Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using single-step GBLUP under a scenario that includes purebred genotypes.     

Evidence of biases in genetic evaluations due to genomic preselection in dairy cattle

A genomic preselection step of young sires is now often included in dairy cattle breeding schemes. Young sires are selected based on their genomic breeding values. They have better Mendelian sampling contribution so that the assumption of random Mendelian sampling term in genetic evaluations is clearly violated. When these sires and their progeny are evaluated using BLUP, it is feared that estimated breeding values are biased. The effect of genomic selection on genetic evaluations was studied through simulations keeping the structure of the Holstein population in France. The quality of genetic evaluations was assessed by computing bias and accuracy from the difference and correlation between true and estimated breeding values, respectively, and also the mean square error of prediction. Different levels of heritability, selection intensity, and accuracy of genomic evaluation were tested. After only one generation and whatever the scenario, breeding values of preselected young sires and their daughters were significantly underestimated and their accuracy was decreased. Genomic preselection needs to be accounted for in genetic evaluation models.     

Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds.     

Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations

This study investigated the possibility of increasing the reliability of direct genomic values (DGV) by combining reference populations. The data were from 3,735 bulls from Danish, Swedish, and Finnish Red dairy cattle populations. Single nucleotide polymorphism markers were fitted as random variables in a Bayesian model, using published estimated breeding values as response variables. In total, 17 index traits were analyzed. Reliabilities were estimated using a 5-fold cross validation, and calculated as the within-year squared correlation between estimated breeding values and DGV. Marker effects were estimated using reference populations from individual countries, as well as using a combined reference population from all 3 countries. Single-country reference populations gave mean reliabilities across 17 traits of 0.19 to 0.23, whereas the combined reference gave mean reliabilities of 0.26 for all populations. Using marker effects from 1 population to predict the other 2 gave a loss in mean reliability of 0.14 to 0.21 when predicting Swedish or Finnish animals with Danish marker effects, or vice versa. Using Swedish or Finnish marker effects to predict each other only showed a loss in mean reliability of 0.03 to 0.05. A combined Swedish-Finnish reference population led to an average reliability as high as that from the 3-country reference population, but somewhat different for individual traits. The results from this study show that it is possible to increase the reliability of DGV by combining reference populations from related populations.     

Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit

The objective of this study was to evaluate a genomic breeding scheme in a small dairy cattle population that was intermediate in terms of using both young bulls (YB) and progeny-tested bulls (PB). This scheme was compared with a conventional progeny testing program without use of genomic information and, as the extreme case, a juvenile scheme with genomic information, where all bulls were used before progeny information was available. The population structure, cost, and breeding plan parameters were chosen to reflect the Danish Jersey cattle population, being representative for a small dairy cattle population. The population consisted of 68,000 registered cows. Annually, 1,500 bull dams were screened to produce the 500 genotyped bull calves from which 60 YB were selected to be progeny tested. Two unfavorably correlated traits were included in the breeding goal, a production trait (h² = 0.30) and a functional trait (h² = 0.04). An increase in reliability of 5 percentage points for each trait was used in the default genomic scenario. A deterministic approach was used to model the different breeding programs, where the primary evaluation criterion was annual monetary genetic gain (AMGG). Discounted profit was used as an indicator of the economic outcome. We investigated the effect of varying the following parameters: (1) increase in reliability due to genomic information, (2) number of genotyped bull calves, (3) proportion of bull dam sires that are young bulls, and (4) proportion of cow sires that are young bulls. The genomic breeding scheme was both genetically and economically superior to the conventional breeding scheme, even in a small dairy cattle population where genomic information causes a relatively low increase in reliability of breeding values. Assuming low reliabilities of genomic predictions, the optimal breeding scheme according to AMGG was characterized by mixed use of YB and PB as bull sires. Exclusive use of YB for production cows increased AMGG up to 3 percentage points. The results from this study supported our hypothesis that strong interaction effects exist. The strongest interaction effects were obtained between increased reliabilities of genomic estimated breeding values and more intensive use of YB. The juvenile scheme was genetically inferior when the increase in reliability was low (5 percentage points), but became genetically superior at higher reliabilities of genomic estimated breeding values. The juvenile scheme was always superior according to discounted profit because of the shorter generation interval and minimizing costs for housing and feeding waiting bulls.     

Differences among methods to validate genomic evaluations for dairy cattle

Two methods of testing predictions from genomic evaluations were investigated. Data used were from the August 2006 and April 2010 official USDA genetic evaluations of dairy cattle. The training data set consisted of both cows and bulls that were proven (had own or daughter information) as of August 2006 and included 8,022, 1,959, and 1,056 Holsteins, Jerseys, and Brown Swiss, respectively. The validation data set consisted of bulls that were unproven as of August 2006 and were proven by April 2010 with 2,653, 411, and 132 Holsteins, Jerseys, and Brown Swiss for the production traits. Method 1 used the training animal's predicted transmitting ability (PTA) from August of 2006. Method 2 used the training animal's April 2010 PTA to estimate single nucleotide polymorphism effects. Both methods were tested using several regressions with the same validation animals. In both cases, the validation animals were tested using the deregressed April 2010 PTA. All traits that had genomic evaluations from the official USDA April 2010 genetic evaluations were tested. Results included bias, differences from expected regressions (calculated using selection intensities), and the coefficients of determination. The genomic information increased the predictive ability for most of the traits in all of the breeds. The 2 methods of testing resulted in some differences that would affect interpretation of results. The coefficient of determination was higher for all traits using method 2. This was the expected result as the data were not independent because evaluations of the validation bulls contributed to their sires’ evaluations. The regression coefficients from method 2 were often higher than the regression coefficients from method 1. Many traits had regression coefficients that were higher than 2 standard deviations from the expected regressions when using method 2. This was partially due to the lack of independence of the training and validation data sets. Most traits did have some level of bias in the prediction equations, regardless of breed. The use of method 1 made it possible to evaluate the increased accuracy in proven first-crop bull evaluations by using genomic information. Proven first-crop bulls had an increase in accuracy from the addition of genomic information. It is advised to use method 1 for validation of genomic evaluations.     

The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa

Cost-effective high-density (HD) genotypes of livestock species can be obtained by genotyping a proportion of the population using a HD panel and the remainder using a cheaper low-density panel, and then imputing the missing genotypes that are not directly assayed in the low-density panel. The efficacy of genotype imputation can largely be affected by the structure and history of the specific target population and it should be checked before incorporating imputation in routine genotyping practices. Here, we investigated the efficacy of imputation in crossbred dairy cattle populations of East Africa using 4 different commercial single nucleotide polymorphisms (SNP) panels, 3 reference populations, and 3 imputation algorithms. We found that Minimac and a reference population, which included a mixture of crossbred and ancestral purebred animals, provided the highest imputation accuracy compared with other scenarios of imputation. The accuracies of imputation, measured as the correlation between real and imputed genotypes averaged across SNP, were around 0.76 and 0.94 for 7K and 40K SNP, respectively, when imputed up to a 770K panel. We also presented a method to maximize the imputation accuracy of low-density panels, which relies on the pairwise (co)variances between SNP and the minor allele frequency of SNP. The performance of the developed method was tested in a 5-fold cross-validation process where various densities of SNP were selected using the (co)variance method and also by alternative SNP selection methods and then imputed up to the HD panel. The (co)variance method provided the highest imputation accuracies at almost all marker densities, with accuracies being up to 0.19 higher than the random selection of SNP. The accuracies of imputation from 7K and 40K panels selected using the (co)variance method were around 0.80 and 0.94, respectively. The presented method also achieved higher accuracy of genomic prediction at lower densities of selected SNP. The squared correlation between genomic breeding values estimated using imputed genotypes and those from the real 770K HD panel was 0.95 when the accuracy of imputation was 0.64. The presented method for SNP selection is straightforward in its application and can ensure high accuracies in genotype imputation of crossbred dairy populations in East Africa.     

Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle

Genomic selection (GS) permits accurate breeding values to be obtained for young animals, shortening the generation interval and accelerating the genetic gain, thereby leading to reduced costs for proven bulls. Genotyping a large number of animals using high-density single nucleotide polymorphism marker arrays is nevertheless expensive, and therefore, a method to reduce the costs of GS is desired. The aim of this study was to investigate an influence of enlarging the reference population, with either genotyped animals or individuals with predicted genotypes, on the accuracy of genomic estimated breeding values. A dairy cattle population was simulated in which proven bulls with 100 daughters were used as a reference population for GS. Phenotypic records were simulated for bulls with heritability equal to the reliability of daughter yield deviations based on 100 daughters. The simulated traits represented heritabilities at the level of individual daughter performance of 0.3, 0.05, and 0.01. Three scenarios were considered in which (1) the reference population consisted of 1,000 genotyped animals, (2) 1,000 ungenotyped animals were added to the reference population, and (3) the 1,000 animals added in scenario 2 were genotyped in addition to the 1,000 animals from scenario 1. Genotypes for ungenotyped animals were predicted with an average accuracy of 0.58. Additionally, an adjustment of the diagonal elements of the G matrix was proposed for animals with predicted genotypes. The accuracy of genomic estimated breeding values for juvenile animals was the highest for the scenario with 2,000 genotyped animals, being 0.90, 0.79, and 0.60 for the heritabilities of 0.3, 0.05, and 0.01, respectively. Accuracies did not differ significantly between the scenario with 1,000 genotyped animals only and the scenario in which 1,000 ungenotyped animals were added and the adjustment of the G matrix was applied. The absence of significant increase in the accuracy of genomic estimated breeding values was attributed to the low accuracy of predicted genotypes. Although the differences were not significant, the difference between scenario 1 and 2 increased with decreasing heritability. Without the adjustment of the diagonal elements of the G matrix, accuracy decreased. Results suggest that inclusion of ungenotyped animals is only expected to enhance the accuracy of GS when the unknown genotypes can be predicted with high accuracy.     

Assessment of accuracy of genomic prediction for French Lacaune dairy sheep

Genomic selection in Lacaune dairy sheep was investigated based on genotypes from the OvineSNP50 BeadChip (Illumina Inc., San Diego, CA). Historical artificial insemination progeny-tested rams formed a population of 2,892 genotyped rams. Additional ungenotyped rams and females were included by single-step genomic BLUP (ssGBLUP). Three prediction strategies were tried: pseudo-BLUP (using all rams and daughter yield deviations), pseudo-ssGBLUP (using all rams and daughter yield deviations), and regular ssGBLUP (using all phenotypes and pedigree in an animal model). The population linkage disequilibrium was determined, with an average squared correlation coefficient of 0.11 for markers closer than 0.1 cM (lower than in dairy cattle). The estimated effective population is 370 individuals. Gain in accuracy of genomic selection over parent averages ranged from 0.10 to 0.20. Highest accuracies and lowest bias were found using regular ssGBLUP. Transition to a genomic breeding scheme is possible but costs need to be carefully evaluated.     

Cow genotyping strategies for genomic selection in a small dairy cattle population

This study compares how different cow genotyping strategies increase the accuracy of genomic estimated breeding values (EBV) in dairy cattle breeds with low numbers. In these breeds, few sires have progeny records, and genotyping cows can improve the accuracy of genomic EBV. The Guernsey breed is a small dairy cattle breed with approximately 14,000 recorded individuals worldwide. Predictions of phenotypes of milk yield, fat yield, protein yield, and calving interval were made for Guernsey cows from England and Guernsey Island using genomic EBV, with training sets including 197 de-regressed proofs of genotyped bulls, with cows selected from among 1,440 genotyped cows using different genotyping strategies. Accuracies of predictions were tested using 10-fold cross-validation among the cows. Genomic EBV were predicted using 4 different methods: (1) pedigree BLUP, (2) genomic BLUP using only bulls, (3) univariate genomic BLUP using bulls and cows, and (4) bivariate genomic BLUP. Genotyping cows with phenotypes and using their data for the prediction of single nucleotide polymorphism effects increased the correlation between genomic EBV and phenotypes compared with using only bulls by 0.163 ± 0.022 for milk yield, 0.111 ± 0.021 for fat yield, and 0.113 ± 0.018 for protein yield; a decrease of 0.014 ± 0.010 for calving interval from a low base was the only exception. Genetic correlation between phenotypes from bulls and cows were approximately 0.6 for all yield traits and significantly different from 1. Only a very small change occurred in correlation between genomic EBV and phenotypes when using the bivariate model. It was always better to genotype all the cows, but when only half of the cows were genotyped, a divergent selection strategy was better compared with the random or directional selection approach. Divergent selection of 30% of the cows remained superior for the yield traits in 8 of 10 folds.     

Efficient use of genomic information for sustainable genetic improvement in small cattle populations

In this study, we compared genetic gain, genetic variation, and the efficiency of converting variation into gain under different genomic selection scenarios with truncation or optimum contribution selection in a small dairy population by simulation. Breeding programs have to maximize genetic gain but also ensure sustainability by maintaining genetic variation. Numerous studies have shown that genomic selection increases genetic gain. Although genomic selection is a well-established method, small populations still struggle with choosing the most sustainable strategy to adopt this type of selection. We developed a simulator of a dairy population and simulated a model after the Slovenian Brown Swiss population with ～10,500 cows. We compared different truncation selection scenarios by varying (1) the method of sire selection and their use on cows or bull-dams, and (2) selection intensity and the number of years a sire is in use. Furthermore, we compared different optimum contribution selection scenarios with optimization of sire selection and their usage. We compared scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, rate of coancestry, effective population size, and conversion efficiency. The results showed that early use of genomically tested sires increased genetic gain compared with progeny testing, as expected from changes in selection accuracy and generation interval. A faster turnover of sires from year to year and higher intensity increased the genetic gain even further but increased the loss of genetic variation per year. Although maximizing intensity gave the lowest conversion efficiency, faster turnover of sires gave an intermediate conversion efficiency. The largest conversion efficiency was achieved with the simultaneous use of genomically and progeny-tested sires that were used over several years. Compared with truncation selection, optimizing sire selection and their usage increased the conversion efficiency by achieving either comparable genetic gain for a smaller loss of genetic variation or higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations implement sustainable genomic selection.     

Detection and assessment of copy number variation using PacBio long-read and Illumina sequencing in New Zealand dairy cattle

Single nucleotide polymorphisms have been the DNA variant of choice for genomic prediction, largely because of the ease of single nucleotide polymorphism genotype collection. In contrast, structural variants (SV), which include copy number variants (CNV), translocations, insertions, and inversions, have eluded easy detection and characterization, particularly in nonhuman species. However, evidence increasingly shows that SV not only contribute a substantial proportion of genetic variation but also have significant influence on phenotypes. Here we present the discovery of CNV in a prominent New Zealand dairy bull using long-read PacBio (Pacific Biosciences, Menlo Park, CA) sequencing technology and the Sniffles SV discovery tool (version 0.0.1; https://github.com/fritzsedlazeck/Sniffles). The CNV identified from long reads were compared with CNV discovered in the same bull from Illumina sequencing using CNVnator (read depth–based tool; Illumina Inc., San Diego, CA) as a means of validation. Subsequently, further validation was undertaken using whole-genome Illumina sequencing of 556 cattle representing the wider New Zealand dairy cattle population. Very limited overlap was observed in CNV discovered from the 2 sequencing platforms, in part because of the differences in size of CNV detected. Only a few CNV were therefore able to be validated using this approach. However, the ability to use CNVnator to genotype the 557 cattle for copy number across all regions identified as putative CNV allowed a genome-wide assessment of transmission level of copy number based on pedigree. The more highly transmissible a putative CNV region was observed to be, the more likely the distribution of copy number was multimodal across the 557 sequenced animals. Furthermore, visual assessment of highly transmissible CNV regions provided evidence supporting the presence of CNV across the sequenced animals. This transmission-based approach was able to confirm a subset of CNV that segregates in the New Zealand dairy cattle population. Genome-wide identification and validation of CNV is an important step toward their inclusion in genomic selection strategies.     

Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle

The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach.     

Marker selection and genomic prediction of economically important traits using imputed high-density genotypes for 5 breeds of dairy cattle

Marker sets used in US dairy genomic predictions were previously expanded by including high-density (HD) or sequence markers with the largest effects for Holstein breed only. Other non-Holstein breeds lacked enough HD genotyped animals to be used as a reference population at that time, and thus were not included in the genomic prediction. Recently, numbers of non-Holstein breeds genotyped using HD panels reached an acceptable level for imputation and marker selection, allowing HD genomic prediction and HD marker selection for Holstein plus 4 other breeds. Genotypes for 351,461 Holsteins, 347,570 Jerseys, 42,346 Brown Swiss, 9,364 Ayrshires (including Red dairy cattle), and 4,599 Guernseys were imputed to the HD marker list that included 643,059 SNP. The separate HD reference populations included Illumina BovineHD (San Diego, CA) genotypes for 4,012 Holsteins, 407 Jerseys, 181 Brown Swiss, 527 Ayrshires, and 147 Guernseys. The 643,059 variants included the HD SNP and all 79,254 (80K) genetic markers and QTL used in routine national genomic evaluations. Before imputation, approximately 91 to 97% of genotypes were unknown for each breed; after imputation, 1.1% of Holstein, 3.2% of Jersey, 6.7% of Brown Swiss, 4.8% of Ayrshire, and 4.2% of Guernsey alleles remained unknown due to lower density haplotypes that had no matching HD haplotype. The higher remaining missing rates in non-Holstein breeds are mainly due to fewer HD genotyped animals in the imputation reference populations. Allele effects for up to 39 traits were estimated separately within each breed using phenotypic reference populations that included up to 6,157 Jersey males and 110,130 Jersey females. Correlations of HD with 80K genomic predictions for young animals averaged 0.986, 0.989, 0.985, 0.992, and 0.978 for Jersey, Ayrshire, Brown Swiss, Guernsey, and Holstein breeds, respectively. Correlations were highest for yield traits (about 0.991) and lowest for foot angle and rear legs–side view (0.981and 0.982, respectively). Some HD effects were more than twice as large as the largest 80K SNP effect, and HD markers had larger effects than nearby 80K markers for many breed-trait combinations. Previous studies selected and included markers with large effects for Holstein traits; the newly selected HD markers should also improve non-Holstein and crossbred genomic predictions and were added to official US genomic predictions in April 2020.     

Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation

Since the introduction of genome-enabled prediction for dairy cattle in 2009, genomic selection has markedly changed many aspects of the dairy genetics industry and enhanced the rate of response to selection for most economically important traits. Young dairy bulls are genotyped to obtain their genomic predicted transmitting ability (GPTA) and reliability (REL) values. These GPTA are a main factor in most purchasing, marketing, and culling decisions until bulls reach 5 yr of age and their milk-recorded offspring become available. At that time, daughter yield deviations (DYD) can be compared with the GPTA computed several years earlier. For most bulls, the DYD align well with the initial predictions. However, for some bulls, the difference between DYD and corresponding GPTA is quite large, and published REL are of limited value in identifying such bulls. A method of bootstrap aggregation sampling (bagging) using genomic BLUP (GBLUP) was applied to predict the GPTA of 2,963, 2,963, and 2,803 young Holstein bulls for protein yield, somatic cell score, and daughter pregnancy rate (DPR), respectively. For each trait, 50 bootstrap samples from a reference population comprising 2011 DYD of 8,610, 8,405, and 7,945 older Holstein bulls were used. Leave-one-out cross validation was also performed to assess prediction accuracy when removing specific bulls from the reference population. The main objectives of this study were (1) to assess the extent to which current REL values and alternative measures of variability, such as the bootstrap standard deviation (SD) of predictions, could detect bulls whose daughter performance deviates significantly from early genomic predictions, and (2) to identify factors associated with the reference population that inform about inaccurate genomic predictions. The SD of bootstrap predictions was a mildly useful metric for identifying bulls whose future daughter performance may deviate significantly from early GPTA for protein and DPR. Leave-one-out cross validation allowed us to identify groups of reference population bulls that were influential on other reference population bulls for protein yield and observe their effects on predictions of testing set bulls, as a whole and individually.     

Comparison between sire-maternal grandsire and animal models for genetic evaluation of longevity in a dairy cattle population with small herds

Survival analysis techniques for sire-maternal grandsire (MGS) and animal models were used to test the genetic evaluation of longevity in a Slovenian Brown cattle population characterized by small herds. Three genetic models were compared: a sire-MGS model for bulls and an approximate animal model based on estimated breeding values (EBV) from the sire-MGS model for cows, an animal model, and an animal model based on the estimated variance components from the sire-MGS model. In addition, modeling the contemporary group effect was defined as either a herd or a herd-year (HY) effect. With various restrictions on the minimum HY group size (from 1 to 10 cows per HY), changes in estimates of variance components, and consequently also in EBV, were observed for the sire-MGS and animal models. Variance of contemporary group effects decreased when an HY effect was fitted instead of a herd effect. In the case of a sire-MGS model, estimates of additive genetic variance were mostly robust to changes in minimum HY group size or fitting herd or HY effect, whereas they increased in the animal model when HY instead of herd effects was fitted, possibly revealing some confounding between cow EBV and contemporary group effect. Estimated heritabilities from sire-MGS models were between 0.091 and 0.119 and were mainly influenced by the restriction on the HY group size. Estimated heritabilities from animal models were higher: between 0.125 and 0.160 when herd effect was fitted and between 0.171 and 0.210 when HY effect was fitted. Rank correlations between the animal model and the approximate animal model based on EBV from the sire-MGS model were high: 0.94 for cows and 0.93 for sires when a herd effect was fitted and 0.90 for cows and 0.93 for sires when an HY effect was fitted. Validation performed on the independent validation data set revealed that the correlation between sire EBV and daughter survival were slightly higher with the approximate animal model based on EBV from the sire-MGS model compared with the animal model. The correlations between the sire EBV and daughter survival were higher when the model included an HY effect instead of a herd effect. To avoid confounding and reduce computational requirements, it is suggested that the approximate animal model based on EBV from the sire-MGS model and HY as a contemporary group effect is an interesting compromise for practical applications of genetic evaluation of longevity in cattle populations.     

Comparison between linear models and survival analysis for genetic evaluation of clinical mastitis in dairy cattle

Clinical mastitis was analyzed with mixed linear models (LM) and survival analysis (SA) using data from the first 3 lactations of >200,000 Swedish Holstein cows having their first calving between 1995 and 2000. The model for both methods included fixed effects of year-month and age at calving, fixed regressions of proportions of heterosis and North American Holstein genes, and random effects of herd-year at calving and sire. For the LM, clinical mastitis was defined as a binary trait measured from 10 d before to 150 d after calving. For the SA, clinical mastitis was defined either as the time period from 10 d before calving to the day of first treatment or culling because of mastitis (uncensored record) or from 10 d before to the day of next calving, culling for reasons other than mastitis, movement to a new herd, or to lactation d 240 (censored record). The heritability estimates from SA (0.03 to 0.04) were higher than those obtained with the LM (0.01 to 0.03). Consequently, the accuracies of estimated transmitting abilities were also higher for the trait analyzed with SA. The difference between estimates from the 2 methods was greater for later lactations. This study reveals the potential of analyzing clinical mastitis data with SA.     

Within- and across-breed genomic predictions and genomic relationships for Western Pyrenees dairy sheep breeds Latxa,Manech, and Basco-Béarnaise

Genotypes, phenotypes and pedigrees of 6 breeds of dairy sheep (including subdivisions of Latxa, Manech, and Basco-Béarnaise) from the Spain and France Western Pyrenees were used to estimate genetic relationships across breeds (together with genotypes from the Lacaune dairy sheep) and to verify by forward cross-validation single-breed or multiple-breed genetic evaluations. The number of rams genotyped fluctuated between 100 and 1,300 but generally represented the 10 last cohorts of progeny-tested rams within each breed. Genetic relationships were assessed by principal components analysis of the genomic relationship matrices and also by the conservation of linkage disequilibrium patterns at given physical distances in the genome. Genomic and pedigree-based evaluations used daughter yield performances of all rams, although some of them were not genotyped. A pseudo-single step method was used in this case for genomic predictions. Results showed a clear structure in blond and black breeds for Manech and Latxa, reflecting historical exchanges, and isolation of Basco-Béarnaise and Lacaune. Relatedness between any 2 breeds was, however, lower than expected. Single-breed genomic predictions had accuracies comparable with other breeds of dairy sheep or small breeds of dairy cattle. They were more accurate than pedigree predictions for 5 out of 6 breeds, with absolute increases in accuracy ranging from 0.05 to 0.30 points. They were significantly better, as assessed by bootstrapping of candidates, for 2 of the breeds. Predictions using multiple populations only marginally increased the accuracy for a couple of breeds. Pooling populations does not increase the accuracy of genomic evaluations in dairy sheep; however, single-breed genomic predictions are more accurate, even for small breeds, and make the consideration of genomic schemes in dairy sheep interesting.     

Economic evaluation of progeny-testing and genomic selection schemes for small-sized nucleus dairy cattle breeding programs in developing countries

In developing countries minimal and erratic performance and pedigree recording impede implementation of large-sized breeding programs. Small-sized nucleus programs offer an alternative but rely on their economic performance for their viability. We investigated the economic performance of 2 alternative small-sized dairy nucleus programs [i.e., progeny testing (PT) and genomic selection (GS)] over a 20-yr investment period. The nucleus was made up of 453 male and 360 female animals distributed in 8 non-overlapping age classes. Each year 10 active sires and 100 elite dams were selected. Populations of commercial recorded cows (CRC) of sizes 12,592 and 25,184 were used to produce test daughters in PT or to create a reference population in GS, respectively. Economic performance was defined as gross margins, calculated as discounted revenues minus discounted costs following a single generation of selection. Revenues were calculated as cumulative discounted expressions (CDE, kg) × 0.32 (€/kg of milk) × 100,000 (size commercial population). Genetic superiorities, deterministically simulated using pseudo-BLUP index and CDE, were determined using gene flow. Costs were for one generation of selection. Results show that GS schemes had higher cumulated genetic gain in the commercial cow population and higher gross margins compared with PT schemes. Gross margins were between 3.2- and 5.2-fold higher for GS, depending on size of the CRC population. The increase in gross margin was mostly due to a decreased generation interval and lower running costs in GS schemes. In PT schemes many bulls are culled before selection. We therefore also compared 2 schemes in which semen was stored instead of keeping live bulls. As expected, semen storage resulted in an increase in gross margins in PT schemes, but gross margins remained lower than those of GS schemes. We conclude that implementation of small-sized GS breeding schemes can be economically viable for developing countries.     

Accuracy of genomic evaluation with weighted single-step genomic best linear unbiased prediction for milk production traits,udder type traits,and somatic cell scores in French dairy goats

Genomic evaluation of French dairy goats is routinely conducted using the single-step genomic BLUP (ssGBLUP) method. This method has the advantage of simultaneously using all phenotypes, pedigrees, and genotypes. However, ssGBLUP assumes that all SNP explain the same amount of genetic variance, which is unlikely in the case of traits whose major genes or QTL are segregating. In this study, we investigated the effect of weighted ssGBLUP and its alternatives, which give more weight to SNP associated with the trait, on the accuracy of genomic evaluation of milk production, udder type traits, and somatic cell scores. The data set included 2,955 genotyped animals and 2,543,680 pedigree animals. The number of phenotypes varied with the trait. The accuracy of genomic evaluation was assessed on 205 genotyped Alpine and 146 genotyped Saanen goats born between 2009 and 2012. For traits with unknown QTL, weighted ssGBLUP was less accurate than, or as accurate as, ssGBLUP. For traits with identified QTL (i.e., QTL only present in the Saanen breed), weighted ssGBLUP outperformed ssGBLUP by between 2 and 14%.