Neil P. O'Sullivan

Hen welfare in different housing systems

Egg production systems have become subject to heightened levels of scrutiny. Multiple factors such as disease, skeletal and foot health, pest and parasite load, behavior, stress, affective states, nutrition, and genetics influence the level of welfare hens experience. Although the need to evaluate the influence of these factors on welfare is recognized, research is still in the early stages. We compared conventional cages, furnished cages, noncage systems, and outdoor systems. Specific attributes of each system are shown to affect welfare, and systems that have similar attributes are affected similarly. For instance, environments in which hens are exposed to litter and soil, such as noncage and outdoor systems, provide a greater opportunity for disease and parasites. The more complex the environment, the more difficult it is to clean, and the larger the group size, the more easily disease and parasites are able to spread. Environments such as conventional cages, which limit movement, can lead to osteoporosis, but environments that have increased complexity, such as noncage systems, expose hens to an increased incidence of bone fractures. More space allows for hens to perform a greater repertoire of behaviors, although some deleterious behaviors such as cannibalism and piling, which results in smothering, can occur in large groups. Less is understood about the stress that each system imposes on the hen, but it appears that each system has its unique challenges. Selective breeding for desired traits such as improved bone strength and decreased feather pecking and cannibalism may help to improve welfare. It appears that no single housing system is ideal from a hen welfare perspective. Although environmental complexity increases behavioral opportunities, it also introduces difficulties in terms of disease and pest control. In addition, environmental complexity can create opportunities for the hens to express behaviors that may be detrimental to their welfare. As a result, any attempt to evaluate the sustainability of a switch to an alternative housing system requires careful consideration of the merits and shortcomings of each housing system.

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

BackgroundGenomic 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.MethodsThe 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.ResultsUsing 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.

Extent and Consistency Across Generations of Linkage Disequilibrium in Commercial Layer Chicken Breeding Populations

Recent studies report a surprisingly high degree of marker-to-marker linkage disequilibrium (LD) in ruminant livestock populations. This has important implications for QTL mapping and marker-assisted selection. This study evaluated LD between microsatellite markers in a number of breeding populations of layer chickens using the standardized chi-square (χ2′) measure. The results show appreciable LD among markers separated by up to 5 cM, decreasing rapidly with increased separation between markers. The LD within 5 cM was strongly conserved across generations and differed among chromosomal regions. Using marker-to-marker LD as an indication for marker-QTL LD, a genome scan of markers spaced 2 cM apart at moderate power would have good chances of uncovering most QTL segregating in these populations. However, of markers showing significant trait associations, only 57% are expected to be within 5 cM of the responsible QTL, and the remainder will be up to 20 cM away. Thus, high-resolution LD mapping of QTL will require dense marker genotyping across the region of interest to allow for interval mapping of the QTL.

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

BackgroundThe 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.MethodsThe 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.ResultsPedigree 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.

Extent and Consistency of Linkage Disequilibrium and Identification of DNA Markers for Production and Egg Quality Traits in Commercial Layer Chicken Populations

BackgroundThe 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.ResultsThe 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 (φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses.ConclusionHigh 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.

Mapping QTL affecting resistance to Marek's disease in an F6 advanced intercross population of commercial layer chickens

BackgroundMareks disease (MD) is a T-cell lymphoma of chickens caused by the Mareks disease virus (MDV), an oncogenic avian herpesvirus. MD is a major cause of economic loss to the poultry industry and the most serious and persistent infectious disease concern. A full-sib intercross population, consisting of five independent families was generated by crossing and repeated intercrossing of two partially inbred commercial White Leghorn layer lines known to differ in genetic resistance to MD. At the F6 generation, a total of 1615 chicks were produced (98 to 248 per family) and phenotyped for MD resistance measured as survival time in days after challenge with a very virulent plus (vv+) strain of MDV.ResultsQTL affecting MD resistance were identified by selective DNA pooling using a panel of 15 SNPs and 217 microsatellite markers. Since MHC blood type (BT) is known to affect MD resistance, a total of 18 independent pool pairs were constructed according to family × BT combination, with some combinations represented twice for technical reasons. Twenty-one QTL regions (QTLR) affecting post-challenge survival time were identified, distributed among 11 chromosomes (GGA1, 2, 3, 4, 5, 8, 9, 15, 18, 26 and Z), with about two-thirds of the MD resistance alleles derived from the more MD resistant parental line. Eight of the QTLR associated with MD resistance, were previously identified in a backcross (BC) mapping study with the same parental lines. Of these, 7 originated from the more resistant line, and one from the less resistant line.ConclusionThere was considerable evidence suggesting that MD resistance alleles tend to be recessive. The width of the QTLR for these QTL appeared to be reduced about two-fold in the F6 as compared to that found in the previous BC study. These results provide a firm basis for high-resolution linkage disequilibrium mapping and positional cloning of the resistance genes.

Genome-wide association study for egg production and quality in layer chickens

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.

Rooster semen cryopreservation: Effect of pedigree line and male age on postthaw sperm function

The fertility rates of cryopreserved poultry semen are highly variable and not reliable for use in preservation of commercial genetic stocks. Our objective was to evaluate the cryosurvival of semen from 8 pedigreed layer lines at 2 different ages: the onset and end of commercial production. Semen from 160 roosters (20/line) was frozen individually with 11% glycerol at 6 and 12 mo of age. Glycerol was removed from thawed semen by Accudenz gradient centrifugation. The viability of thawed sperm from each male was determined using fluorescent live-dead staining and flow cytometry; sperm velocity parameters were measured using computerized motion analysis. The fertilizing ability of thawed sperm was evaluated in vitro by assessing hydrolysis of the inner perivitelline membrane. The postthaw function of sperm from the elite lines varied widely, despite the fact that fresh semen from all of these lines typically yielded high fertility rates. The percentage of thawed sperm with intact plasma membranes ranged from 27.8 + or - 2.1 to 49.6 + or - 1.9 and varied among lines and between age groups. Thawed sperm from 2 lines consistently demonstrated the highest and lowest motility parameters, whereas the velocity parameters of the remaining 6 lines varied widely. The mean number of hydrolysis points per square millimeter of inner perivitelline membrane ranged from 12.5 + or - 4.1 (line 2) to 103.3 + or - 30.2 (line 6). Age effects were observed for 4 out of 8 lines; however, improved postthaw sperm function at 12 mo of age was not consistent for all 3 assays. These results demonstrate variability among pedigreed lines in withstanding glycerol-based semen cryopreservation and provide a model for delineating genotypic and phenotypic factors affecting sperm cryosurvival.

Mapping Quantitative Trait Loci Affecting Susceptibility to Marek's Disease Virus in a Backcross Population of Layer Chickens

Mareks disease (MD), caused by the oncogenic MD avian herpes virus (MDV), is a major source of economic losses to the poultry industry. A reciprocal backcross (BC) population (total 2052 individuals) was generated by crossing two partially inbred commercial Leghorn layer lines known to differ in MDV resistance, measured as survival time after challenge with a (vv+) MDV. QTL affecting resistance were identified by selective DNA pooling using a panel of 198 microsatellite markers covering two-thirds of the chicken genome. Data for each BC were analyzed separately, and as a combined data set. Markers showing significant association with resistance generally appeared in blocks of two or three, separated by blocks of nonsignificant markers. Defined this way, 15 chromosomal regions (QTLR) affecting MDV resistance, distributed among 10 chromosomes (GGA 1, 2, 3, 4, 5, 7, 8, 9, 15, and Z), were identified. The identified QTLR include one gene and three QTL associated with resistance in previous studies of other lines, and three additional QTL associated with resistance in previous studies of the present lines. These QTL could be used in marker-assisted selection (MAS) programs for MDV resistance and as a platform for high-resolution mapping and positional cloning of the resistance genes.

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

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.