Today’s egg layer industry has been shaped by implementation of some of the genetic advancements and market feedback, which has guided egg quality trait definitions aligned to specific customer demands. In this chapter we will discuss the definition of egg quality traits from their genetic perspective.

Accuracy of genomic prediction was compared in two layer lines using medium density SNP chip and lowpass sequencing. Three thousand individuals representing two lines were sequenced for the project. Thirtyfour genomic regions were identified as genome-wide significant across 11 egg quality traits and their genotypes were added to the chip. Multi-trait single-step animal model was used for genomic prediction. A slight increase in accuracy for some of the traits was obtained by adding the significant SNPs to those present on the chip. Fitting all sequence SNPs had no advantage for predictive ability over chip despite explaining 15% more of phenotypic variance in the Genome Wide Association Study. Further optimization of the utilization of sequence data and cost reduction are required to make low-pass sequence competitive to medium density SNP chips for genomic prediction.

Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.

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.

Avian Leukosis Virus subgroup E (ALVE) integrations are endogenous retroviral elements found in the chicken genome. The presence of ALVE has been reported to have negative impacts on multiple traits, including egg production and body weight. The recent development of rapid, inexpensive and specific ALVE detection methods has facilitated their characterization in elite commercial egg production lines across multiple generations. The presence of 20 ALVE was examined in 8 elite lines, from 3 different breeds. Seventeen of these ALVE (85%) were informative and found to be segregating in at least one of the lines. To test for an association between specific ALVE inserts and traits, a large genotype by phenotype study was undertaken. Genotypes were obtained for 500 to 1500 males per line, and the phenotypes used were siredaughter averages. Phenotype data were analyzed by line with a linear model that included the effects of generation, ALVE genotype and their interaction. If genotype effect was significant, the number of ALVE copies was fitted as a regression to estimate additive ALVE gene substitution effect. Significant associations between the presence of specific ALVE inserts and 18 commercially relevant performance and egg quality traits, including egg production, egg weight and albumen height, were observed. When an ALVE was segregating in more than one line, these associations did not always have the same impact (negative, positive or none) in each line. It is hypothesized that the presence of ALVE in the chicken genome may influence production traits by 3 mechanisms: viral protein production may modulate the immune system and impact overall production performance (virus effect); insertional mutagenesis caused by viral integration may cause direct gene alterations or affect gene regulation (gene effect); or the integration site may be within or adjacent to a quantitative trait region which impacts a performance trait (linkage disequilibrium, marker effect).

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.

The i-STAT1 clinical analyzer has become an increasingly popular tool in clinical production animal medicine as it can provide pen-side results in a cost effective and timely manner when compared to standard benchtop serum biochemistry blood gas and chemistry analyses. This study compares the results of the portable Abbott i-STAT1 analyzer and the Abaxis VetScan VS2 for glucose (Glu, mg/dL), ionized Ca (mmol/L), Na (mmol/L), and K (mmol/L) values. Three genetically distinct commercial varieties (CV) of Hy-Line white-egg laying hens are used in this study (Hy-Line W-36, Hy-Line W-80, and Hy-Line W-80+). Thirty blood samples (n = 10 per CV) were obtained in the production house from the brachial vein and concurrently analyzed by the i-STAT1 portable device. Serum from 22 of these same samples was analyzed via VetScan VS2, a benchtop serum clinical biochemistry analyzer, using VetScan Avian/Reptilian Profile Plus reagent rotors. A paired T-test was used to test for statistical differences in means between the 2 instruments for each of the parameters. Parameters with significant mean differences were then subject to correlation and regression analysis to further evaluate relationships between the results from the 2 methods. Significant differences between means were found for Glu, Na, and K levels. Ca levels were found to be not directly comparable by the 2 analysis instruments. This comparison elucidates the importance of clinical analyzer validations when applying different strategies of diagnostic medicine in poultry.

Different genetic lines of laying hens may show varying levels of fearfulness in response to stressful events or situations. It is important to select appropriate genetic strains when keeping hens in alternative housing systems to minimize health and production issues caused by fear and stress. In this study, data were obtained from two strains of Rhode Island Red (RIR1-2), two strains of White Plymouth Rock (WPR1-2) and three strains of White Leghorn (WL1-3) breeds. One hundred hens from each strain were used to compare strain differences in fear and stress responses. Each hen was scored for fearfulness based on her responses to the following tests: novel object, physical restraint, pencil, human reaction, inversion, and tonic immobility (TI). Stress was assessed based on physical asymmetry (ASYM) of the metatarsal and middle toe, and on corticosterone (CORT) concentrations in blood plasma and egg albumen. Differences between brown (RIR; WPR) and white (WL) egg layers were observed for all measures: WL had more head movements and took more time to right themselves during TI (P < 0.001), but higher flapping intensity was observed in RIR and WPR (P < 0.001). Hens in the WL strains also scored lower than RIR and WPR on the struggle test (P < 0.001), and had lower ASYM scores (P = 0.002) and concentrations of albumen CORT (P = 0.0065) and plasma CORT (P = 0.002), but scored higher during the remaining fear tests (P < 0.001). Differences among WL strains were also observed for all measures: WL2 had the most head movements and took the longest to right during TI (P < 0.001). During inversion, WP2 and RIR2 had the highest flapping intensity (P < 0.001) whereas WL2 and WL3 had the lowest flapping intensity (P < 0.001). There was variation among strains in plasma CORT, albumen CORT, and scores for the struggle test and pencil test. Only the WPR2 strain showed significant differences in ASYM (P < 0.001). Genetic strains of laying hens show clear variation between brown and white shell color varieties regarding fear and stress responses. Brown hens tend to actively avoid perceived threats whereas white hens use passive avoidance. Using several methods to test fear and stress to better understand individual birds' responses to stressors will help to design breeding strategies to maximize hens' success in alternative housing systems.

Background: The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL（quantitative trait loci） for recombination rate in two purebred layer chicken lines.Methods: A total of 1200 white-egg layers（WL） were genotyped with 580 K SNPs and 5108 brown-egg layers（BL）were genotyped with 42 K SNPs（single nucleotide polymorphisms）. Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL.Results: Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL,respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines.Conclusions: Differences in the recombination rates, hotspot locations, and QTL regions associated with genomewide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait.

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.

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.

High environmental temperature is a serious stress affecting economic and biological efficiency of poultry production in tropical and subtropical countries that is expected to become more prominent with global climate change. Iowa experienced 3 acute heat waves of 11, 3, and 4 d of heat index above 38°C in the summer of 2012, which led to production losses and increased bird mortality. For the current study, the proportion of daughters that died from heat stress during this period was calculated for 118 sire families of an elite White Leghorn layer line. The number of daughters per sire ranged from 25 to 111 and averaged 68. Average mortality due to heat stress was 8.2%, ranging from 0 to 24.6%. All sires were genotyped using a 600 K Affymetrix chip. After stringent quality filtering (clustering quality, parentage, missing genotypes, MAF) 113,344 SNPs were retained for the analysis. Method BayesB with π equal to 0.999, for the number of markers fitted not to exceed the number of observations, was applied. Markers explained 8% of the phenotypic variance. One 1-Mb window on chromosome 5 explained 1.2% of genetic variance. When the number of daughters was fitted as a weight in the analysis, the proportion of variance explained by markers dropped to 1%, but 9 1-Mb windows explained more than 1% of genetic variance on chromosomes 1, 3, 5 (the same top window as in the unweighted analysis), 9, 17, and 18. Although the support of the genomic regions associated with heat stress resistance identified in this study was not very strong, they overlapped with previously reported quantitative trait loci regions for immune response and physiological traits in chickens and contained genes that have been associated with response to heat stress in other studies. Further research is needed to validate the results.

A.S. Mason1, A.Wolc2,3, J. Arango2, P. Settar2, A.R. Lund2, D.W. Burt4, & *J.E. Fulton2 1The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian EH25 9RG, UK 2Hy-Line International, PO Box 310, Dallas Center IA 50063 USA jfulton@hyline.com (Corresponding Author) 3Dept. of Animal Science, 1221 Kildee Hall, Iowa State University, Ames, Iowa 50011-3150, USA 4University of Queensland, St. Lucia, QLD 4072, Australia

More energy-efficient, readily dimmable, long-lasting and more affordable light-emitting diode (LED) lights are increasingly finding applications in poultry production facilities. Despite anecdotal evidence about the benefits of such lighting on bird performance and behavior, concrete research data were lacking. In this study, a commercial poultry-specific LED light (dim-to-blue, controllable correlated color temperature (CCT) from 4500 to 5300 K) and a typical compact fluorescent light (CFL) (soft white, CCT=2700 K) were compared with regards to their effects on growing performance, activity levels, and feather and comb conditions of non-beak-trimmed W-36 pullets during a 14-week rearing period. A total of 1280-day-old pullets in two successive batches, 640 birds each, were used in the study. For each batch, pullets were randomly assigned to four identical litter-floor rooms equipped with perches, two rooms per light regimen, 160 birds per room. Body weight, BW uniformity (BWU), BW gain (BWG) and cumulative mortality rate (CMR) of the pullets were determined every 2 weeks from day-old to 14 weeks of age (WOA). Activity levels of the pullets at 5 to 14 WOA were delineated by movement index. Results revealed that pullets under the LED and CFL lights had comparable BW (1140±5 g v. 1135±5 g, P=0.41), BWU (90.8±1.0% v. 91.9±1.0%, P=0.48) and CMR (1.3±0.6% v. 2.7±0.6%, P=0.18) at 14 WOA despite some varying BWG during the rearing. Circadian activity levels of the pullets were higher under the LED light than under the CFL light, possibly resulting from differences in spectrum and/or perceived light intensity between the two lights. No feather damage or comb wound was apparent in either light regimen at the end of the rearing period. The results contribute to understanding the impact of emerging LED lights on pullets rearing which is a critical component of egg production.

The development of SNP chips has enabled rapid genotyping of hundreds of thousands of loci at a relatively low cost. In addition to providing SNP genotype information, copy number variation (CNV) can also be inferred from intensity data generated by the same chips. The aim of this study was to detect and describe CNVs in five lines of layer chickens using different SNP chips. A total of 18,719 individuals from four pure lines and one commercial cross were genotyped using four different SNP chips (Illumina 42K, Affymetrix 600K, and two customized Affymetrix 50K chips). Analysis software Axiom® CNV Summary Tools and PennCNV were used to identify CNVs from Affymetrix chips and cnvPartition in Genome Studio was used to identify CNV’s from the Illumina chip. The CNV regions (CNVR) within lines were defined using the BedTools software, through merging CNVs overlapping by at least 1 bp. CNVRs identified across all panels were selected with BedTools intersect to choose regions with highest confidence. Gene enrichment analysis was performed for genes that overlap identified CNVs to detect overrepresented biological processes and pathways. The mean number of detected CNVs per individual varied depending on the population and size of the SNP set, from 0.50 with the 50K chip in one of the white layer lines up to 4.87 with the 600K chip in one of the brown layer lines. There were considerable differences in the number of detected CNVs between lines, probably due to breeding history. The length of detected CNVs ranged from 1.16 kb to 3.16 Mb. The mean length of detected CNVs was higher for the 42K and 50K chips than for the 600K chip, which is most likely a result of low detectability of shorter CNVs due to larger distances between markers in comparison to the 600K chip. The low frequency of CNVs detected on the 50K panels could also result from their custom design, which intentionally eliminated poorly clustering SNPs. Most of the detected CNVs had low population frequencies. In total CNVs were merged into 2687 CNVRs and overlapped 495.73 Mb of the genome. Intersecting CNVRs across all lines and panels yielded 4131 CNVRs from which 2139 were observed in at least two individuals. In conclusion, commonly used SNP chip platforms and analysis using relevant software can be used to identify CNVs in commercially relevant chicken layer lines. The number of detected CNVs and their length depends on the population and density of SNP on the chip.

The objective of this study was to estimate repeatability within and between ages for dynamic stiffnessin two lines of layer chickens in order to verify if multiple records are necessary to adequately describe a bird’s genetic merit for egg shell quality.Repeatability was low across ages to moderate within age,which suggests that for accurate evaluation eggs should be collected at different stages of laying cycle,with additional benefit from analyzing more than one egg within age.

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.

Genomic selection using high-density SNP panels was first implemented in dairy cattle breeding programs. It provided benefits in terms of reduced generation intervals, improved accuracies of selecting young animals, and reductions in costs associated with the requirement of progeny testing bulls in traditional breeding programs (Hayes et al., 2009). Several distinct features of the poultry breeding industry differ markedly from dairy cattle breeding and influence the manner in which genomic selection can be used for genetic improvement in poultry breeding: · Traditional genetic improvement programs in poultry already have short generation intervals (multiple overlapping generations per year with selection every 6 wk in broilers, non-overlapping annual generations in layers). There is some scope for shortening the generation interval in layers but not as much as was the case for dairy cattle breeding where bull pathways could be reduced in length from greater than 6 yr to less than 3 yr (Schaeffer, 2006, Schefers and Weigel, 2012).