Data from the first four cycles of the Germplasm Evaluation program at the U.S. Meat Animal Research Center were used to evaluate weights of Angus, Hereford, and F1 cows produced by crosses of 22 sire and 2 dam (Angus and Hereford) breeds. Four weights per year were available for cows from 2 through 8 yr of age (AY) with age in months (AM). Weights (n = 61,798) were analyzed with REML using covariance function-random regression models (CF-RRM), with regression on orthogonal (Legendre) polynomials of AM. Models included fixed regression on AM and effects of cow line, age in years, season of measurement, and their interactions; year of birth; and pregnancy-lactation codes. Random parts of the models fitted RRM coefficients for additive (a) and permanent environmental (c) effects. Estimates of CF were used to estimate covariances among all ages. Temporary environmental effects were modeled to account for heterogeneity of variance by AY. Quadratic fixed regression was sufficient to model population trajectory and was fitted in all analyses. Other models varied order of fit and rank of coefficients for a and c. A parsimonious model included linear and quartic regression coefficients for a and c, respectively. A reduced cubic order sufficed for c. Estimates of all variances increased with age. Estimates for older ages disagreed with estimates using traditional bivariate models. Plots of covariances for c were smooth for intermediate, but erratic for extreme ages. Heritability estimates ranged from 0.38 (36 mo) to 0.78 (94 mo), with fluctuations especially for extreme ages. Estimates of genetic correlations were high for most pairs of ages, with the lowest estimate (0.70) between extreme ages (19 and 103 mo). Results suggest that although cow weights do not fit a repeatability model with constant variances as well as CF-RRM, a repeatability model might be an acceptable approximation for prediction of additive genetic effects.
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. 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. 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.
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.
A 3,072 single nucleotide polymorphism (SNP) panel was used to identify genetic markers linked to quantitative trait loci (QTL). Two association methods were used to search for QTL, SNP-wise and genome-wise models. The QTL associated with SNPs, found using both of these methods, can be applied to breeding programs in marker assisted selection (MAS). The extent and consistency of linkage disequilibrium (LD) was measured in two lines of commercial egg laying chickens by analysis of SNPs. Correlations were drawn between measurements of two consecutive years to determine consistency. At short distances, LD is retained which allows for markers at high LD with a trait to be effectively applied in MAS.
To study the genetic relationship between three grouped reasons for sow removal (SR) in consecutive parities, accounting for censoring, 13,838 records from Large White sows were analyzed. Data were from seven pure-line farms having, on average, 5.9% unknown SR. Three traits were subjectively defined, each corresponding to a classification of SR (reproductive [RR], nonreproductive [RN], and others [RO]). Records for each trait could take one of five categories, according to parity at removal (0 to 4 or later). A multivariate linear censored model was implemented. The model to estimate (co)variance components and parameters included the effects of year-season, region, contemporary group, and additive genetic effects. The most common SR was related to reproduction (48.5%). Diseases of different origin and cause, old age/parity, and sow death or loss accounted for about 18, 7, and 4% of total culls, respectively. Estimates of variance components showed heterogeneity of additive genetic and residual variances for the three traits. Estimates of heritability were 0.18, 0.13, and 0.15 for RR, RN, and RO, respectively. Genetic correlations between removal codes were high (≥0.90). Results suggest sizeable additive genetic variances exist for parity at removal and different codes of removal. Different SR reasons seem to operate similarly or as a closely related genetic trait associated with fitness. In particular, RN and RO seem to be genetically indistinguishable. Data structure, definition, and volume are major limitations in studies of sow survival. A multiple-trait censored model is preferred to evaluate reasons of sow disposal. Grouped removal causes seem to be strongly genetically correlated but with heterogeneous variances, suggesting that combining all removal causes and treating the trait as parity at disposal is an alternative approach.
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).
Providing high-quality food for the increasing world population with limited natural resources is a challenge for animal agriculture. Over the past decades, poultry production has undergone remarkable advancements to adapt to emerging challenges and evolving changes in consumer expectations. Among these changes, the need for an animal protein production system that considers the social, economic, and environmental aspects of sustainability has increased. With that in mind, efforts were and will continue to be made toward improving various aspects of the poultry production chain. Genetic selection has evolved from a simple phenotypic mass selection to the use of genomics, focusing not only on efficiency, but also on animal welfare and the demand from niche markets. Precision poultry farming technologies should be further innovated to develop the core component of an integrated imaging system for evaluating poultry production and wellbeing. Moreover, feed formulation will continue to be adjusted as the birds' nutritional requirements, feed ingredient availability, and cost change, and bird processing will continue to adopt technologies that can improve meat quality and reduce labor intensity and demand. These adaptations highlight a dynamic aspect of the poultry industry and its continuous effort to produce a safe, cost-effective, and environment friendly protein source while maintaining animal welfare.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)