Genomic evaluation of regional dairy cattle breeds in single‐breed and multibreed contexts

An important prerequisite for high prediction accuracy in genomic prediction is the availability of a large training population, which allows accurate marker effect estimation. This requirement is not fulfilled in case of regional breeds with a limited number of breeding animals. We assessed the efficiency of the current French routine genomic evaluation procedure in four regional breeds (Abondance, Tarentaise, French Simmental and Vosgienne) as well as the potential benefits when the training populations consisting of males and females of these breeds are merged to form a multibreed training population. Genomic evaluation was 5–11% more accurate than a pedigree‐based BLUP in three of the four breeds, while the numerically smallest breed showed a < 1% increase in accuracy. Multibreed genomic evaluation was beneficial for two breeds (Abondance and French Simmental) with maximum gains of 5 and 8% in correlation coefficients between yield deviations and genomic estimated breeding values, when compared to the single‐breed genomic evaluation results. Inflation of genomic evaluation of young candidates was also reduced. Our results indicate that genomic selection can be effective in regional breeds as well. Here, we provide empirical evidence proving that genetic distance between breeds is only one of the factors affecting the efficiency of multibreed genomic evaluation.     

Accuracy of genomic prediction using different models and response variables in the Nordic Red cattle population

Breeding animals can be accurately evaluated using appropriate genomic prediction models, based on marker data and phenotype information. In this study, direct genomic values (DGV) were estimated for 16 traits of Nordic Total Merit (NTM) Index in Nordic Red cattle population using three models and two different response variables. The three models were as follows: a linear mixed model (GBLUP), a Bayesian variable selection model similar to BayesA (BayesA*) and a Bayesian least absolute shrinkage and selection operator model (Bayesian Lasso). The response variables were deregressed proofs (DRP) and conventional estimated breeding values (EBV). The reliability of genomic predictions was measured on bulls in the validation data set as the squared correlation between DGV and DRP divided by the reliability of DRP. Using DRP as response variable, the reliabilities of DGV among the 16 traits ranged from 0.151 to 0.569 (average 0.317) for GBLUP, from 0.152 to 0.576 (average 0.318) for BayesA* and from 0.150 to 0.570 (average 0.320) for Bayesian Lasso. Using EBV as response variable, the reliabilities ranged from 0.159 to 0.580 (average 0.322) for GBLUP, from 0.157 to 0.578 (average 0.319) for BayesA* and from 0.159 to 0.582 (average 0.325) for Bayesian Lasso. In summary, Bayesian Lasso performed slightly better than the other two models, and EBV performed slightly better than DRP as response variable, with regard to prediction reliability of DGV. However, these differences were not statistically significant. Moreover, using EBV as response variable would result in problems with the scale of the resulting DGV and potential problem due to double counting.     

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G‐matrices) across these two breeds. Single‐trait genomic best linear unbiased prediction (GBLUP) model and two‐trait GBLUP model were used for single‐breed and two‐breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two‐breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two‐breed predictions. Weighting the two‐breed G‐matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two‐breed predictions.     

The benefit of native uniqueness in a local red cattle breed from Northern Germany

During last decades, native uniqueness decreased in local livestock breeds due to the introgression of high‐yielding breeds. Recovery of native uniqueness became important because of conservation aspects regarding native genetic diversity and native traits. Thereby the expectation exists, that the relation between native uniqueness and genetic gain is contradictory. The aim of this study was to explore the influence of native uniqueness on performance traits and the total merit index in a local red cattle breed from Northern Germany. Data contained a pedigree file of 178,255 Red Dual‐Purpose cattle, 809 target genotypes and 3,581 reference genotypes from introgressed breeds. Native genetic contributions were tested for correlation with performance traits of milk yield, longevity, foundation, somatic cells, fertility and maternal calving and the total merit index. The study revealed that native uniqueness is favourably related to longevity (0.16), foundation (0.23), and somatic cells (0.08), and the total merit index (0.10). Selection on native uniqueness could probably lead to an increased longevity, udder health and genetic gain of the Red Dual‐Purpose cattle. Moreover, it was shown that the Red Dual‐Purpose cattle was not upgraded through introgression of high‐yielding breeds.     

Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM

Genomic selection is based on breeding values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic breeding values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total breeding value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.     

Impacts of genotyping strategies on long‐term genetic response in genomic selection

The present study investigated the effects of the choices of animals of reference populations on long‐term responses to genomic selection. Simulated populations comprised 300 individuals and 10 generations of selection practiced for a trait with heritability of 0.1, 0.3 or 0.5. Thirty individuals were randomly selected in the first five generations and selected by estimated breeding values from best linear unbiased prediction (BLUP) and genomic BLUP in the subsequent five generations. The reference populations comprise all animals for all generations (scenario 1), all animals for 6‐10 generations (scenario 2) and 2‐6 generations (scenario 3), and half of the animals for all generations (scenario 4). For all heritability levels, the genetic gains in generation 10 were similar in scenarios 1 and 2. Among scenarios 2 to 4, the highest genetic gains were obtained in scenario 2, with heritabilities of 0.1 and 0.3 as well as scenario 4 with heritability of 0.5. The inbreeding coefficients in scenarios 1, 2 and 4 were lower than those in BLUP, especially within cases with low heritability. These results indicate an appropriate choice of reference population can improve genetic gain and restrict inbreeding even when the reference population size is limited.     

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.     

Effects of a breeding scheme combined by genomic pre‐selection and progeny testing on annual genetic gain in a dairy cattle population

The effectiveness of the incorporation of genomic pre‐selection into dairy cattle progeny testing (GS‐PT) was compared with that of progeny testing (PT) where the fraction of dam to breed bull (DB) selected was 0.01. When the fraction of sires to breed bulls (SB) selected without being progeny tested to produce young bulls (YB) in the next generation was 0.2, the annual genetic gain from GS‐PT was 13% to 43% greater when h² = 0.3 and 16% to 53% greater when h² = 0.1 compared with that from PT. Given h² = 0.3, a selection accuracy of 0.8 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, GS‐PT produced 40% to 43% greater annual genetic gain than PT. Given h² = 0.1, a selection accuracy of 0.6 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, annual genetic gain from GS‐PT was 48% to 53% greater than that from PT. When h² = 0.3, progeny testing capacity had little effect on annual genetic gain from GS‐PT. However, when h² = 0.1, annual genetic gain from GS‐PT increased with increasing progeny testing capacity.     

Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single-step genomic reaction norm model

Economically important traits are usually complex traits influenced by genes, environment and genotype-by-environment (G × E) interactions. Ignoring G × E interaction could lead to bias in the estimation of breeding values and selection decisions. A total of 1,778 pigs were genotyped using the PorcineSNP80 BeadChip. The existence of G × E interactions was investigated using a single-step reaction norm model for growth traits of days to 100 kg (AGE) and backfat thickness adjusted to 100 kg (BFT), based on a pedigree-based relationship matrix (A) or a genomic–pedigree joint relationship matrix (H). In the reaction norm model, the herd-year-season effect was measured as the environmental variable (EV). Our results showed no G × E interactions for AGE, but for BFT. For both AGE and BFT, the genomic reaction norm model (H) produced more accurate predictions than the conventional reaction norm model (A). For BFT, the accuracies were greater based on the reaction norm model than those based on the reduced model without exploiting G × E interaction, with EV ranging from 0.5 to 1, and accuracy increasing by 3.9% and 4.6% in the reaction norm model based on A and H matrices, respectively, while reaction norm model yielded approximately 8.4% and 7.9% lower accuracy for EVs ranging from 0 to 0.4, based on A and H matrices, respectively. In addition, for BFT, the highest accuracy was obtained in the BJLM6 farm for realizing directional selection. This study will help to apply G × E interactions to practical genomic selection.     

The influence of age, breed, rearing intensity and exercise on the incidence of spavin in Swedish dairy cattle. A clinical and morphological investigation

The hocks of tied cows of a Swedish dairy herd of different breeds were radiographed. Osteoarthrosis of the arthrodial joints (spavin) was found in 37 % of the animals. Changes were seen before the cows were 2 years old, and they increased in severity with increasing age. Macroscopic and histologic examination revealed that there was osteoarthrosis also in many radiologically normal hocks. The study shows that the frequency of spavin in tied dairy cows is higher than clinical signs indicate. Apparently only cows with fusion of the arthrodial joints show the stiff hind leg movements considered typical of spavin. Differences in the incidence of spavin were found between the different breeds. Cows of the Swedish Friesian breed had the lowest (20%) and cows of the Jersey breed had the highest (50%) incidence of spavin. However, as regards the Jersey breed this was due to the high incidence (71%) found in cows raised on a high intensity feeding during the young stock period. Among the SJB cows raised on a normal intensity feeding the incidence of spavin was 25 %. Such a relationship between high young stock feeding intensity and the incidence of spavin was not seen within the other breeds. The cows in another herd with loose housing had a lower frequency of spavin than the tied cows. Offspring of animals with spavin had a higher incidence of spavin than the offspring of animals without spavin.     

Influence of bushdumps on the herbaceous understorey in a mesic Eastern Cape savanna

Abstract

Studies of the effects of woody vegetation on herbaceous vegetation in southern Africa have focused almost exclusively on savannas with isolated trees with no attention given to multi‐species bushclump savannas. The influence of multi‐species bushdumps on herbaceous vegetation was investigated in a mesic savanna in the Eastern Cape. Development of bushdumps appeared to reduce herbaceous production underneath the clumps by up to 90%. The herbaceous layer underneath the bushdumps was distinctly different to that of the open grassland and it also had slightly higher quality compared to that of grassland. Effective bush control measures are warranted if these savanna areas are to sustain beef and mutton production as is currently the case.     

Application of single‐step genomic best linear unbiased prediction with a multiple‐lactation random regression test‐day model for Japanese Holsteins

This study aimed to evaluate a validation reliability of single‐step genomic best linear unbiased prediction (ssGBLUP) with a multiple‐lactation random regression test‐day model and investigate an effect of adding genotyped cows on the reliability. Two data sets for test‐day records from the first three lactations were used: full data from February 1975 to December 2015 (60 850 534 records from 2 853 810 cows) and reduced data cut off in 2011 (53 091 066 records from 2 502 307 cows). We used marker genotypes of 4480 bulls and 608 cows. Genomic enhanced breeding values (GEBV) of 305‐day milk yield in all the lactations were estimated for at least 535 young bulls using two marker data sets: bull genotypes only and both bulls and cows genotypes. The realized reliability (R²) from linear regression analysis was used as an indicator of validation reliability. Using only genotyped bulls, R² was ranged from 0.41 to 0.46 and it was always higher than parent averages. The very similar R² were observed when genotyped cows were added. An application of ssGBLUP to a multiple‐lactation random regression model is feasible and adding a limited number of genotyped cows has no significant effect on reliability of GEBV for genotyped bulls.     

Genomic selection in American mink (Neovison vison) using a single-step genomic best linear unbiased prediction model for size and quality traits graded on live mink

Genomic selection relies on single-nucleotide polymorphisms (SNPs), which are often collected using medium-density SNP arrays. In mink, no such array is available; instead, genotyping by sequencing (GBS) can be used to generate marker information. Here, we evaluated the effect of genomic selection for mink using GBS. We compared the estimated breeding values (EBVs) from single-step genomic best linear unbiased prediction (SSGBLUP) models to the EBV from ordinary pedigree-based BLUP models. We analyzed seven size and quality traits from the live grading of brown mink. The phenotype data consisted of ~20,600 records for the seven traits from the mink born between 2013 and 2016. Genotype data included 2,103 mink born between 2010 and 2014, mostly breeding animals. In total, 28,336 SNP markers from 391 scaffolds were available for genomic prediction. The pedigree file included 29,212 mink. The predictive ability was assessed by the correlation (r) between progeny trait deviation (PTD) and EBV, and the regression of PTD on EBV, using 5-fold cross-validation. For each fold, one-fifth of animals born in 2014 formed the validation set. For all traits, the SSGBLUP model resulted in higher accuracies than the BLUP model. The average increase in accuracy was 15% (between 3% for fur clarity and 28% for body weight). For three traits (body weight, silky appearance of the under wool, and guard hair thickness), the difference in r between the two models was significant (P < 0.05). For all traits, the regression slopes of PTD on EBV from SSGBLUP models were closer to 1 than regression slopes from BLUP models, indicating SSGBLUP models resulted in less bias of EBV for selection candidates than the BLUP models. However, the regression coefficients did not differ significantly. In conclusion, the SSGBLUP model is superior to conventional BLUP model in the accurate selection of superior animals, and, thus, it would increase genetic gain in a selective breeding program. In addition, this study shows that GBS data work well in genomic prediction in mink, demonstrating the potential of GBS for genomic selection in livestock species.     

Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

The objective of this study was to investigate the impact of accounting for parent average (PA) and genotyped daughters’ average (GDA) on the estimation of deregressed estimated breeding values (dEBVs) used as pseudo‐phenotypes in multiple‐step genomic evaluations. Genomic estimated breeding values (GEBVs) were predicted, in eight different simulated scenarios, using dEBVs calculated based on four methods. These methods included PA and GDA in the dEBV (VR) or only GDA (VRpa) and excluded both PA and GDA from the dEBV with either all information or only information from PA and GDA (JA and NEW, respectively). In general, VR and NEW showed the lowest and highest GEBV reliabilities across scenarios, respectively. Among all deregression methods, VRpa and NEW provided the most consistent bias estimates across the majority of scenarios, and they significantly yielded the least biased GEBVs. Our results indicate that removing PA and GDA information from dEBVs used in multiple‐step genomic evaluations can increase the reliability of GEBVs, when both bulls and their daughters are included in the training population.     

Effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci detection and genomic evaluation in a simulated cattle population

The objective of this study was to assess the effect of genotyped bulls with different numbers of phenotyped progenies on quantitative trait loci (QTL) detection and genomic evaluation using a simulated cattle population. Twelve generations (G1–G12) were simulated from the base generation (G0). The recent population had different effective population sizes, heritability, and number of QTL. G0–G4 were used for pedigree information. A total of 300 genotyped bulls from G5–G10 were randomly selected. Their progenies were generated in G6–G11 with different numbers of progeny per bull. Scenarios were considered according to the number of progenies and whether the genotypes were possessed by the bulls or the progenies. A genome‐wide association study and genomic evaluation were performed with a single‐step genomic best linear unbiased prediction method to calculate the power of QTL detection and the genomic estimated breeding value (GEBV). We found that genotyped bulls could be available for QTL detection depending on conditions. Additionally, using a reference population, including genotyped bulls, which had more progeny phenotypes, enabled a more accurate prediction of GEBV. However, it is desirable to have more than 4,500 individuals consisting of both genotypes and phenotypes for practical genomic evaluation.     

Selection of core animals in the Algorithm for Proven and Young using a simulation model

The Algorithm for Proven and Young (APY) enables the implementation of single‐step genomic BLUP (ssGBLUP) in large, genotyped populations by separating genotyped animals into core and non‐core subsets and creating a computationally efficient inverse for the genomic relationship matrix ( G ). As APY became the choice for large‐scale genomic evaluations in BLUP‐based methods, a common question is how to choose the animals in the core subset. We compared several core definitions to answer this question. Simulations comprised a moderately heritable trait for 95,010 animals and 50,000 genotypes for animals across five generations. Genotypes consisted of 25,500 SNP distributed across 15 chromosomes. Genotyping errors and missing pedigree were also mimicked. Core animals were defined based on individual generations, equal representation across generations, and at random. For a sufficiently large core size, core definitions had the same accuracies and biases, even if the core animals had imperfect genotypes. When genotyped animals had unknown parents, accuracy and bias were significantly better (p ≤ .05) for random and across generation core definitions.     

The estimated long-term impact of tsetse control on the size of the population of cattle in the Didessa Valley,Western Ethiopia

The long-term impact of tsetse control on cattle population size in the Didessa Valley, western Ethiopia, was analysed using an age-structured population model. A prior analytical assessment revealed that the risk of cattle dying in the tsetse-unprotected villages ranged from 4 to 9 times higher than in the tsetse-protected village. Model results show that during a period of 10 years the cattle population in the tsetse-protected village of Meti is likely to increase from 167 to 583 animals, while that in the adjacent tsetse-unprotected village of Gale remains almost constant. Model simulations also predict that improving the survival rate of calves in the tsetse-unprotected villages of Taikiltu and Temoloko (which presently have calf mortality rates of up to 35%) would bring a substantial increase in their cattle population.     

Effects of a novel missense polymorphism within the SIGLEC5 gene on fertility traits in Holstein‐Friesian cattle

The aim of the study was to find functional polymorphism within two exons of the SIGLEC5 (sialic acid‐binding Ig‐like lectin‐5) gene and to examine its effects on the production and fertility traits of cows and bulls. Two hundred seventytwo Holstein‐Friesian cows and 574 bulls were included in the study. Novel missense polymorphism (A > G) within exon 3 causing substitution of amino acid arginine by glutamate in position 260 of SIGLEC5 protein (R260Q) was identified by sequencing and digestion by restriction enzyme Msp I. Basic production and fertility traits of cows and estimated breeding values (EBV) of bulls were analysed. The study demonstrated a significant association of SIGLEC5 R260Q polymorphism with days open and calving interval in cows as well as with breeding value for calving interval in bulls. An opposite effect of SIGLEC5 alleles for production and fertility traits was observed: the allele G increased the breeding value for the protein yield, while the allele A increased the breeding value for the calving interval. The current study suggests the involvement of SIGLEC5 R260Q polymorphism in biological processes related to fertility traits. This finding can be applied as a biomarker for a genetic improvement programme in Holstein‐Friesian cattle.