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Accuracy of Predicted Genomic Breeding Values in Purebred and Crossbred Pigs
View ORCID ProfileAndré M. Hidalgo, John W. M. Bastiaansen, Marcos S. Lopes, Barbara Harlizius, Martien A. M. Groenen and Dirk-Jan de Koning
André M. Hidalgo
Wageningen University, Animal Breeding and Genomics Centre, Wageningen, the Netherlands 6700 AHSwedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden 750 07
ORCID record for André M. Hidalgo
For correspondence: andre.hidalgo@wur.nl
John W. M. Bastiaansen
Wageningen University, Animal Breeding and Genomics Centre, Wageningen, the Netherlands 6700 AH
Marcos S. Lopes
Wageningen University, Animal Breeding and Genomics Centre, Wageningen, the Netherlands 6700 AHTopigs Norsvin Research Center, Beuningen, the Netherlands 6640 AA
Barbara Harlizius
Topigs Norsvin Research Center, Beuningen, the Netherlands 6640 AA
Martien A. M. Groenen
Dirk-Jan de Koning
Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden 750 07
Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals. In pigs, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. Therefore, the objective of this study was to determine the accuracy of predicted breeding values in crossbred pigs using purebred genomic and phenotypic data. A second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations for four traits: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). We performed marker-based and pedigree-based predictions. Within-population predictions for the four traits ranged from 0.21 to 0.72. Multi-population prediction yielded accuracies ranging from 0.18 to 0.67. Predictions across purebred populations as well as predicting genetic merit of crossbreds from their purebred parental lines for AFI performed poorly (not significantly different from zero). In contrast, accuracies of across-population predictions and accuracies of purebred to crossbred predictions for LBW and LVR ranged from 0.08 to 0.31 and 0.11 to 0.31, respectively. Accuracy for TNB was zero for across-population prediction, whereas for purebred to crossbred prediction it ranged from 0.08 to 0.22. In general, marker-based outperformed pedigree-based prediction across populations and traits. However, in some cases pedigree-based prediction performed similarly or outperformed marker-based prediction. There was predictive ability when purebred populations were used to predict crossbred genetic merit using an additive model in the populations studied. AFI was the only exception, indicating that predictive ability depends largely on the genetic correlation between PB and CB performance, which was 0.31 for AFI. Multi-population prediction was no better than within-population prediction for the purebred validation set. Accuracy of prediction was very trait-dependent.
GenPred
shared data resource
across-population
multi-population
reproduction traits
within-population
Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals (Berry et al. 2009; VanRaden et al. 2009; Hayes et al. 2009b). In pigs and poultry, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. In pig breeding, multiple sire and dam lines are used, with a minimum of two lines (typically for crossbred sows) and often additional sire lines to produce a three-way or four-way cross finisher pig (Merks and De Vries 2002; Lutaaya et al. 2001).
Selection based on genomic estimated breeding values (GEBV) for purebreds (PB) using phenotypes on crossbreds (CB) is expected to increase the response to selection observed in CB compared to the situation in which only PB phenotypes are used. This increased response is expected when the genetic correlation between the PB and CB trait is less than 1, especially when the genetic correlation is 0.7 or less (Dekkers 2007). Genetic correlations between PB and CB performance vary and can be considerably less than 1 (Lutaaya et al. 2001; Zumbach et al. 2007; Cecchinato et al. 2010). Adding CB individuals to the training data is very expensive because, besides genotyping, it also requires additional identification and individual recording of target traits. Breeding companies are not inclined to make these investments unless there is evidence that predictions yield greater gains and higher accuracies. Simulation studies have shown that the response to selection is greater when PB animals are selected based on CB performance and that accuracy of prediction is high (Dekkers 2007; Ibánez-Escriche et al. 2009; Kinghorn et al. 2010; Toosi et al. 2010; Zeng et al. 2013). There is, however, a lack of studies using real data. The number of genotyped CB is not yet large enough to test the superiority of training on CB for PB selection. A first step toward finding the optimal genomic selection scenario for pigs is to determine predictive ability (accuracy), in real data, of GEBV for CB pigs based on PB genomic and phenotypic data. This will show how CB performance responds to the current practice of selection on GEBV in PB pigs.
Recently, accuracies of within-population genomic prediction in pigs have been reported (Cleveland et al. 2010; Forni et al. 2011; Christensen et al. 2012; Tusell et al. 2013; Badke et al. 2014). These studies have shown that all traits had more than zero predictive ability within population in a variety of pig breeds using different methods. It has also been shown that using genomic information generally increased the accuracy of prediction compared to using only pedigree information (Forni et al. 2011; Christensen et al. 2012; Tusell et al. 2013). Using multi-population training might be a way to increase the accuracy of prediction further. This is especially relevant to enable genomic selection for small populations when a closely related breed, or the same breed from another country, is added to the training set (Lund et al. 2014). An unresolved question is how to obtain accurate predictions from multi-population datasets. The effectiveness of a multi-population genomic evaluation depends on many factors, e.g., differences in allele frequency and consistency of linkage disequilibrium (LD) between quantitative trait loci (QTL) and single nucleotide polymorphism (SNP), which could reduce the accuracy of prediction (Wientjes et al. 2013), whereas the larger reference population would potentially improve the accuracy.
The objective of our study was to determine predictive ability (accuracy) in CB pigs using real PB genomic and phenotypic data. The outcome is a first step toward determining the optimal genomic selection scenario to select PB for CB performance. As in cattle, studying accuracy of prediction for multi-population datasets is important for species in which population size imposes upper limits to the training population size. Therefore, a second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations in pigs.
Genotypes were available from sows with own-performance information of three pig populations born from 2005 through 2012: 1070 Dutch Landrace-based (DL) sows from 19 farms; 1389 Large White-based (LW) sows from 14 farms; and 287 individuals from an F1 cross between these two commercial lines (DL sire/LW dams) originating from three farms. The genotyped CB animals had no specific family structure and the majority of them were not offspring of the genotyped PB animals, i.e., a number of generations separated PB and CB. The 287 CB animals were offspring from 76 sires and 170 dams. Four female reproduction traits were analyzed: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). AFI consisted of the age at the second estrus, which was the time that the first insemination was performed. TNB was the sum of all piglets born alive and stillborn. LBW was the sum of individual birth weights of all piglets born in the same litter. Finally, LVR consisted of the standard deviation (SD) of individual birth weight of the piglets from the same litter.
The PB and CB sows that were selected for genotyping have phenotypic records from multiple parities on multiple traits and have a large genetic contribution to future descendants. All PB sows were breeding animals from nucleus farms, whereas the CB sows belonged to farms where combined crossbred and pure line selection (CCPS) is applied. There was no strong selection for first parity performance in the genotyped sows, reducing any possible bias in TNB and LBW due to culling after first parity.
Deregressed estimated breeding values (DEBV) were used as response variable for each trait undergoing study. The estimated breeding values (EBV) were deregressed for each trait separately using the methodology proposed by Garrick et al. (2009). DEBVs, instead of EBVs, were used to compute the GEBV accuracy because this removes the influence of the parents' EBVs and rescales the EBV according to its accuracy, i.e., the DEBV of the animals reflect their genetic merit. Ostersen et al. (2011) have shown that using DEBVs rather than EBVs for genomic prediction yields higher GEBV accuracies. The number of animals and records used to estimate the EBVs are in Table 1. The EBV of each animal was obtained from the routine genetic evaluation by Topigs Norsvin using MiXBLUP (Mulder et al. 2012) in a multi-trait model (including all measured reproduction traits). The genetic evaluation was done across lines with phenotypes from the different populations treated as the same trait. A fixed line effect was included in the model for estimating EBVs. In multi-population prediction scenarios, this line effect was added back to the random additive genetic effect after estimating the EBVs, and subsequently, the line effect was again included in the genomic prediction model. Adding back the line effect allows the differences of the level of EBV between-population to be maintained in the data. Therefore, in the genomic prediction step, the mean differences between populations are still present, and this allows SNP effects (that differ in allele frequencies between lines) to explain these differences between lines.
Table 1 Number of phenotypes on crossbreds and purebreds that were used to estimate the breeding values
The model for obtaining the EBVs for AFI included genetic line and herd-year-season as fixed effects and an additive genetic effect (animal) as random effect. For TNB, the fixed effects were genetic line, parity, interval between weaning and pregnancy (days), whether more than one insemination procedure was performed (yes or no), and herd-year-season. The random effects consisted of service sire, a permanent effect to account for the repeated observations of a single sow, and an additive genetic effect (animal). EBVs for LBW were obtained with a model that included genetic line, parity number, TNB, and herd-year-season as fixed effects and a permanent effect and an additive genetic effect (animal) as random effects. The model used for LVR was similar to the one used for LBW, except that TNB was removed. The reliabilities per animal, needed for deregression, were extracted from the genetic evaluation based on the methodology of Tier and Meyer (2004). The heritabilities (h2) used for deregression were estimated via restricted maximum likelihood (REML) using a pedigree-based relationship matrix and were also obtained from the routine genetic evaluation. The h2 of the traits were 0.30 for AFI, 0.11 for TNB, 0.38 for LBW, and 0.14 for LVR. The genomic h2 of the DEBVs were estimated via REML using ASREML 3.0 (Gilmour et al. 2009).
Sows were genotyped using the Illumina PorcineSNP60 BeadChip (Ramos et al. 2009). SNPs with GenCall <0.15, unmapped SNPs, and SNPs located on either the X or the Y chromosome, according to the Sscrofa10.2 assembly of the reference genome (Groenen et al. 2012), were excluded. Quality control was performed in all populations simultaneously, which involved excluding SNPs with call rate <0.95, minor allele frequency <0.01, and strong deviations of Hardy-Weinberg equilibrium (χ2 > 600). After quality control, 42,139 SNPs remained out of the initial 64,232 SNPs. Individuals with missing genotype frequency >0.05 were also removed. Missing genotypes of the remaining animals were imputed using BEAGLE 3.3.2 (Browning and Browning 2007).
GEBVs were computed based on the genomic best linear unbiased prediction method (GBLUP). GBLUP uses a genomic relationship matrix (G) instead of the numerator relationship matrix (A). The G matrix contains genomic kinship indicating relatedness between animals and was used for prediction in all scenarios with the model:
where y is the vector of DEBVs, μ is the overall mean, g is the vector of random additive genetic effects assumed to be ∼N(0, G
), Z is a design matrix allocating g to y, and e is a residual with heterogeneous variance due to differences in reliabilities of the DEBVs (Garrick et al. 2009). In predictions where the training set contained more than one population, the fixed line effect present in the model for estimating EBVs was also included in the GBLUP model as a fixed effect.
The G matrix for within-population prediction was built according to VanRaden (2008), which was computed as
, where Z is a matrix of centered genotypes and
are the allelic frequencies of the ith marker based on observed genotypes. In predictions where the training set contained more than one population, the G matrix was built according to Chen et al. (2013), accounting for differences in allele frequencies between populations.
We used ASREML 3.0 (Gilmour et al. 2009) to predict the GEBVs, with the G matrix entered as a user-defined matrix. Animals assigned to the prediction set had their DEBVs removed before predicting GEBV.
All scenarios were also analyzed using the A matrix, which contains the average additive genetic relationships of the animals based on the pedigree (PED-BLUP). The model for these analyses was similar to the GBLUP one; however, the g vector of the random additive genetic effect was assumed to be ∼N(0, A
Genetic correlations between PB and CB performance were estimated for the four traits. We used records for DL, LW, and F1 animals born from 2005 through 2012 (Supporting Information, Table S1). Genetic correlations were estimated in bivariate analyses using REML in ASREML 3.0 (Gilmour et al. 2009). The effects of bivariate models were the same as those used to obtain the EBVs (see above); however, to estimate genetic correlations, PB performance and CB performance were treated as different traits (Falconer 1952), which in matrix notation is:
where yi is the vector of observations with i being 1 for purebred and 2 for crossbred data, Zi is the incidence matrix for gi, which is a vector of random additive genetic effects. The additive genetic variance is expressed as:
where A is the numerator relationship matrix and G0 is a 2×2 covariance matrix with the purebred and crossbred variances in the diagonals and the covariances in the off-diagonals.
Scenarios and accuracy of prediction
Seventeen scenarios were investigated that can be divided into four groups according to composition of the training and validation data sets as follows:
Scenarios 1–3: Training and validation data were subsets from the same population, DL, LW, and F1, respectively, i.e., prediction was within-population. These scenarios determine how well the within-population prediction performs for the different traits.
Scenarios 4–7: Same as scenarios 1–3 but the remaining PB population(s) was/were added to the training data, i.e., prediction was multi-population. These scenarios determine whether adding data from a different PB population to the training data would increase the accuracy compared to the within-population prediction.
Scenarios 8–11: One PB population was used for training to predict the other PB population. F1 data were not used in these scenarios, i.e., prediction was across breeds. These scenarios determine how well across-population predictions would perform.
Scenarios 12–17: PB populations were used for training and CB animals were used for validation. These scenarios determine how well CB genetic merit can be predicted from PB data alone, and whether inclusion of more than one parental PB population increases the accuracy.
The accuracy of prediction was estimated as the correlation between the GEBV/EBV and the DEBV of the validation set animals for GBLUP/PED-BLUP. Prediction bias was calculated by regressing the validation variables (DEBV) on the prediction variables (GEBV/EBV). Accuracies were the average of 20 random training-validation populations in scenarios 1–7, 9, 11, 13, 15, and 17. For scenarios 1–7, we randomly set aside part of the genotyped animals (N = 50) and used those in a later step to determine the accuracy of prediction. These 50 were not included in the training for those scenarios. In scenarios 9, 11, 13, 15, and 17, not all the available animals were used for training. Subsets of the training populations were sampled such that the same number of animals was used from each population per trait undergoing study. Any differences in accuracies would then be due to the different populations used, and not to differences in the number of animals. Scenarios 8, 10, 12, 14, and 16 only had one estimate of accuracy because all the animals were used in the training population to maximize prediction accuracy of animals in another population.
Estimates of genomic h2 of the DEBVs across traits and populations ranged from 0.04 to 0.58 (Table 2). Estimates of pedigree-based h2 of the DEBVs across traits and populations ranged from 0.03 to 0.78 (Table S2). The genomic and pedigree-based heritabilities were similar in general. Genetic correlations between PB performance and CB performance for the four traits undergoing study ranged from 0.31 for AFI to 0.90 for LBW (Table 3).
Table 2 Estimated genomic heritability (h2) of the deregressed estimated breeding values across traits and populations under study
Table 3 Genetic correlations between purebred and crossbred performance for the four traits undergoing study
Accuracies for within-population predictions for scenarios 1–3 ranged from 0.22 to 0.72 for GBLUP and from 0.21 to 0.64 for PED-BLUP across the four traits and different training sets, indicating a modest to good predictive ability (Table 4). The regression coefficient of the GEBV/EBV on the DEBV for scenarios 1–3 ranged from 1.03 to 1.70 for GBLUP and from 0.90 to 2.21 for PED-BLUP.
Table 4 GEBV accuracies from within-population prediction using GBLUP and PED-BLUP (scenarios 1–3)
For multi-population prediction of PB populations (scenarios 4 and 5) the accuracies ranged from 0.18 to 0.67, whereas for multi-population prediction (two PB + one CB) of the CB population (scenarios 6 and 7) the accuracies ranged from 0.17 to 0.45 for GBLUP and from 0.32 to 0.42 for PED-BLUP. When predicting PB (scenarios 4 and 5; Table 5), the addition of the other PB population resulted in lower accuracies for all four traits in comparison to within-population prediction for GBLUP. When predicting CB (scenarios 6 and 7; Table 5), the addition of PB populations resulted in lower accuracies for AFI and TNB but higher accuracies for LBW and LVR. The regression coefficient of the GEBV/EBV on the DEBV for scenarios 4 and 5 ranged from 0.86 to 1.18 for GBLUP, whereas for scenarios 6 and 7 it ranged from 0.80 to 3.11 for GBLUP and from 0.97 to 5.00 for PED-BLUP. Accuracies and regression coefficients of the EBV on the DEBV were not computed for PED-BLUP for scenarios 4 and 5 because the other PB population to be added is not related according to the pedigree.
Table 5 GEBV accuracies from multi-population prediction using GBLUP and PED-BLUP (scenarios 4–7)
GEBV accuracy of across-breed prediction, i.e., predicting genetic merit of one PB from a different PB population, performed poorly for AFI and TNB (Table 6); accuracies were not significantly different from zero (P > 0.05). Accuracies for LBW and LVR ranged from 0.13 to 0.26 across the different training sets for GBLUP. The regression coefficient of the GEBV on the DEBV for AFI and TNB ranged from −0.71 to 1.37, whereas for LBW and LVR it ranged from 0.70 to 1.40. Accuracies and regression coefficients of the EBV on the DEBV were not computed for PED-BLUP because the two PB populations are not related according to the pedigree.
Table 6 GEBV accuracies from across-population prediction using GBLUP (scenarios 8–11)
Accuracy of prediction in scenarios 12–17 that predicted genetic merit of CB using PB parental populations as training data performed poorly for AFI (Table 7); accuracies were not significantly different from zero for both GBLUP and PED-BLUP (P > 0.05). For the other three traits, TNB, LBW, and LVR, however, predictive ability was observed. Accuracies ranged from 0.11 to 0.31 for GBLUP and from 0.08 to 0.22 for PED-BLUP. The regression coefficient of the GEBV/EBV on the DEBV for AFI ranged from −1.14 to −0.15 for GBLUP and from 0.15 to 0.95 for PED-BLUP, whereas for TNB, LBW, and LVR it ranged from 0.48 to 3.82 for GBLUP and from 0.53 to 7.76 for PED-BLUP.
Table 7 GEBV accuracies from prediction of crossbred genetic merit from purebred training data using GBLUP and PED-BLUP (scenarios 12–17)
Accuracies of genomically predicted breeding values in CB and PB pigs were estimated for four female reproduction traits in 17 scenarios to optimize the use of genomic data for crossbred animals. We have used DEBVs as a response variable with a moderate to high mean reliability (ranging from 0.33 to 0.80) for the different traits and populations. The SD of the accuracies in scenarios in which we had replicates of training validation populations varied according to the type of prediction (within, multi-, across, or PB to CB). Within-population and multi-population predictions showed higher SDs because the relationship between training and validation in each replicate could substantially vary due to different degrees of relationships within a population. For across-population and PB to CB predictions, the relationship between training and validation populations was naturally lower; therefore, in each replicate there was less variation.
Within-population prediction
LBW and LVR showed generally higher accuracies than AFI and TNB. This difference between traits may occur due to the lower reliability of the DEBV for AFI and TNB, which lowers the accuracy when the number of observations is preset. Another possibility is that there are non-additive genetic effects (e.g., dominance, epistasis) affecting AFI and TNB more, whereas LBW and LVR may be regulated mainly by an additive action of the genes. Therefore, the importance of non-additive effects needs to be further investigated. Even with the low number of genotyped CB pigs, all traits showed predictive ability within the CB. Therefore, a greater number of genotyped CB should increase these accuracies. In general, GBLUP outperformed PED-BLUP across populations and traits, which is mainly a result of a better estimation of relationships among individuals by the markers. Similar results have also been reported in other studies using pigs (Forni et al. 2011; Tusell et al. 2013). The regression coefficients of the GEBV/EBV on the DEBV for both GBLUP and PED-BLUP were, in general, close to 1, indicating that the predictions were not severely biased, except for TNB, where some of the them deviated considerably from 1.
The level of accuracy found here is concordant with those found in other studies on pigs (Cleveland et al. 2010; Forni et al. 2011; Christensen et al. 2012; Tusell et al. 2013; Badke et al. 2014). In these studies, as well as in ours, many traits and breeds were studied and within-population prediction always had predictive ability. One of the studies (Tusell et al. 2013) also studied TNB for two PB populations and their F1 cross and also found that prediction within the F1 cross has greater accuracy than within-PB prediction. They argued that this might be caused by the structure and effective sample size of the populations undergoing study. Accuracies found by Christensen et al. (2012) were not statistically different between single-step BLUP (SS-BLUP) and GBLUP, but both were higher than pedigree-based prediction and GBLUP was shown to be more biased. The advantage of using SS-BLUP was an increase of accuracy for non-genotyped animals. Because our aim was to predict genotyped animals, we studied accuracies of prediction using GBLUP.
Multi-population prediction
Adding data from a different PB population to the training data (scenarios 4 and 5) decreased the accuracy of prediction compared with within-population predictions (scenarios 1–3) for GBLUP. Adding data from the two PB populations to the CB training data (scenarios 6 and 7) had different results depending on the trait. LBW and LVR that had high genetic correlation between PB and CB performance had an increase in accuracy, whereas for AFI that had a low genetic correlation there was a decrease in accuracy. TNB had a high genetic correlation; however, the accuracy also decreased, which was unexpected.
If traits are genetically very different (low genetic correlation between PB and CB), then adding more animals with the other trait to the training is not expected to increase the accuracy. When the trait is the same, however (high genetic correlation), including more animals with the other trait (PB vs. CB) is expected to increase the accuracy. Besides having a high genetic correlation between the traits, the additional animals also need to have some (genomic) relationship to the validation animals. In addition to a low genetic correlation between PB and CB performance, the degradation of accuracy might result from differences in non-additive effects.
For PED-BLUP, adding the two parental PB populations in the training also had different results depending on the trait. AFI and LBW had an increase in accuracy, whereas TNB and LVR had a slight decrease in accuracy. The regression coefficient of the GEBV/EBV on the DEBV estimated to investigate bias for scenarios 4–7 was, in general, close to 1, indicating that the predictions did not suffer from a large bias, except for AFI and TNB in scenarios 6 and 7. For these traits, whenever the PB parental populations were used as training and CB used as the validation set, the regression coefficient of the GEBV/EBV on the DEBV indicated that the estimates were severely biased.
A review regarding multi-population prediction in cattle (Lund et al. 2014) has shown that combining populations, in general, increases the accuracy of prediction when the breeds are the same but from different countries, to a lesser degree when the breeds are closely related, and has little or no benefit when the breeds are distantly related. Another study (Hayes et al. 2009a) has reported slightly higher accuracies when using multi-population prediction compared to within-population prediction in dairy cattle. Chen et al. (2013) used Angus and Charolais steers to determine the accuracy of prediction with GBLUP for within-population and multi-population predictions. In their study, accuracies did not always increase, suggesting that noise was being added to the predictions. The maximum increment in accuracy that they obtained was 0.05, whereas a decrement of 0.07 was also obtained, which is within the same range as the differences observed in the current study. These studies showed that adding another PB population to the training data in cattle did not necessarily increase the accuracy of prediction, similar to our current results in pigs.
De Roos et al. (2009), using simulated data, also showed that increasing the size of the training data by adding animals from a different population does not always increase the accuracy. An increase in accuracy higher than within-population was only found when the populations were closely related, when marker density was high, or when the size of the initial within-population training data set was small. In our case, the number of markers was reasonable and in some scenarios the size of the within-population training data set was small, but we still did not have a great increase in accuracy of prediction. This suggests that the marker density might not be sufficient to have similar LD levels between QTL and markers in the different populations that are mixed. The genetic distance between the populations was probably an important factor that limited the benefit of adding training data from other populations.
Across-population prediction
Some predictive ability was observed when predicting across populations for LBW and LVR, whereas for AFI and TNB all the accuracies were null. Increasing the size of the training population slightly improved the accuracies of prediction, on average by 0.05. Greater accuracies were found when DL predicted LW genetic merit, rather than the other way around (scenario 9 vs. scenario 11). The regression coefficients of the GEBV on the DEBV for scenarios 8–11 were, in general, close to 1 for LBW and LVR, indicating that the predictions did not suffer from much bias. For AFI and TNB, however, regression values greatly deviated from 1, sometimes with negative values, which we attribute to the very low accuracies we found.
In a study by Harris et al. (2008), the prediction across Holstein-Friesian and Jersey cattle breeds was also investigated. Predictions were not accurate, ranging from −0.1 to 0.3 for 25 traits. In another study, Hayes et al. (2009a) predicted the GEBV of Jersey animals using a Holstein population as training data and vice versa, resulting in accuracies ranging from −0.06 to 0.23 for five traits. Both studies report results that were very similar to ours that ranged from −0.05 to 0.26.
The simulation study by De Roos et al. (2009) indicated that across-population prediction was substantially less accurate than within-population or multiple-population prediction. These lower accuracies were due to differences in marker–QTL LD phase between the populations. A marker may be in LD with QTL in a given population, but it is not necessarily in LD with those QTL in the other population, resulting in poor predictions for the other population. These simulation results suggested that, for our analyses, a higher marker density would be required. However, results of Veroneze et al. (2014) show that with the same 60K porcine SNP panel, the density of SNPs is high enough to obtain reasonable levels of LD. This would predict that our SNP panel should be able to capture marker effects across breeds.
Using purebred training data to predict crossbred genetic merit
Using only the PB population(s) to predict the CB genetic merit with GBLUP has some predictive ability for TNB, LBW, and LVR, whereas all the accuracies for AFI were null. Increasing the size of the training data by adding another PB population increased the accuracy for TNB and LBW, whereas for AFI and LVR it did not. However, when we increased the size of the training population by adding more animals of the same PB population, the accuracies usually increased. The accuracy of prediction for predicting CB animals based on PB animals appears to depend largely on the genetic correlation between PB and CB performance. As our results demonstrate, the greater the genetic correlation, the higher the chances of having any or more predictive ability. AFI, for which the genetic correlation between PB and CB was very poor, had zero accuracy of prediction showing that selection on PB is expected to have no effect on CB genetic merit.
For PED-BLUP, the accuracies were, in general, lower than for GBLUP, especially for LBW and LVR. Adding the second PB population in the training slightly increased the accuracy of prediction.
The greater accuracies found for TNB, LBW, and LVR when training with DL rather than LW population can be explained by the slightly greater relationship between DL and F1 populations than between LW and F1. This higher relationship is specific for the animals included in this study. The F1 animals that were genotyped are more closely related to the DL animals that were genotyped than to the LW animals that were genotyped.
To test the impact of the relationship between training and validation populations on the accuracy, we split the training data into the 50% of animals that are MOST related to the validation set and the 50% that are LEAST related to the validation set (Table S3, Table S4). For AFI, TNB, and LBW, using the 50% MOST related animals resulted in greater accuracies, whereas for LVR it did not. This indicates that if CB animals have closer genomic relationships to the PB animals used as training, then higher accuracies for scenarios 12–17 could generally be expected.
In cattle, Harris et al. (2008) used PB populations (Holstein-Friesian and Jersey) to predict the genomic breeding values of a cross between these two breeds. They used data from 4500 sires genotyped for approximately 44K SNPs. Their results show that using the two breeds as the training population increased the accuracy by 5–10% compared to using only one of the breeds to predict the cross. The actual level of accuracy was not reported in their study. Our results were similar to theirs for TNB, LBW, and LVR, where the genetic correlation between PB and CB performance is close to 1, but not for AFI.
Results indicate that using a PB population to predict CB genetic merit can generate reasonable predictions. This, however, is not consistent for all traits. Although these results do not reflect the actual practice of genomic selection in pig breeding, they do provide an estimate of the accuracy of genomic prediction between CB and PB populations using real data. The results make a strong case for the genotyping and recording of CB animals, at least for a subset of traits where genetic correlations are away from 1.
The low genetic correlation between PB and CB performance for AFI was also found in another study (Nagyné-Kiszlinger et al. 2013). They have reported values of 0.28 and 0.39 for Hungarian Large White and Hungarian Landrace with their reciprocal cross. Possible reasons for this low genetic correlation were reported: 1) genes that affect the trait might be different among populations; 2) this trait is affected by non-additive effects or environmental factors due to different management of PB and CB animals (Nagyné-Kiszlinger et al. 2013). One clear environmental factor that probably reduces the genetic correlation of AFI between PB and CB is the use of batch farrowing systems in the production environment of CB sows. Suppression of estrus is used to synchronize the heat of the CB gilts, which impacts the measurement of the trait and leads to these low correlations.
Standardized EBVs were used; therefore, a bias would possibly be introduced during deregression due to different reliabilities between breeds (Garrick et al. 2009). Additional sources for potential bias affecting the SNP effect estimates are the differences in the population mean of the breeds. The differences in the mean between populations were remedied by reintroducing the line effect after deregression. To test the impact of deregression on bias, we investigated all 17 scenarios for the trait AFI by analyzing phenotypes, which are not standardized, instead of DEBVs. The correlation between the accuracies obtained by the two different approaches was 0.92, with a mean regression coefficient of the GEBV on the phenotype of 0.70. This correlation shows that using the phenotypes has good agreement with the accuracies calculated using DEBVs; therefore, any bias due to the process of standardization and deregression is expected to be limited.
The reasonable accuracy for PB predicting CB genetic merit shows that in a current typical breeding program, selection in the PB does result in a phenotypic response in CB. AFI was an exception in our study, because the genetic correlation between PB and CB performance was very low.
Further studies to compare the accuracy of genomic selection of PB for CB performance are needed. Other genomic models including breed-specific effects of SNP alleles or dominance (Ibánez-Escriche et al. 2009; Zeng et al. 2013) were described and were found to outperform an additive model only in specific cases, e.g., with high dominance levels or when the number of SNPs is small relative to the size of the training population. Using these more complex models or a multiple-trait model (Christensen et al. 2014) with real data is needed.
In conclusion, there was predictive ability for purebred population(s) predicting crossbred genetic merit using an additive model in the populations studied when PB and CB traits have high genetic correlation. For practical implementation, estimation of genomic breeding values of PB animals for CB performance needs to be further studied with models that take into account the crossbred nature of training data. Multi-population prediction was no better than within-population prediction for PB populations. Accuracy of prediction was shown to be very trait-dependent; hence, for the utility of data from other breeds in the application of genomic selection, each trait needs to be studied separately and no generalizations should be made. Finally, real data accuracies were lower than what simulation studies have reported.
A.M.H. currently benefits from a joint grant from the European Commission (within the framework of the Erasmus-Mundus joint doctorate "EGS-ABG"), Breed4Food (a public-private partnership in the domain of animal breeding and genomics), and Topigs Norsvin. J.W.M.B. was supported by PPP Breed4Food. Topigs Norsvin had a role in study design, data collection, and preparation of the manuscript.
Supporting information is available online at www.g3journal.org/lookup/suppl/doi:10.1534/g3.115.018119/-/DC1
Communicating editor: G. A. de los Campos
Received March 20, 2015.
Accepted May 21, 2015.
Copyright © 2015 Hidalgo et al.
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You are going to email the following Accuracy of Predicted Genomic Breeding Values in Purebred and Crossbred Pigs
A Bayesian Decision Theory Approach for Genomic Selection
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Using OpenCV's Haar cascade face detection functionality, I was able to throw together a real time head tracking program. I coupled this with a several demos, including a Johnny Lee style 3D targets demo. Performance is surprisingly good. On my older 2.16 Ghz MacBook, I'm tracking at 25 frames per second - sufficient for real time interaction without obtrusive reaction delays.
Check out our paper on "Pseudo-3D Video Conferencing with a Generic Webcam"


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E&P | NGI All News Access | NGI The Weekly Gas Market Report | Regulatory
Trump Plan to 'Free Up' Pipeline Infrastructure Projects Encouraging, Says Chevron CEO
By Carolyn Davis
Chevron Corp. sowed a second consecutive quarterly profit in the last three months of 2016 following sharp cost containment and a modest price rebound, but earnings still came in well below Wall Street consensus.
Net profits totaled $415 million (22 cents/share) in 4Q2016, versus a year-ago loss of $588 million (minus 31 cents). Sales and other operating revenues were $30 billion in the final quarter, compared with $28 billion in the year-ago period. The Street's consensus was earnings of 64 cents/share on revenue of $33.3 billion.
Improved earnings and balanced cash flow should be the hallmarks of 2017 as more belt-tightening and production growth pad revenue, CEO John Watson said during a conference call Friday morning.
"Our 2016 earnings reflect the low oil and gas prices we saw during the year," he said. "We responded aggressively to those conditions, cutting capital and operating expenses by $14 billion," allowing capital expenses to decline by more than expected.
Upstream earnings totaled $930 million, compared with a loss of $1.36 million a year ago. U.S. upstream profits totaled $121 million from a loss of $1.95 billion in 4Q2015. For 2016, Chevron swung to a $497 million (minus 27 cents/share) loss from 2015 profits of $4.59 billion ($2.46).
Cash flow from operations totaled $12.8 billion in 2016, versus $19.5 billion in 2015. Capital and exploratory expenditures fell to $22.4 billion last year from $34 billion in 2015. Nowhere are the efficiency gains more apparent than in the drilling program, where the "trend of spend" is down, Watson said.
"Trend of Spend Is Down'
For example, for the wells drilled in the deepwater Gulf of Mexico (GOM) last year, "if we had had the productivity we had in 2014, we would have spent $1 billion more…and that was just in the deepwater. So the efficiencies we've put in place have allowed us to bring down costs. The trend of spend is down…We have some major capital projects that are being completed. If we're still in the $50-55/bbl world, you'll see us tracking at the bottom-end of that range…
"Notably, I would expect that we would see an increase in unconventional spending," led in the U.S. onshore by the Permian Basin.
"We've talked about ramping up the Permian, and I think that will be the case," the CEO said. "We're budgeting about $2 billion this year, but you could easily see another $1 billion there. We have very little activity in the Marcellus Shale now…We have gotten very efficient there, but we would expect better market conditions and offtake capability there. We've made good progress in the Duvernay" in Canada and in Argentina. "Just in the shale and tight area, you could see some increases…That's short-cycle high-return activity."
In the Permian, for example, plans this year are to add about one rig every two months to the current 15-rig fleet (10 operated), to end 2017 at around 15 operated rigs. Capital spending in the Permian is set at around $2 billion for 2017. More details are expected during an investor conference in March.
Opportunities also exist in the portfolio to progress concepts and delineation drilling at projects in the GOM and overseas.
"We have a good queue of projects, but we need to make sure that those have kind of the right economics associated with them," Watson said. "But all of that can comfortably fit in the range that we've talked about…"
Global supply across the board, not specifically to Chevron, has remained stable, despite the falling rig count and reduced spending, which was not anticipated.
"I have been surprised at how resilient production has been in many locations around the world," Watson said. "Some of that is, we just keep getting better…We've been on plateau in some of our Gulf of Mexico projects, and I think we and others are getting very good at extending plateaus. And technology only goes in one direction. We hear about it in the context of the shales, but the same thing is true in other conventional activities…It's made some of…base activity more competitive.
"Ultimately, however, you do need new major capital projects to fill the gap if you look out a few years. And we're just not seeing final investment decisions being taken on significant new greenfield opportunities…At some point, we do…expect to see, at least in the conventional area, some declines in production. And there's a limit to this, and it has surprised us that it's held up as well as it has. But at some point, you're going to need new activity."
Trump Agenda Encouraging
During the question and answer period, Watson was asked about the changes undertaken since President Trump took office. He was asked about the president's agenda, and specifically about the GOP-backed border tax adjustment plan, part of a proposed corporate tax reform, which would adjust taxes by subsidizing exports and taxing imports.
"In an overall sense, I've been very pleased with the agenda that the Trump administration has," Watson said. "We have seen an avalanche of regulation over the last decade, and putting a much more balanced cost benefit framework in place to assess the value of those regulations, freeing up infrastructure pipelines — all of that is quite positive for our business, for the country, job creation and a lot of things. So that is very much a positive.
"And we all know that our tax system is not competitive. We want American companies to be able to compete, and so there's a lot of work being done to try to bring down corporate rates so that we can compete both at home and abroad for capital. And of course, the administration has a focus on bringing jobs and capital back to the U.S., and lower rates will help that."
In Watson's view, the administration is looking for "ways to make those lower rates happen, and so they're looking at a variety of different concepts. The truth is, there are a lot of different ideas being floated right now, and I think they're looking for input. And we'll continue to provide it. President Trump has indicated that the border adjustment concept is complex, and I would agree with that…I think we need to take a close look at perhaps the consequences of that, both some that could be positive and the unintended consequences in terms of impact on consumers, exchange rates and knock-on effects on the global economy."
Watson said he has "no doubt that the administration will do a good job of doing that and will settle on the right kind of tax reform at the end of the day. I think we need to have a little patience for the different ideas that are being put out there, and hopefully we'll get to the right outcome."
Chevron last year had "noteworthy milestones," and Watson highlighted ramping up production from Gorgon, the first of two major liquefied natural gas (LNG) export facilities in Australia that Chevron operates. The other operated Australian LNG project is Wheatstone.
About 900 million boe of proved reserves were added last year, equal to 95% of net boe, with the "largest additions" from the Permian, Wheatstone and a planned project at the Tengiz field in Kazakhstan.
Worldwide net oil and gas production reached 2.67 million boe/d in 4Q2016, essentially flat year/year. For the year, output was 2.59 million boe/d, down 1%. In the United States, production totaled 682,000 boe/d in the final three months, off 5%, or 37,000 boe/d, from 4Q2015.
Production increases from base business in the GOM, as well as shale and tight properties in the Permian, were offset by the impact of asset sales of 58,000 boe/d and normal field declines. The net liquids component of U.S. output increased 2% to 508,000 boe/d, while net natural gas production decreased 21% to 1.04 Bcf/d.
NGI is covering many North American-focused oil and gas operator results over the coming weeks. A calendar listing is available on the website.
Related topics: Capital production Watson

redpajama_set_name: RedPajamaCommonCrawl


In the wake of the tragic events in Dallas and Baton Rouge, please pause to honor local law enforcement.
It's hard to find words that adequately express our feelings about the senseless, hate-inspired violence that claimed the lives of eight law enforcement officers in Dallas and Baton Rouge these last two weekends. We wish to convey our support and admiration for all the men and women across America who risk their lives every day to protect and serve our cities and neighborhoods.
ezVote is proud to support local law enforcement. And we encourage anyone who may be reading this to take a moment in the next few days to offer an expression of gratitude to the law enforcement personnel in your community. The sooner we reach out to one another, the sooner we can begin to heal.
NOTE: For the last few years, ezVote has had the honor and privilege of providing services to the Illinois Association of Chiefs of Police. We thank them for their selfless commitment to the people of Illinois, and we join them in mourning their fallen colleagues in Texas and Louisiana.


What if when you went home tonight, the police came to your door and said your husband was under arrest? What if your husband went to prison?
Your child trusts you to be honest with her. If there is an absence in the family due to incarceration, it is better to be up front with your child than to "protect them" from the truth. Your child will have her own process to go through as she adjusts to this new way of life. She needs you to be constant and open with her during this time.
Trust does not mean blindly believing what your teenager tells you. Trust means not giving up on your child, no matter what she does. Trust means never walking away from the relationship in frustration, because you trust that she needs you and that you will find a way to work things out.
There is a common adage that says, "respect must be earned." But how can a child earn respect if they do not understand what it is? Respect is not an innate characteristic of a small child. As a parent, you play the most important role in teaching children what respect means.
Let your children open up to you.
Your children are learning from your example. They will learn how to respond to confrontation, stress, and fear by how you respond.
If you tell your child it's disrespectful to interrupt when an adult is speaking, but then interrupt when your child is talking, what example are giving? Your words about being respectful don't make a difference if you aren't teaching by example.
As a single parent, you will feel pressured to be both father and mother, parent and breadwinner. Your child needs to spend time with you, but if you are stretched thin between responsibilities and obligations, you can't give your child quality time. Gather around you a strong support group upon whom you can rely.
So many things can affect a child's self-esteem. The way you treat your child should not tear them down. Encouraging words can influence how their day goes despite how their friends and others treat them. Children need to know that you have their back and that they have a safe refuge from the world. Pray with and for them before sending your kids out the door with a smile. Children who feel good about themselves are more likely to give respect to others.
Think of your child as a plant who is programmed to grow and grow. Your kid is like that they are given to us to nurture and grow. Encourage them to keep going, tell them they are doing a great job, tell them you are proud of them, when you show them they are doing a great job and When kids learn to do things for themselves and feel proud of what they can do, they feel capable. I feel this is very important if a parent is incarcerated because as a child they may feel like they are losing out on things, they may blame themselves and act out but if we show them we are proud of them, it makes a world of difference.


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Kashihara (橿原市, Kashihara-shi) és una ciutat i municipi de la prefectura de Nara, a la regió de Kansai, Japó. És el segon municipi més populós de tota la prefectura després de la ciutat de Nara, la capital prefectural. La ciutat de Kashihara té una gran importància històrica per al Japó, ja que es on hi hagué Fujiwara-kyō antiga capital del Japó des de l'any 694 al 710 i on l'Emperador Jinmu creà el Japó i ascendí al tron del crisantem.

Geografia
La ciutat de Kashihara es troba localitzada a la part central de la prefectura de Nara. Tot i trobar-se a la part sud de la conca de Nara, al municipi s'hi troben nombroses muntanyes com ara el mont Unebi, el mont Amanokagu i el mont Miminashi, tots ells pertanyents als tres monts de Yamato o Yamato sanzan (大和三山). El terme municipal de Kashihara limita amb els de Kōryō i Tawaramoto al nord; amb Gose i Yamato-Takada a l'oest; amb Sakurai a l'est i amb Asuka i Takatori al sud.

Història
Des del període Nara fins a la fi del període Tokugawa, la zona on actualment es troba el modern municipi de Kashihara va pertànyer a l'antiga província de Yamato. L'actual ciutat de Kashihara va ser fundada l'11 de febrer de 1956 fruit de la fusió de les viles de Yagi, Imai i Unebi i els pobles de Kamokimi, Kanahashi, Niizawa i Masuge, tots ells pertanyents al districte de Takaichi, així com el poble de Miminashi, pertanyent al districte de Shiki. La toponímia dels antics municipis encara roman als noms dels barris i de les estacions de ferrocarril.

La ciutat de Kashihara, tot i ser de recent creació, té un lloc important a la història i la mitologia nacional japonesa. Entre el 694 i el 710, la capital del Japó, la ciutat de Fujiwara-kyō, es trobà a aquesta zona. També s'ha cregut sempre que l'Emperador Jinmu, fundador del Japó, va conquerir el tron amb l'ajut de l'ocell daurat de tres potes (八咫烏, Yatagarasu) trobat a la zona (el qual té un monument a la plaça de l'estació de Yamato-Yagi). Per últim, cal assenyalar que Imai (avui dia integrada dins de Kashihara) va ser una de les ciutats autònomes més riques i prosperes de tot el Japó durant el segle XVI, d'on vé la dita popular "Umi no Sakai, riku no Imai" (en català: "de la mar Sakai, de l'interior Imai").

Administració
Aquesta és la llista dels alcaldes de Kashihara des de la fundació de la ciutat:

Alcaldes

 Saburō Yoshikawa (1956-1972)
 Yoshimi Mori (1972-1975)
 Tarō Miura (1975-1992)
 Shirō Okahashi (1992-1995)
 Yutaka Asoda (1995-2007)
 Yutaka Morishita (2007-2019)
 Tadahiko Kameda (2019-present)

Demografia

Transport

Ferrocarril
 Companyia de Ferrocarrils del Japó Occidental (JR West)
 Kaguyama - Unebi - estació de Kanahashi
 Ferrocarril Kinki Nippon (Kintetsu)
 Masuga - Yamato-Yagi - Miminashi - Ninokuchi - Yagi-Nishiguchi - Unebi-goryōmae - Kashiharajingū-mae - Bōjō - Kashiharajingū-nishiguchi - Okadera

Carretera
 Autopista de Kyoto-Nara-Wakayama (Keinawa)
 Nacional 24 - Nacional 165 - Nacional 166 - Nacional 169

Agermanaments
   Miyazaki, prefectura de Miyazaki, Japó. (11 de febrer de 1966)
  Luoyang, província de Henan, RPX. (12 de febrer de 2006)

Referències

Ciutats de la prefectura de Nara

redpajama_set_name: RedPajamaWikipedia


StarTimes is reducing its subscription fees for its Kenyan market by 50 per cent. Viewers will be able to view its 'Nyota' bouquet on a daily basis at $0.25 per day.
The monthly subscription fee has dropped from $42 to $22 on its 'Classic' bouquet, and to $7 for the monthly Nyota rate.StarTimes is also adding 6 more services including National Geographic channel, Fox and Star Plus.
StarTimes director of marketing Japhet Akhulia said the changes are expected to lead to a stronger subscriber offer in terms of price and content. "We have made strategic improvements by adding six more channels key in extending value for money to our Classic bouquet subscribers," he said.
As for Star's DTH service, subscribers on its satellite television platform have the option of the 'Super' bundle retailing at $42; 'Smart' option at $25 or 'Nova' at $12 respectively.


Photo booth is a very popular term which defines the photo session in simple terms. It has been a long known fact that photo booths have been playing an important role in life of billions of the people present worldwide. We are here with the discussion on the open air photo booth which was designed with the idea of long portability and it is thus both functional as well as unique. This kind of photo booth was discovered a long back and is highly been in trend. Its set up is quite amazing which is based on capturing effective photos in simple manner. It captures the goodies of traditionally enclosed photos which may confront you with the best pixel qualities at once.
What open air photo booth actually is?
Did you know what the open air named photo booth is actually all about? Well if no than here we are with the descriptive discussion on the photo booth which may help you find various impressive details. The open air photo booth has been one of the popular photo booth set up which has been offering the effective photo sessions and the impressive picture qualities. It is typically all about the camera set up with an open back drop. This type of photo booth was named as the open air booth and was actually introduced long ago. It has been holding the best features to capture as many people into a single photo graph. The open air type photo booth has been highly preferred still by many people as it has been holding the feature to manage the best photo session. This type of photo booth holds several benefits but still many are yet unknown with it.
ü It is completely easy and simple to take photo.
ü It can capture images of as many people into single photo.
ü It is available by every photo studio.
ü It is cost effective i.e. affordable.
ü It can help you share your photos both online as well as offline.
Although it is one of the old method for the photo capturing but is still in use by billions of the people present all over the world. It avails the best picture quality and help people keep the memorable as well as beautiful moments with them. They are widely been in use in great events for the photography sessions.


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WW1 LESSON PLANS
TIMELINE OF WW1
WW1 SCHOOL WORKSHOP
BRING THE HISTORY OF WW1 TO LIFE IN YOUR SCHOOL
At 11am on the 11th November 1918, the First World War ended with an armistice signed by both sides in a railway carriage in Compiègne, France that came to be known as the 'Treaty of Versailles'.
At long last one of the worst wars in human history had come to an end and people could return to their homes, many of which had either been damaged by war. In addition, over 17 million people had died, 7 million of which were innocent civilians and 10 million of which were soldiers from all sides.
This terrible 'cost' of the war is still felt now, one hundred years later, when families recall Great Grandparents and other ancestors who either died, were injured or were otherwise affected by the war.
During war time, families from both sides had been separated from their loved ones, who had often had to endure the most horrible conditions of trench warfare, machine gun fire and constant shell bombardment, while their loved ones had to wait at home in fear of a telegram boy bringing a telegram to say that their Brother, Father, Son or Uncle had died.
Meanwhile, back home, families had been forced to endure incredible hardship without income from the men in the family, with mothers and daughters working in factories and farms, and children having to grow up without their Father helping to raise them in the house.
Unfortunately the killing of the First World War lasted right up until the last minute, with the family of the War poet Wilfred Owen, receiving news that he had died just as the church bells started ringing to signal the end of the war at 11am on the 11th November 1918.
THE FIRST WORLD WAR ENDS
WW1 school workshop / First World War primary school timeline / The First World War ends
Go back to the clickable WW1 timeline for children
BALESTRA SCHOOL SPORT LTD, trading as BALESTRA SCHOOL WORKSHOPS co. no. 5903136.
Delivering fun and interactive primary school workshops in North West of England in the following areas: Manchester, Oldham, Wigan, Sefton, Trafford, Rochdale, St Helens, Warrington, Stockport, Bury, Knowsley, Halton, Tameside, Bolton, Liverpool, Salford, Cheshire, Cumbria, Lancashire, Staffordshire, North Yorkshire, South Yorkshire, Derbyshire and many more NW areas.
We are also happy to consider travelling further afield for a small mileage surcharge - see here for further details.
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Camp LEAD (Leadership and Education through Admiration and Dedication) is a summer camp for students ages 8 to 13. Students will engage in a transnational program for five days. It is designed to harness the students' knowledge of responsible leadership. Camp LEAD is comprised of a series of workshops and activities that will develop the students' critical thinking, problem-solving, communication and financial skills.
Camp LEAD challenges students physically on the ropes courses, mentally in the classroom, and emotionally as they learn to communicate with others. An atmosphere is created where students facilitate their own learning and set goals in an experiential setting. Students understand what their strengths are as leaders, the role they play on a team and how to bring out the best in others. Students discover what is possible with little to no resources, aside from their brain and their willingness to take action.


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Virginia Democrats are in turmoil these days, as Gov. Ralph Northam and the two Democrats behind him in the line of succession have all been caught up in possible career-ending scandals involving racist costumes and alleged sexual assault. Republicans, unsurprisingly, are striking poses of outrage and demanding the immediate resignation of all three officials. Democrats, meanwhile, are charging Republicans with hypocrisy, because the GOP continues to support its own politicians who are racist or sexually abusive — such as President Trump, who has a long track record of racism and was recorded bragging about sexual assault.
Hypocrisy is a favorite charge in American politics — arguably because it's the only one that seems to consistently stick — but this series of scandals shows that Republicans are something worse than hypocrites. They are political nihilists, who use scandals like this to further their agenda of emptying politics of any morality, hope or substance, clearing the path for a naked power grab.
First, the facts: Last week, a trolly right-wing website published a photo, taken from Northam's personal page in his 1984 medical school yearbook, that shows one man in blackface and another in a KKK-style hood. At first, Northam admitted to being in the photo and apologized. Then backtracked, claiming that neither of the people in the picture was actually him, while also admitting to have done blackface at another point to impersonate Michael Jackson.
Then allegations were published — by the same website — that Lt. Gov. Justin Fairfax, who would replace Northam if he resigned, had been accused of sexual assault. Shortly after that, Fairfax's accuser, a California academic and a Democrat who seems to have no relationship to Fairfax outside of this one encounter, released a public statement that is both credible and disturbing.
Third in line is Virginia Attorney General Mark Herring, who then stepped forward and admitted to doing blackface in college. This time, the story isn't being driven by right-wing blogs, but it seems likely that Herring decided it was wise to get ahead of the story before any photos came out.
Without a doubt, these scandals are the result of a coordinated political attack by right-wing forces who are mobilizing to prevent Democrats from passing legislation supporting women's equality. So even as many Democrats agree there should be serious political consequences for these three men, they are also calling Republicans hypocrites for their outraged denunciations.
They have a point. After all, the same conservatives who are professing outrage over stories of racist behavior and sexual misconduct supported Republicans Ed Gillespie and Corey Stewart in their statewide campaigns — for governor and U.S. senator, respectively — even though both made overt appeals to racism and Stewart has ties to fringe white nationalist groups. These same conservatives largely support Trump, of course, and were quick to defend Supreme Court Justice Brett Kavanaugh from allegations that were strikingly similar to the ones now raised against Fairfax.
But I'd argue that what Republicans are up to here is more sinister than garden variety hypocrisy. Their larger purpose is to push the idea that no one actually cares about racism or sexism, and that all protestations to the contrary are just postures that liberals adopt for political gain. Their strategy is to seed the notion that everyone is a bigot motivated by greed and naked self-interest, so they can then turn around and hold Republicans out as slightly better because hey, at least they're honest about it.
That's what all the discourse about "political correctness" is ultimately about. Intrinsic to these complaints about is the idea that people who seek to enforce social norms against racist or sexist slurs are not motivated by sincere moral conviction but by a will to power. That's why the related term "virtue signaling" has taken off in right wing circles. That refers to the idea that no one actually believes in their professed egalitarian values, but only say they do in order to gain status and power.
This was the premise of Donald Trump's entire campaign and now his presidency: He was smashing through the strictures of "political correctness" and freeing people to admit their true racist and sexist nature. This premise requires painting his opponents as disingenuous. Otherwise, the "politically incorrect" would have to admit that they're just bigoted bullies, rather than righteous freedom fighters struggling against hypocritical moral scolds.
"Wealthy politicians and donors push for open borders while living their lives behind walls and gates and guards," Trump said. The implication was clear: Liberals are a pack of hypocrites who say they want racial diversity but actually wall themselves up in enclaves of privilege. So conservatives should build a border wall as a way of sticking it to those phonies.
So Republicans don't much care if they come off like hypocrites when they express outrage at Democrats who do racist or sexist things, while defending and indulging people on their own team who are often far worse. It's because they aren't really hypocrites. They are people who believe outrage at bigotry is never sincere, but only performed for political gain. So they perform it for political gain whenever that's convenient, and never lose a moment's sleep.
Republican nihilism, on its surface, seems to be working for them. Sen. Al Franken, the Minnesota Democrat, was forced to resign after eight women accused him of sexual harassment, but Kavanaugh is now on the Supreme Court despite credible allegations that were more serious. Trump was elected president even after being revealed as bragging about transgressions that would likely qualify as criminal assault. Ralph Northam is under immense pressure to resign, understandably and justifiably. But any number of Republicans, including the president, continue to skate while invoking white nationalist ideas. It's maddening.
That's why some on the left are inclined to wonder if it's time for Democrats to do what Republicans do: White-knuckle your way through a scandal, hoping that eventually everyone will move on and you can keep your seat and the power. There are quite a few embittered fans of Al Franken fans who believed he could used "due process" to drag out the scandal until it went away.
This approach has it all backward. That might save individual politicians from doom — Northam is clearly making that bet by refusing to resign and pretending to investigate — but in the long run, that dig-in-the-heels strategy only bolsters the nihilistic Republican narrative that no one actually cares about bigotry and only pretends to when it's politically advantageous.
But by taking a moral stand and cleaning their own house, Democrats can show voters that they actually do care. They take sexual assault and racist conduct seriously and will consistently denounce such things, even when their own elected leaders have committed the offenses and must face the consequences. This isn't just the right thing to do. It's also the winning strategy in the long run.
The Franken scandal shows why. If the Minnesota senator had toughed it out through the sexual harassment scandal, he would have been sitting on the Senate Judiciary Committee while Brett Kavanaugh was forced to respond to sexual assault allegations. Franken's presence would have completely undermined the moral authority Democrats brought to the proceedings. But he was gone, and Democrats could send a strong, unmistakable signal to the nation, and to women in particular: They take sexual assault seriously and will fight for women's right not to be victimized.
Sure, Kavanaugh was confirmed anyway. But taking the high road paid off for Democrats almost immediately with a sweeping victory in the 2018 midterm elections. That win was due to women voters, who not only showed up in larger numbers than men but overwhelmingly voted for Democrats while men split almost evenly between the two parties. It's doubtful that would have happened if women felt that both parties put protecting their own politicians over women's health and safety.


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Operates all surveillance computer equipment. Monitors all areas of the property and closely observes a target game, staff member, or customer. Ensures compliance with applicable gaming regulations. Reports improper activity or disturbances to supervisor or security team for further investigation. May require a high school diploma or its equivalent. Typically reports to a supervisor or manager. May require 0-1 year of general work experience. Possesses a moderate understanding of general aspects of the job. Works under the close direction of senior personnel in the functional area.
These charts show the average base salary (core compensation), as well as the average total compensation for the job of Surveillance Operator in the United States. The base salary for Surveillance Operator ranges from $30,802 to $37,909 with the average base salary of $33,927. The total compensation, which includes bonus, health and retirement, can vary anywhere from $31,005 to $38,346 with the average total compensation of $34,236.


This session will introduce you to the topic of plagiarism and how it can be avoided by referencing sources correctly. Learn about basic referencing features and terminology. Hands on practice during the session will give you the opportunity to find out more about your required referencing style, supported by library staff.


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