Neil P. O’Sullivan

Genome-wide association analysis and genetic architecture of egg weight and egg uniformity in layer chickens.

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.

Genetic parameters of egg defects and egg quality in layer chickens

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.

Pedigree and genomic analyses of feed consumption and residual feed intake in laying hens

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.

Analysis of egg production in layer chickens using a random regression model with genomic relationships

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.

Effects of number of training generations on genomic prediction for various traits in a layer chicken population

AbstractBackground 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.MethodsPhenotypic 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.ResultsOn 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.ConclusionsThe 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.

Effect of lighting programs during the pullet phase on skeletal integrity of egg-laying strains of chickens

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.

Genome wide association study for heat stress induced mortality in a white egg layer line

&NA; 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 &pgr; 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.

Investigating the genetic determination of clutch traits in laying hens

&NA; 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.