The effect of selection and sex on genetic parameters of body weight at different ages in a commercial broiler chicken population

Sex-specific genetic parameter estimates of body weight in Mazandaran indigenous chickens

Body weight is an economically important trait in poultry that shows sexual dimorphism (SD). In the present study, variation in SD in Mazandaran native chickens was investigated in terms of the (Co) variance components and genetic parameters of body weight between males and females. Studied traits were body weights at hatch (BW1), 8 weeks (BW8) and 12 weeks of age (BW12). Also, for weight at sexual maturity (WSM) covariance components were only estimated in females. Cross-sex direct and maternal correlations were also estimated for studied traits except for WSM. For this purpose, a deep 21-generation pedigree and body weight data (57,576 BW1, 72,925 BW8, 62,727 BW12 and, 42,496 WSM) were used. Evaluation of SD of body weight was performed using six bivariate animal models with and without considering the genetic and permanent maternal environmental effects under the restricted maximum likelihood method in WOMBAT software. Model with direct additive genetic effects and maternal genetic effects without covariance between them was identified as the best model for BW1 and BW8. The Model including direct additive genetic effects and permanent maternal environmental effects was the best model for BW12 and WSM. Direct heritability (h2) estimates for BW1, BW8 and, BW12 were, respectively, 0.05 ± 0.013, 0.17 ± 0.02 and, 0.25 ± 0.03 in males and, 0.05 ± 0.012, 0.15 ± 0.01 and 0.21 ± 0.01 in females. Also, the direct heritability of WSM based on univariate analysis in females was estimated to be 0.40 ± 0.01. Maternal heritability (h m 2 ) varied from 0.39 ± 0.01 (BW1) to 0.04 ± 0.009 (BW8) in males, and 0.36 ± 0.10 (BW1) to 0.04 ± 0.006 (BW8) in females. The correlation between direct genetic effects between males and females for BW1 was not significantly different from one. The direct genetic correlation between the two sexes for BW8 and BW12 was significantly different from 1 concluding that these traits are dimorphic in terms of direct genetic effects and therefore independent selection in both sexes is possible.

Genome-wide association analysis identify candidate genes for feed efficiency and growth traits in Wenchang chickens

BACKGROUND

Wenchang chickens are one of the most popular local chicken breeds in the Chinese chicken industry. However, the low feed efficiency is the main shortcoming of this breed. Therefore, there is a need to find a more precise breeding method to improve the feed efficiency of Wenchang chickens. In this study, we explored important candidate genes and variants for feed efficiency and growth traits through genome-wide association study (GWAS) analysis.

RESULTS

Estimates of genomic heritability for growth and feed efficiency traits, including residual feed intake (RFI) of 0.05, average daily food intake (ADFI) of 0.21, average daily weight gain (ADG) of 0.24, body weight (BW) at 87, 95, 104, 113 days of age (BW87, BW95, BW104 and BW113) ranged from 0.30 to 0.44. Important candidate genes related to feed efficiency and growth traits were identified, such as PLCE1, LAP3, MED28, QDPR, LDB2 and SEL1L3 genes.

CONCLUSION

The results identified important candidate genes for feed efficiency and growth traits in Wenchang chickens and provide a theoretical basis for the development of new molecular breeding technology.

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.

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 (G_f: estimated with only set of SNP selected on significance level, G_r: estimated with the remaining SNP), led to a gain in accuracy from 1% to 70%, depending on the proportion of SNP used to define G_f. 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.

Improving a nonlinear Gompertz growth model using bird-specific random coefficients in two heritage chicken lines

Growth models describe body weight (BW) changes over time, allowing information from longitudinal measurements to be combined into a few parameters with biological interpretation. Nonlinear mixed models (NLMM) allow for the inclusion of random factors. Random factors can account for a relatively large subset of the total variance explained by bird-specific measurement correlation. The aim of this study was to evaluate different NLMM using birds from 2 heritage chicken lines; New Hampshire (NH) and Brown Leghorn (BL). A total of 32 birds (16 mixed sex birds from each strain) were raised to 17 wk of age. After 12 wk, half were continued on ad libitum (AL) feed intake, and half were pair-fed, using a precision feeding system; they were given 95% of the AL intake of a paired bird closest in BW. Residual feed intake (RFI) of birds, as an indicator of production efficiency, was increased in pair-fed BL birds as a result of minor feed restriction. Growth data of the birds were fit to a mixed Gompertz model with a variety of different bird-specific random coefficients. The model had the form: BW=Wm×exp−exp−b(t−tinf); where Wm was the mature BW, b was the rate of maturing, t was age (d), t_inf was the inflection point (d). This fixed-effects model was compared with NLMM using model evaluation criteria to evaluate relative model suitability. Random coefficients, Wm_u ∼ N(0,V_Wm) and b_u ∼ N(0,V_b)_, were tested separately and together and their differences, for strains, sex, and feeding treatments, were reported as different where P ≤ 0.05. The model with both random coefficients was determined to be the most parsimonious model, based on an assessment of serial correlation of the residuals. NLMM coefficients allow stochastic prediction of the mean age and its variation that birds need to achieve a certain BW, allowing for unique new decision support modeling applications; these could be used in stochastic modeling to evaluate the economic impact of management decisions.

Genetic evaluation for latent variables derived from factor analysis in broilers

1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.

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

A multivariate model was developed and used to estimate genetic parameters of body weight (BW) at 1-6 weeks of age of broilers raised in a commercial environment. The development of model was based on the predictive ability of breeding values evaluated from a cross-validation procedure that relied on half-sib correlation. The multivariate model accounted for heterogeneous variances between sexes through standardization applied to male and female BWs differently. It was found that the direct additive genetic, permanent environmental maternal and residual variances for BW increased drastically as broilers aged. The drastic increase in variances over weeks of age was mainly due to scaling effects. The ratio of the permanent environmental maternal variance to phenotypic variance decreased gradually with increasing age. Heritability of BW traits ranged from 0.28 to 0.33 at different weeks of age. The direct genetic effects on consecutive weekly BWs had high genetic correlations (0.85-0.99), but the genetic correlations between early and late BWs were low (0.32-0.57). The difference in variance components between sexes increased with increasing age. In conclusion, the permanent environmental maternal effect on broiler chicken BW decreased with increasing age from weeks 1 to 6. Potential bias of the model that considered identical variances for sexes could be reduced when heterogeneous variances between sexes are accounted for in the model.

Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens

Background

The objectives of this study were to (1) simultaneously estimate genetic parameters for BW, feed intake (FI), and body weight gain (Gain) during a FI test in broiler chickens using multi-trait Bayesian analysis; (2) derive phenotypic and genetic residual feed intake (RFI) and estimate genetic parameters of the resulting traits; and (3) compute a Bayesian measure of direct and correlated superiority of a group selected on phenotypic or genetic residual feed intake. A total of 56,649 male and female broiler chickens were measured at one of two ages (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{t}}$$\end{document}t or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{t}} - 6$$\end{document}t-6 days). BW, FI, and Gain of males and females at the two ages were considered as separate traits, resulting in a 12-trait model. Phenotypic RFI (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{P}}$$\end{document}RFIP) and genetic RFI (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{G}}$$\end{document}RFIG) were estimated from a conditional distribution of FI given BW and Gain using partial phenotypic and partial genetic regression coefficients, respectively.

Results

Posterior means of heritability for BW, FI and Gain were moderately high and estimates were significantly different between males and females at the same age for all traits. In addition, the genetic correlations between male and female traits at the same age were significantly different from 1, which suggests a sex-by-genotype interaction. Genetic correlations between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{P}}$$\end{document}RFIP and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{G }}$$\end{document}RFIG were significantly different from 1 at an older age but not at a younger age.

Conclusions

The results of the multivariate Bayesian analyses in this study showed that genetic evaluation for production and feed efficiency traits should take sex and age differences into account to increase accuracy of selection and genetic gain. Moreover, for communicating with stakeholders, it is easier to explain results from selection on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{G}}$$\end{document}RFIG than selection on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{P}}$$\end{document}RFIP, since \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RFI}}_{\text{G}}$$\end{document}RFIG is genetically independent of production traits and it explains the efficiency of birds in nutrient utilization independently of energy requirements for production and maintenance.

Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

BACKGROUND

The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment.

METHODS

A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only.

RESULTS

Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds.

CONCLUSIONS

The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

Genome wide association study of body weight and feed efficiency traits in a commercial broiler chicken population, a re-visitation

Genome wide association study was conducted using a mixed linear model (MLM) approach that accounted for family structure to identify single nucleotide polymorphisms (SNPs) and candidate genes associated with body weight (BW) and feed efficiency (FE) traits in a broiler chicken population. The results of the MLM approach were compared with the results of a general linear model approach that does not take family structure in to account. In total, 11 quantitative trait loci (QTL) and 21 SNPs, were identified to be significantly associated with BW traits and 5 QTL and 5 SNPs were found associated with FE traits using MLM approach. Besides some overlaps between the results of the two GWAS approaches, there are considerable differences in the detected QTL. Even though the genomic inflation factor (λ) values indicate that there is no strong family structure in this population, using models that account for the existing family structure may reduce bias and increase accuracy of the estimated SNP effects in the association analysis. The SNPs and candidate genes identified in this study provide information on the genetic background of BW and FE traits in broiler chickens and might be used as prior information for genomic selection.

Macro-environmental sensitivity for growth rate in Danish Duroc pigs is under genetic control

The aim of this study was to examine (i) the genetic variation in macro-environmental sensitivity (macro-ES) for ADG in Danish Duroc pigs, (ii) the genetic heterogeneity among sexes, and (iii) residual variance heterogeneity among herds. Record of ADG for 32,297 boars (19 herds) and 42,724 gilts (16 herds) was used for analysis. The data were provided by the National Danish Pig Research Centre. The analysis was performed by fitting univariate reaction norm models with the herd-year-month on test (HYM) effect as environmental covariates and herd-specific residual variance for boars and gilts separately under a Bayesian setting. The environmental covariate was inferred simultaneously with other parameters of the model. Gibbs sampling was used to sample model dispersion and location parameters. The posterior means and highest posterior density intervals of the additive genetic variance, genetic correlations for ADG, and heritability were calculated over the continuous environmental range of -3σh to +3σh (SD of the HYM effect). The coheritability of ADG at the average environmental level and ADG in the environments along the -3σh to +3σh environmental gradient were also calculated. The analysis showed significant variation in macro-ES, revealing genotype by environment interactions (G × E) for ADG. The presence of G × E resulted in changes in additive genetic variance and heritability across the -3σh to +3σh range. The genetic correlations were high and positive between ADG in environments differing by 1σh units or less and decreased to moderately positive between ADG in the extreme environments in both sexes. The coheritability of ADG in the environment at the average level and the -3σh environment for boars were greater than the heritability in the environment at the average level, while it was less for gilts. The coheritability of ADG in the environment at the average level and the +3σh environment for boars was less than heritability in the environment at the average level, while it was either the same or greater for gilts, depending on the residual variance. Boars had larger additive genetic and residual variances than gilts. Heterogeneity of residual variances across herds was shown for both sexes. In conclusion, this study shows the presence of macro-ES, genetic variance heterogeneity among sexes for ADG in pigs, and residual variance heterogeneity across herds.