[Show abstract][Hide abstract] ABSTRACT: 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.
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.
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.
Preview · Article · Dec 2015 · Genetics Selection Evolution
[Show abstract][Hide abstract] ABSTRACT: The ANR UtOplGe project aims at assessing the value of genomic selection in pyramidal selection schemes, wherethe selection is conducted on a small size nucleus, before the wide dissemination of genetic progress throughsuccessive stages of multiplication and production. We present preliminary results in a selection scheme of layinghens and we also analyze possible interactions genotype x environment (G x E) through two diets differentiated onthe given energy leve! (Low Energy, BE with 2455 kea! vs. High Energy, HE with 2881 kea!). The referencepopulation consisted of 453 roosters of a pure li ne, genotyped with a high density chip (Aft)m etrix® Axiom® HD600k SNPs chip). Associated phenotypes were those of3 1,38ldaughters ofthese males, obtained by crossing withanother line, housed in multiple cages and equally allocated to one or the other of the two diets. The val idation wasbased on the genetic evaluation and selection within 551 males candidates, sons of the preceding ones. Thesecandidates were simply genotyped when selected and performance oftheir sisters were not yet available. Genomicindices were estimated according to the "Single Step" procedure. This analysis focused on egg weight at 65 weeksof age. While genetic parameters estimated with the markers information were quite similar to those obtained withthe conventional pedigree information in pure Iine, calculated genomic indices were significantly more accuratethan pedigree index, with a reliability (CD) twice as high for candidates known only on genotype. Moreover, foregg weight, no significant G x E interaction (estimated genetic correlation> 0.90) between diets was observed.These encouraging results support the establishment of a routine genomic selection in laying hens.
[Show abstract][Hide abstract] ABSTRACT: Abstract Text:
Taking advantage of the 600K Affymetrix® Axiom® HD genotyping array, the aim of this study is to detect QTL influencing egg quality traits in layers. A population of 438 sires from a pure line was genotyped. Their 31,381 F1 crossbred daughters were phenotyped for egg quality traits at 50 and 70 weeks of age. Moreover, to observe the putative existence of QTL by environment interactions, these layers were divided into 2 groups, fed with two different diets. The egg shell color and its components (redness, yellowness, lightness), the egg shell strength, stiffness and shape, the short length of egg and the egg weight were recorded. GWAS analyses have been showed 13,547 SNP with a significant (1% chromosome wide level) association with at least one egg quality trait. Thus, QTL have been detected and genotype by environment interactions have been observed.
[Show abstract][Hide abstract] ABSTRACT: Abstract Text:
This paper presents the preliminary results obtained during the implementation of a genomic evaluation for egg weight in laying hens. One originality of the project is to have used a population of crossbred hens to estimate GEBV of purebred sires. Moreover, hens were divided in 2 groups fed with 2 diets of Low Energy and High Energy. The genomic evaluation from performances of crossbred daughters is more accurate than traditional genetic evaluation in purebred. The interaction genotype x diet seems to be low.
[Show abstract][Hide abstract] ABSTRACT: Taking advantage of the 600K Affymet-rix® Axiom® HD genotyping array, the aim of this study is to detect QTL influencing egg quality traits in layers. A population of 438 sires from a pure line was genotyped. Their 31,381 F1 crossbred daughters were phenotyped for egg quality traits at 50 and 70 weeks of age. Moreover, to observe the putative existence of QTL by environment interactions, these layers were divided into 2 groups, fed with two different diets. The egg shell color and its components (redness, yellowness, lightness), the egg shell strength, stiffness and shape, the short length of egg and the egg weight were recorded. GWAS analyses have been showed 13,547 SNP with a significant (1% chromosome wide level) association with at least one egg quality trait. Thus, QTL have been detected and genotype by environment interactions have been observed.
[Show abstract][Hide abstract] ABSTRACT: Meat quality depends on skeletal muscle structure and metabolic properties. While most studies carried on pigs focus on the Longissimus muscle (LM) for fresh meat consumption, Semimembranosus (SM) is also of interest because of its importance for cooked ham production. Even if both muscles are classified as glycolytic muscles, they exhibit dissimilar myofiber composition and metabolic characteristics. The comparison of LM and SM transcriptome profiles undertaken in this study may thus clarify the biological events underlying their phenotypic differences which might influence several meat quality traits.
Muscular transcriptome analyses were performed using a custom pig muscle microarray: the 15 K Genmascqchip. A total of 3823 genes were differentially expressed between the two muscles (Benjamini-Hochberg adjusted P value ≤0.05), out of which 1690 and 2133 were overrepresented in LM and SM respectively. The microarray data were validated using the expression level of seven differentially expressed genes quantified by real-time RT-PCR. A set of 1047 differentially expressed genes with a muscle fold change ratio above 1.5 was used for functional characterization. Functional annotation emphasized five main clusters associated to transcriptome muscle differences. These five clusters were related to energy metabolism, cell cycle, gene expression, anatomical structure development and signal transduction/immune response.
This study revealed strong transcriptome differences between LM and SM. These results suggest that skeletal muscle discrepancies might arise essentially from different post-natal myogenic activities.
[Show abstract][Hide abstract] ABSTRACT: Coccidiosis is a major parasitic disease that causes huge economic losses to the poultry industry. Its pathogenicity leads to depression of body weight gain, lesions and, in the most serious cases, death in affected animals. Genetic variability for resistance to coccidiosis in the chicken has been demonstrated and if this natural resistance could be exploited, it would reduce the costs of the disease. Previously, a design to characterize the genetic regulation of Eimeria tenella resistance was set up in a Fayoumi x Leghorn F2 cross. The 860 F2 animals of this design were phenotyped for weight gain, plasma coloration, hematocrit level, intestinal lesion score and body temperature. In the work reported here, the 860 animals were genotyped for a panel of 1393 (157 microsatellites and 1236 single nucleotide polymorphism (SNP) markers that cover the sequenced genome (i.e. the 28 first autosomes and the Z chromosome). In addition, with the aim of finding an index capable of explaining a large amount of the variance associated with resistance to coccidiosis, a composite factor was derived by combining the variables of all these traits in a single variable. QTL detection was performed by linkage analysis using GridQTL and QTLMap. Single and multi-QTL models were applied.
Thirty-one QTL were identified i.e. 27 with the single-QTL model and four with the multi-QTL model and the average confidence interval was 5.9 cM. Only a few QTL were common with the previous study that used the same design but focused on the 260 more extreme animals that were genotyped with the 157 microsatellites only. Major differences were also found between results obtained with QTLMap and GridQTL.
The medium-density SNP panel made it possible to genotype new regions of the chicken genome (including micro-chromosomes) that were involved in the genetic control of the traits investigated. This study also highlights the strong variations in QTL detection between different models and marker densities.
Full-text · Article · Feb 2014 · Genetics Selection Evolution
[Show abstract][Hide abstract] ABSTRACT: Better understanding of the mechanisms underlying interindividual variation in stress responses and their links with production traits is a key issue for sustainable animal breeding. In this study, we searched for quantitative trait loci (QTL) controlling the magnitude of the plasma cortisol stress response and compared them to body size traits in five F2 full-sib families issued from two rainbow trout lines divergently selected for high or low post-confinement plasma cortisol level. Approximately 1000 F2 individuals were individually tagged and exposed to two successive acute confinement challenges (1 month interval). Post-stress plasma cortisol concentrations were determined for each fish. A medium density genome scan was carried out (268 markers, overall marker spacing less than 10 cM). QTL detection was performed using qtlmap software, based on an interval mapping method (http://www.inra.fr/qtlmap). Overall, QTL of medium individual effects on cortisol responsiveness (<10% of phenotypic variance) were detected on 18 chromosomes, strongly supporting the hypothesis that control of the trait is polygenic. Although a core array of QTL controlled cortisol concentrations at both challenges, several QTL seemed challenge specific, suggesting that responses to the first and to a subsequent exposure to the confinement stressor are distinct traits sharing only part of their genetic control. Chromosomal location of the steroidogenic acute regulatory protein (STAR) makes it a good potential candidate gene for one of the QTL. Finally, comparison of body size traits QTL (weight, length and body conformation) with cortisol-associated QTL did not support evidence for negative genetic relationships between the two types of traits.
[Show abstract][Hide abstract] ABSTRACT: Genomic selection is based on an evaluation of the genetic values of the candidates for selection through a «molecular score» calculated from their genotypes for a great number of DNA markers. A first step consists in estimating the effects of the markers on a reference population that has been genotyped and phenotyped for the traits that need improving. The establishment of this table of genotypic values allows for the calculation of the genetic values of the candidates in the subsequent generations, for which genotyping might then prove sufficient. This strategy was implemented in dairy cattle in just a few years. The operators in charge of selection in the other animal production sectors have witnessed this change and hence legitimately regard this approach as useful input for them. The existence of a 600k SNP chip for the chicken now allows for the application of genomic selection in layers and broilers. Tools for high-throughput genotyping in other poultry species are being developed. There are three components of genetic progress that can be improved, regardless of the species. First, selection intensity can be increased for traits non-measurable in routine (product quality, feed efficiency, resistance to diseases). Then, the accuracy of genetic values can be improved, especially for males whenever traits are expressed by females only. Finally, the generation interval can be reduced through early evaluation of candidates (egg production). What is more, genomic evaluation provides, for the first time, the opportunity to select pure-bred individuals from the selection nucleus for their usefulness in crossbreeding and production. The implementation costs of genomic selection are quite high. This is due to the size of the reference population needed for accurate evaluation, the huge number of genotyping operations to be carried out on candidates and the great diversity of populations to be selected. However, use of large numbers of breeding animals in the poultry industry could help counterbalance these costs.
[Show abstract][Hide abstract] ABSTRACT: For decades, genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. However, this conventional approach is hindered by antagonistic genetic correlations between some traits and the high cost of measuring body composition traits. Marker-assisted selection should overcome these problems by selecting loci that have effects on either one trait only or on more than one trait but with a favorable genetic correlation. In the present study, identification of such loci was done by genotyping an F2 intercross between fat and lean lines divergently selected for abdominal fatness genotyped with a medium-density genetic map (120 microsatellites and 1302 single nucleotide polymorphisms). Genome scan linkage analyses were performed for growth (body weight at 1, 3, 5, and 7 weeks, and shank length and diameter at 9 weeks), body composition at 9 weeks (abdominal fat weight and percentage, breast muscle weight and percentage, and thigh weight and percentage), and for several physiological measurements at 7 weeks in the fasting state, i.e. body temperature and plasma levels of IGF-I, NEFA and glucose. Interval mapping analyses were performed with the QTLMap software, including single-trait analyses with single and multiple QTL on the same chromosome.
Sixty-seven QTL were detected, most of which had never been described before. Of these 67 QTL, 47 were detected by single-QTL analyses and 20 by multiple-QTL analyses, which underlines the importance of using different statistical models. Close analysis of the genes located in the defined intervals identified several relevant functional candidates, such as ACACA for abdominal fatness, GHSR and GAS1 for breast muscle weight, DCRX and ASPSCR1 for plasma glucose content, and ChEBP for shank diameter.
The medium-density genetic map enabled us to genotype new regions of the chicken genome (including micro-chromosomes) that influenced the traits investigated. With this marker density, confidence intervals were sufficiently small (14 cM on average) to search for candidate genes. Altogether, this new information provides a valuable starting point for the identification of causative genes responsible for important QTL controlling growth, body composition and metabolic traits in the broiler chicken.
[Show abstract][Hide abstract] ABSTRACT: Abstract Mapping quantitative trait loci (QTL) using genetic marker information is a time-consuming analysis that has interested the mapping community in recent decades. The increasing amount of genetic marker data allows one to consider ever more precise QTL analyses while increasing the demand for computation. Part of the difficulty of detecting QTLs resides in finding appropriate critical values or threshold values, above which a QTL effect is considered significant. Different approaches exist to determine these thresholds, using either empirical methods or algebraic approximations. In this article, we present a new implementation of existing software, QTLMap, which takes advantage of the data parallel nature of the problem by offsetting heavy computations to a graphics processing unit (GPU). Developments on the GPU were implemented using Cuda technology. This new implementation performs up to 75 times faster than the previous multicore implementation, while maintaining the same results and level of precision (Double Precision) and computing both QTL values and thresholds. This speedup allows one to perform more complex analyses, such as linkage disequilibrium linkage analyses (LDLA) and multiQTL analyses, in a reasonable time frame.
Preview · Article · Sep 2013 · Journal of computational biology: a journal of computational molecular cell biology
[Show abstract][Hide abstract] ABSTRACT: Longissimus lumborum (LM) and semimembranosus (SM) are used for different meat consumption. Both are classified as glycolytic muscles but have different myofiber composition and metabolic properties. Compare LM and SM transcriptome profiles may clarify the biological events which could explain their phenotypic differences. The 90 pigs used in this study were produced as an inter-cross between 2 commercial sire lines. Muscle samples were collected 20 minutes post-mortem, snap frozen and used for total RNA isolation. Transcriptome analysis was undertaken using a pig muscle microarray: the 15K Genmascqchip. Analyses were performed using R software. Raw data were submitted to quality filtration and normalization. Probes with the smallest expression variability were filtered out. Normalized data were analyzed using a linear model of variance taking into account fixed effects of slaughter date, sex, sire and muscle. Carcass weight was used as a covariate. Genes wh ich were differentially expressed between muscles were clustered according to their semantic similarities. Semantic similarities were computed according to Wang’s method using Gene Ontology (GO) Biological Process (BP) terms. Thus, functional characterizations of genes clusters were performed with WebGestalt using GO BP terms. A total of 3,867 genes were differentially expressed between the 2 muscles, out of which 1,729 and 2,138 were over-represented respectively in LM and in SM. A set of 1,047 differentially expressed genes with a muscle fold change ratio above 1.5 was used for functional characterization. Five clusters related to energy metabolism, cell cycle, gene expression, anatomical structure development and signal transduction/immune response were identified. These results shed light on differential transcriptome profiles between LM and SM. This variability could affect muscle development and hence meat quality.