348 PEGASE, Domaine de La Prise, 35590, Saint-Gilles, France. helene.rome@rennes.inra.fr.|Agrocampus Ouest, UMR1348 PEGASE, 65 Rue de Saint Brieuc, 35042, Rennes, France. helene.rome@rennes.inra.fr.
Hélène
H
Romé
Novogen, Mauguérand, 22800, Le Foeil, France. amandine.varenne@novogen-layers.com.
Amandine
A
Varenne
INRA, UMR1348 PEGASE, Domaine de La Prise, 35590, Saint-Gilles, France. frederic.herault@rennes.inra.fr.|Agrocampus Ouest, UMR1348 PEGASE, 65 Rue de Saint Brieuc, 35042, Rennes, France. frederic.herault@rennes.inra.fr.
Frédéric
F
Hérault
SYSAAF, INRA UR83 Recherches Avicoles, 37380, Nouzilly, France. herve.chapuis@tours.inra.fr.
Hervé
H
Chapuis
Zootests, Parc Technologique Du Zoopôle, 5 Rue Gabriel Calloet Kerbrat, 22440, Ploufragan, France. christophe.alleno@zootests.fr.
Christophe
C
Alleno
INRA, UMR1388 GenPhySe, Auzeville BP52627, 31326, Castanet-Tolosan, France. patrice.dehais@toulouse.inra.fr.
Patrice
P
Dehais
INRA, UMR1388 GenPhySe, Auzeville BP52627, 31326, Castanet-Tolosan, France. alain.vignal@stoulouse.inra.fr.
Alain
A
Vignal
Novogen, Mauguérand, 22800, Le Foeil, France. thierry.burlot@novogen-layers.com.
Thierry
T
Burlot
INRA, UMR1348 PEGASE, Domaine de La Prise, 35590, Saint-Gilles, France. pascale.leroy@rennes.inra.fr.|Agrocampus Ouest, UMR1348 PEGASE, 65 Rue de Saint Brieuc, 35042, Rennes, France. pascale.leroy@rennes.inra.fr.
Pascale
P
Le Roy
D000818:Animals; D002645:Chickens / Q000235:genetics / Q000502:physiology*; D002874:Chromosome Mapping; D004032:Diet; D005260:Female; D059647:Gene-Environment Interaction; D055106:Genome-Wide Association Study; D052287:Oviparity; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci