How does a beef x dairy calving affect the dairy cow's following lactation?

For beef semen usage on dairy cows, much of the research has focused on the performance of the crossbred calves, yet little focus has been given to the subsequent performance of the cow herself. This study aimed to evaluate the performance of dairy cows for milk yield, fertility, and survival traits after giving birth to beef x dairy crossbred calves and compare this with the performance after giving birth to purebred dairy calves. Further, we aimed to study if the effect of a difficult calving was the same regardless of whether the calf was purebred dairy or beef x dairy crossbred. Phenotypic records from 587,288 calving events from 1997 to 2020 were collected from the Swedish milk recording system from cows of the dairy breeds Swedish Red (SR) and Swedish Holstein (SH). The sire beef breeds studied were Aberdeen Angus, Hereford (combined in category LIGHT), Charolais, Limousin, and Simmental (category HEAVY). Sixteen traits were defined and grouped in 3 categories: cumulative and 305-d milk, fat, and protein yield, daily milk yield, and 75-d milk yield as yield traits; calving to first insemination interval, calving to last insemination interval, first to last insemination interval, calving interval, and number of inseminations as fertility traits; and survival to 75 d or to next calving and lactation length as measures of survival. The data were analyzed for all traits for first and second parities separately using mixed linear models, with a focus on the estimates of cow breed by service sire breed combinations. All traits in parity 2 were adjusted for previous 305-d milk yield based on the expectation that low-yielding cows would more likely to be inseminated with beef semen. Overall, milk yield was lower after beef x dairy calvings compared with the purebred dairy calvings. The largest effects were found on cumulative yields and in second parity, with lower effects for yields early in lactation and yields in first parity. The largest decrease was 13-14 kg (0.12 phenotypic SD) for cumulative fat yield when breeding beef breed sires with purebred SR dams. For fertility traits, for most breed combinations, the effects were not large enough to be significant. Conversely, all beef x dairy crossbred combinations showed significantly lower results for survival to the next lactation, and mostly also for lactation length. There was some indication that dairy cows with beef x dairy calvings in parity 2 that were the result of maximum 2 inseminations in parity 1, had lower survival than corresponding calvings resulting from more than 2 inseminations. This could indicate that the former cows were marked for culling already when inseminated. There was generally an unfavorable effect of a difficult calving on all traits, however, there were almost no significant interactions between calving performance and dam by sire breed combination, and these interactions were never significant in first parity.

Inheritance of genomic regions and genes associated with number of oocytes and embryos in Gir cattle through daughter design.

Over the past decades, daughter designs, including genotyped sires and their genotyped daughters, have been used as an approach to identify quantitative trait loci (QTL) related with economic traits. The aim of this study was to identify genomic regions inherited by Gir sire families and genes associated with number of viable oocytes (VO), total number of oocytes (TO) and number of embryos (EMBR) based on a daughter design approach. In total, 15 Gir sire families were selected. The number of daughters per family ranged from 26 to 395, which were genotyped with different SNP panels and imputed to the Illumina BovineHD BeadChip (777K) and had phenotypes for oocyte and embryo production. Daughters had phenotypic data for VO, TO, and EMBR. The search for QTL was performed through Genome-Wide Association Study (GWAS) based on genomic best linear unbiased prediction (gBLUP). QTL were found for each trait among and within families based on the top 10 genomic windows with the greatest genetic variance. For EMBR, genomic windows identified among families were located on BTA4, BTA5, BTA6, BTA7, BTA8, BTA13, BTA16 and BTA17, and they were most frequent on BTA7 within families. For VO, genomic windows were located on BTA2, BTA4, BTA5, BTA7, BTA17, BTA21, BTA22, BTA23 and BTA27 among families, being most frequent on BTA8 within families. For TO, top 10 genomic windows were identified on BTA2, BTA4, BTA5, BTA7, BTA17, BTA21, BTA22, BTA26 and BTA27, being most frequent on BTA7 and BTA8 within families. Considering all results, the greatest number of genomic windows was found on BTA7, where VCAN, XRCC4, TRNAC-ACA, HAPLN1 and EDIL3 genes were identified in the common regions. In conclusion, 15 Gir sire families with 26 to 395 daughters per family with phenotypes for oocyte and embryo production helped to identify the inheritance of several genomic regions, especially on BTA7, where EDIL3, HAPLN1 and VCAN candidate genes were associated with number of oocytes and embryos in Gir cattle families.

Animal board invited review: The purebred-crossbred genetic correlation in poultry.

The purebred-crossbred genetic correlation (rpc) is a key parameter to determine whether the optimal selection of purebred animals to improve crossbred performance should rely on crossbred phenotypes, purebred phenotypes, or both. We reviewed published estimates of the rpc in poultry. In total, 19 studies were included, of which four were on broilers and 15 on laying hens, with 150 rpc estimates for nine different trait categories. Average reported rpc estimates were highest for egg weight, egg quality and egg colour (0.74-0.82), intermediate for BW, maturity and mortality (0.61-0.70) and egg number (0.58), and low for resilience (0.40) and body conformation (0.14). Most studies were based on measuring purebred and crossbred phenotypes in the same environment and thus did not capture the contribution of genotype by environment interactions to the rpc, suggesting that the presented average estimates may be higher than values that apply in practice. Nearly all studies were based on two-way crossbred animals. We hypothesised that rpc values for a two-way cross are good proxies for rpc of a four-way cross. Only eight out of 19 studies were published in the last 25 years, and only two of those used genomic data. We expect that more studies using genomic data may be published in the coming years, as the required data may be generated when implementing genomic selection for crossbred performance, which will lead to more accurate rpc estimates. Future studies that aim to estimate rpc are encouraged to capture the genotype by environment interaction component by housing purebred and crossbred animals differently as is done in practice. Moreover, there is a need for further studies that enable to explicitly estimate the magnitude of genotype by environment versus genotype by genotype interactions for multiple trait categories. Further, studies are advised to report: the specific housing conditions of the animals, any differences between measurements of purebred versus crossbred performance, and the heritabilities of purebred and crossbred performance.

Estimation of inbreeding, between-breed genomic relatedness and definition of sub-populations in red-pied cattle breeds.

Currently, enhancing the collaboration between related breeds is of main importance to increase the competitivity and the sustainability of local breeds. One type of collaboration is the development of an across-breed reference population that will allow a better management of local breeds. For this purpose, the genomic relatedness between the local target breed and possible breeds to be included in the reference population should be estimated. In Europe, there are several local red-pied cattle breeds that would benefit from this kind of collaboration. However, how different red-pied cattle breeds from the Benelux are related to each other and can collaborate is still unclear. The objectives of this study were therefore: (1) to estimate the level of inbreeding of the East Belgian Red and White (EBRW), the Red-Pied of the Ösling (RPO) and Dutch red-pied cattle breeds; (2) to determine the genomic relatedness of several red-pied cattle breeds, with a special focus on two endangered breeds: the EBRW and the RPO, and (3) based on the second objective, to detect animals from other breeds that were genomically close enough to be considered as advantageous in the creation of an across-breed reference population of EBRW or RPO. The estimated inbreeding levels based on runs of homozygosity were relatively low for almost all the studied breeds and especially for the EBRW and RPO. This would imply that inbreeding is currently not an issue in these two endangered breeds and that their sustainability is not threatened by their level of inbreeding. The results from the principal component analysis, the phylogenetic tree and the clustering all highlighted that the EBRW and RPO breeds were included in the genomic continuum of the studied red-pied cattle breeds and can be therefore considered as genomically close to Dutch red-pied cattle breeds, highlighting the possibility of a collaboration between these breeds. Especially, EBRW animals were closely related to Deep Red and Improved Red animals while, to a lesser extent, the RPO animals were closely related to the Meuse-Rhine-Yssel breed. Based on these results, we could use distance measures, based either on the principal component analysis or clustering, to detect animals from Dutch breeds that were genomically closest to the EBRW or RPO breeds. This will finally allow the building of an across-breed reference population for EBRW or RPO for further genomic evaluations, considering these genomically closest animals from other breeds.

Variation in faecal digestibility values related to feed efficiency traits of grower-finisher pigs.

Providing pigs a diet that matches their nutrient requirements involves optimizing the diet based on the nutrient digestibility values of the considered feed ingredients. Feeding the same quantity of a diet to pigs with similar BW but with different requirements, however, can result in a different average daily gain (ADG) and backfat thickness (BF) between pigs. Digestibility may contribute to this variation in efficiency. We investigated variation in feed efficiency traits in grower-finisher pigs associated with variation in faecal digestibility values, independent of feed intake at the time of measuring faecal digestibility. Considered traits were ADG, average daily feed intake (ADFI), feed conversion ratio (FCR), BF and residual feed intake (RFI). Feed intake, BW, and BF data of one hundred and sixty three-way crossbreed grower-finisher pigs (eighty female and eighty male) were collected during two phases, from day 0 of the experiment (mean BW 23 kg) till day 56 (mean BW 70 kg) and from day 56 to slaughter (mean BW 121 kg). Pigs were either fed a diet based on corn/soybean meal or a more fibrous diet based on wheat/barley/by-products, with titanium dioxide as indigestible marker. Faecal samples of one hundred and five pigs were collected on the day before slaughter and used to determine apparent faecal digestibility of DM, ash, organic matter (OM), CP, crude fat (CFat), crude fibre (CF), and to calculate the digestibility of nonstarch polysaccharides (NSPs) and energy (E). The effects of diet, sex and covariate feed intake at sampling (FIs) on faecal digestibility values were estimated and were significant for all except for CFat. Faecal digestibility values of each individual pig determined at the day before slaughter, corrected for diet, sex and FIs, were used to estimate their association with ADG, ADFI, FCR, BF, and RFI. In the first phase, a one percent unit increase in faecal digestibility of DM, ash, OM, E, CP, CFat, CF, NSP, and Ash individually was related to 0.01-0.03 unit reduction in FCR and 6-23 g/day reduction in RFI. A unit increase in CP digestibility was related to 0.1 mm increase in BF and 10 g/day increase in ADG. In the second phase, a one percent unit increase in faecal digestibility of DM, CP and Ash was related to a decrease of 16-20 g/day in RFI. In conclusion, the relationship between variation in feed efficiency traits and faecal digestibility values is different across the developmental stages of a pig.

Relatedness between numerically small Dutch Red dairy cattle populations and possibilities for multibreed genomic prediction.

Red dairy breeds are a valuable cultural and historical asset, and often a source of unique genetic diversity. However, they have difficulties competing with other, more productive, dairy breeds. Improving competitiveness of Red dairy breeds, by accelerating their genetic improvement using genomic selection, may be a promising strategy to secure their long-term future. For many Red dairy breeds, establishing a sufficiently large breed-specific reference population for genomic prediction is often not possible, but may be overcome by adding individuals from another breed. Relatedness between breeds strongly decides the benefit of adding another breed to the reference population. To prioritize among available breeds, the effective number of chromosome segments (Me) can be used as an indicator of relatedness between individuals from different breeds. The Me is also an important parameter in determining the accuracy of genomic prediction. The Me can be estimated both within a population and between 2 populations or breeds, as the reciprocal of the variance of genomic relationships. We investigated relatedness between 6 Dutch Red cattle breeds, Groningen White Headed (GWH), Dutch Friesian (DF), Meuse-Rhine-Yssel (MRY), Dutch Belted (DB), Deep Red (DR), and Improved Red (IR), focusing primarily on the Me, to predict which of those breeds may benefit from including reference animals of the other breeds. All of these breeds, except MRY, are under high risk of extinction. Our results indicated high variability of Me, especially between Me ranging from ∼3,500 to ∼17,400, indicating different levels of relatedness between the breeds. Two clusters are especially important, one formed by MRY, DR, and IR, and the other comprising DF and DB. Although relatedness between breeds within each of these 2 clusters is high, across-breed genomic prediction is still limited by the current number of genotyped individuals, which for many breeds is low. However, adding MRY individuals would increase the reference population of DR substantially. We estimated that between 11 and 133 individuals from other breeds are needed to achieve accuracy of genomic prediction equivalent to using one additional individual from the same breed. Given the variation in size of the breeds in this study, the benefit of a multibreed reference population is expected to be lower for larger breeds than for the smaller ones.

Short communication: Genetic correlations between methane and milk production, conformation, and functional traits.

Livestock produce CH4, contributing to the global warming effect. One of the currently investigated solutions to reduce CH4 production is selective breeding. The goal of this study was to estimate the genetic correlations between CH4 and milk production, conformation, and functional traits used in the selection index for Polish-Holstein cows. In total, 34,429 daily CH4 production observations collected from 483 cows were available, out of which 281 cows were genotyped. The CH4 was measured using a so-called sniffer device installed in an automated milking system. Breeding values for CH4 were estimated with the use of single-step genomic BLUP, and breeding values for remaining traits were obtained from the Polish national genomic evaluation. Genetic correlations between CH4 production and remaining traits were estimated using bivariate analyses. The estimated genetic correlations were in general low. The highest values were estimated for fat yield (0.21), milk yield (0.15), chest width (0.15), size (0.15), dairy strength (0.11), and somatic cell count (0.11). These estimates, as opposed to estimates for the remaining traits, were significantly different from zero.

Efficient and accurate computation of base generation allele frequencies.

Allele frequencies are used for several aspects of genomic prediction, with the assumption that these are equal to the allele frequency in the base generation of the pedigree. The current standard method, however, calculates allele frequencies from the current genotyped population. We compared the current standard method with BLUP and general least squares (GLS) methods explicitly targeting the base population to determine whether there is a more accurate and still efficient method of calculating allele frequencies that better represents the base generation. A data set based on a typical dairy population was simulated for 325,266 animals; the last 100,078 animals in generations 9 to 12 of the population were genotyped, with 1,670 SNP markers. For the BLUP method, several SNP genotypes were analyzed with a multitrait model by assuming a heritability of 0.99 and no genetic correlation among them. This method was limited by the time required for each BLUP to converge (approximately 6 min per BLUP run of 15 SNP). The GLS method had 2 implementations. The first implementation, using imputation on the fly and multiplication of sparse matrices, was very efficient and required just 49 s and 1.3 GB of random access memory. The second implementation, using a dense full A22-1 matrix, was very inefficient and required more than 1 d of wall clock time and more than 118.2 GB of random access memory. When no selection was considered in the simulations, all methods predicted equally well. When selection was introduced, higher correlations between the estimated allele frequency and known base generation allele frequency were observed for BLUP (0.96 ± 0.01) and GLS (0.97 ± 0.01) compared with the current standard method (0.87 ± 0.01). The GLS method decreased in accuracy when introducing incomplete pedigree, with 25% of sires in the first 5 generations randomly replaced as unknown to erroneously identify founder animals (0.93 ± 0.01) and a further decrease for 8 generations (0.91 ± 0.01). There was no change in accuracy when introducing 5% genotyping errors (0.97 ± 0.01), 5% missing genotypes (0.97 ± 0.01), or both 5% genotyping errors and missing genotypes (0.97 ± 0.01). The GLS method provided the most accurate estimates of base generation allele frequency and was only slightly slower compared with the current method. The efficient implementation of the GLS method, therefore, is very well suited for practical application and is recommended for implementation.

Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection.

Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS) model. This model allowed for evidence of quantitative trait loci (QTL) to be accumulated across breeds or for both QTL that segregate across breeds and breed-specific QTL. In both cases, single nucleotide polymorphism effects were estimated with information from a single breed. Other models considered were a single-trait and multitrait genomic residual maximum likelihood (GREML) model, with breeds considered as different traits, and a single-trait BSSVS model. All single-trait models were applied to each of the 2 breeds separately and to the pooled data of both breeds. The data used included a training data set of 6,278 Holstein and 722 Jersey bulls, as well as 374 Jersey validation bulls. All animals had genotypes for 474,773 single nucleotide polymorphisms after editing and phenotypes for milk, fat, and protein yields. Using the same training data, BSSVS consistently outperformed GREML. The multitask BSSVS, however, did not outperform single-trait BSSVS, which used pooled Holstein and Jersey data for training. Thus, the rigorous assumption that the traits are the same in both breeds yielded a slightly better prediction than a model that had to estimate the correlation between the breeds from the data. Adding the Holstein data significantly increased the accuracy of the single-trait GREML and BSSVS in predicting the Jerseys for milk and protein, in line with estimated correlations between the breeds of 0.66 and 0.47 for milk and protein yields, whereas only the BSSVS model significantly improved the accuracy for fat yield with an estimated correlation between breeds of only 0.05. The relatively high genetic correlations for milk and protein yields, and the superiority of the pooling strategy, is likely the result of the observed admixture between both breeds in our data. The Bayesian model was able to detect several QTL in Holsteins, which likely enabled it to outperform GREML. The inability of the multitask Bayesian models to outperform a simple pooling strategy may be explained by the fact that the pooling strategy assumes equal effects in both breeds; furthermore, this assumption may be valid for moderate- to large-sized QTL, which are important for multibreed genomic prediction.

Accuracies of breeding values for dry matter intake using nongenotyped animals and predictor traits in different lactations.

Given the interest of including dry matter intake (DMI) in the breeding goal, accurate estimated breeding values (EBV) for DMI are needed, preferably for separate lactations. Due to the limited amount of records available on DMI, 2 main approaches have been suggested to compute those EBV: (1) the inclusion of predictor traits, such as fat- and protein-corrected milk (FPCM) and live weight (LW), and (2) the addition of genomic information of animals using what is called genomic prediction. Recently, several methodologies to estimate EBV utilizing genomic information (EBV) have become available. In this study, a new method known as single-step ridge-regression BLUP (SSRR-BLUP) is suggested. The SSRR-BLUP method does not have an imposed limit on the number of genotyped animals, as the commonly used methods do. The objective of this study was to estimate genetic parameters using a relatively large data set with DMI records, as well as compare the accuracies of the EBV for DMI. These accuracies were obtained using 4 different methods: BLUP (using pedigree for all animals with phenotypes), genomic BLUP (GBLUP; only for genotyped animals), single-step GBLUP (SS-GBLUP), and SSRR-BLUP (for genotyped and nongenotyped animals). Records from different lactations, with or without predictor traits (FPCM and LW), were used in the model. Accuracies of EBV for DMI (defined as the correlation between the EBV and pre-adjusted DMI phenotypes divided by the average accuracy of those phenotypes) ranged between 0.21 and 0.38 across methods and scenarios. Accuracies of EBV for DMI using BLUP were the lowest accuracies obtained across methods. Meanwhile, accuracies of EBV for DMI were similar in SS-GBLUP and SSRR-BLUP, and lower for the GBLUP method. Hence, SSRR-BLUP could be used when the number of genotyped animals is large, avoiding the construction of the inverse genomic relationship matrix. Adding information on DMI from different lactations in the reference population gave higher accuracies in comparison when only lactation 1 was included. Finally, no benefit was obtained by adding information on predictor traits to the reference population when DMI was already included. However, in the absence of DMI records, having records on FPCM and LW from different lactations is a good way to obtain EBV with a relatively good accuracy.

BOARD INVITED REVIEW: The purebred-crossbred correlation in pigs: A review of theory, estimates, and implications.

Pig and poultry production relies on crossbreeding of purebred populations to produce production animals. In those breeding schemes, selection takes place within the purebred population to improve crossbred performance (CB performance). The genetic correlation between purebred performance (PB performance) and CB performance () is, however, lower than unity for many traits. When is low, the use of CB performance in selection is required to achieve sizable genetic progress. The objectives of this paper were to describe the different components and importance of , and to review existing literature that report estimates in pigs. The has 3 components: 1) genotype by genotype interactions, 2) genotype by environment interactions, and 3) differences in trait measurements. We theoretically showed that direct selection for CB performance reduces the response to selection in purebreds for.

Conservation priorities for the different lines of Dutch Red and White Friesian cattle change when relationships with other breeds are taken into account.

From a genetic point of view, the selection of breeds and animals within breeds for conservation in a national gene pool can be based on a maximum diversity strategy. This implies that priority is given to conservation of breeds and animals that diverge most and overlap of conserved diversity is minimized. This study investigated the genetic diversity in the Dutch Red and White Friesian (DFR) cattle breed and its contribution to the total genetic diversity in the pool of the Dutch dairy breeds. All Dutch cattle breeds are clearly distinct, except for Dutch Friesian breed (DF) and DFR and have their own specific genetic identity. DFR has a small but unique contribution to the total genetic diversity of Dutch cattle breeds and is closely related to the Dutch Friesian breed. Seven different lines are distinguished within the DFR breed and all contribute to the diversity of the DFR breed. Two lines show the largest contributions to the genetic diversity in DFR. One of these lines comprises unique diversity both within the breed and across all cattle breeds. The other line comprises unique diversity for the DFR but overlaps with the Holstein Friesian breed. There seems to be no necessity to conserve the other five lines separately, because their level of differentiation is very low. This study illustrates that, when taking conservation decisions for a breed, it is worthwhile to take into account the population structure of the breed itself and the relationships with other breeds.

Genetic changes of survival traits over the past 25 yr in Dutch dairy cattle.

Genetic correlations and heritabilities for survival were investigated over a period of 25 yr to evaluate if survival in first lactation has become a different trait and if this is affected by adjusting for production level. Survival after first calving until 12mo after calving (surv_12mo) and survival of first lactation (surv_1st_lac) were analyzed in Dutch black-and-white cows. The data set contained 1,108,745 animals for surv_12mo and 1,062,276 animals for surv_1st_lac, with first calving between 1989 and 2013. The trait survival as recorded over 25 yr was split in five 5-yr intervals to enable a multitrait analysis. Bivariate models using subsets of the full data set and multitrait and autoregressive models using the full data set were used. Survival and functional survival were analyzed. Functional survival was defined as survival adjusted for within-herd production level for 305-d yield of combined kilograms of fat and protein. Mean survival increased over time, whereas genetic variances and heritability decreased. Bivariate models yielded large standard errors on genetic correlations due to poor connectedness between the extreme 5-yr intervals. The more parsimonious models using the full data set gave nonunity genetic correlations. Genetic correlations for survival were below 0.90 between intervals separated by 1 or more 5-yr intervals. Genetic correlations for functional survival did not indicate that definition of survival changed (≥0.90). The difference in genetic correlations between survival and functional survival is likely explained by lower emphasis of dairy farmers on culling in first lactation for low yield in more recent years. This suggests that genetic evaluation for longevity using historical data should analyze functional survival rather than survival.

Validation of simultaneous deregression of cow and bull breeding values and derivation of appropriate weights.

Training of genomic prediction in dairy cattle may use deregressed proofs (DRP) as phenotypes. In this case, DRP should be estimated breeding values (EBV) corrected for information of relatives included in the data used for genomic prediction, and adjusted for regression to the mean (i.e., their reliability). Deregression is especially important when combining animals with EBV with low reliability, as commonly the case for cows, and high reliability. The objective of this paper, therefore, was to compare the performance of different deregression procedures for data that include both cow and bull EBV, and to develop and test procedures to obtain the appropriate deregressed weights for the DRP. Considered DRP were EBV: without any adjustment, adjusted for information of parents and regression to the mean, or adjusted for information of all relatives and regression to the mean. Considered deregressed weights were weights of initial EBV: without any adjustment, adjusted for information of parents, or adjusted for information of all relatives. The procedures were compared using simulated data based on an existing pedigree with 1,532 bulls and 13,720 cows that were considered to be included in the data used for genomic prediction. For each cow, 1 to 5 records were simulated. For each bull, an additional 50 to 200 daughters with 1 record each were simulated to generate a source of data that was not used for genomic prediction. The simulated trait had either a heritability of 0.05 or 0.3. The validation involved 3 steps: (1) computation of initial EBV and weights, (2) deregression of those EBV and weights, (3) using deregressed EBV and weights to compute final EBV, (4) comparison of the initial and final EBV and weights. The methods developed to compute appropriate weights for the DRP were either very precise and computationally somewhat demanding for larger data sets, or were less precise but computationally trivial due their approximate nature. Adjusting DRP for all relatives, known as matrix deregression, yields by definition final EBV that are identical to the original EBV. Matrix deregression is therefore preferred over other approaches that only correct for information of parents or not performing any deregression at all. It is important to use appropriate weights for the DRP, properly corrected for information of relatives, especially when individual reliabilities of final EBV are computed based on the prediction error variance of the model.

Accuracy of genomic prediction of purebreds for cross bred performance in pigs.

In pig breeding, as the final product is a cross bred (CB) animal, the goal is to increase the CB performance. This goal requires different strategies for the implementation of genomic selection from what is currently implemented in, for example dairy cattle breeding. A good strategy is to estimate marker effects on the basis of CB performance and subsequently use them to select pure bred (PB) breeding animals. The objective of our study was to assess empirically the predictive ability (accuracy) of direct genomic values of PB for CB performance across two traits using CB and PB genomic and phenotypic data. We studied three scenarios in which genetic merit was predicted within each population, and four scenarios where PB genetic merit for CB performance was predicted based on either CB or a PB training data. Accuracy of prediction of PB genetic merit for CB performance based on CB training data ranged from 0.23 to 0.27 for gestation length (GLE), whereas it ranged from 0.11 to 0.22 for total number of piglets born (TNB). When based on PB training data, it ranged from 0.35 to 0.55 for GLE and from 0.30 to 0.40 for TNB. Our results showed that it is possible to predict PB genetic merit for CB performance using CB training data, but predictive ability was lower than training using PB training data. This result is mainly due to the structure of our data, which had small-to-moderate size of the CB training data set, low relationship between the CB training and the PB validation populations, and a high genetic correlation (0.94 for GLE and 0.90 for TNB) between the studied traits in PB and CB individuals, thus favouring selection on the basis of PB data.

Accuracy of genomic prediction using imputed whole-genome sequence data in white layers.

There is an increasing interest in using whole-genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole-genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole-genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non-synonymous and non-coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non-synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.

Updating the reference population to achieve constant genomic prediction reliability across generations.

The reliability of genomic breeding values (DGV) decays over generations. To keep the DGV reliability at a constant level, the reference population (RP) has to be continuously updated with animals from new generations. Updating RP may be challenging due to economic reasons, especially for novel traits involving expensive phenotyping. Therefore, the goal of this study was to investigate a minimal RP update size to keep the reliability at a constant level across generations. We used a simulated dataset resembling a dairy cattle population. The trait of interest was not included itself in the selection index, but it was affected by selection pressure by being correlated with an index trait that represented the overall breeding goal. The heritability of the index trait was assumed to be 0.25 and for the novel trait the heritability equalled 0.2. The genetic correlation between the two traits was 0.25. The initial RP (n=2000) was composed of cows only with a single observation per animal. Reliability of DGV using the initial RP was computed by evaluating contemporary animals. Thereafter, the RP was used to evaluate animals which were one generation younger from the reference individuals. The drop in the reliability when evaluating younger animals was then assessed and the RP was updated to re-gain the initial reliability. The update animals were contemporaries of evaluated animals (EVA). The RP was updated in batches of 100 animals/update. First, the animals most closely related to the EVA were chosen to update RP. The results showed that, approximately, 600 animals were needed every generation to maintain the DGV reliability at a constant level across generations. The sum of squared relationships between RP and EVA and the sum of off-diagonal coefficients of the inverse of the genomic relationship matrix for RP, separately explained 31% and 34%, respectively, of the variation in the reliability across generations. Combined, these parameters explained 53% of the variation in the reliability across generations. Thus, for an optimal RP update an algorithm considering both relationships between reference and evaluated animals, as well as relationships among reference animals, is required.

Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP.

BACKGROUND: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). RESULTS: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M e ) across the populations. CONCLUSION: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M e . Across populations, M e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.

Genotype by environment interaction for livability of dairy calves from first parity cows.

Death of calves around parturition is a matter of concern for dairy farmers. Relatively high stillbirth rates and unfavourable trends have been reported for Holstein heifers in the Netherlands and several other countries. In our study, we investigated herd differences, genetic parameters and genotype by environment interaction for heifer calf livability. A large dataset with data from calvings between 1993 and 2012 of Dutch dairy farms was used. There were considerable differences between herds in livability of calves from heifers, with averages ranging from 74% to 95%. Both herds with relatively high and low averages showed the same negative trend between 1993 and 2012, with largest declines in herds with the lowest averages. We found that heritability and genetic variation of first parity livability were substantially larger in herd environments where the likelihood of stillbirth was high v. environments where stillbirth was at a low level. The genetic correlations between herd environment levels were all very close to unity, indicating that ranking of sires was similar for all environments. However, for herds with a relatively high stillbirth incidence selecting sires with favourable breeding values is expected to be twice as profitable as in herds with a relatively low stillbirth incidence.