Genetic analysis of feed efficiency and novel feeding behavior traits measured in group-housed broilers using electronic feeders.

This study aimed to estimate genetic parameters for feeding behavior (FB) traits and to assess their genetic relationship with performance traits in group-housed broilers. In total, 99,472,151 visits were recorded for 95,711 birds between 2017 and 2022 using electronic feeders. The visits were first clustered into 2,667,617 daily observations for ten FB traits: daily feed intake (DFI), daily number of visits (NVIS), time spent at the feeders (TSF), number of visited feeders (NVF), visiting activity interval (VAI), feeding rate (FR), daily number of meals (NMEAL), average intake per meal (INTMEAL), number of visits per meal (VISMEAL) and interval between meals (MEALIVL). All FB traits were then considered as the average per bird across the feeding test period. Three growth traits (body weight at the start - SBW and at the end of the feeding test - FBW, and weight gain over the test period - BWG), and 2 feed efficiency (FE) traits (Feed Conversion Rate - FCR and Residual Feed Intake - RFI) were also recorded. The (co)variance components were estimated using multitrait animal mixed models. For growth and FE, the heritability (h2) estimates were moderate, ranging from 0.20 ± 0.01 (BWG) to 0.32 ± 0.02 (RFI). Overall, the h2 estimates for FB traits were higher than for productive traits, ranging from 0.31 ± 0.01 (DFI) to 0.56 ± 0.02 (TSF). DFI presented high genetic correlations (0.53-0.86) with all performance traits. Conversely, the remaining FB traits presented null to moderate genetic correlations with these traits, ranging from -0.38 to 0.42 for growth traits and between -0.14 and 0.25 for FE traits. Genetic selection for favorable feeding behavior is expected to exhibit a fast genetic response. The results suggest that it is possible to consider different feeding strategies without compromising the genetic progress of FE. Conversely, breeding strategies prioritizing a higher bird activity might result in lighter broiler lines in the long term, given the negative genetic correlations between visit-related traits (NV, NVF, and NMEAL) and growth traits (SBW and FBW).

Bone Metabolite Profile Differs between Normal and Femur Head Necrosis (FHN/BCO)-Affected Broilers: Implications for Dysregulated Metabolic Cascades in FHN Pathophysiology.

Femur head necrosis (FHN), also known as bacterial chondronecrosis with osteomyelitis (BCO), has remained an animal welfare and production concern for modern broilers regardless of efforts to select against it in primary breeder flocks. Characterized by the bacterial infection of weak bone, FHN has been found in birds without clinical lameness and remains only detectable via necropsy. This presents an opportunity to utilize untargeted metabolomics to elucidate potential non-invasive biomarkers and key causative pathways involved in FHN pathology. The current study used ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) and identified a total of 152 metabolites. Mean intensity differences at p < 0.05 were found in 44 metabolites, with 3 significantly down-regulated and 41 up-regulated in FHN-affected bone. Multivariate analysis and a partial least squares discriminant analysis (PLS-DA) scores plot showed the distinct clustering of metabolite profiles from FHN-affected vs. normal bone. Biologically related molecular networks were predicted using an ingenuity pathway analysis (IPA) knowledge base. Using a fold-change cut off of -1.5 and 1.5, top canonical pathways, networks, diseases, molecular functions, and upstream regulators were generated using the 44 differentially abundant metabolites. The results showed the metabolites NAD+, NADP+, and NADH to be downregulated, while 5-Aminoimidazole-4-carboxamide ribonucleotide (AICAR) and histamine were significantly increased in FHN. Ascorbate recycling and purine nucleotides degradation were the top canonical pathways, indicating the potential dysregulation of redox homeostasis and osteogenesis. Lipid metabolism and cellular growth and proliferation were some of the top molecular functions predicted based on the metabolite profile in FHN-affected bone. Network analysis showed significant overlap across metabolites and predicted upstream and downstream complexes, including AMP-activated protein kinase (AMPK), insulin, collagen type IV, mitochondrial complex, c-Jun N-terminal kinase (Jnk), extracellular signal-regulated kinase (ERK), and 3ß-hydroxysteroid dehydrogenase (3ß HSD). The qPCR analysis of relevant factors showed a significant decrease in AMPKα2 mRNA expression in FHN-affected bone, supporting the predicted downregulation found in the IPA network analysis. Taken as a whole, these results demonstrate a shift in energy production, bone homeostasis, and bone cell differentiation that is distinct in FHN-affected bone, with implications for how metabolites drive the pathology of FHN.

(Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers.

This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits (CT1 and CT2) measured in purebred broiler chickens. The MT models also included information on indicator traits measured in vivo [Growth and feed efficiency trait (FE)]. We proposed an approach termed (quasi) multitask SVR (QMTSVR), with hyperparameter optimization performed via genetic algorithm. ST and MT Bayesian shrinkage and variable selection models [genomic best linear unbiased predictor (GBLUP), BayesC (BC), and reproducing kernel Hilbert space (RKHS) regression] were employed as benchmarks. MT models were trained using 2 validation designs (CV1 and CV2), which differ if the information on secondary traits is available in the testing set. Models' predictive ability was assessed with prediction accuracy (ACC; i.e. the correlation between predicted and observed values, divided by the square root of phenotype accuracy), standardized root-mean-squared error (RMSE*), and inflation factor (b). To account for potential bias in CV2-style predictions, we also computed a parametric estimate of accuracy (ACCpar). Predictive ability metrics varied according to trait, model, and validation design (CV1 or CV2), ranging from 0.71 to 0.84 for ACC, 0.78 to 0.92 for RMSE*, and between 0.82 and 1.34 for b. The highest ACC and smallest RMSE* were achieved with QMTSVR-CV2 in both traits. We observed that for CT1, model/validation design selection was sensitive to the choice of accuracy metric (ACC or ACCpar). Nonetheless, the higher predictive accuracy of QMTSVR over MTGBLUP and MTBC was replicated across accuracy metrics, besides the similar performance between the proposed method and the MTRKHS model. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike-slab multivariate priors.

Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds.

BACKGROUND: In genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a 'J-factor', which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors. RESULTS: For body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV. CONCLUSIONS: Based on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

Classifying aneuploidy in genotype intensity data using deep learning.

Aneuploidy is the loss or gain of one or more chromosomes. Although it is a rare phenomenon in liveborn individuals, it is observed in livestock breeding populations. These breeding populations are often routinely genotyped and the genotype intensity data from single nucleotide polymorphism (SNP) arrays can be exploited to identify aneuploidy cases. This identification is a time-consuming and costly task, because it is often performed by visual inspection of the data per chromosome, usually done in plots of the intensity data by an expert. Therefore, we wanted to explore the feasibility of automated image classification to replace (part of) the visual detection procedure for any diploid species. The aim of this study was to develop a deep learning Convolutional Neural Network (CNN) classification model based on chromosome level plots of SNP array intensity data that can classify the images into disomic, monosomic and trisomic cases. A multispecies dataset enriched for aneuploidy cases was collected containing genotype intensity data of 3321 disomic, 1759 monosomic and 164 trisomic chromosomes. The final CNN model had an accuracy of 99.9%, overall precision was 1, recall was 0.98 and the F1 score was 0.99 for classifying images from intensity data. The high precision assures that cases detected are most likely true cases, however, some trisomy cases may be missed (the recall of the class trisomic was 0.94). This supervised CNN model performed much better than an unsupervised k-means clustering, which reached an accuracy of 0.73 and had especially difficult to classify trisomic cases correctly. The developed CNN classification model provides high accuracy to classify aneuploidy cases based on images of plotted X and Y genotype intensity values. The classification model can be used as a tool for routine screening in large diploid populations that are genotyped to get a better understanding of the incidence and inheritance, and in addition, avoid anomalies in breeding candidates.

Estimation of dam line composition of 3-way crossbred animals using genomic information.

BACKGROUND: In genomic prediction including data of 3- or 4-way crossbred animals, line composition is usually fitted as a regression on expected line proportions, which are 0.5, 0.25 and 0.25, respectively, for 3-way crossbred animals. However, actual line proportions for the dam lines can vary between ~ 0.1 and 0.4, and ignoring this variation may affect the genomic estimated breeding values of purebred selection candidates. Our aim was to validate a proposed gold standard to evaluate different approaches for estimating line proportions using simulated data, and to subsequently use this in actual 3-way crossbred broiler data to evaluate several other methods. RESULTS: Analysis of simulated data confirmed that line proportions computed from assigned breed-origin-of-alleles (BOA) provide a very accurate gold standard, even if the parental lines are closely related. Alternative investigated methods were linear regression of genotypes on line-specific allele frequencies, maximum likelihood estimation using the program ADMIXTURE, and the genomic relationship of crossbred animals with their maternal grandparents. The results from the simulated data showed that the genomic relationship with the maternal grandparent was most accurate, and least affected by closer relationships between the dam lines. Linear regression and ADMIXTURE performed similarly for unrelated lines, but their accuracy dropped considerably when the dam lines were more closely related. In almost all cases, estimates improved after adjusting them to ensure that the sum of dam line contributions within animals was equal to 0.5, and within dam line and across animals the average was equal to 0.25. Results from the broiler data were much more similar between methods. In both cases, stringent linkage disequilibrium pruning of genotype data led to a relatively low accuracy of predicted line proportions, due to the loss of too many single nucleotide polymorphisms. CONCLUSIONS: With relatively unrelated parental lines as typical in crosses in pigs and poultry, linear regression of crossbred genotypes on line-specific allele frequencies and ADMIXTURE are very competitive methods. Thus, linear regression may be the method of choice, as it does not require genotypes of grandparents, is computationally very efficient, and easily implemented and adapted for considering the specific nature of the crossbred animals analysed.

A low-tech, cost-effective and efficient method for safeguarding genetic diversity by direct cryopreservation of poultry embryonic reproductive cells.

Chickens are an important resource for smallholder farmers who raise locally adapted, genetically distinct breeds for eggs and meat. The development of efficient reproductive technologies to conserve and regenerate chicken breeds safeguards existing biodiversity and secures poultry genetic resources for climate resilience, biosecurity, and future food production. The majority of the over 1600 breeds of chicken are raised in low and lower to middle income countries under resource-limited, small-scale production systems, which necessitates a low-tech, cost-effective means of conserving diversity is needed. Here, we validate a simple biobanking technique using cryopreserved embryonic chicken gonads. The gonads are quickly isolated, visually sexed, pooled by sex, and cryopreserved. Subsequently, the stored material is thawed and dissociated before injection into sterile host chicken embryos. By using pooled GFP and RFP-labelled donor gonadal cells and Sire Dam Surrogate mating, we demonstrate that chicks deriving entirely from male and female donor germ cells are hatched. This technology will enable ongoing efforts to conserve chicken genetic diversity for both commercial and smallholder farmers, and to preserve existing genetic resources at poultry research facilities.

Avian Primordial Germ Cells Are Bipotent for Male or Female Gametogenesis.

In birds, males are the homogametic sex (ZZ) and females are the heterogametic sex (ZW). Here, we investigate the role of chromosomal sex and germ cell competition on avian germ cell differentiation. We recently developed genetically sterile layer cockerels and hens for use as surrogate hosts for primordial germ cell (PGC) transplantation. Using in vitro propagated and cryopreserved PGCs from a pedigree Silkie broiler breed, we now demonstrate that sterile surrogate layer hosts injected with same sex PGCs have normal fertility and produced pure breed Silkie broiler offspring when directly mated to each other in Sire Dam Surrogate mating. We found that female sterile hosts carrying chromosomally male (ZZ) PGCs formed functional oocytes and eggs, which gave rise to 100% male offspring after fertilization. Unexpectedly, we also observed that chromosomally female (ZW) PGCs carried by male sterile hosts formed functional spermatozoa and produced viable offspring. These findings demonstrate that avian PGCs are not sexually restricted for functional gamete formation and provide new insights for the cryopreservation of poultry and other bird species using diploid stage germ cells.

Investigating the persistence of accuracy of genomic predictions over time in broilers.

Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees and 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over 7 years, of which 154,318 from the last 4 years were genotyped. Training populations were constructed adding 1 year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first 3 years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of 1 year of genotypes in addition to 4 years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included 5 or 2 years of data, and a decrease of ~7% was observed when the training population included only 1 year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared with training sets without genomic data. The two most recent years of pedigree, phenotypic, and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

Kinetics of the Cellular and Transcriptomic Response to Eimeria maxima in Relatively Resistant and Susceptible Chicken Lines.

Eimeria maxima is a common cause of coccidiosis in chickens, a disease that has a huge economic impact on poultry production. Knowledge of immunity to E. maxima and the specific mechanisms that contribute to differing levels of resistance observed between chicken breeds and between congenic lines derived from a single breed of chickens is required. This study aimed to define differences in the kinetics of the immune response of two inbred lines of White Leghorn chickens that exhibit differential resistance (line C.B12) or susceptibility (line 15I) to infection by E. maxima. Line C.B12 and 15I chickens were infected with E. maxima and transcriptome analysis of jejunal tissue was performed at 2, 4, 6 and 8 days post-infection (dpi). RNA-Seq analysis revealed differences in the rapidity and magnitude of cytokine transcription responses post-infection between the two lines. In particular, IFN-γ and IL-10 transcript expression increased in the jejunum earlier in line C.B12 (at 4 dpi) compared to line 15I (at 6 dpi). Line C.B12 chickens exhibited increases of IFNG and IL10 mRNA in the jejunum at 4 dpi, whereas in line 15I transcription was delayed but increased to a greater extent. RT-qPCR and ELISAs confirmed the results of the transcriptomic study. Higher serum IL-10 correlated strongly with higher E. maxima replication in line 15I compared to line C.B12 chickens. Overall, the findings suggest early induction of the IFN-γ and IL-10 responses, as well as immune-related genes including IL21 at 4 dpi identified by RNA-Seq, may be key to resistance to E. maxima.

Modeling genetic differences of combined broiler chicken populations in single-step GBLUP.

The introduction of animals from a different environment or population is a common practice in commercial livestock populations. In this study, we modeled the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations. The pedigree was composed of 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the origin of parents and to use unknown parent groups (UPG) or metafounders (MF). Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP using the Algorithm for Proven and Young. Bias, dispersion, and accuracy of GEBV for the validation birds, that is, from the most recent generation, were computed. The bias and dispersion were estimated with the linear regression (LR) method,whereas accuracy was estimated by the LR method and predictive ability. When fixed UPG were fit without estimated inbreeding, the model did not converge. In contrast, models with fixed UPG and estimated inbreeding or random UPG converged and resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation. Genomic predictions with MF were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy can be obtained by adding an extra fixed effect to account for the origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR method is more consistent among scenarios, whereas the predictive ability greatly depends on the model specification.

Direct allele introgression into pure chicken breeds using Sire Dam Surrogate (SDS) mating.

Poultry is the most abundant livestock species with over 60 billion chickens raised globally per year. The majority of chicken are produced from commercial flocks, however many indigenous chicken breeds play an important role in rural economies as they are well adapted to local environmental and scavenging conditions. The ability to make precise genetic changes in chicken will permit the validation of genetic variants responsible for climate adaptation and disease resilience, and the transfer of beneficial alleles between breeds. Here, we generate a novel inducibly sterile surrogate host chicken. Introducing donor genome edited primordial germ cells into the sterile male and female host embryos produces adult chicken carrying only exogenous germ cells. Subsequent direct mating of the surrogate hosts, Sire Dam Surrogate (SDS) mating, recreates the donor chicken breed carrying the edited allele in a single generation. We demonstrate the introgression and validation of two feather trait alleles, Dominant white and Frizzle into two pure chicken breeds using the SDS surrogate hosts.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

BACKGROUND: Deep neural networks (DNN) are a particular case of artificial neural networks (ANN) composed by multiple hidden layers, and have recently gained attention in genome-enabled prediction of complex traits. Yet, few studies in genome-enabled prediction have assessed the performance of DNN compared to traditional regression models. Strikingly, no clear superiority of DNN has been reported so far, and results seem highly dependent on the species and traits of application. Nevertheless, the relatively small datasets used in previous studies, most with fewer than 5000 observations may have precluded the full potential of DNN. Therefore, the objective of this study was to investigate the impact of the dataset sample size on the performance of DNN compared to Bayesian regression models for genome-enable prediction of body weight in broilers by sub-sampling 63,526 observations of the training set. RESULTS: Predictive performance of DNN improved as sample size increased, reaching a plateau at about 0.32 of prediction correlation when 60% of the entire training set size was used (i.e., 39,510 observations). Interestingly, DNN showed superior prediction correlation using up to 3% of training set, but poorer prediction correlation after that compared to Bayesian Ridge Regression (BRR) and Bayes Cπ. Regardless of the amount of data used to train the predictive machines, DNN displayed the lowest mean square error of prediction compared to all other approaches. The predictive bias was lower for DNN compared to Bayesian models, across all dataset sizes, with estimates close to one with larger sample sizes. CONCLUSIONS: DNN had worse prediction correlation compared to BRR and Bayes Cπ, but improved mean square error of prediction and bias relative to both Bayesian models for genome-enabled prediction of body weight in broilers. Such findings, highlights advantages and disadvantages between predictive approaches depending on the criterion used for comparison. Furthermore, the inclusion of more data per se is not a guarantee for the DNN to outperform the Bayesian regression methods commonly used for genome-enabled prediction. Nonetheless, further analysis is necessary to detect scenarios where DNN can clearly outperform Bayesian benchmark models.

Correction to: Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles.

Following publication of original article [1], we noticed that there was an error: Eq. (3) on page 5 is the genomic relationship matrix that.

Reviving rare chicken breeds using genetically engineered sterility in surrogate host birds.

In macrolecithal species, cryopreservation of the oocyte and zygote is not possible due to the large size and quantity of lipid deposited within the egg. For birds, this signifies that cryopreserving and regenerating a species from frozen cellular material are currently technically unfeasible. Diploid primordial germ cells (PGCs) are a potential means to freeze down the entire genome and reconstitute an avian species from frozen material. Here, we examine the use of genetically engineered (GE) sterile female layer chicken as surrogate hosts for the transplantation of cryopreserved avian PGCs from rare heritage breeds of chicken. We first amplified PGC numbers in culture before cryopreservation and subsequent transplantation into host GE embryos. We found that all hatched offspring from the chimera GE hens were derived from the donor rare heritage breed broiler PGCs, and using cryopreserved semen, we were able to produce pure offspring. Measurement of the mutation rate of PGCs in culture revealed that 2.7 × 10-10 de novo single-nucleotide variants (SNVs) were generated per cell division, which is comparable with other stem cell lineages. We also found that endogenous avian leukosis virus (ALV) retroviral insertions were not mobilized during in vitro propagation. Taken together, these results show that mutation rates are no higher than normal stem cells, essential if we are to conserve avian breeds. Thus, GE sterile avian surrogate hosts provide a viable platform to conserve and regenerate avian species using cryopreserved PGCs.

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 ([Formula: see text] or [Formula: see text] 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 ([Formula: see text]) and genetic RFI ([Formula: see text]) 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 [Formula: see text] and [Formula: see text] 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 [Formula: see text] than selection on [Formula: see text], since [Formula: see text] is genetically independent of production traits and it explains the efficiency of birds in nutrient utilization independently of energy requirements for production and maintenance.

Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset.

Broiler breeding programs rely on crossbreeding. With genomic selection, widespread use of crossbred performance in breeding programs comes within reach. Commercial crossbreds, however, may have unknown pedigrees and their genomes may include DNA from 2 to 4 different breeds. Our aim was, for a broiler dataset with a limited number of sires having both purebred and crossbred offspring generated using natural mating, to rapidly derive parentage, assess the distribution of the sire contribution to the offspring generation, and to assess breed-of-origin of alleles in crossbreds. The dataset contained genotypes for 56,075 SNPs for 5,882 purebred and 10,943 3-way crossbred offspring generated by natural mating of 164 purebred sires to 1,016 purebred and 1,386 F1 crossbred hens. Using our algorithm FindParents, joint parentage derivation for the offspring and parent generations required only 1 m 29 s to retrieve parentage for 20,253 animals considering 4,504 possible parents. FindParents was similarly accurate as a maximum likelihood based method, apart from situations where settings of FindParents did not match the genotyping error rate in the data. Numbers of offspring per sire had a very skewed distribution, ranging from 1 to 270 crossbreds and 1 to 154 purebreds. Derivation of breed-of-origin of alleles relied on phasing all genotypes, including 8,205, 372, and 720 animals from the 3 pure lines involved, and allocating haplotypes in the crossbreds to purebred lines based on observed frequencies in the purebred lines. Breed-of-origin could be derived for 96.94% of the alleles of the 1,386 F1 crossbred hens and for 91.88% of the alleles of the 10,943 3-way crossbred offspring, of which 49.49% to the sire line. The achieved percentage of assignment to the sire line was sufficient to proceed with subsequent analyses requiring only the breed-of-origin of the paternal alleles to be known. Although required number of animals may be population dependent, to increase the total percentage of assigned alleles, it seems advisable to use at least approx. 1,000 genotyped purebred animals for each of the lines involved.

Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles.

BACKGROUND: Pig and poultry breeding programs aim at improving crossbred (CB) performance. Selection response may be suboptimal if only purebred (PB) performance is used to compute genomic estimated breeding values (GEBV) because the genetic correlation between PB and CB performance ([Formula: see text]) is often lower than 1. Thus, it may be beneficial to use information on both PB and CB performance. In addition, the accuracy of GEBV of PB animals for CB performance may improve when the breed-of-origin of alleles (BOA) is considered in the genomic relationship matrix (GRM). Thus, our aim was to compare scenarios where GEBV are computed and validated by using (1) either CB offspring averages or individual CB records for validation, (2) either a PB or CB reference population, and (3) a GRM that either accounts for or ignores BOA in the CB individuals. For this purpose, we used data on body weight measured at around 7 (BW7) or 35 (BW35) days in PB and CB broiler chickens and evaluated the accuracy of GEBV based on the correlation GEBV with phenotypes in the validation population (validation correlation). RESULTS: With validation on CB offspring averages, the validation correlation of GEBV of PB animals for CB performance was lower with a CB reference population than with a PB reference population for BW35 ([Formula: see text] = 0.96), and about equal for BW7 ([Formula: see text] = 0.80) when BOA was ignored. However, with validation on individual CB records, the validation correlation was higher with a CB reference population for both traits. The use of a GRM that took BOA into account increased the validation correlation for BW7 but reduced it for BW35. CONCLUSIONS: We argue that the benefit of using a CB reference population for genomic prediction of PB animals for CB performance should be assessed either by validation on CB offspring averages, or by validation on individual CB records while using a GRM that accounts for BOA in the CB individuals. With this recommendation in mind, our results show that the accuracy of GEBV of PB animals for CB performance was equal to or higher with a CB reference population than with a PB reference population for a trait with an [Formula: see text] of 0.8, but lower for a trait with an [Formula: see text] of 0.96. In addition, taking BOA into account was beneficial for a trait with an [Formula: see text] of 0.8 but not for a trait with an [Formula: see text] of 0.96.

Using fecal microbiota as biomarkers for predictions of performance in the selective breeding process of pedigree broiler breeders.

Much work has been dedicated to identifying members of the microbial gut community that have potential to augment the growth rate of agricultural animals including chickens. Here, we assessed any correlations between the fecal microbiome, a proxy for the gut microbiome, and feed efficiency or weight gain at the pedigree chicken level, the highest tier of the production process. Because selective breeding is conducted at the pedigree level, our aim was to determine if microbiome profiles could be used to predict feed conversion or weight gain in order to improve selective breeding. Using 16s rRNA amplicon sequencing, we profiled the microbiomes of high and low weight gain (WG) birds and good and poor feed efficient (FE) birds in two pedigree lineages of broiler chickens. We also aimed to understand the dynamics of the microbiome with respect to maturation. A time series experiment was conducted, where fecal samples of chickens were collected at 6 points of the rearing process and the microbiome of these samples profiled. We identified OTUs differences at different taxonomic levels in the fecal community between high and low performing birds within each genetic line, indicating a specificity of the microbial community profiles correlated to performance factors. Using machine-learning methods, we built a classification model that could predict feed conversion performance from the fecal microbial community. With respect to maturation, we found that the fecal microbiome is dynamic in early life but stabilizes after 3 weeks of age independent of lineage. Our results indicate that the fecal microbiome profile can be used to predict feed conversion, but not weight gain in these pedigree lines. From the time series experiments, it appears that these predictions can be evaluated as early as 20 days of age. Our data also indicates that there is a genetic factor for the microbiome profile.

Estimating the purebred-crossbred genetic correlation of body weight in broiler chickens with pedigree or genomic relationships.

BACKGROUND: In pig and poultry breeding programs, the breeding goal is to improve crossbred (CB) performance, whereas selection in the purebred (PB) lines is often based on PB performance. Thus, response to selection may be suboptimal, because the genetic correlation between PB and CB performance ([Formula: see text]) is generally lower than 1. Accurate estimates of the [Formula: see text] are needed, so that breeders can decide if they should collect data from CB animals. [Formula: see text] can be estimated either from pedigree or genomic relationships, which may produce different results. With genomic relationships, the [Formula: see text] estimate could be improved when relationships between purebred and crossbred animals are based only on the alleles that originate from the PB line of interest. This work presents the first comparison of estimated [Formula: see text] and variance components of body weight in broilers, using pedigree-based or genotype-based models, where the breed-of-origin of alleles was either ignored or considered. We used genotypes and body weight measurements of PB and CB animals that have a common sire line. RESULTS: Our results showed that the [Formula: see text] estimates depended on the relationship matrix used. Estimates were 5 to 25% larger with genotype-based models than with pedigree-based models. Moreover, [Formula: see text] estimates were similar (max. 7% difference) regardless of whether the model considered breed-of-origin of alleles or not. Standard errors of [Formula: see text] estimates were smaller with genotype-based than with pedigree-based methods, and smaller with models that ignored breed-of-origin than with models that considered breed-of-origin. CONCLUSIONS: We conclude that genotype-based models can be useful for estimating [Formula: see text], even when the PB and CB animals that have phenotypes are closely related. Considering breed-of-origin of alleles did not yield different estimates of [Formula: see text], probably because the parental breeds of the CB animals were distantly related.