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Additional file 5: Figure S1. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under RANDOM selection. Figure S2. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under MASS selection. Figure S3. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under PBLUP selection with own performance (PBLUP_OP). Figure S4. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection without own performance (GBLUP_NoOP). Figure S5. Scatterplot of statistical additive effects in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection with own performance (GBLUP_OP). Figure S6. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under RANDOM selection. Figure S7. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under MASS selection. Figure S8. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under PBLUP selection with own performance (PBLUP_OP). Figure S9. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection without own performance (GBLUP_NoOP). Figure S10. Scatterplot of statistical additive effects in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection with own performance (GBLUP_OP).

Additional file 4: Figure S1. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under RANDOM selection. Figure S2. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under MASS selection. Figure S3. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under PBLUP selection with own performance (PBLUP_OP). Figure S4. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under GBLUP selection without own performance (GBLUP_NoOP). Figure S5. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive effects (Model A) under GBLUP selection with own performance (GBLUP_OP). Figure S6. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under RANDOM selection. Figure S7. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under MASS selection. Figure S8. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under PBLUP selection with own performance (PBLUP_OP). Figure S9. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection without own performance (GBLUP_NoOP). Figure S10. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive and dominance effects (Model AD) under GBLUP selection with own performance (GBLUP_OP). Figure S11. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under RANDOM selection. Figure S12. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under MASS selection. Figure S13. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under PBLUP selection with own performance (PBLUP_OP). Figure S14. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection without own performance (GBLUP_NoOP). Figure S15. Scatterplot of allele frequencies of all causal variants in different generations for the genetic model with additive, dominance and epistatic effects (Model ADE) under GBLUP selection with own performance (GBLUP_OP).

Additional file 2: Figure S1. Allele frequency distribution of segregating causal loci over 50 generations without selection. Figure S2. Extent of LD (r2) in the simulated population before selection as a function of distance in one random replicate. Figure S3. Trend in the variation in minor allele frequency (MAF) of segregating causal loci for the five selection methods and three genetic models. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S4. The difference between additive genic and additive genetic variance which represents a transient loss in genetic variance for the five selection methods and three genetic models. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S5. Phenotypic trend for the GBLUP model with own performance records and with and without a dominance effect for the genetic models with non-additive effects. The phenotypic trend is scaled by the additive genetic standard deviation in the generation before selection in order to make the results comparable across the genetic models. The two genetic models were a model with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error. Figure S6. Trend in additive genetic (A, B) and additive genic (C, D) variance for the GBLUP model with own performance and with and without a dominance effect for the genetic models with non-additive effects. The trend is scaled by the additive genetic or additive genic variance in the generation before selection in order to make the results comparable across the genetic models. The two genetic models were a model with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). Results are shown as averages of 20 replicates and the width of the lines represents the average plus and minus one standard error.

Additional file 3. Decomposition of additive genetic variance. This file provides a theoretical decomposition of the additive genetic variance.

Additional file 6: Table S1. Maximum genetic gain1 that is still possible after 50 generations of selection for the five selection methods and three genetic models2. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1The maximum genetic gain in generation 50 is estimated as the genetic gain when all loci would be fixed for the favourable allele, using the statistical additive effects of generation 50 and neglecting mutations. 2Results are shown as averages across the 20 replicates with their corresponding standard errors of the mean between brackets. Table S2. Percentual change in the components of the genetic variance after 10 and 50 generations of selection for the five selection methods and three genetic models1. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets. Increases in the value of a component are represented in bold. Table S3. Average pedigree inbreeding coefficient after 50 generations of selection for the five selection methods and three genetic models1. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets. Table S4. Average and variance of change in allele frequency of causal loci1 across 50 generations of selection for the five selection methods and three genetic models2. The five selection methods were: RANDOM selection, MASS selection, PBLUP selection with own performance (PBLUP_OP), GBLUP selection without own performance (GBLUP_NoOP) or with own performance (GBLUP_OP). The three genetic models were a model with only additive effects (A), with additive and dominance effects (AD), or with additive, dominance and epistatic effects (ADE). 1Causal loci included only the causal loci segregating in generation 0. 2Results are shown as averages of 20 replicates with their corresponding standard errors of the mean between brackets.

It is important to detect negative behavior of animals for breeding in order to improve their health and welfare. In this work, AI is employed to assist individual animal detection and tracking, which enables the future analysis of behavior for individual animals. The study involves animal groups of pigs and laying hens. First, two state-of-the-art deep learning-based Multi-Object Tracking (MOT) methods are investigated, namely Joint Detection and Embedding (JDE) and FairMOT. Both models detect and track individual animals automatically and continuously. Second, a weighted association algorithm is proposed, which is feasible for both MOT methods to optimize the object re-identification (re-ID), thereby improving the tracking performance. The proposed methods are evaluated on manually annotated datasets. The best tracking performance on pigs is obtained by FairMOT with the weighted association, resulting in an IDF1 of 90.3%, MOTA of 90.8%, MOTP of 83.7%, number of identity switches of 14, and an execution rate of 20.48 fps. For the laying hens, FairMOT with the weighted association also achieves the best tracking performance, with an IDF1 of 88.8%, MOTA of 86.8%, MOTP of 72.8%, number of identity switches of 2, and an execution rate of 21.01 fps. These results show a promising high accuracy and robustness for the individual animal tracking.

Additional file 1: Table S1. Minimum, mean, and maximum values of realized r_pc for crosses between lines that were divergently selected for 10 (P10-N10), 25 (P25-N25), or 50 generations of selection (P50-N50).

Table S1 contains mean (over 20 replicates) observed scale heritability estimates for disease status for different genetic standard deviations in susceptibility. FigureS2 contains a schematic overview of the steps used in the simulations.FileS3 contains the R-code used to simulate populations with genetic and permanent environmental variation in susceptibility.FileS4 contains the R-code used to simulate the endemic infectious disease.

Additional file 2: Table S1. Construction of the R matrix for the ADR-model: SNP coding for ROH status and ROH-frequency per SNP and example of genotypes and ROH-status for 30 SNPs and two animals (j and k) in Table S1. Table S2. Construction of the R matrix for the ADR-model: example of calculating the ROH-based relationship between two animals (j and k) based on their ROH-status ( $${x}_{ij}$$ x ij and $${x}_{ik}$$ x ik ) for the 30 SNPs in Table S1. Applying Eq. (S1), the ROH-based relationship between j and k is 3.0471/3.4829 = 0.8749. Note that the frequency of each SNP being in a ROH ( $${p}_{i}^{*}$$ p i ∗ ) was assumed to be known.

Additional file 1. Simulation of the TGV coming from the sire for four-way crossbreds. Explanation of how the TGV coming from the sire was simulated for the four-way crossbreds.

Innovations in the breeding and management of pigs are needed to improve the performance and welfare of animals raised in social groups, and in particular to minimise biting and damage to group mates. Depending on the context, social interactions between pigs can be frequent or infrequent, aggressive, or non-aggressive. Injuries or emotional distress may follow. The behaviours leading to damage to conspecifics include progeny savaging, tail, ear or vulva biting, and excessive aggression. In combination with changes in husbandry practices designed to improve living conditions, refined methods of genetic selection may be a solution reducing these behaviours. Knowledge gaps relating to lack of data and limits in statistical analyses have been identified. The originality of this paper lies in its proposal of several statistical methods for common use in analysing and predicting unwanted behaviours, and for genetic use in the breeding context. We focus on models of interaction reflecting the identity and behaviour of group mates which can be applied directly to damaging traits, social network analysis to define new and more integrative traits, and capture-recapture analysis to replace missing data by estimating the probability of behaviours. We provide the rationale for each method and suggest they should be combined for a more accurate estimation of the variation underlying damaging behaviours.

Additional file 3. Partial relationships. Histogram of the partial relationships for purebreds, two-way crossbreds and four-way crossbreds.

Additional file 6: Figure S6. Dominance effects for yield traits, estimated by GREML (model ADR) with back-solving, for a region on chromosome 5 from 10 to 45 Mb. MY: 305-day milk yield (kg); FY: 305-day fat yield (kg); PY: 305-day protein yield (kg). Effects were multiplied by 100 and divided by the genetic standard deviation ( $${\sigma }_{g}$$ σ g ) of the corresponding trait. Figure S7. Statistical significance of dominance effects for yield traits, estimated by single SNP GWAS, for a region on chromosome 5 from 10 to 45 Mb. MY: 305-day milk yield.

Additional file 5. Results with estimated genetic variance components. Average accuracies and regression coefficients for purebreds, two-way-crossbreds or four-way crossbred reference population when all variance components were estimated.

Additional file 8: Table S5. Correlations between variance components estimates1 from the ADR model for nine traits. Correlations were calculated from the average information matrix from mtg2 output.

Breeding is based on selecting the genetically best individuals as parents of the next generation. Traditionally, selection relied on breeding values estimated from phenotypic records of the individual itself and its relatives, connected using pedigree information. In the last decade, many livestock and plant breeding programs have adopted genomic selection, which estimates breeding values based on genome-wide marker data. Genomic prediction enables accurate selection early in life, leading to faster genetic improvement. In pig and poultry breeding the production individuals are crosses between three or four breeding lines. Similar strategies are used in crops like maize, where the breeding lines are intentionally inbred, while in livestock breeding lines inbreeding is restricted. In all these cases, the breeding goal is to improve crossbred performance. Selection in pigs and poultry, however, takes place among individuals within a breeding line, and selection decisions are often based on purebred performance. The genetic correlation between purebred and crossbred performance is generally lower than unity, which can limit the progress in crossbred performance. This limitation can be removed by using a crossbred reference population in genomic prediction. Crossbred individuals carry alleles from several parental lines, which may affect genomic prediction. We developed a method to derive the Breed-Origin-of-Alleles (BOA) for individuals resulting from crossing non-inbred breeding lines, based on phased genotype data and haplotype frequencies observed in the parental lines. We were able to derive BOA for 92-95% of the alleles in crossbred broilers and pigs. We hypothesized that using BOA in genomic prediction models would increase genomic prediction accuracy for crossbred performance. Empirical accuracies, however, were at best slightly improved, despite the observation that allele frequencies and estimated allelic effects for crossbred performance did differ among the parental lines. Using BOA, relationships with respect to a focal breeding line are only based on alleles from this line, while alleles from other lines are ignored. The limited benefit in accuracy when using BOA is likely because alleles from other lines contribute little to variation in relationships of the focal line. Nevertheless, the BOA methodology is potentially beneficial in genomic prediction in admixed populations in other species, especially if founder populations are very different. The most likely explanation for this limited benefit is that the alleles from other breeding lines had little impact on variation in genomic relationships with respect to the breeding line of interest, so that considering BOA hardly affected the genomic estimated breeding values.

Made available in DSpace on 2019-10-04T12:14:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-08-01 Genus PIC (Hendersonville, TN) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Pooling semen of multiple boars is commonly used in swine production systems. Compared with single boar systems, this technique changes family structure creating maternal half-sib families. The aim of this simulation study was to investigate how pooling semen affects the accuracy of estimating direct and maternal effects for individual piglet birth weight, in purebred pigs. Different scenarios of pooling semen were simulated by allowing the same female to mate from 1 to 6 boars, per insemination, whereas litter size was kept constant (N = 12). In each pooled boar scenario, genomic information was used to construct either the genomic relationship matrix (G) or to reconstruct pedigree in addition to G. Genotypes were generated for 60,000 SNPs evenly distributed across 18 autosomes. From the 5 simulated generations, only animals from generations 3 to 5 were genotyped (N = 36,000). Direct and maternal true breeding values (TBV) were computed as the sum of the effects of the 1,080 QTLs. Phenotypes were constructed as the sum of direct TBV, maternal TBV, an overall mean of 1.25 kg, and a residual effect. The simulated heritabilities for direct and maternal effects were 0.056 and 0.19, respectively, and the genetic correlation between both effects was -0.25. All simulations were replicated 5 times. Variance components and direct and maternal heritability were estimated using average information REML. Predictions were computed via pedigree-based BLUP and single-step genomic BLUP (ssGBLUP). Genotyped littermates in the last generation were used for validation. Prediction accuracies were calculated as correlations between EBV and TBV for direct (acc(direct)) and maternal (acc(mat)) effects. When boars were known, acc(direct) were 0.21 (1 boar) and 0.26 (6 boars) for BLUP, whereas for ssGBLUP, they were 0.38 (1 boar) and 0.43 (6 boars). When boars were unknown, acc(direct) was lower in BLUP but similar in ssGBLUP. For the scenario with known boars, acc(mat) was 0.58 and 0.63 for 1 and 6 boars, respectively, under ssGBLUP. For unknown boars, acc(mat) was 0.63 for 2 boars and 0.62 for 6 boars in ssGBLUP. In general, acc(direct) and acc(mat) were lower in the single-boar scenario compared with pooled semen scenarios, indicating that a half-sib structure is more adequate to estimate direct and maternal effects. Using pooled semen from multiple boars can help us to improve accuracy of predicting maternal and direct effects when maternal half-sib families are larger than 2. Sao Paulo State Univ, Dept Anim Sci, BR-14884900 Jaboticabal, SP, Brazil Univ Georgia, Dept Anim & Dairy Sci, Athens, GA 30602 USA Wageningen Univ & Res, Anim Breeding & Genet Ctr, NL-6700 AH Wageningen, Netherlands Genus PIC, Hendersonville, TN 37075 USA Sao Paulo State Univ, Dept Anim Sci, BR-14884900 Jaboticabal, SP, Brazil CAPES: 001 CAPES: 88881.131671/2016-01

Additional file 6: Table S2. Simulated and estimated parameters for the random design versus the two-family design when the number of groups was fixed (ngâ =â 500).

Additional file 5: Figure S4. Lower and upper confidence intervals for all parameters (dilution, variance of DGE, variance of IGE, and genetic correlation between direct and indirect effects) for different group sizes (schemes) with different CV and $${\bar{\text{n}}} = 8$$ n ÂŻ = 8 . The black horizontal lines show the true simulated values and the black dots show the estimates. The group compositions are random.

International audience; AbstractHygienic behaviour (HB) in honeybees reflects social immunity against diseases and parasites. Young bees showing HB detect, uncap, and remove infested brood from a colony. We developed a new variant of freeze-killed brood (FKB*) test to optimise the duration of the HB test, the costs, and safety for the operator. In 2016, we performed a comparison between traditional FKB and FKB* on 25 unselected and unrelated colonies in the apiary of the University of Milano. To estimate repeatability and heritability, in 2017 and 2018, FKB* was used to phenotype, respectively, 56 and 95 colonies twice, in the context of a breeding programme. FKB* took less time and required a smaller amount of liquid nitrogen. The two methods showed a correlation between colony effects of 0.93, indicating that they measure the same trait. For single records, the phenotypic correlation between both methods was 0.64. Estimated heritability and repeatability for single records HB were 0.23 and 0.24, respectively, whilst heritability for the average HB value of two records was 0.37.

Additional file 7: Figure S5. Lengths of confidence intervals for two-family versus random schemes. Description: The lengths of confidence intervals for all parameters (dilution, variance of DGE, variance of IGE, and genetic correlation between direct and indirect effects) for schemes 2, 14; 4, 12; and 6, 10 (with $$\bar{n} = 8$$ n ¯ = 8 ) where the random design was compared with the two-family design. The number of groups were fixed $$(n_{g} = 500)$$ ( n g = 500 ) for different group sizes.

Additional file 1. Fixed effect estimates. Table of fixed effect estimates and standard errors on the log scale from Model 1.

Additional file 4: Figure S3. Lower and upper confidence intervals for all parameters (dilution, variance of DGE, variance of IGE, and genetic correlation between direct and indirect effects) for different group sizes (schemes) with different CV and $${\bar{\text{n}}} = 6$$ n ÂŻ = 6 . The black horizontal lines show the true simulated values and the black dots show the estimates. The group compositions are random.

Additional file 4: Figure S3. Effect of a 1% increase in ROH-based inbreeding ( $$F_{ROH}$$ F ROH ) for cumulative measures. Error bars represent one standard error and stars indicate significance for non-nullity (*P

Additional file 3: Figure S2. Description: Lower and upper confidence intervals for all parameters (dilution, variance of DGE, variance of IGE, and genetic correlation between direct and indirect effects) for different group sizes (schemes) with different CV and $${\bar{\text{n}}} = 4$$ n ÂŻ = 4 . The black horizontal lines show the true simulated values and the black dots show the estimates. The group compositions are random.

Additional file 1: Table S1. Simulated and estimated parameters for the random designs.

Additional file 1: Figure S1. Distribution of the number of complete generations (NCG) and complete generation equivalent (CGE) for cows included in pedigree-based analyses (nâ =â 37,061).

Additional file 3: Fig. S2. Moving correlation (of 51 markers) between changes in allele frequency in the 1996â 2000 and 2001â 2005 periods.

Additional file 1. Allele frequency distribution of markers and causal loci. Figure S1: Allele frequency distribution of HDP markers for the two populations. Figure S2: Allele frequency distribution of causal loci for the two populations.

Additional file 2: Table S1. Number of QTL extracted from AnimalQTLdb per trait and trait category.

Additional file 4. Power of direct and indirect SNP effects.

Additional file 3. Number of direct and indirect SNP effects with pâ

Additional file 2. QQ-plots of indirect SNP effects for STM, RMM.t, and GLMM.

Additional file 1: Fig. S1. Number of SNPs per bin of 50Â kb per Bos taurus autosome (BTA).

Additional file 4: Table S2. Genomic regions of ≥ 7 Mb with strong negative correlation (r ≤ − 0.6) between changes in allele frequency in the 1996–2000 and 2001–2005 periods, and fraction of QTL in these regions per trait category.

Additional file 1: Fig. S1. Frequency of treatments (a) and the mean number of parasites per host (b) when the level of expenditures is optimized for the value of R0 in the numerical example. When the level of expenditures is optimized for the value of R0 in the numerical example, the frequency of treatments and the mean number of parasites per host both decrease at an increasing rate as R0 decreases. As a result, E and L both decrease at an increasing rate as R0 decreases, hence the economic value increases as R0 decreases.

Additional file 1. QQ-plots of direct SNP effects for STM, RMM.t, and GLMM.

As the classical Breeder’s Equation often fails to accurately predict evolutionary responses, concepts of ‘total’ heritability incorporating ‘non-genetic inheritance’, such as maternal effects (MEs), have been explored. Despite ample evidence for MEs in a variety of traits and theoretical indications for their evolutionary importance, the consequences of MEs for evolutionary responses in natural populations remain unclear. We here explored whether MEs can alter the rate of adaptation of a key life-history trait, clutch size, undergoing directional selection following an environmental shift (e.g. when increased phenological mismatch due to climate warming leads to a smaller optimal clutch size). To test this, we parameterized an individual-based model with data from a long-term (1973–2013) study of great tits (Parus major). Using these realistic parameter values we show that MEs, whether ‘environmentally plastic’ or fixed, have little effect on evolutionary responses. This demonstrates the importance of using realistic parameters values in theoretical models and questions the importance of MEs for adaptation to environmental changes, such as climate change.

Indirect genetic effects (IGEs) are the basis of social interactions among conspecifics, and can affect genetic variation of nonsocial and social traits. We used flour beetles (Tribolium castaneum) of two phenotypically distinguishable populations to estimate genetic (co)variances and the effect of IGEs on three life-history traits: development time (DT), growth rate (GR), and pupal body mass (BM). We found that GR was strongly affected by social environment with IGEs accounting for 18% of the heritable variation. We also discovered a sex-specific social effect: male ratio in a group significantly affected both GR and BM; that is, beetles grew larger and faster in male-biased social environments. Such sex-specific IGEs have not previously been demonstrated in a nonsocial insect. Our results show that beetles that achieve a higher BM do so via a slower GR in response to social environment. Existing models of evolution in age-structured or stage-structured populations do not account for IGEs of social cohorts. It is likely that such IGEs have played a key role in the evolution of developmental plasticity shown by Tenebrionid larvae in response to density. Our results document an important source of genetic variation for GR, often overlooked in life-history theory.

Additional file 5: Table S1. Cross-validation results for lines B1 and BD, with both genotyped and non-genotyped sires as reference population for ssGBLUP and pedigree-BLUP.

Additional file 3. Predictive ability of MA and MAD based on GBLUP. Prediction accuracy for Landrace and Yorkshire boars under two genomic models.

Additional file 1. Within-line prediction accuracy. Results for within-line prediction accuracy for Landrace and Yorkshire sows under two genomic models.

Additional file 2: Table S1. Genetic parameters for the mean and the variance of Box-Cox transformed harvest weight. Table S1 contains genetic parameters for the mean and the variance of Box-Cox transformed harvest weight estimated using a double hierarchical generalized linear model.

Additional file 3: Table S2. Estimates of fixed effects for variance of the level of harvest weight, length, depth, and width. Table S2 contains estimates of the fixed effects for variance of the level of harvest weight, length, depth, and width, obtained from the reduced model with main fixed effects (not the interactions) and random effects.

Additional file 1: Figure S1. Example of the block design used to allocate two families to each group. Figure S1 represents an example of block design used to allocate two families to each group. All families in the block are unrelated to each other.

Additional file 2. Distribution of estimated additive and dominance effects of SNPs. Distribution of estimated additive and dominance effects of SNPs for Landrace and Yorkshire lines.

Additional file 2. Comparison of Scenario 1 with an additive scenario. We compared the performance of Scenario 1 to an additive scenario (Scenario Additive) where training was on purebred animals, mean performance of crossbred progeny was used as response variable and an additive model was used to estimate genomic estimated breeding values. The size of the reference population for the additive scenario was 1000 within each pure line and each of the animals in the training set had 10 crossbred progeny.

Additional file 1. Partitioning accuracies of breeding values due to additive and dominance effects for Scenario 1 and Scenario 5.

Theoretical basis of the effect of selection on reliability. Detailed illustration on how selection reduces the reliability of selection and how this effect differs between types of estimated breeding values [31]. (PDF 7873 kb)