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Livestock produce CH 4 , contributing to the global warming effect. One of the currently investigated solutions to reduce CH 4 production is selective breeding. The goal of this study was to estimate the genetic correlations between CH 4 and milk production, conformation, and functional traits used in the selection index for Polish-Holstein cows. In total, 34,429 daily CH 4 production observations collected from 483 cows were available, out of which 281 cows were genotyped. The CH 4 was measured using a so-called sniffer device installed in an automated milking system. Breeding values for CH 4 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 CH 4 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. [ABSTRACT FROM AUTHOR]

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 × 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 × 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. The sire beef breeds studied were Aberdeen Angus, Hereford (combined in category LHT), Charolais, Limousin, and Simmental (category HVY). 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 × 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 to 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 × 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 × 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over the past decades, daughter designs, including genotyped sires and their genotyped daughters, have been used as an approach to identify QTL related to 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 GWAS based on GBLUP. The 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, the 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 the 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 the EDIL3, HAPLN1 , and VCAN candidate genes were associated with number of oocytes and embryos in Gir cattle families. [ABSTRACT FROM AUTHOR]

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 (M e) can be used as an indicator of relatedness between individuals from different breeds. The M e is also an important parameter in determining the accuracy of genomic prediction. The M e 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 M e , 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 M e , especially between M e 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. [ABSTRACT FROM AUTHOR]

The article looks at the impact of growing genomic data on animal genomic prediction models. Topics covered include the process for estimating breeding values for genomic selection and the occurrence of conditions that impact the performance of a model. Also discussed is the use of single nucleotide polymorphisms (SNPs) to estimate the effects on models.

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. [ABSTRACT FROM AUTHOR]

When animals are selected for one specific allele, for example for inclusion in a gene bank, this may result in the loss of diversity in other parts of the genome. The aim of this study was to quantify the risk of losing diversity across the genome when targeting a single allele for conservation when storing animals in a gene bank. From a small Holstein population, genotyped for 54 001 SNP loci, animals were prioritized for a single allele while maximizing the genomewide diversity using optimal contribution selection. Selection for a single allele was done for five different target frequencies: (i) no restriction on a target frequency; (ii) target frequency = original frequency in population; (iii) target frequency = 0.50; (iv) target frequency of the major allele = 1 (fixation); and (v) target frequency of the major allele = 0 (elimination). To do this, optimal contribution selection was extended with an extra constraint on the allele frequency of the target SNP marker. Results showed that elimination or fixation of alleles can result in substantial losses in genetic diversity around the targeted locus and also across the rest of the genome, depending on the allele frequency and the target frequency. It was concluded that losses of genetic diversity around the target allele are the largest when the target frequency is very different from the current allele frequency. [ABSTRACT FROM AUTHOR]

Genetic diversity is often evaluated using pedigree information. Currently, diversity can be evaluated in more detail over the genome based on large numbers of SNP markers. Pedigree- and SNP-based diversity were compared for two small related groups of Holstein animals genotyped with the 50 k SNP chip, genome-wide, per chromosome and for part of the genome examined. Diversity was estimated with coefficient of kinship (pedigree) and expected heterozygosity (SNP). SNP-based diversity at chromosome regions was determined using 5-Mb sliding windows, and significance of difference between groups was determined by bootstrapping. Both pedigree- and SNP-based diversity indicated more diversity in one of the groups; 26 of the 30 chromosomes showed significantly more diversity for the same group, as did 25.9% of the chromosome regions. Even in small populations that are genetically close, differences in diversity can be detected. Pedigree- and SNP-based diversity give comparable differences, but SNP-based diversity shows on which chromosome regions these differences are based. For maintaining diversity in a gene bank, SNP-based diversity gives a more detailed picture than pedigree-based diversity. [ABSTRACT FROM AUTHOR]

Summary Up to now, prioritization of animals for conservation has been mainly based on pedigree information; however, genomic information may improve prioritization. In this study, we used two Holstein populations to investigate the consequences for genetic diversity when animals are prioritized with optimal contributions based on pedigree or genomic data and whether consequences are different at the chromosomal level. Selection with genomic kinships resulted in a higher conserved diversity, but differences were small. Largest differences were found when few animals were prioritized and when pedigree errors were present. We found more differences at the chromosomal level, where selection based on genomic kinships resulted in a higher conserved diversity for most chromosomes, but for some chromosomes, pedigree-based selection resulted in a higher conserved diversity. To optimize conservation strategies, genomic information can help to improve the selection of animals for conservation in those situations where pedigree information is unreliable or absent or when we want to conserve diversity at specific genome regions. [ABSTRACT FROM AUTHOR]

Digestibility of nutrients in pig diets is an important component of overall feed efficiency. Targeted improvement of digestibility is currently mainly achieved by optimization of pig diets, based on information generated from digestibility trials that aim to establish fecal digestibility coefficients of different nutrients across a variety of ingredients. Genetic selection for nutrient digestibility is hampered by shortage of data on individual digestibility, but might help to further improve efficiency of pork production. The present study aimed to estimate the repeatability of fecal digestibility in pigs, as a first step to judge the perspectives for a breeding approach of nutrient digestibility. To achieve this, data was accumulated across nine digestibility trials, containing 1150 digestibility records of 416 growing pigs, measured across the trials. The data was analyzed with a model estimating variances for trial, diet, common litter, and individual animal effect for digestibility of Dry Matter, Ash, Organic Matter, Crude Protein, Crude fat and Non-Starch Polysaccharides. The factors diet and trial together explained the majority of the phenotypic variance, due to the design of the trials. Within diet and trial, common litter and individual animal effect contributed 0–10% of the phenotypic variance. The repeatability estimates ranged from 7% for Ash to 16% for Crude Protein, which suggests there may be genetic variation between pigs in digestibility. In conclusion, the repeatability estimates indicate it is worthwhile to collect phenotypic data that enable the estimation of genetic parameters for digestibility, if these data can be obtained at reasonable cost. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify differences in genetic variation of fatty acid (FA) composition in milk in different breeds. Data used included Meuse-Rhine-Yssel (MRY) and Holstein Friesian (HF) cattle breeds which were raised in the Netherlands. Both populations participated in the same milk recording system, but differed in selection history, where in the MRY there has been relatively very little emphasis on selection for high-input high-output production systems compared to HF. Differences in genetic variation were investigated by estimating breed specific additive genetic variances and heritabilities for FA contents in milk of MRY and HF. Mid Infrared Spectrometry spectra were used to predict total fat percentage and detailed FA contents in milk (14 individual FA and 14 groups of FA in g of fat/dL of milk). The dataset for MRY contained 2916 records from 2049 registered cows having at least 50% genes of MRY origin and the dataset used for HF contained 155,319 records from 96,315 registered cows having at least 50% genes of HF origin. Variance components of individual FA content in milk for the different breeds were estimated using a single trait animal model. Additive genetic variances for FA produced through de novo synthesis (short chain FA, C12:0, C14:0, and partly C16:0), C14:1 c- 9 and C16:1 c- 9 were significantly higher ( P <0.001) for HF compared to MRY. Heritabilities of the individual FA, C4:0 to C18:0, for HF ranged from 0.28 to 0.52 and for MRY from 0.17 to 0.34. Heritabilities of the individual C18 unsaturated FA for HF ranged from 0.11 to 0.34 and for MRY from 0.10 to 0.26. Although the mean content in milk for the FA C18:2 c- 9, t- 11 was low in both breeds, the additive genetic variance in our dataset was significantly higher for MRY ( P <0.05) compared to HF. Heritabilities of the groups of FA for HF ranged from 0.19 to 0.53 and for MRY from 0.11 to 0.28. For the majority of the FA, the additive genetic variances for HF were significantly higher compared to MRY, except for most of the poly-unsaturated FA. The results for the poly-unsaturated FA, however, may be affected by the lower accuracy of the predictions for these FA. In conclusion, our results show that the HF breed has substantially larger genetic variance for most FA compared to MRY. [ABSTRACT FROM AUTHOR]