Background: The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and HAM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. Results: VC estimates from HAM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in HAM estimates arises when selective genotyping is used to construct the H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from HAM under random genotyping have the expected values. Predicted breeding values from HAM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions. Conclusions: We conclude that VC estimates from HAM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of HAM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation.

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.

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.

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 ($${\text{t}}$$ t or $${\text{t}} - 6$$ 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 ($${\text{RFI}}_{\text{P}}$$ RFI P ) and genetic RFI ($${\text{RFI}}_{\text{G}}$$ RFI G ) 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 $${\text{RFI}}_{\text{P}}$$ RFI P and $${\text{RFI}}_{\text{G }}$$ RFI G 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 $${\text{RFI}}_{\text{G}}$$ RFI G than selection on $${\text{RFI}}_{\text{P}}$$ RFI P , since $${\text{RFI}}_{\text{G}}$$ RFI G is genetically independent of production traits and it explains the efficiency of birds in nutrient utilization independently of energy requirements for production and maintenance.

Effects of lactation length and weaning-to-conception interval on the subsequent litter size of purebred sows were estimated using an animal model. Data on 2,847 Landrace sows with 7,125 litters born between January 1989 and May 1997 and on 1,234 Yorkshire sows with 2,999 litters born between January 1990 and May 1997 were obtained from two Canadian selection herds. Sows having a lactation of less than 14 d (MMEW) were usually not mated until their second estrus, whereas sows weaned after at least 14 d of lactation (later weaning) were usually mated on their first estrus. Litter size included both number of pigs born alive and those stillborn. Linear, quadratic, and logarithmic effects of lactation length were tested. The effect of weaning-to-conception interval on litter size was modeled using an approach based on threshold variables and an approach using segmented polynomials. Results indicated linear and logarithmic effects of lactation length on subsequent litter size for Yorkshire and Landrace breeds, respectively. Litter size decreased as weaning-to-conception interval increased up to 7 and 10 d for Yorkshire and Landrace, respectively, then increased with further increases in weaning-to-conception interval up to 35 and 30 d for the two breeds, and then remained constant. The MMEW sows did not have lower subsequent litter sizes than later-weaned sows because the negative effect of a shorter lactation was offset by the positive effect of a longer weaning-to-conception interval. However, average time spent open per parity was longer for MMEW sows than for later-weaned sows. Both lactation length and weaning-to-conception interval should be considered in models for the genetic evaluation of litter size in purebred swine. Segmented polynomials can be used to predict litter size as a continuous function of weaning-to-conception interval or to derive weaning-to-conception interval adjustment factors for litter size.