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The cuticle is an invisible barrier that protects the internal egg contents from microorganisms entering through gas exchange pores. Eggs which have a good cuticle are least likely to be penetrated by microorganisms and improved cuticle cover should reduce vertical transmission of microorganisms and improve biosecurity. The aim was to carry out a genome wide association study for cuticle deposition in 3 independent populations of laying hens using tartrazine and lissamine green staining. Eggs from ∼8,000 hens represented 2 White Leghorn and 1 Rhode Island Red breed. Estimates of heritability using pedigree or genomic relationship matrices were in the 0.2 to 0.3 range. The results were breed specific. Across the populations, genomic regions on chromosomes 1, 2, 4, 5, and 8 were identified as significantly associated with cuticle deposition. No single loci had a large effect. A comparison was made with genes differentially expressed in the shell gland when cuticle deposition was manipulated, however none were obvious candidates for cuticle deposition. The results support the polygenic nature of the trait and the information will help in the future to understand the genetic variance and what might control cuticle deposition and the microbiological safety of the egg., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines., Results: The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were - 0.26 ± 0.14 and - 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability., Conclusions: In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens., (Copyright © 2020. Published by Elsevier Inc.)

Egg production on a flock level can be summarized into several phases determined by biology of individual birds: rapid increase in production reflecting achieving sexual maturity, peak production related to maximum laying potential, followed by gradual decrease in the rate of lay as the birds age. In 1989 Yang et al. proposed a mathematical model (modified compartmental model) to describe this process. In this study a biphasic modified compartmental model was proposed for modeling, classifying, and predicting egg production in single cycle and molted flocks. Goodness-of-fit was high for both single cycle (average R 2 = 0.99) and molted flocks (average R 2 = 0.97), suggesting that the model could be used for benchmarking molted flocks. The difference in R 2 between the biphasic model and the model used by Yang et al in 1989 can be used to differentiate between single cycle and molted flocks. The biphasic model was shown to predict future records well up to 8 wk in advance, but as with any regression model, caution is recommended when predicting records outside of the observed age range., (Copyright © 2019. Published by Elsevier Inc.)

The ability to produce viable progeny is a complex trait, involving both male and female components. In poultry, mating ratios are usually 1 male to 6 to 12 females. Consequently, the impact of male reproductive failure is much greater than that for a female. In this study, the genetic determination of male reproductive performance, by natural mating and artificial insemination (AI), was evaluated. Semen quality was studied in 1,575 pre-selected (using a selection index of multiple egg production and quality traits) White Leghorn males of a single pure line from multiple generations. A subset of individuals with satisfactory semen quality (based on sperm count and motility) were further tested for subsequent fertility and hatchability. Genetic parameters for fertility (FER), hatch of fertile (HOF), hatch of set (HOS), sperm motility (SM), sperm count (SC), and fertility using AI (FER-AI) were estimated using single- and multi-trait animal models, with generation as fixed effect. Selected birds were genotyped using the 600K Affymetrix SNP chip. Genomic data were analyzed with the BayesB method. FER, HOS, and HOF were highly correlated, both genetically (0.82 to 0.99) and phenotypically (0.28 to 0.99), but genetic correlations with semen quality traits were not strong (0.05 to 0.43) and phenotypic correlations varied between generations (-0.13 to 0.14). Birds used for fertility and hatchability tests were pre-selected based on SM and SC, which could contribute to the lack of strong correlations between these traits (due to truncation of the distribution). Based on pedigree information, low to moderate heritabilities were estimated for reproductive traits (0.08 to 0.21). Markers explained a low proportion of phenotypic variance (0.04 to 0.15), probably due to stringent selection of genotyped individuals and the limited training set size. No genes with large effects were identified. Genomic estimated breeding values were more accurate than pedigree-based estimates but only for HOF and FERT-AI. Despite low estimates of accuracy in validation, genetic trends were positive for all analyzed traits. In conclusion, continued long-term selection can result in genetic improvement of reproductive performance of roosters., (© 2019 Poultry Science Association Inc.)

Clutch traits were proposed as a more detailed description of egg-laying patterns than simple total egg production. In this study, egg production of 23,809 Rhode Island Red (RIR) and 22,210 White Leghorn (WL) hens was described in terms of number of clutches, average and maximum clutch size, age at first egg, total saleable egg production, and percentage of egg defects. Genetic parameters were estimated using a six-trait animal model. Of the phenotyped birds, 1433 RIR hens and 1515 WL hens were genotyped with line specific 50K Affymetrix Axiom single nucleotide polymorphism chips to perform genome-wide association analyses. Moderate heritabilities were estimated for clutch traits of 0.20 to 0.42 in the RIR line and 0.29 to 0.41 in the WL line. Average and maximum clutch size was positively genetically correlated with total saleable egg number in both lines. Genome-wide association analysis identified seven regions that were associated with egg production in the RIR line and 12 regions in the WL line. The regions identified were line and trait specific, except for one region on chromosome 6 from 28 to 29 Mb that influenced number of clutches and maximum and average clutch size in WL hens. Regions associated with egg production identified here overlapped with 260 genes, with some strong positional candidates based on gene ontology including WASH1, which is involved in oocyte maturation, NPVF, involved in regulation of follicle-stimulating hormone secretion, and FOXO3, involved in oocyte maturation and ovulation from the ovarian follicle. Confirmation of the role of these genes in regulation of egg production pattern will require further studies.

An outbreak of H5N2 highly pathogenic avian influenza (HPAI) in 2015, resulting in mandatory euthanization of millions of chickens, was one of the most fatal in the US history. The aim of this study was to detect genes associated with survival following natural infection with HPAI during this outbreak. Blood samples were collected from 274 individuals from 3 commercial varieties of White Leghorn. Survivors and age and genetics matched non-affected controls from each variety were included in the comparison. All individuals were genotyped on the 600k SNP array. A genome-wide association study (GWAS) with the standard frequency test in PLINK was performed within each variety, whereas logistic regression with the first 3 multidimensional scaling components as covariates was used for joined analysis of all varieties. Several SNPs located within 3 regions reached the 5% Bonferroni genome-wide threshold of significance (P < 3.87E-06). The associations were identified for 2 varieties and only within genetic variety on chromosomes 11 (variety 1), 5, and 18 (variety 3). A genome-wide scan with FST was also performed for 40, 100, and 500 kb windows to support the genome-wide association analyses. The regions with highest FST values between cases and controls were located on chromosomes 1 and Z, and overlapped a number of genes with immunological function and QTL connected to health. Only a few regions were consistent between the analyses, and were significant in the FST genome-wide scan and approaching significance in GWAS. This study confirms that resistance to HPAI is a complex, polygenic trait and that mechanisms of resistance may be population specific. Further study utilizing much larger sample sizes and/or sequence data is needed to detect genes responsible for HPAI survival.

Compromised eggshell quality causes considerable economic losses for the egg industry. Breeding for improved eggshell quality has been very challenging. Eggshell quality is a trait that would greatly benefit from marker-assisted selection, which would allow the selection of sires for their direct contribution to the trait and would also allow implementation of measurements integrating a number of shell parameters that are difficult to measure. In this study, we selected the most promising autosomal quantitative trait loci (QTL) affecting eggshell quality on chromosomes 2, 3, 6 and 14 from earlier experiments and we extended the F 2 population to include 1599 F 2 females. The study was repeated on two commercial populations: Lohmann Tierzucht Rhode Island Red line (n = 692 females) and a Hy-Line White Plymouth Rock line (n = 290 progeny tested males). We analyzed the selected autosomal QTL regions on the three populations with SNP markers at 4-13 SNPs/Mb density. QTL for eggshell quality were replicated on all studied regions in the F 2 population. New QTL were detected for eggshell color on chromosomes 3 and 6. Marker associations with eggshell quality traits were validated in the tested commercial lines on chromosomes 2, 3 and 6, thus paving the way for marker-assisted selection for improved eggshell quality., (© 2018 Stichting International Foundation for Animal Genetics.)

Two highly pathogenic avian influenza (HPAI) outbreaks have affected commercial egg production flocks in the American continent in recent years; a H7N3 outbreak in Mexico in 2012 that caused 70% to 85% mortality and a H5N2 outbreak in the United States in 2015 with over 99% mortality. Blood samples were obtained from survivors of each outbreak and from age and genetics matched non-affected controls. A total of 485 individuals (survivors and controls) were genotyped with a 600 k single nucleotide polymorphism (SNP) array to detect genomic regions that influenced the outcome of highly pathogenic influenza infection in the two outbreaks. A total of 420458 high quality, segregating SNPs were identified across all samples. Genetic differences between survivors and controls were analyzed using a logistic model, mixed models and a Bayesian variable selection approach. Several genomic regions potentially associated with resistance to HPAI were identified, after performing multidimensional scaling and adjustment for multiple testing. Analysis conducted within each outbreak identified different genomic regions for resistance to the two virus strains. The strongest signals for the Iowa H5N2 survivor samples were detected on chromosomes 1, 7, 9 and 15. Positional candidate genes were mainly coding for plasma membrane proteins with receptor activity and were also involved in immune response. Three regions with the strongest signal for the Mexico H7N3 samples were located on chromosomes 1 and 5. Neuronal cell surface, signal transduction and immune response proteins coding genes were located in the close proximity of these regions.

Shell quality is one of the most important traits for improvement in layer chickens. Proper consideration of repeated records can increase the accuracy of estimated breeding values and thus genetic improvement of shell quality. The objective of this study was to compare different models for genetic evaluation of the collected data. For this study, 81,646 dynamic stiffness records on 21,321 brown egg layers and 93,748 records on 24,678 white egg layers from 4 generations were analyzed. Across generations, data were collected at 2 to 4 ages (at approximately 26, 42, 65, and 86 wk), with repeated records at each age. Seven models were compared, including 5 repeatability models with increasing complexity, a random regression model, and a multitrait model. The models were compared using Akaike Information Criteria with significance testing of nested models with a Log Likelihood Ratio test. Estimates of heritability were 0.31-0.36 for the brown line and 0.23-0.26 for the white line, but repeatability was higher for the model with age-specific permanent environment effects (0.59 for both lines) than for the model with an overall permanent environmental effects (0.47 for the brown and 0.41 for the white line). The model that allowed for permanent environmental effect within age and heterogeneous residual variance between ages resulted in improved fit compared to the traditional model that fits single permanent environment and residual effects, but was inferior in fit and predictive ability to the full multiple-trait model. The random regression model had better fit to the data than repeatability models but slightly worse than the multiple-trait model. For traits with repeated records at different ages, repeatability within and across ages as well as genetic correlations should be considered while choosing the number of records collected per individual as well as the model for genetic evaluation.

Reducing the incidence of egg shell breakage is an important selection goal in egg layer hens breeding. Breaking strength provides an indicator of static shell resistance correlated with shell thickness. Acoustic egg tests combine shell's resonance profile with egg mass to calculate dynamic stiffness (KDyn) a quantitative indicator of integral shell resistance, and a novel direct detection of both cracks and micro-cracks (MCr) making it possible for use in selection programs aiming improvement of shell quality. A shell quality bio-complex was defined to improve overall shell quality, including: breaking strength at equator (BSe) and poles (BSp), KDyn, and MCr, on multiple eggs/hen-age. A total of 81,667; 101,113; and 72,462 records from 4 generations of three pure lines were evaluated. Two models were tested in the brown-egg line: I) four-trait linear repeatability model and II) three-trait linear (BS, KDyn)-threshold (MCr) in the three lines. Models were implemented with AIREMLF90 and THRGIBBS1F90. Heritability and repeatability (Model I) estimates were: h 2 = 0.14, 0.18, 0.33, and 0.02; r = 0.16, 0.28, 0.43, and 0.03 for BSe, BSp, KDyn, and MCr, respectively. Corresponding values in White Plymouth Rock were h 2 = 0.14, 0.17, 0.33, and 0.02; r = 0.21, 0.33, 0.44, and 0.04, and in White Leghorn were h 2 = 0.14, 0.23, 0.36, and 0.02; r = 0.24, 0.38, 0.52, and 0.02. Genetic correlations between BSe and BSp were between 0.51 and 0.68. The BS traits were moderately correlated with KDyn (+0.23 to +0.51), and tended to be negatively correlated with MCr. KDyn, and MCr (-0.46 to -0.62). Model II had similar results; except for increased h 2 = 0.06 and r = 0.09 for MCr. Results indicate that BSe and BSp are different traits; while incidence of MCr is low in heritable but showed negative genetic correlations with the other traits. This makes MCr unsuitable for direct selection; but favors indirect selection against MCr via BSe, BSp, and KDyn for a holistic selection to improve shell quality, in particular to achieve the ultimate goal, reduction of egg breakage., (© 2016 Poultry Science Association Inc.)

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population., (© 2016 Blackwell Verlag GmbH.)

Background: Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line., Methods: Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated., Results: On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions., Conclusions: The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

Venous blood gas and chemistry reference ranges were determined for commercial Hy-Line W-36 pullets and laying hens utilizing the portable i-STAT®1 analyzer and CG8+ cartridges. A total of 632 samples were analyzed from birds between 4 and 110 wk of age. Reference ranges were established for pullets (4 to 15 wk), first cycle laying hens (20 to 68 wk), and second cycle (post molt) laying hens (70 to 110 wk) for the following traits: sodium (Na mmol/L), potassium (K mmol/L), ionized calcium (iCa mmol/L), glucose (Glu mg/dl), hematocrit (Hct% Packed Cell Volume [PCV]), pH, partial pressure carbon dioxide (PCO2 mm Hg), partial pressure oxygen (PO2 mm Hg), total concentration carbon dioxide (TCO2 mmol/L), bicarbonate (HCO3 mmol/L), base excess (BE mmol/L), oxygen saturation (sO2%), and hemoglobin (Hb g/dl). Data were analyzed using ANOVA to investigate the effect of production status as categorized by bird age. Trait relationships were evaluated by linear correlation and their spectral decomposition. All traits differed significantly among pullets and mature laying hens in both first and second lay cycles. Levels for K, iCa, Hct, pH, TCO2, HCO3, BE, sO2, and Hb differed significantly between first cycle and second cycle laying hens. Many venous blood gas and chemistry parameters were significantly correlated. The first 3 eigenvalues explained ∼2/3 of total variation. The first 2 principal components (PC) explained 51% of the total variation and indicated acid-balance and relationship between blood O2 and CO2. The third PC explained 16% of variation and seems to be related to blood iCa. Establishing reference ranges for pullet and laying hen blood gas and chemistry with the i-STAT®1 handheld unit provides a mechanism to further investigate pullet and layer physiology, evaluate metabolic disturbances, and may potentially serve as a means to select breeder candidates with optimal blood gas or chemistry levels on-farm., (© The Author 2015. Published by Oxford University Press on behalf of the Poultry Science Association.)

Background: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken., Methods: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production., Results: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line., Conclusions: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

The objectives of this study were to evaluate the effects of feeding 5 different energy and nutrient dense diets to Hy-Line W-36 hens on long-term performance and economics. A total of 480 19 wk old Hy-Line W-36 Single Comb White Leghorn hens were weighed and randomly allocated to 6 replicate groups of 16 hens each (2 adjacent cages containing 8 hens per cage, 60.9×58.4 cm) per dietary treatment in a randomized complete block design. The hens were fed 5 treatment diets formulated to contain 85, 90, 95, 100, and 105% of the energy and nutrient recommendations stated in the 2009 Hy-Line Variety W-36 Commercial Management Guide. Production performance was measured for 52 wk from 19 to 70 wk age. Over the course of the trial, a significant increasing linear response to increasing energy and nutrient density was seen for hen-day egg production, egg weight, egg mass, feed efficiency, energy intake, and body weight (BW). Feed intake showed no significant linear level response to increasing energy and nutrient density except during the early production cycle. No consistent responses were noted for egg quality, percent yolk, and percent egg solids throughout the study. Significant linear responses due to energy and nutrient density were seen for egg income, feed cost, and income minus feed cost. In general, as energy and nutrient density increased, egg income and feed cost per hen increased, but income minus feed cost decreased. Overall, these results indicate that feeding Hy-Line W-36 hens increasing energy and nutrient-dense diets will increase egg production, egg weight, egg mass, feed efficiency, energy intake, BW, egg income, and feed cost, but decrease egg income minus feed cost. However, these benefits do not take effect in early production and seem to be most effective in later stages of the production cycle, perhaps "priming" the birds for better egg-production persistency with age., (© 2015 Poultry Science Association Inc.)

The Mx protein is one of the best-characterized interferon-stimulated antiviral mediators. Mx homologs have been identified in most vertebrates examined; however, their location within the cell, their level of activity, and the viruses they inhibit vary widely. Recent studies have demonstrated multiple Mx alleles in chickens and some reports have suggested a specific variant (S631N) within exon 14 confers antiviral activity. In the current study, the complete genome of nine elite egg-layer type lines were sequenced and multiple variants of the Mx gene identified. Within the coding region and upstream putative promoter region 36 SNP variants were identified, producing a total of 12 unique haplotypes. Each elite line contained from one to four haplotypes, with many of these haplotypes being found in only one line. Observation of changes in haplotype frequency over generations, as well as recombination, suggested some unknown selection pressure on the Mx gene. Trait association analysis with either individual SNP or haplotypes showed a significant effect of Mx haplotype on several egg production related traits, and on mortality following Marek's disease virus challenge in some lines. Examination of the location of the various SNP within the protein suggests synonymous SNP tend to be found within structural or enzymatic regions of the protein, while non-synonymous SNP are located in less well defined regions. The putative resistance variant N631 was found in five of the 12 haplotypes with an overall frequency of 47% across the nine lines. Two Mx recombinants were identified within the elite populations, indicating that novel variation can arise and be maintained within intensively selected lines. Collectively, these results suggest the conflicting reports in the literature describing the impact of the different SNP on chicken Mx function may be due to the varying context of haplotypes present in the populations studied.

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome-wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13,000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. (2012). The Bayesian whole genome prediction model--BayesB (Meuwissen et al. 2001) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non-zero effect with posterior probability > 0.9 for one or more traits., (© 2014 Blackwell Verlag GmbH.)

Efficiency of production is increasingly important with the current escalation of feed costs and demands to minimize the environmental footprint. The objectives of this study were 1) to estimate heritabilities for daily feed consumption and residual feed intake and their genetic correlations with production and egg-quality traits; 2) to evaluate accuracies of estimated breeding values from pedigree- and marker-based prediction models; and 3) to localize genomic regions associated with feed efficiency in a brown egg layer line. Individual feed intake data collected over 2-wk trial periods were available for approximately 6,000 birds from 8 generations. Genetic parameters were estimated with a multitrait animal model; methods BayesB and BayesCπ were used to estimate marker effects and find genomic regions associated with feed efficiency. Using pedigree information, feed efficiency was found to be moderately heritable (h(2) = 0.46 for daily feed consumption and 0.47 for residual feed intake). Hens that consumed more feed and had greater residual feed intake (lower efficiency) had a genetic tendency to lay slightly more eggs with greater yolk weights and albumen heights. Regions on chromosomes 1, 2, 4, 7, 13, and Z were found to be associated with feed intake and efficiency. The accuracy from genomic prediction was higher and more persistent (better maintained across generations) than that from pedigree-based prediction. These results indicate that genomic selection can be used to improve feed efficiency in layers.

Background: A fresh, good quality egg has a firm and gelatinous albumen that anchors the yolk and restricts growth of microbiological pathogens. As the egg ages, the gel-like structure collapses, resulting in thin and runny albumen. Occasionally thin albumen is found in a fresh egg, giving the impression of a low quality product. A mapping population consisting of 1599 F2 hens from a cross between White Rock and Rhode Island Red lines was set up, to identify loci controlling albumen quality. The phenotype for albumen quality was evaluated by albumen height and in Haugh units (HU) measured on three consecutive eggs from each F2 hen at the age of 40 weeks. For the fine-mapping analysis, albumen height and HU were used simultaneously to eliminate contribution of the egg size to the phenotype., Results: Linkage analysis in a small population of seven half-sib families (668 F2) with 162 microsatellite markers spread across 27 chromosomes revealed two genome-wide significant regions with additive effects for HU on chromosomes 7 and Z. In addition, two putative genome-wide quantitative trait loci (QTL) regions were identified on chromosomes 4 and 26. The QTL effects ranged from 2 to 4% of the phenotypic variance. The genome-wide significant QTL regions on chromosomes 7 and Z were selected for fine-mapping in the full set composed of 16 half-sib families. In addition, their existence was confirmed by an association analysis in an independent commercial Hy-Line pure line., Conclusions: We identified four chicken genomic regions that affect albumen quality. Our results also suggest that genes that affect albumen quality act both directly and indirectly through several different mechanisms. For instance, the QTL regions on both fine-mapped chromosomes 7 and Z overlapped with a previously reported QTL for eggshell quality, indicating that eggshell membranes may play a role in albumen quality.

A genome-wide association study (GWAS) using Bayesian variable selection was performed to determine genomic regions associated with mortality due to Marek's disease virus (MDV) infection in layers. Mortality (%) under experimental disease challenge (500 plaque-forming units of a very virulent plus MDV strain) was recorded for progeny groups (average 15.5 birds; range 3 to 30) of 253 genotyped sires from four generations of a brown-egg layer line. An additional generation of 43 sires with progeny data was used to validate results. Sires were genotyped with a 42K Illumina single-nucleotide polymorphism (SNP) chip. Methods BayesB (pi = 0.995) and BayesCpi, with or without weighting residuals by the size of progeny groups were applied. The proportion of genetic variance contributed by SNPs within each 1-megabase (Mb) genomic region was quantified. Average mortality was 33% but differed significantly between generations. Genetic markers explained about 11% of phenotypic variation in mortality. Correlations between genomic estimated breeding values and percentage of progeny mortality for the validation generation (sons of individuals in training) were 0.12, 0.17, 0.02, and 0.16 for BayesB, weighted BayesB, BayesCpi, and weighted BayesCpi, respectively, when using the whole genome, and 0.03, 0.20, -0.06, and 0.14, when using only SNP from the 10, 1-Mb regions, explaining the largest proportion of genetic variance according to each method. Results suggest that regions on chromosomes 2, 3, 4, 9, 15, 18, and 21 are associated with Marek's disease resistance and can be used for selection and that accounting for the size of progeny groups has a large impact on correct localization of such genomic regions.

A challenge test following inoculation with a standard amount of a vv+ strain of the Marek's disease (MD) virus in multiple lines and multiple generations of egg type chicken and the corresponding phenotypic trend are described. This program significantly reduced mortality of progeny from selected sires for three to 11 generations in eight of the nine elite lines studied herein. In brown egg lines, a retrospective analysis of DNA indicated an association between the blood type B (major histocompatibility complex) of the sire and the MD mortality in the challenge of its progeny. As a result of the multigeneration stock amplification and crossbreeding processes used in the commercial breeding industry, improvement in survival after challenge at the elite level will translate to improved welfare for millions of birds at the commercial production level.

Random regression models allow for analysis of longitudinal data, which together with the use of genomic information are expected to increase accuracy of selection, when compared with analyzing average or total production with pedigree information. The objective of this study was to estimate variance components for egg production over time in a commercial brown egg layer population using genomic relationship information. A random regression reduced animal model with a marker-based relationship matrix was used to estimate genomic breeding values of 3,908 genotyped animals from 6 generations. The first 5 generations were used for training, and predictions were validated in generation 6. Daily egg production up to 46 wk in lay was accumulated into 85,462 biweekly (every 2 wk) records for training, of which 17,570 were recorded on genotyped hens and the remaining on their nongenotyped progeny. The effect of adding additional egg production data of 2,167 nongenotyped sibs of selection candidates [16,037 biweekly (every 2 wk) records] to the training data was also investigated. The model included a 5th order Legendre polynomial nested within hatch-week as fixed effects and random terms for coefficients of quadratic polynomials for genetic and permanent environmental components. Residual variance was assumed heterogeneous among 2-wk periods. Models using pedigree and genomic relationships were compared. Estimates of residual variance were very similar under both models, but the model with genomic relationships resulted in a larger estimate of genetic variance. Heritability estimates increased with age up to mid production and decreased afterward, resulting in an average heritability of 0.20 and 0.33 for pedigree and genomic models. Prediction of total egg number was more accurate with the genomic than with the pedigree-based random regression model (correlation in validation 0.26 vs. 0.16). The genomic model outperformed the pedigree model in most of the 2-wk periods. Thus, results of this study show that random regression reduced animal models can be used in breeding programs using genomic information and can result in substantial improvements in the accuracy of selection for trajectory traits.

Avian eggshell quality is an important trait for commercial egg production, as the eggshell is the primary packaging material and antimicrobial barrier for the internal food resource. Strong eggshells are essential to ensure that eggs can reach their final destination without damage. Ovocalyxin-32 (OCX32) is a matrix protein found within the outer layers of the eggshell and in the cuticle. Numerous reports in the literature have identified association between variants in the gene encoding this protein, OCX32, and various eggshell quality traits. Thus, OCX32 is a candidate gene for selection for eggshell traits in commercial poultry populations. Sequencing of exons 2-6 of the OCX32 gene in eight elite brown and white eggshell commercial egg-laying lines revealed 28 SNPs and one SNP/indel. Eighteen of these SNPs were predicted to alter the amino acid sequence of the protein. Clusters of SNPs in complete linkage disequilibrium were found in both exons 2 and 6. A total of 19 different versions or protein-sequence haplotypes of the OCX32 protein were inferred, revealing considerable variation within commercial lines. Genotypes for 13 of the SNPs were determined for 330-1819 individuals per line. Trait association studies revealed a significant effect of OCX32 on shell color in white egg lines and line-specific significant effects on albumen height, early egg weight, puncture score, and yolk weight. Three of the lines showed a significant change in OCX32 frequency over time, indicating selection pressure for certain variants of this gene during the breeding program., (© 2012 The Authors, Animal Genetics © 2012 Stichting International Foundation for Animal Genetics.)

The pioneering work by Professor Soller et al., among others, on the use of genetic markers to analyze quantitative traits has provided opportunities to discover their genetic architecture in livestock by identifying quantitative trait loci (QTL). The recent availability of high-density single nucleotide polymorphism (SNP) panels has advanced such studies by capitalizing on population-wide linkage disequilibrium at positions across the genome. In this study, genomic prediction model Bayes-B was used to identify genomic regions associated with the mean and standard deviation of egg weight at three ages in a commercial brown egg layer line. A total of 24,425 segregating SNPs were evaluated simultaneously using over 2900 genotyped individuals or families. The corresponding phenotypic records were represented as individual measurements or family means from full-sib progeny. A novel approach using the posterior distribution of window variances from the Monte Carlo Markov Chain samples was used to describe genetic architecture and to make statistical inferences about regions with the largest effects. A QTL region on chromosome 4 was found to explain a large proportion of the genetic variance for the mean (30%) and standard deviation (up to 16%) of the weight of eggs laid at specific ages. Additional regions with smaller effects on chromosomes 2, 5, 6, 8, 20, 23, 28 and Z showed suggestive associations with mean egg weight and a region on chromosome 13 with the standard deviation of egg weight at 26-28 weeks of age. The genetic architecture of the analyzed traits was characterized by a limited number of genes or genomic regions with large effects and many regions with small polygenic effects. The region on chromosome 4 can be used to improve both the mean and standard deviation of egg weight by marker-assisted selection., (© 2012 The Authors, Animal Genetics © 2012 Stichting International Foundation for Animal Genetics.)

Genetic parameters were estimated for egg defects, egg production, and egg quality traits. Eggs from 11,738 purebred brown-egg laying hens were classified as salable or as having one of the following defects: bloody, broken, calcium deposit, dirty, double yolk, misshapen, pee-wee, shell-less, and soft shelled. Egg quality included albumen height, egg weight, yolk weight, and puncture score. Body weight, age at sexual maturity, and egg production were also recorded. Heritability estimates of liability to defects using a threshold animal model were less than 0.1 for bloody and dirty; between 0.1 and 0.2 for pee-wee, broken, misshapen, soft shelled, and shell-less; and above 0.2 for calcium deposit and double yolk. Quality and production traits were more heritable, with estimates ranging from 0.29 (puncture score) to 0.74 (egg weight). High-producing hens had a lower frequency of egg defects. High egg weight and BW were associated with an increased frequency of double yolks, and to a lesser extent, with more shell quality defects. Estimates of genetic correlations among defect traits that were related to shell quality were positive and moderate to strong (0.24-0.73), suggesting that these could be grouped into one category or selection could be based on the trait with the highest heritability or that is easiest to measure. Selection against defective eggs would be more efficient by including egg defect traits in the selection criterion, along with egg production rate of salable eggs and egg quality traits.

Egg-laying strains of chickens are highly susceptible to osteoporosis, a noninfectious disease characterized by a decrease in structural bone as hens age. To minimize the onset of osteoporosis, it was hypothesized that a delay in sexual maturity may allow a pullet to develop a stronger skeletal frame before egg laying, leading to improved skeletal mineralization at end of lay. One management tool that can easily be implemented by pullet growers to delay sexual maturity is length of photoperiod. The objective of the current study was to determine whether lighting programs used during the pullet phase of egg-laying strains of chickens can be manipulated to allow for improved skeletal mineralization in laying hens at end of lay. Two experiments were conducted in which 1,000 pullets/experiment were exposed to 1 of 3 varying step-down lighting programs (2 to 17 wk of age), referred to as rapid, moderate, and slow. For both experiments, 2 strains of chickens were used. Experiment 1 compared the Hy-Line W-36 with the Hy-Line W-98, and experiment 2 compared the Hy-Line Brown with the Hy-Line W-98. At 66 wk of age, all hens remaining in the study were weighed individually and the drum stick and wing were retrieved for determination of bone mineralization and bone size traits. Bone data were analyzed using an analysis of covariance with BW as the covariant, and BW was analyzed as an ANOVA. Skeletal frame development was affected by lighting regimen. Pullets exposed to the slow lighting photoperiod had longer bones and more bone area (experiment 2) than those exposed to the rapid photoperiod, most likely because of a delay in bone growth plate closure, which occurs at sexual maturity. However, this delay in sexual maturity, as indicated by longer bones, did not improve bone mineralization at 66 wk of age. It was concluded that pullet lighting regimen had little effect on bone mineralization at end of lay.

Background: The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information., Methods: The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability., Results: Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.

Background: Occurrence of blood and meat inclusions is an internal egg quality defect. Mass candling reveals most of the spots, but because brown eggshell hampers selection in brown chicken lines it has not been possible to eliminate the defect by selection. Estimated frequency of blood and meat inclusions in brown layers is about 18% whereas it is 0.5% in white egg layers. Several factors are known to increase the incidence of this fault: genetic background, low level of vitamin A and/or D, stress or infections, for instance. To study the genetic background of the defect, a mapping population of 1599 F2 hens from a cross of White Rock and Rhode Island Red lines was set up., Results: Our histopathological analyses show that blood spots consist of mainly erythrocytes and that meat spots are accumulations of necrotic material. Linkage analysis of 27 chromosomes with 162 microsatellite markers revealed one significant quantitative trait locus (QTL) affecting blood spot and meat spot frequency. We sequenced a fragment of a candidate gene within the region, ZO-2, coding for a tight junction protein. Nine polymorphisms were detected and two of them were included in fine-mapping and association analysis. Fine-mapping defined the QTL result. To further verify the QTL, association analyses were carried out in two independent commercial breeding lines with the marker MCW241 and surrounding SNPs. Association was found mainly in a 0.8 Mb-wide chromosomal area on GGAZ., Conclusions: There was good agreement between the location of the QTL region on chromosome Z and the association results in the commercial breeds analyzed. Variations found in tight junction protein ZO-2 and microRNA gga-mir-1556 may predispose egg layers to blood and meat spot defects. This paper describes the first results of detailed QTL analyses of the blood and meat spots trait(s) in chickens., (© 2011 Honkatukia et al; licensee BioMed Central Ltd.)

Background: Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line., Methods: The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV., Results: Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.

The objectives of this study were to estimate genetic parameters for egg production over the age trajectory in 3 layer lines, which represent different biotypes for egg production, and to validate the use of breeding values for slope as a measure of persistency to be used in the selection program. Egg production of more than 26,000 layers per line from 6 consecutive generations were analyzed with a random regression model with a within-hatch-nested fifth-order fixed-regression polynomial and linear polynomials for random additive genetic and permanent environmental effects. Daily records were cumulated into biweekly periods. In all lines, a nonzero genetic variance for mean and slope and a positive genetic correlation between mean and slope were estimated. Genetic variance of egg production by 2-wk period was low at the beginning of lay and increased as the birds aged for all 3 lines, which resulted in heritability estimates increasing with age. Breeding values for slope reflected the shape of the egg production curve well and can be used to directly select for persistency of egg production.

Background: The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes., Results: The LD between markers pairs was high at short distances (r2 > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (phi > 0.2) and an additional ten 3-SNP windows that had a sum of phi greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses., Conclusion: High LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.