Transcriptomic analysis of intestinal organoids, derived from pigs divergent in feed efficiency, and their response to Escherichia coli.

BACKGROUND: There is increasing interest in using intestinal organoids to study complex traits like feed efficiency (FE) and host-microbe interactions. The aim of this study was to investigate differences in the molecular phenotype of organoids derived from pigs divergent for FE as well as their responses to challenge with adherent and invasive Escherichia coli (E. coli). RESULTS: Colon and ileum tissue from low and high FE pigs was used to generate 3D organoids and two dimensional (2D) monolayers of organoid cells for E. coli challenge. Genome-wide gene expression was used to investigate molecular differences between pigs that were phenotypically divergent for FE and to study the difference in gene expression after challenge with E. coli. We showed, (1) minor differences in gene expression of colon organoids from pigs with low and high FE phenotypes, (2) that an E. coli challenge results in a strong innate immune gene response in both colon and ileum organoids, (3) that the immune response seems to be less pronounced in the colon organoids of high FE pigs and (4) a slightly stronger immune response was observed in ileum than in colon organoids. CONCLUSIONS: These findings demonstrate the potential for using organoids to gain insights into complex biological mechanisms such as FE.

Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms.

Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW). Recently, prediction using single-output machine learning algorithms and information from SNPs as predictor variables have been proposed for genomic selection in growing pigs, but like in other species, the prediction quality achieved for RFI has been generally poor. However, it has been suggested that it could be improved through multi-output or stacking methods. For this purpose, four strategies were implemented to predict RFI. Two of them correspond to the computation of RFI in an indirect way using the predicted values of its components obtained from (i) individual (multiple single-output strategy) or (ii) simultaneous predictions (multi-output strategy). The other two correspond to the direct prediction of RFI using (iii) the individual predictions of its components as predictor variables jointly with the genotype (stacking strategy), or (iv) using only the genotypes as predictors of RFI (single-output strategy). The single-output strategy was considered the benchmark. This research aimed to test the former three hypotheses using data recorded from 5828 growing pigs and 45,610 SNPs. For all the strategies two different learning methods were fitted: random forest (RF) and support vector regression (SVR). A nested cross-validation (CV) with an outer 10-folds CV and an inner threefold CV for hyperparameter tuning was implemented to test all strategies. This scheme was repeated using as predictor variables different subsets with an increasing number (from 200 to 3000) of the most informative SNPs identified with RF. Results showed that the highest prediction performance was achieved with 1000 SNPs, although the stability of feature selection was poor (0.13 points out of 1). For all SNP subsets, the benchmark showed the best prediction performance. Using the RF as a learner and the 1000 most informative SNPs as predictors, the mean (SD) of the 10 values obtained in the test sets were: 0.23 (0.04) for the Spearman correlation, 0.83 (0.04) for the zero-one loss, and 0.33 (0.03) for the rank distance loss. We conclude that the information on predicted components of RFI (DFI, ADG, MW, and BFT) does not contribute to improve the quality of the prediction of this trait in relation to the one obtained with the single-output strategy.

Impact of genomic preselection on subsequent ssGBLUP evaluation of preselected animals for scarcely recorded feed intake in pigs.

We have previously shown that single-step genomic best linear unbiased prediction (ssGBLUP) estimates breeding values of genomically preselected animals without preselection bias for widely recorded traits, that is traits recorded for the majority of animals in the breeding population. This study investigated the impact of genomic preselection (GPS) on accuracy and bias in ssGBLUP evaluation of genomically preselected animals for a scarcely recorded trait, that is a trait recorded for only a small proportion of the animals, which generally has a lower prediction accuracy than widely recorded traits, mainly due to having a much smaller number of phenotypes available. We used data from a commercial pig breeding program, considering feed intake as a scarcely recorded target trait, being available for ~30% of the animals with phenotypes for any trait, and average daily gain, backfat thickness and loin depth as widely recorded predictor traits, being available for >95% of the animals with phenotypes for any trait. The data contained the routine GPS implemented by commercial animal breeding programs, and we retrospectively implemented two scenarios with additional layers of GPS by discarding pedigree, genotypes and phenotypes of animals without progeny. The ssGBLUP evaluation following GPS used records only from the target trait, only from the predictor traits, or both. Accuracy for feed intake did not differ statistically across GPS scenarios, although it tended to decrease with more intense GPS. The accuracy had average values of 0.37, 0.44, and 0.45 across all GPS scenarios when, respectively, records from only the target trait, only the predictor traits, or both were used in the ssGBLUP evaluation. Considerable deflation of the genomic breeding values for feed intake was observed in the most stringent GPS scenario, due to the variance components being underestimated as a result of the limited amount of strongly preselected data. As long as (co)variance components were unbiased, no or only marginal bias was observed. These results for accuracy and bias were observed whether records of the scarcely recorded target trait, of the predictor traits, or both were used in the ssGBLUP evaluation. Our results show that for the scarcely recorded feed intake in pigs, ssGBLUP is able to estimate breeding values of preselected animals without preselection bias, similarly as previously observed for widely recorded traits.

Impact of genomic preselection on subsequent genetic evaluations with ssGBLUP using real data from pigs.

BACKGROUND: Empirically assessing the impact of preselection on genetic evaluation of preselected animals requires comparing scenarios that take different approaches into account, including scenarios without preselection. However, preselection is almost always performed in animal breeding programs, so it is difficult to have a dataset without preselection. Hence, most studies on preselection have used simulated datasets, and have concluded that genomic estimated breeding values (GEBV) from subsequent single-step genomic best linear unbiased prediction (ssGBLUP) evaluations are unbiased. The aim of this study was to investigate the impact of genomic preselection (GPS) on accuracy and bias in subsequent ssGBLUP evaluations, using data from a commercial pig breeding program. METHODS: We used data on average daily gain during performance testing, average daily gain throughout life, backfat thickness, and loin depth from one sire line and one dam line of pigs. As these traits have different weights in the breeding goals of the two lines, we analyzed the lines separately. For each line, we implemented a reference GPS scenario that kept all available data, against which the next two scenarios were compared. We then implemented two other scenarios with additional layers of GPS by removing all animals without progeny either (i) only in the validation generation, or (ii) in all generations. We conducted subsequent ssGBLUP evaluations for each GPS scenario, using all the data remaining after implementing the GPS scenario. Accuracy and bias were computed by comparing GEBV against progeny yield deviations of validation animals. RESULTS: Results for all traits and in both lines showed a marginal loss in accuracy due to the additional layers of GPS. Average accuracies across all GPS scenarios in the two lines were 0.39, 0.47, 0.56, and 0.60, for average daily gain during performance testing and throughout life, backfat thickness, and loin depth, respectively. Biases were largely absent, and when present, did not differ greatly between the GPS scenarios. CONCLUSIONS: We conclude that the impact of preselection on accuracy and bias in subsequent ssGBLUP evaluations of selection candidates in pigs is generally minimal. We expect this conclusion to apply for other animal breeding programs as well, since preselection of any type or intensity generally has the same effect in animal breeding programs.

Convergence behavior of single-step GBLUP and SNPBLUP for different termination criteria.

BACKGROUND: The preconditioned conjugate gradient (PCG) method is the current method of choice for iterative solving of genetic evaluations. The relative difference between two successive iterates and the relative residual of the system of equations are usually chosen as a termination criterion for the PCG method in animal breeding. However, our initial analyses showed that these two commonly used termination criteria may report that a PCG method applied to a single-step single nucleotide polymorphism best linear unbiased prediction (ssSNPBLUP) is not converged yet, whereas the solutions are accurate enough for practical use. Therefore, the aim of this study was to propose two termination criteria that have been (partly) developed in other fields, but are new in animal breeding, and to compare their behavior to that of the two termination criteria widely used in animal breeding for the PCG method applied to ssSNPBLUP. The convergence patterns of ssSNPBLUP were also compared to the convergence patterns of single-step genomic BLUP (ssGBLUP). RESULTS: Building upon previous work, we propose two termination criteria that take the properties of the system of equations into account. These two termination criteria are directly related to the relative error of the iterates with respect to the true solutions. Based on pig and dairy cattle datasets, we show that the preconditioned coefficient matrices of ssSNPBLUP and ssGBLUP have similar properties when using a second-level preconditioner for ssSNPBLUP. Therefore, the PCG method applied to ssSNPBLUP and ssGBLUP converged similarly based on the relative error of the iterates with respect to the true solutions. This similar convergence behavior between ssSNPBLUP and ssGBLUP was observed for both proposed termination criteria. This was, however, not the case for the termination criterion defined as the relative residual when applied to the dairy cattle evaluations. CONCLUSION: Our results showed that the PCG method can converge similarly when applied to ssSNPBLUP and to ssGBLUP. The two proposed termination criteria always depicted these similar convergence behaviors, and we recommend them for comparing convergence properties of different models and for routine evaluations.

The effects of birth weight and estimated breeding value for protein deposition on nitrogen efficiency in growing pigs.

The effects of birth weight (BiW; low BiW [LBW] vs. high BiW [HBW]) and estimated breeding value (EBV) for protein deposition (low EBV [LBV] vs. high EBV [HBV]) on N retention, N efficiency, and concentrations of metabolites in plasma and urine related to N efficiency in growing pigs were studied. At an age of 14 wk, 10 LBW-LBV (BiW: 1.07 ± 0.09 [SD] kg; EBV: -2.52 ± 3.97 g/d, compared with an average crossbred pig with a protein deposition of 165 g/d), 10 LBW-HBV (BiW: 1.02 ± 0.13 kg; EBV: 10.47 ± 4.26 g/d), 10 HBW-LBV (BiW: 1.80 ± 0.13 kg; EBV: -2.15 ± 2.28 g/d), and 10 HBW-HBV (BiW: 1.80 ± 0.15 kg; EBV: 11.18 ± 3.68 g/d) male growing pigs were allotted to the experiment. The pigs were individually housed in metabolism cages and were subjected to an N balance study in two sequential periods of 5 d, after an 11-d dietary adaptation period. Pigs were assigned to a protein adequate (A) or protein restricted (R, 70% of A) regime in a change-over design. Pigs were fed 2.8 times the energy requirements for maintenance. Nontargeted metabolomics analyses were performed in urine and blood plasma samples. The N retention (in g/d) was higher in the HBW than in the LBW pigs (P < 0.001). The N retention (in g/[kg metabolic body weight (BW0.75) · d]) and N efficiency, however, were not affected by the BiW of the pigs. The N retention (P = 0.04) and N efficiency (P = 0.04) were higher in HBV than in LVB pigs on the A regime but were not affected by EBV in pigs on the R regime. Restricting the dietary protein supply with 30% decreased the N retention (P < 0.001) but increased the N efficiency (P = 0.003). Nontargeted metabolomics showed that a hexose, free amino acids (AA), and lysophosphatidylcholines were the most important metabolites in plasma for the discrimination between HBV and LBV pigs, whereas metabolites of microbial origin contributed to the discrimination between HBV and LBV pigs in urine. This study shows that BiW does not affect N efficiency in the later life of pigs. Nitrogen efficiency and N retention were higher in HBV than in LBV pigs on the A regime but similar in HBV and LBV pigs on the R regime. In precision feeding concepts aiming to further optimize protein and AA efficiency in pigs, the variation in EBV for protein deposition of pigs should be considered as a factor determining N retention, growth performance, and N efficiency.

Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning.

Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources. In genomics, FS allows identifying relevant markers and designing low-density SNP chips to evaluate selection candidates. In this research, several univariate and multivariate FS algorithms combined with various parametric and non-parametric learners were applied to the prediction of feed efficiency in growing pigs from high-dimensional genomic data. The objective was to find the best combination of feature selector, SNP subset size, and learner leading to accurate and stable (i.e., less sensitive to changes in the training data) prediction models. Genomic best linear unbiased prediction (GBLUP) without SNP pre-selection was the benchmark. Three types of FS methods were implemented: (i) filter methods: univariate (univ.dtree, spearcor) or multivariate (cforest, mrmr), with random selection as benchmark; (ii) embedded methods: elastic net and least absolute shrinkage and selection operator (LASSO) regression; (iii) combination of filter and embedded methods. Ridge regression, support vector machine (SVM), and gradient boosting (GB) were applied after pre-selection performed with the filter methods. Data represented 5,708 individual records of residual feed intake to be predicted from the animal's own genotype. Accuracy (stability of results) was measured as the median (interquartile range) of the Spearman correlation between observed and predicted data in a 10-fold cross-validation. The best prediction in terms of accuracy and stability was obtained with SVM and GB using 500 or more SNPs [0.28 (0.02) and 0.27 (0.04) for SVM and GB with 1,000 SNPs, respectively]. With larger subset sizes (1,000-1,500 SNPs), the filter method had no influence on prediction quality, which was similar to that attained with a random selection. With 50-250 SNPs, the FS method had a huge impact on prediction quality: it was very poor for tree-based methods combined with any learner, but good and similar to what was obtained with larger SNP subsets when spearcor or mrmr were implemented with or without embedded methods. Those filters also led to very stable results, suggesting their potential use for designing low-density SNP chips for genome-based evaluation of feed efficiency.

Variance estimates are similar using pedigree or genomic relationships with or without the use of metafounders or the algorithm for proven and young animals1.

With an increase in the number of animals genotyped there has been a shift from using pedigree relationship matrices (A) to genomic ones. As the use of genomic relationship matrices (G) has increased, new methods to build or approximate G have developed. We investigated whether the way variance components are estimated should reflect these changes. We estimated variance components for maternal sow traits by solving with restricted maximum likelihood, with four methods of calculating the inverse of the relationship matrix. These methods included using just the inverse of A (A-1), combining A-1 and the direct inverse of G (HDIRECT-1), including metafounders (HMETA-1), or combining A-1 with an approximated inverse of G using the algorithm for proven and young animals (HAPY-1). There was a tendency for higher additive genetic variances and lower permanent environmental variances estimated with A-1 compared with the three H-1 methods, which supports that G-1 is better than A-1 at separating genetic and permanent environmental components, due to a better definition of the actual relationships between animals. There were limited or no differences in variance estimates between HDIRECT-1, HMETA-1, and HAPY-1. Importantly, there was limited differences in variance components, repeatability or heritability estimates between methods. Heritabilities ranged between <0.01 to 0.04 for stayability after second cycle, and farrowing rate, between 0.08 and 0.15 for litter weight variation, maximum cycle number, total number born, total number still born, and prolonged interval between weaning and first insemination, and between 0.39 and 0.44 for litter birth weight and gestation length. The limited differences in heritabilities suggest that there would be very limited changes to estimated breeding values or ranking of animals across models using the different sets of variance components. It is suggested that variance estimates continue to be made using A-1, however including G-1 is possibly more appropriate if refining the model, for traits that fit a permanent environmental effect.

Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms.

This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced genotyping cost. Data consisted of average daily gain (ADG) and residual feed intake (RFI) records and genotypes of 5,708 purebred (PB) boars and 5,007 CB pigs. Prediction models were fitted using individual PB genotypes and phenotypes (trn.1); genotypes of PB sires and average of CB records per PB sire (trn.2); and individual CB genotypes and phenotypes (trn.3). The average of CB offspring records was the trait to be predicted from PB sire's genotype using cross-validation. Single nucleotide polymorphisms (SNPs) were ranked based on the Spearman Rank correlation with the trait. Subsets with an increasing number (from 50 to 2,000) of the most informative SNPs were used as predictor variables in SVM. Prediction performance was the median of the Spearman correlation (SC, interquartile range in brackets) between observed and predicted phenotypes in the testing set. The best predictive performances were obtained when sire phenotypic information was included in trn.1 (0.22 [0.03] for RFI with SVM and 250 SNPs, and 0.12 [0.05] for ADG with SVM and 500-1,000 SNPs) or when trn.3 was used (0.29 [0.16] with Genomic best linear unbiased prediction (GBLUP) for RFI, and 0.15 [0.09] for ADG with just 50 SNPs). Animals from the last two generations were assigned to the testing set and remaining animals to the training set. Individual's PB own phenotype and genotype improved the prediction ability of CB offspring of young animals for ADG but not for RFI. The highest SC was 0.34 [0.21] and 0.36 [0.22] for RFI and ADG, respectively, with SVM and 50 SNPs. Predictive performance using CB data for training leads to a SC of 0.34 [0.19] with GBLUP and 0.28 [0.18] with SVM and 250 SNPs for RFI and 0.34 [0.15] with SVM and 500 SNPs for ADG. Results suggest that PB candidates could be evaluated for CB performance with SVM and low-density SNP chip panels after collecting their own RFI or ADG performances or even earlier, after being genotyped using a reference population of CB animals.

Prediction of nutrient digestibility in grower-finisher pigs based on faecal microbiota composition.

Microbiota play an important role in total tract nutrient digestion, especially when fibrous diets are fed to pigs. This study aimed to use metagenomics to predict faecal nutrient digestibility in grower-finisher pigs. The study design consisted of 160 three-way crossbreed grower-finisher pigs (80 female and 80 male) which were either fed a diet based on corn/soybean meal or a more fibrous diet based on wheat/barley/by-products. On the day before slaughter, faecal samples were collected and used to determine faecal digestibility of dry matter, ash, organic matter, crude protein, crude fat, crude fibre and non-starch polysaccharides. The faecal samples were also sequenced for the 16S hypervariable region of bacteria (V3/V4) to profile the faecal microbiome. With these data, we calculated the between-animal variation in faecal nutrient digestibility associated with variation in the faecal microbiome, that is the "microbiability". The microbiability values were significantly greater than zero for dry matter, organic matter, crude protein, crude fibre and non-starch polysaccharides, ranging from 0.58 to 0.93, as well as for crude fat with a value of 0.37, but not significantly different from zero for ash. Using leave-one-out cross-validation, we estimated the accuracy of predicting digestibility values of individual pigs based on their faecal microbiota composition. The accuracies of prediction for crude fat and ash digestibility were virtually 0, and for the other nutrients, the accuracies ranged from 0.42 to 0.63. In conclusion, the faecal microbiota composition gave high microbiability values for faecal digestibility of dry matter, organic matter, crude protein, crude fibre and non-starch polysaccharides. The accuracies of prediction are relatively low if the interest is in precisely predicting faecal nutrient digestibility of individual pigs, but are promising from the perspective of ranking animals in a genetic selection context.

Genetic correlations between growth performance and carcass traits of purebred and crossbred pigs raised in tropical and temperate climates1.

In pig breeding, selection commonly takes place in purebred (PB) pigs raised mainly in temperate climates (TEMP) under optimal environmental conditions in nucleus farms. However, pork production typically makes use of crossbred (CB) animals raised in nonstandardized commercial farms, which are located not only in TEMP regions but also in tropical and subtropical regions (TROP). Besides the differences in the genetic background of PB and CB, differences in climate conditions, and differences between nucleus and commercial farms can lower the genetic correlation between the performance of PB in the TEMP (PBTEMP) and CB in the TROP (CBTROP). Genetic correlations (rg) between the performance of PB and CB growing-finishing pigs in TROP and TEMP environments have not been reported yet, due to the scarcity of data in both CB and TROP. Therefore, the present study aimed 1) to verify the presence of genotype × environment interaction (G × E) and 2) to estimate the rg for carcass and growth performance traits when PB and 3-way CB pigs are raised in 2 different climatic environments (TROP and TEMP). Phenotypic records of 217,332 PB and 195,978 CB, representing 2 climatic environments: TROP (Brazil) and TEMP (Canada, France, and the Netherlands) were available for this study. The PB population consisted of 2 sire lines, and the CB population consisted of terminal 3-way cross progeny generated by crossing sires from one of the PB sire lines with commercially available 2-way maternal sow crosses. G × E appears to be present for average daily gain, protein deposition, and muscle depth given the rg estimates between PB in both environments (0.64 to 0.79). With the presence of G × E, phenotypes should be collected in TROP when the objective is to improve the performance of CB in the TROP. Also, based on the rg estimates between PBTEMP and CBTROP (0.22 to 0.25), and on the expected responses to selection, selecting based only on the performance of PBTEMP would give limited genetic progress in the CBTROP. The rg estimates between PBTROP and CBTROP are high (0.80 to 0.99), suggesting that combined crossbred-purebred selection schemes would probably not be necessary to increase genetic progress in CBTROP. However, the calculated responses to selection show that when the objective is the improvement of CBTROP, direct selection based on the performance of CBTROP has the potential to lead to the higher genetic progress compared with indirect selection on the performance of PBTROP.

Effect of feeding cereals-alternative ingredients diets or corn-soybean meal diets on performance and carcass characteristics of growing-finishing gilts and boars.

Pig-breeding businesses have resulted in global breeding programs that select pigs to perform well on high-energy high-protein diets, which are traditionally based on corn and soybean meal. Nowadays, there is a shift toward diets based on cereals and co-products, therefore, high dietary inclusion of co-products can modify the expected performance of these pigs. The objective of this study was to evaluate the effect of feeding a cereals-alternative ingredients diet (CA-diet) compared to a corn-soybean meal diet (CS-diet) on the growth performance, feed efficiency, and carcass characteristics of genetically similar growing-finishing gilts and boars. In total, 160 pigs, 80 gilts and 80 boars, coming from 18 litters were used. The pigs were blocked based on litter, to ensure no genetic differences between the 2 treatments. For the starter phase, pigs fed the CA-diet performed in terms of growth, and feed efficiency, as good as the pigs fed CS-diet (P > 0.05). For the grower phase, pigs fed the CA-diet had the same ADFI (P > 0.05), but a lower daily energy intake (ADEI) (P < 0.001), and same growth performance (P > 0.05) than pig fed the CS-diet, therefore pigs fed the CA-diet were more efficient in terms of residual energy intake (REI) (P < 0.001). For the finisher phase, interaction between diet and sex had an effect on ADFI (P < 0.001), ADEI (P < 0.001), ADG (P = 0.010), and lipid deposition (Ld) (P = 0.016). Pigs fed the CA-diet were less efficient than pigs fed the CS-diet, i.e., G:F (P < 0.001), RFI (P < 0.001), and REI (P = 0.007). In general, feeding a CA-diet to pigs showed to improve the ratio between Pd and Ld, especially for boars. Also, pigs fed the CA-diet showed thinner back fat thickness (P < 0.001), same loin depth thickness (P > 0.05), but lower dressing percentage (P < 0.001), than pigs fed the CS-diet.

Fecal microbial composition associated with variation in feed efficiency in pigs depends on diet and sex.

Dietary fiber content and composition affect microbial composition and activity in the gut, which in turn influence energetic contribution of fermentation products to the metabolic energy supply in pigs. This may affect feed efficiency (FE) in pigs. The present study investigated the relationship between the fecal microbial composition and FE in individual growing-finishing pigs. In addition, the effects of diet composition and sex on the fecal microbiome were studied. Fecal samples were collected of 154 grower-finisher pigs (3-way crossbreeds) the day before slaughter. Pigs were either fed a diet based on corn/soybean meal (CS) or a diet based on wheat/barley/by-products (WB). Fecal microbiome was characterized by 16S ribosomal DNA sequencing, clustered by operational taxonomic unit (OTU), and results were subjected to a discriminant approach combined with principal component analysis to discriminate diets, sexes, and FE extreme groups (10 high and 10 low FE pigs for each diet by sex-combination). Pigs on different diets and males vs. females had a very distinct fecal microbiome, needing only 2 OTU for diet (P = 0.020) and 18 OTU for sex (P = 0.040) to separate the groups. The 2 most important OTU for diet, and the most important OTU for sex, were taxonomically classified as the same bacterium. In pigs fed the CS diet, there was no significant association between FE and fecal microbiota composition based on OTU (P > 0.05), but in pigs fed the WB diet differences in FE were associated with 17 OTU in males (P = 0.018) and to 7 OTU in females (P = 0.010), with 3 OTU in common for both sexes. In conclusion, our results showed a diet and sex-dependent relationship between FE and the fecal microbial composition at slaughter weight in grower-finisher pigs.

Correction to: Genomic evaluation for a three-way crossbreeding system considering breed-of-origin of alleles.

After publication of our article [1], we found a typo in the formula to build the genomic relationship matrix using allele frequencies across all genotyped pigs (matrix) and the genomic relationship matrix using breed-specific allele frequencies (matrix), and we noted that the description to this formula is not very clear.

Genomic evaluation for a three-way crossbreeding system considering breed-of-origin of alleles.

BACKGROUND: Genomic prediction of purebred animals for crossbred performance can be based on a model that estimates effects of single nucleotide polymorphisms (SNPs) in purebreds on crossbred performance. For crossbred performance, SNP effects might be breed-specific due to differences between breeds in allele frequencies and linkage disequilibrium patterns between SNPs and quantitative trait loci. Accurately tracing the breed-of-origin of alleles (BOA) in three-way crosses is possible with a recently developed procedure called BOA. A model that accounts for breed-specific SNP effects (BOA model), has never been tested empirically on a three-way crossbreeding scheme. Therefore, the objectives of this study were to evaluate the estimates of variance components and the predictive accuracy of the BOA model compared to models in which SNP effects for crossbred performance were assumed to be the same across breeds, using either breed-specific allele frequencies ([Formula: see text] model) or allele frequencies averaged across breeds ([Formula: see text] model). In this study, we used data from purebred and three-way crossbred pigs on average daily gain (ADG), back fat thickness (BF), and loin depth (LD). RESULTS: Estimates of variance components for crossbred performance from the BOA model were mostly similar to estimates from models [Formula: see text] and [Formula: see text]. Heritabilities for crossbred performance ranged from 0.24 to 0.46 between traits. Genetic correlations between purebred and crossbred performance ([Formula: see text]) across breeds ranged from 0.30 to 0.62 for ADG and from 0.53 to 0.74 for BF and LD. For ADG, prediction accuracies of the BOA model were higher than those of the [Formula: see text] and [Formula: see text] models, with significantly higher accuracies only for one maternal breed. For BF and LD, prediction accuracies of models [Formula: see text] and [Formula: see text] were higher than those of the BOA model, with no significant differences. Across all traits, models [Formula: see text] and [Formula: see text] yielded similar predictions. CONCLUSIONS: The BOA model yielded a higher prediction accuracy for ADG in one maternal breed, which had the lowest [Formula: see text] (0.30). Using the BOA model was especially relevant for traits with a low [Formula: see text]. In all other cases, the use of crossbred information in models [Formula: see text] and [Formula: see text], does not jeopardize predictions and these models are more easily implemented than the BOA model.