Genetic analysis on body weight at different ages in broiler chicken raised in commercial environment

Delineating maternal influence in regulation of variance in major economic traits of White Leghorns: Bayesian insights

Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks). Model 2 and Model 3 with maternal effects were found to be the best having the highest accuracy for almost all the traits. The direct additive genetic heritability was moderate for ASM, moderate to high for body weight traits and egg weight traits and low to moderate for egg production traits. Though the maternal heritability (h2mat) and permanent environmental effect (c2mpe) was low (<0.1) for most of the traits, they formed an important component of trait variance. Traits like egg weight at 28 weeks (0.14±0.06) and egg production at 72 weeks (0.13±0.07) reported comparatively higher values for c2mpe and h2mat respectively. Additive genetic correlation was high and positive between body weight traits, between egg weight traits, between consecutive egg production traits and between body weight and egg weight traits. However, a negative genetic correlation existed between egg production and egg weight traits, egg production and body weight traits, ASM and early egg production traits. Overall, a moderate positive genetic correlation was estimated between ASM and body weight traits and ASM and egg weight traits. Based on our findings, we can deduce that maternal effects constitute an important source of variation for all the major economic traits in White Leghorn and should be necessarily considered in genetic evaluation programs.

Animal board invited review: The purebred-crossbred genetic correlation in poultry

The purebred-crossbred genetic correlation (rpc) is a key parameter to determine whether the optimal selection of purebred animals to improve crossbred performance should rely on crossbred phenotypes, purebred phenotypes, or both. We reviewed published estimates of the rpc in poultry. In total, 19 studies were included, of which four were on broilers and 15 on laying hens, with 150 rpc estimates for nine different trait categories. Average reported rpc estimates were highest for egg weight, egg quality and egg colour (0.74-0.82), intermediate for BW, maturity and mortality (0.61-0.70) and egg number (0.58), and low for resilience (0.40) and body conformation (0.14). Most studies were based on measuring purebred and crossbred phenotypes in the same environment and thus did not capture the contribution of genotype by environment interactions to the rpc, suggesting that the presented average estimates may be higher than values that apply in practice. Nearly all studies were based on two-way crossbred animals. We hypothesised that rpc values for a two-way cross are good proxies for rpc of a four-way cross. Only eight out of 19 studies were published in the last 25 years, and only two of those used genomic data. We expect that more studies using genomic data may be published in the coming years, as the required data may be generated when implementing genomic selection for crossbred performance, which will lead to more accurate rpc estimates. Future studies that aim to estimate rpc are encouraged to capture the genotype by environment interaction component by housing purebred and crossbred animals differently as is done in practice. Moreover, there is a need for further studies that enable to explicitly estimate the magnitude of genotype by environment versus genotype by genotype interactions for multiple trait categories. Further, studies are advised to report: the specific housing conditions of the animals, any differences between measurements of purebred versus crossbred performance, and the heritabilities of purebred and crossbred performance.

Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits

Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999-2001 to 2020-2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.

Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers

BACKGROUND

Maternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values.

RESULTS

This model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam-offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance.

CONCLUSIONS

In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.

Selective genotyping strategies for a sib test scheme of a broiler breeder program

BACKGROUND

In broiler breeding, genotype-by-environment interaction is known to result in a genetic correlation between body weight measured in bio-secure and commercial environments that is substantially less than 1. Thus, measuring body weights on sibs of selection candidates in a commercial environment and genotyping them could increase genetic progress. Using real data, the aim of this study was to evaluate which genotyping strategy and which proportion of sibs placed in the commercial environment should be genotyped to optimize a sib-testing breeding program in broilers. Phenotypic body weight and genomic information were collected on all sibs raised in a commercial environment, which allowed to retrospectively analyze different sampling strategies and genotyping proportions.

RESULTS

Accuracies of genomic estimated breeding values (GEBV) obtained with the different genotyping strategies were assessed by computing their correlation with GEBV obtained when all sibs in the commercial environment were genotyped. Results showed that, compared to random sampling (RND), genotyping sibs with extreme phenotypes (EXT) resulted in higher GEBV accuracy across all genotyping proportions, especially for genotyping proportions of 12.5% or 25%, which resulted in correlations of 0.91 vs 0.88 for 12.5% and 0.94 vs 0.91 for 25% genotyped. Including pedigree on birds with phenotype in the commercial environment that were not genotyped increased accuracy at lower genotyping proportions, especially for the RND strategy (correlations of 0.88 vs 0.65 at 12.5% and 0.91 vs 0.80 at 25%), and a smaller but still substantial increase in accuracy for the EXT strategy (0.91 vs 0.79 for 12.5% and 0.94 vs 0.88 for 25% genotyped). Dispersion bias was virtually absent for RND if 25% or more birds were genotyped. However, GEBV were considerably inflated for EXT, especially when the proportion genotyped was low, which was further exacerbated if the pedigree of non-genotyped sibs was excluded.

CONCLUSIONS

When less than 75% of all animals placed in a commercial environment are genotyped, it is recommended to use the EXT strategy, because it yields the highest accuracy. However, caution should be taken when interpreting the resulting GEBV because they will be over-dispersed. When 75% or more of the animals are genotyped, random sampling is recommended because it yields virtually no bias of GEBV and results in similar accuracies as the EXT strategy.

Autosomal and Z-linked genetic evaluation for body weight in Mazandaran native chicken using different models for dosage compensation on the Z chromosome

The objectives of this study were to assess the autosomal and sex-linked genetic inheritance of growth traits and identify the effective dosage compensation on the Z chromosome in Mazandaran native chickens. The data included body weights at hatching (BW0), 8 weeks (BW8) and 12 weeks (BW12) of age, related to the first 21 generations of selection, were collected from Mazandaran native chicken breeding centre. The fixed effects included sex of birds in two classes, hatch in five classes and generation in 21 classes. The inverse of the sex-linked additive genetic relationship matrix was constructed using nadiv package in R considering different models for dosage compensation on the Z chromosome. The setup inversed matrix was then supplied externally to WOMBAT using the GIN option. Twelve univariate animal models separating participation of autosomal additive genetic, sex-linked additive genetic and maternal effects (both genetic and permanent environment effects) with considering the five different dosage compensation methods for models with sex-linked effects were analysed by WOMBAT software. BW0 was not affected by sex-linked additive genetic effects. For BW8 and BW12 the model which included autosomal, sex-linked direct additive and maternal effects with no global dosage compensation for the Z chromosome was the most appropriate model. Autosomal heritability estimates were 0.05 ± 0.02, 0.10 ± 0.01 and 0.11 ± 0.01, for BW0, BW8 and BW12, respectively. For BW8 and BW12, sex-linked heritability estimates were 0.07 and 0.27, respectively. Spearman rank correlation coefficient between autosomal and sex-linked breeding values were 0.45 and 0.12 for BW8 and BW12, respectively. Spearman rank correlation coefficient between autosomal and sex-linked breeding values were 0.45 and 0.12 for BW8 and BW12, respectively. The autosomal direct additive genetic correlations between all traits were positive. The estimate of direct sex-linked additive genetic correlation between BW8 and BW12 was high (0.88). Also, maternal genetic correlations were 0.53, 0.54 and 0.91 between BW0-BW8, BW0-BW12 and BW8-BW12, respectively. Given the importance of Z-linked genes for BW8 and BW12, it is recommended that Z-linked effects be separated from autosomal effects in order to increase the accuracy of genetic evaluation of birds for these traits.

Estimation of genetic parameters for important traits using a multi-trait model in late-feathering Qingyuan partridge hens in China

Qingyuan partridge chicken is one of the most well-known Chinese indigenous yellow broilers. In breeding programmes, five traits are usually selected when the chickens are 105 days old, namely body weight (BW), comb height (CH), shank length (SL), shank girth (SG) and feather maturity (FM). The objective of this study was to estimate the genetic parameters of these five traits, especially direct additive genetic correlations, to lay the foundation for balanced selection of Qingyuan partridge chickens. Approximately 9600 records were used for estimation. Variance components for these five traits were estimated using three multi-trait models incorporating different effects via Gibbs sampling. Based on model 1 in which the random effects included direct additive genetic effects and residuals, the estimated direct heritabilities for BW, CH, SL, SG and FM were 0.29 ± 0.04, 0.53 ± 0.04, 0.47 ± 0.04, 0.43 ± 0.05 and 0.18 ± 0.03, respectively. The direct genetic correlations ranged from -0.08 to 0.46. When additionally considering maternal additive genetic effects (model 2), the estimates of direct heritabilities and absolute values of direct additive genetic correlations were smaller. The heritabilities were 0.14 ± 0.04, 0.40 ± 0.02, 0.34 ± 0.05, 0.27 ± 0.05 and 0.12 ± 0.03 for BW, CH, SL, SG and FM, respectively. The direct additive genetic correlations ranged from -0.33 to 0.36. More specifically, the direct additive genetic correlations between BW and CH, SL, SG and FM were 0.19 ± 0.13, 0.15 ± 0.15, 0.36 ± 0.15 and - 0.33 ± 0.21, respectively. The genetic correlations of FM with SL, SG and CH were - 0.15 ± 0.15, -0.08 ± 0.17 and 0.18 ± 0.15, respectively. The direct genetic correlations between CH and SG and SL were - 0.02 ± 0.11 and - 0.20 ± 0.11, respectively, and that between SL and SG was 0.19 ± 0.11. The total heritabilities and maternal additive genetic correlations ranged from 0.16 to 0.44 and from -0.13 to 0.61, respectively. The third model also included the maternal permanent environmental effect for BW. The estimates of direct heritability, direct additive genetic correlation, total heritability and maternal additive genetic correlation were only slightly different from those based on the second model. Therefore, the maternal additive genetic effect has a large effect on the estimation of genetic parameters, and it is better to consider this effect in the genetic evaluation of these five traits. Relatively high direct and maternal additive genetic correlations for most trait pairs suggested that it is better to jointly evaluate these five traits in breeding programmes.

Genome-wide association studies for growth traits in broilers

BACKGROUND

The identification of markers and genes for growth traits may not only benefit for marker assist selection /genomic selection but also provide important information for understanding the genetic foundation of growth traits in broilers.

RESULTS

In the current study, we estimated the genetic parameters of eight growth traits in broilers and carried out the genome-wide association studies for these growth traits. A total of 113 QTNs discovered by multiple methods together, and some genes, including ACTA1, IGF2BP1, TAPT1, LDB2, PRKCA, TGFBR2, GLI3, SLC16A7, INHBA, BAMBI, APCDD1, GPR39, and GATA4, were identified as important candidate genes for rapid growth in broilers.

CONCLUSIONS

The results of this study will provide important information for understanding the genetic foundation of growth traits in broilers.

Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers

In causal relationship studies, the latent variables may summarize the phenotypes in theoretical traits according to their phenotypic correlations, improving the understanding of causal relationships between broilers phenotypes. In this study, we aimed to investigate potential causal relationships among latent variables in broilers using a structural equation model in the context of genetic analysis. The data used in this study comprised 14 traits in broilers with 2,017 records each, and 104,154 animals in pedigree. Four latent variables (WEIGHT, LOSSES, COLOUR, and VISCERA) were defined and validated using Bayesian Confirmatory Factor Analysis. Subsequently, a search for causal linkage structures was performed, obtaining a single causal link structure between the latent variables. Then, this information was used to fit the structural equation model (SEM). The results from the SEM indicated positive causal effects of the variables WEIGHT and LOSSES on the variables VISCERA and COLOUR, respectively, with structural coefficient estimates of 1.006 and 0.040, respectively. On the other hand, an antagonist causal effect of the variable WEIGHT on the variable LOSSES was verified, with a structural coefficient estimate of -4.333. These results highlight the causal relationship between performance and meat quality traits, which may be associated with the natural processes involved in the conversion of muscle into meat and the structural changes in muscle tissues due to intense selection for high growth rates in broilers.

Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

BLUP (best linear unbiased prediction) is the standard for predicting breeding values, where different assumptions can be made on variance-covariance structure, which may influence predictive ability. Herein, we compare accuracy of prediction of four derived-BLUP models: (a) a pedigree relationship matrix (PBLUP), (b) a genomic relationship matrix (GBLUP), (c) a weighted genomic relationship matrix (WGBLUP) and (d) a relationship matrix based on genomic features that consisted of only a subset of SNP selected on a priori information (GFBLUP). We phenotyped a commercial population of broilers for body weight (BW) in five successive weeks and genotyped them using a 50k SNP array. We compared predictive ability of univariate models using conservative cross-validation method, where each full-sib group was divided into two folds. Results from cross-validation showed, with WGBLUP model, a gain in accuracy from 2% to 7% compared with GBLUP model. Splitting the additive genetic matrix into two matrices, based on significance level of SNP (Gf : estimated with only set of SNP selected on significance level, Gr : estimated with the remaining SNP), led to a gain in accuracy from 1% to 70%, depending on the proportion of SNP used to define Gf . Thus, information from GWAS in models improves predictive ability of breeding values for BW in broilers. Increasing the power of detection of SNP effects, by acquiring more data or improving methods for GWAS, will help improve predictive ability.

Effects of Nutritional Restriction during Laying Period of Fat and Lean Line Broiler Breeder Hens on Meat Quality Traits of Offspring

Simple Summary

The meat quality of livestock products is widely appreciated. Maternal nutrition can affect the deposition of nutrients in eggs, and then change the apparent metabolism, development process, and performance of offspring. Our research indicated that meat quality traits are also affected by maternal nutritional level and are related to the nutritional requirements of different genotypes. Some of the effects disappeared at the end of the growth stage. These situations remind poultry producers to consider the impact of feed restrictions on the quality of meat for future generations.

Abstract

The offspring meat quality of hens undergoing a 25% dietary restriction treatment during the laying period were evaluated in fat and lean line breeder. A total of 768 female birds (384/line) were randomly assigned to four groups (12 replicates/group, 16 birds/replicates). Maternal feed restriction (MFR) and normal started at 27 weeks of age. Offspring broilers were fed ad libitum. The offspring meat quality traits and muscle fiber morphology in different periods were measured. At birth, significant interactions were found on breast muscle fiber morphology (p < 0.05). At 28 days, MFR decreased breast water content and increased thigh crude fat content, and significant interactions were observed on breast crude fat and protein contents (p < 0.05). At 56 days, MFR affected morphology of peroneus longus muscle tissue, and significant interactions were found on thigh redness at 48 h and amino acid contents in breast and thigh muscle (p < 0.05). Overall, MRF may lead to offspring birth sarcopenia. Such offspring grow more easily to deposit fat in a nutritious environment, but they will self-regulate adverse symptoms during growth and development. The two lines respond differently to maternal nutritional disturbance due to different nutritional requirements and metabolic patterns.

Estimation of breeding value, genetic parameters and maternal effects of economic traits in rural male parent line chicken using pedigree relationships in an animal model

Breeding value (BV), genetic parameters and additive genetic, and maternal effects were evaluated on growth and production traits utilizing data from eight generations employing animal model in a rural male parent line (PD-6) chicken at ICAR-Directorate of Poultry Research, Hyderabad, India. The least squares means (LSM) for body weight (BW) and shank length (SL) up to 6 weeks of age varied significantly (p ≤ .01) among the generations and hatches. BW increased significantly (p ≤ .01) over the generations and decreased with the hatches. Sex also had a significant effect on BW and shank length except for BW at 0 day (BW0). LSM for BW (BW6) and Shank length (SL6) at 6 weeks of age were 598.84 ± 0.79 g and 74.57 ± 0.04 mm, respectively. Males recorded significantly (p ≤ .01) higher BWs and shank length. All the production traits were significantly (p ≤ .01) influenced by the generation effect. The overall LSM for age at sexual maturity (ASM), egg production at 40 weeks (EP40) and egg weight at 40 weeks (EW40) were 164.93 ± 0.23 days, 74.66 ± 0.40 eggs and 54.79 ± 0.08 g, respectively. Model 3 with additive, maternal permanent environmental and residual effects was the appropriate model for BW2, BW4, BW6, SL4 and SL6, whereas Model 4 with maternal effects was the best for BW0. The heritability estimates for BW6 and SL6 were 0.22 ± 0.02 and 0.18 ± 0.02, respectively. Model 1 with additive direct and residual effects was the best appropriate model for all the production traits. The heritability estimates of EP40 and EW40 were 0.16 ± 0.04 and 0.34 ± 0.05, respectively. BW and shank length were highly correlated with significant (p ≤ .05) positive association from different components. The correlation coefficient from direct additive component between egg production and BW40 was negative, while it was positive with less magnitude between egg production and BW20. The egg production and egg weights had a negative association at different ages. BV of SL6, the primary trait of selection, was significant (p ≤ .05) across the generations and increased linearly with an average genetic gain of 1.05 mm per generation. BV of BW6 was also significant (p ≤ .05) and increased linearly as correlated response with an average genetic response of 22.34 g per generation. BV of EP40 showed an increasing trend with a genetic gain of 0.02 eggs per generation. The EW 40 also increased linearly with an average genetic gain of 0.06 g. The average inbreeding coefficient of the population was 0.015. The study concluded that the population was in ideal status with a linearly increasing trend of average BV with negligible inbreeding over the eight generations of selection.