A comparison of dairy cattle breeding designs that use genomic selection

Different dairy cattle breeding schemes were compared using stochastic simulations, in which the accuracy of the genomic breeding values was dependent on the structure of the breeding scheme, through the availability of new genotyped animals with phenotypic information. Most studies that predict the gain by implementing genomic selection apply a deterministic approach that requires assumptions about the accuracy of the genomic breeding values. The achieved genetic gain, when genomic selection was the only selection method to directly identify elite sires for widespread use and progeny testing was omitted, was compared with using genomic selection for preselection of young bulls for progeny testing and to a conventional progeny test scheme. The rate of inbreeding could be reduced by selecting more sires every year. Selecting 20 sires directly on their genomic breeding values gave a higher genetic gain than any progeny testing scheme, with the same rate of inbreeding as the schemes that used genomic selection for preselection of bulls before progeny testing. The genomic selection breeding schemes could reduce the rate of inbreeding and still increase genetic gain, compared with the conventional breeding scheme. Since progeny testing is expensive, the breeding scheme omitting the progeny test will be the cheapest one. Keeping the progeny test and use of genomic selection for preselection still has some advantages. It gives higher accuracy of breeding values and does not require a complete restructuring of the breeding program. Comparing at the same rate of inbreeding, using genomic selection for elite sire selection only gives a 13% increase in genetic gain, compared with using genomic selection for preselection. One way to reduce the costs of the scheme where genomic selection was used for preselection is to reduce the number of progeny tested bulls. This was here achieved without getting lower genetic gain or a higher rate of inbreeding.     

Assets of imputation to ultra-high density for productive and functional traits

The aim of this study was to evaluate different-density genotyping panels for genotype imputation and genomic prediction. Genotypes from customized Golden Gate Bovine3K BeadChip [LD3K; low-density (LD) 3,000-marker (3K); Illumina Inc., San Diego, CA] and BovineLD BeadChip [LD6K; 6,000-marker (6K); Illumina Inc.] panels were imputed to the BovineSNP50v2 BeadChip [50K; 50,000-marker; Illumina Inc.]. In addition, LD3K, LD6K, and 50K genotypes were imputed to a BovineHD BeadChip [HD; high-density 800,000-marker (800K) panel], and with predictive ability evaluated and compared subsequently. Comparisons of prediction accuracy were carried out using Random boosting and genomic BLUP. Four traits under selection in the Spanish Holstein population were used: milk yield, fat percentage (FP), somatic cell count, and days open (DO). Training sets at 50K density for imputation and prediction included 1,632 genotypes. Testing sets for imputation from LD to 50K contained 834 genotypes and testing sets for genomic evaluation included 383 bulls. The reference population genotyped at HD included 192 bulls. Imputation using BEAGLE software (http://faculty.washington.edu/browning/beagle/beagle.html) was effective for reconstruction of dense 50K and HD genotypes, even when a small reference population was used, with 98.3% of SNP correctly imputed. Random boosting outperformed genomic BLUP in terms of prediction reliability, mean squared error, and selection effectiveness of top animals in the case of FP. For other traits, however, no clear differences existed between methods. No differences were found between imputed LD and 50K genotypes, whereas evaluation of genotypes imputed to HD was on average across data set, method, and trait, 4% more accurate than 50K prediction, and showed smaller (2%) mean squared error of predictions. Similar bias in regression coefficients was found across data sets but regressions were 0.32 units closer to unity for DO when genotypes were imputed to HD density. Imputation to HD genotypes might produce higher stability in the genomic proofs of young candidates. Regarding selection effectiveness of top animals, more (2%) top bulls were classified correctly with imputed LD6K genotypes than with LD3K. When the original 50K genotypes were used, correct classification of top bulls increased by 1%, and when those genotypes were imputed to HD, 3% more top bulls were detected. Selection effectiveness could be slightly enhanced for certain traits such as FP, somatic cell count, or DO when genotypes are imputed to HD. Genetic evaluation units may consider a trait-dependent strategy in terms of method and genotype density for use in the genome-enhanced evaluations.     

Evaluating sire selection practices using lifetime net income functions

Dairy farmers do not take full advantage of opportunities available for genetic improvement through use of artificial insemination, perhaps because economic advantages of good sire selection may not be fully recognized or understood. This study was undertaken to document differences between use of AI and non-AI bulls and to develop prediction equations to compare lifetime economic merit of future progeny from alternative sire selection policies. We describe the use of two methods of measuring lifetime economic merit, with and without adjustment for opportunity cost of a postponed replacement. Comparison of lifetime relative net income adjusted for opportunity cost on groups of cows sired by different kinds of bulls showed that daughters of proven AI bulls generated $148 and $120 more lifetime net income under fluid and manufactured milk market conditions than daughters of non-AI bulls. Daughters of proven AI bulls produced $60 more than daughters of AI young sires in progeny testing programs at the time of daughter conception. We developed prediction equations from combinations of genetic evaluations for production, productive life, SCS, and linear type traits on sires to predict lifetime relative net income of progeny produced from alternative sire selection strategies. Prediction equations explained 14 to 18% of variation in relative net income (not adjusted for opportunity cost), but herd and year of first freshening accounted for considerably more variation than did genetic evaluations on the sire of the cow. Finally, two independent data sets were used to develop and test predictions of lifetime relative net income adjusted for opportunity cost using genetic evaluations based on the eight traits included in the Merit indexes for the sire of each cow. Prediction equations from odd numbered herds were used to predict lifetime economic merit in even numbered herds and vice versa. Coefficients of determination ranged from 0.088 to 0.103 and averaged 0.004 higher than prediction equations with Net or Fluid Merit. Accuracy of predictions showed that Net and Fluid Merit were robust and useful indexes that accurately identified bulls whose daughters generated highest lifetime economic merit.     

Effects of Embryo Transfer on Genetic Change in Dairy Cattle

Impact of embryo transfer on rate of genetic gain was examined for a) development of bulls for progeny test, b) development of replacement females, and c) progeny testing of dams of bulls and replacement females. Increased selection intensity by embryo transfer potentially could improve genetic merit of bull dams by 17% when applied to production of sires for progeny test. Additional benefits would arise from increased availability of sisters to such bulls. Genetic merit of dams of replacement females increases more than genetic merit of dams of bulls with embryo transfer. However, current costs of embryo transfer limit its application to production of replacement females when increased yield is the sole source of added income. Increases in generation interval offset improvement in rate of genetic gain per generation from progeny testing females. Therefore, mass selection on own performance and pedigree produce a higher rate of genetic gain per year than progeny testing females. Application of embryo transfer to selection schemes for multiple traits may prove beneficial.     

Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls

Genomic selection has the potential to revolutionize dairy cattle breeding because young animals can be accurately selected as parents, leading to a much shorter generation interval and higher rates of genetic gain. The aims of this study were to assess the effects of genomic selection and reduction of the generation interval on the rate of genetic gain and rate of inbreeding. Furthermore, the merit of proven bulls relative to young bulls was studied. This is important for breeding organizations as it determines the relative importance of progeny testing. A closed nucleus breeding scheme was simulated in which 1,000 males and 1,000 females were born annually, 200 bulls were progeny tested, and 20 sires and 200 dams were selected to produce the next generation. In the “proven” (PROV) scenario, only cows with own performance records and progeny-tested bulls were selected as parents. The proportion of the genetic variance that was explained by simulated marker information (M) was varied from 0 to 100%. When M increased from 0 to 100%, the rate of genetic gain increased from 0.238 to 0.309 genetic standard deviations (σ) per year (+30%), whereas the rate of inbreeding reduced from 1.00 to 0.42% per generation. Alternatively, when young cows and bulls were selected as parents (YNG scenario), the rate of genetic gain for M = 0% was 0.292 σ/yr but the corresponding rate of inbreeding increased substantially to 3.15% per generation. A realistic genomic selection scheme (YNG with M = 40%) gave 108% higher rate of genetic gain (0.495 σ/yr) and approximately the same rate of inbreeding per generation as the conventional system without genomic selection (PROV with M = 0%). The rate of inbreeding per year, however, increased from 0.18 to 0.52% because the generation interval in the YNG scheme was much shorter. Progeny-testing fewer bulls reduced the rate of genetic gain and increased the rate of inbreeding for PROV, but had negligible effects for YNG because almost all sires were young bulls. In scenario YNG with M = 40%, the best young bulls were superior to the best proven bulls by 1.27 σ difference in genomic estimated breeding value. This superiority increased even further when fewer bulls were progeny tested. This stochastic simulation study shows that genomic selection in combination with a severe reduction in the generation interval can double the rate of genetic gain at the same rate of inbreeding per generation, but with a higher rate of inbreeding per year. The number of progeny-tested bulls can be greatly reduced, although this will slightly affect the quality of the proven bull team. Therefore, it is important for breeding organizations to predict the future demand for proven bull semen in light of the increasing superiority of young bulls.     

Economic evaluation of genomic breeding programs

The objective of this study was to compare a conventional dairy cattle breeding program characterized by a progeny testing scheme with different scenarios of genomic breeding programs. The ultimate economic evaluation criterion was discounted profit reflecting discounted returns minus discounted costs per cow in a balanced breeding goal of production and functionality. A deterministic approach mainly based on the gene flow method and selection index calculations was used to model a conventional progeny testing program and different scenarios of genomic breeding programs. As a novel idea, the modeling of the genomic breeding program accounted for the proportion of farmers waiting for daughter records of genotyped young bulls before using them for artificial insemination. Technical and biological coefficients for modeling were chosen to correspond to a German breeding organization. The conventional breeding program for 50 test bulls per year within a population of 100,000 cows served as a base scenario. Scenarios of genomic breeding programs considered the variation of costs for genotyping, selection intensity of cow sires, proportion of farmers waiting for daughter records of genotyped young bulls, and different accuracies of genomic indices for bulls and cows. Given that the accuracies of genomic indices are greater than 0.70, a distinct economic advantage was found for all scenarios of genomic breeding programs up to factor 2.59, mainly due to the reduction in generation intervals. Costs for genotyping were negligible when focusing on a population-wide perspective and considering additional costs for herdbook registration, milk recording, or keeping of bulls, especially if there is no need for yearly recalculation of effects of single nucleotide polymorphisms. Genomic breeding programs generated a higher discounted profit than a conventional progeny testing program for all scenarios where at least 20% of the inseminations were done by genotyped young bulls without daughter records. Evaluation of levels of annual genetic gain for individual traits revealed the same potential for low heritable traits (h² = 0.05) compared with moderate heritable traits (h² = 0.30), preconditioning highly accurate genomic indices of 0.90. The final economic success of genomic breeding programs strongly depends on the complete abdication of any forms of progeny testing to reduce costs and generation intervals, but such a strategy implies the willingness of the participating milk producers.     

Invited Review: Reliability of genomic predictions for North American Holstein bulls

Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R²) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R² values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R² values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.     

Use of a Bayesian model including QTL markers increases prediction reliability when test animals are distant from the reference population

Relatedness between reference and test animals has an important effect on the reliability of genomic prediction for test animals. Because genomic prediction has been widely applied in practical cattle breeding and bulls have been selected according to genomic breeding value without progeny testing, the sires or grandsires of candidates might not have phenotypic information and might not be in the reference population when the candidates are selected. The objective of this study was to investigate the decreasing trend of the reliability of genomic prediction given distant reference populations, using genomic best linear unbiased prediction (GBLUP) and Bayesian variable selection models with or without including the quantitative trait locus (QTL) markers detected from sequencing data. The data used in this study consisted of 22,242 bulls genotyped using the 54K SNP array from EuroGenomics. Among them, 1,444 Danish bulls born from 2006 to 2010 were selected as test animals. Different reference populations with varying relationships to test animals were created according to pedigree-based relationships. The reference individuals having a relationship with one or more test animals higher than 0.4 (scenario ρ < 0.4), 0.2 (ρ < 0.2), or 0.1 (ρ < 0.1, where ρ = relationship coefficient) were removed from reference sets; these represented the distance between reference and test animals being 2 generations, 3 generations, and 4 generations, respectively. Imputed whole-genome sequencing data of bulls from Denmark were used to conduct a genome-wide association study (GWAS). A small number of significant variants (QTL markers) from the GWAS were added to the array data. To compare the effects of different models, the basic GBLUP model, a Bayesian selection variable model, a GBLUP model with 2 components of genetic effects, and a Bayesian model with pooled array data and QTL markers were used for estimating genomic estimated breeding values (GEBV) of test animals. The reliability of genomic prediction decreased when the test animals were more generations away from the reference population. The reliability of genomic prediction was 0.461 for 1 generation away and 0.396 for 3 generations away, with the same number of individuals in the reference set, using a GBLUP model with chip markers only. The results showed that using the Bayesian method and QTL markers improved the reliability of genomic prediction in all scenarios of relationship between test and reference animals, in a range of 1.3% and 65.1% (4 generations away with only 841 individuals in the reference set). However, most gains were for predictions of milk yield and fat yield. There was little improvement for predictions of protein yield and mastitis, and no improvement for prediction of fertility, except for scenario ρ < 0.1, in which there was a large improvement for predictions of all traits. On the other hand, models including more than 10% polygenic effect decreased prediction reliability when the relationship between test and reference animals was distant.     

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.     

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.     

Optimizing the design of small-sized nucleus breeding programs for dairy cattle with minimal performance recording

Dairy cattle breeding programs in developing countries are constrained by minimal and erratic pedigree and performance recording on cows on commercial farms. Small-sized nucleus breeding programs offer a viable alternative. Deterministic simulations using selection index theory were performed to determine the optimum design for small-sized nucleus schemes for dairy cattle. The nucleus was made up of 197 bulls and 243 cows distributed in 8 non-overlapping age classes. Each year 10 sires and 100 dams were selected to produce the next generation of male and female selection candidates. Conception rates and sex ratio were fixed at 0.90 and 0.50, respectively, translating to 45 male and 45 female candidates joining the nucleus per year. Commercial recorded dams provided information for genetic evaluation of selection candidates (bulls) in the nucleus. Five strategies were defined: nucleus records only [within-nucleus dam performance (DP)], progeny records in addition to nucleus records [progeny testing (PT)], genomic information only [genomic selection (GS)], dam performance records in addition to genomic information (GS+DP), and progeny records in addition to genomic information (GS+PT). Alternative PT, GS, GS+DP, and GS+PT schemes differed in the number of progeny per sire and size of reference population. The maximum number of progeny records per sire was 30, and the maximum size of the reference population was 5,000. Results show that GS schemes had higher responses and lower accuracies compared with other strategies, with the higher response being due to shorter generation intervals. Compared with similar sized progeny-testing schemes, genomic-selection schemes would have lower accuracies but these are offset by higher responses per year, which might provide additional incentive for farmers to participate in recording.     

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

This study investigated the accuracy of direct genomic breeding values (DGV) using a genomic BLUP model, genomic enhanced breeding values (GEBV) using a one-step blending approach, and GEBV using a selection index blending approach for 15 traits of Nordic Red Cattle. The data comprised 6,631 bulls of which 4,408 bulls were genotyped using Illumina Bovine SNP50 BeadChip (Illumina, San Diego, CA). To validate reliability of genomic predictions, about 20% of the youngest genotyped bulls were taken as test data set. Deregressed proofs (DRP) were used as response variables for genomic predictions. Reliabilities of genomic predictions in the validation analyses were measured as squared correlations between DRP and genomic predictions corrected for reliability of DRP, based on the bulls in the test data sets. A set of weighting (scaling) factors was used to construct the combined relationship matrix among genotyped and nongenotyped bulls for one-step blending, and to scale DGV and its expected reliability in the selection index blending. Weighting (scaling) factors had a small influence on reliabilities of GEBV, but a large influence on the variation of GEBV. Based on the validation analyses, averaged over the 15 traits, the reliability of DGV for bulls without daughter records was 11.0 percentage points higher than the reliability of conventional pedigree index. Further gain of 0.9 percentage points was achieved by combining information from conventional pedigree index using the selection index blending, and gain of 1.3 percentage points was achieved by combining information of genotyped and nongenotyped bulls simultaneously applying the one-step blending. These results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls in Nordic Red population, and the one-step blending approach is a good alternative to predict GEBV in practical genetic evaluation program.     

Potential gains in lifetime net merit from genomic testing of cows, heifers, and calves on commercial dairy farms

The objective of this study was to quantify the gains in genetic potential of replacement females that could be achieved by using genomic testing to facilitate selection and culling decisions on commercial dairy farms. Data were simulated for 100 commercial dairy herds, each with 1,850 cows, heifers, and calves. Parameters of the simulation were based on the US Holstein population, and assumed reliabilities of traditional and genomic predictions matched reliabilities of animals that have been genotyped to date. Selection of the top 10, 20, 30, …, 90% of animals within each age group was based on parent averages and predicted transmitting abilities with or without genomic testing of all animals or subsets of animals that had been presorted by traditional predictions. Average gains in lifetime net merit breeding value of selected females due to genomic testing, minus prorated costs of genotyping the animals and their unselected contemporaries, ranged from $28 (top 90% selected) to $259 (top 20% selected) for heifer calves with no pedigrees, $14 (top 90% selected) to $121 (top 10% selected) for heifer calves with known sires, and $7 (top 90% selected) to $87 (top 20% selected) for heifer calves with full pedigrees. In most cases, gains in genetic merit of selected heifer calves far exceeded prorated genotyping costs, and gains were greater for animals with missing or incomplete pedigree information. Gains in genetic merit due to genomic testing were smaller for lactating cows that had phenotypic records, and in many cases, these gains barely exceeded or failed to exceed genotyping costs. Strategies based on selective genotyping of the top, middle, or bottom 50% of animals after presorting by traditional parent averages or predicted transmitting abilities were cost effective, particularly when pedigrees or phenotypes were available and a relatively small proportion of animals were to be selected or culled. Based on these results, it appears that routine genotyping of heifer calves or yearling heifers can be a cost-effective strategy for enhancing the genetic level of replacement females on commercial dairy farms. Increasing the accuracy of predicted breeding values for young females with genomic testing might lead to synergies with other management tools and strategies, such as propagating genetically superior females using advanced reproductive technologies or selling excess females that were generated by the use of sex-enhanced semen.     

Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers

The objective of the present study was to assess the predictive ability of subsets of single nucleotide polymorphism (SNP) markers for development of low-cost, low-density genotyping assays in dairy cattle. Dense SNP genotypes of 4,703 Holstein bulls were provided by the USDA Agricultural Research Service. A subset of 3,305 bulls born from 1952 to 1998 was used to fit various models (training set), and a subset of 1,398 bulls born from 1999 to 2002 was used to evaluate their predictive ability (testing set). After editing, data included genotypes for 32,518 SNP and August 2003 and April 2008 predicted transmitting abilities (PTA) for lifetime net merit (LNM$), the latter resulting from progeny testing. The Bayesian least absolute shrinkage and selection operator method was used to regress August 2003 PTA on marker covariates in the training set to arrive at estimates of marker effects and direct genomic PTA. The coefficient of determination (R²) from regressing the April 2008 progeny test PTA of bulls in the testing set on their August 2003 direct genomic PTA was 0.375. Subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP were created by choosing equally spaced and highly ranked SNP, with the latter based on the absolute value of their estimated effects obtained from the training set. The SNP effects were re-estimated from the training set for each subset of SNP, and the 2008 progeny test PTA of bulls in the testing set were regressed on corresponding direct genomic PTA. The R² values for subsets of 300, 500, 750, 1,000, 1,250, 1,500, and 2,000 SNP with largest effects (evenly spaced SNP) were 0.184 (0.064), 0.236 (0.111), 0.269 (0.190), 0.289 (0.179), 0.307 (0.228), 0.313 (0.268), and 0.322 (0.291), respectively. These results indicate that a low-density assay comprising selected SNP could be a cost-effective alternative for selection decisions and that significant gains in predictive ability may be achieved by increasing the number of SNP allocated to such an assay from 300 or fewer to 1,000 or more.     

Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle

Genomic selection may accelerate genetic progress in breeding programs of indicine breeds when compared with traditional selection methods. We present results of genomic predictions in Gyr (Bos indicus) dairy cattle of Brazil for milk yield (MY), fat yield (FY), protein yield (PY), and age at first calving using information from bulls and cows. Four different single nucleotide polymorphism (SNP) chips were studied. Additionally, the effect of the use of imputed data on genomic prediction accuracy was studied. A total of 474 bulls and 1,688 cows were genotyped with the Illumina BovineHD (HD; San Diego, CA) and BovineSNP50 (50K) chip, respectively. Genotypes of cows were imputed to HD using FImpute v2.2. After quality check of data, 496,606 markers remained. The HD markers present on the GeneSeek SGGP-20Ki (15,727; Lincoln, NE), 50K (22,152), and GeneSeek GGP-75Ki (65,018) were subset and used to assess the effect of lower SNP density on accuracy of prediction. Deregressed breeding values were used as pseudophenotypes for model training. Data were split into reference and validation to mimic a forward prediction scheme. The reference population consisted of animals whose birth year was ≤2004 and consisted of either only bulls (TR1) or a combination of bulls and dams (TR2), whereas the validation set consisted of younger bulls (born after 2004). Genomic BLUP was used to estimate genomic breeding values (GEBV) and reliability of GEBV (R²_PEV) was based on the prediction error variance approach. Reliability of GEBV ranged from ∼0.46 (FY and PY) to 0.56 (MY) with TR1 and from 0.51 (PY) to 0.65 (MY) with TR2. When averaged across all traits, R²_PEV were substantially higher (R²_PEV of TR1 = 0.50 and TR2 = 0.57) compared with reliabilities of parent averages (0.35) computed from pedigree data and based on diagonals of the coefficient matrix (prediction error variance approach). Reliability was similar for all the 4 marker panels using either TR1 or TR2, except that imputed HD cow data set led to an inflation of reliability. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information. A reduced panel of ∼15K markers resulted in reliabilities similar to using HD markers. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information.     

Genotyping strategies for maximizing genomic information in evaluations of the Latxa dairy sheep breed

Genomic selection has been implemented over the years in several livestock species, due to the achievable higher genetic progress. The use of genomic information in evaluations provides better prediction accuracy than do pedigree-based evaluations, and the makeup of the genotyped population is a decisive point. The aim of this work is to compare the effect of different genotyping strategies (number and type of animals) on the prediction accuracy for dairy sheep Latxa breeds. A simulation study was designed based on the real data structure of each population, and the phenotypic and genotypic data obtained were used in genetic (BLUP) and genomic (single-step genomic BLUP) evaluations of different genotyping strategies. The genotyping of males was beneficial when they were genetically connected individuals and if they had daughters with phenotypic records. Genotyping females with their own lactation records increased prediction accuracy, and the connection level has less relevance. The differences in genotyping females were independent of their estimated breeding value. The combined genotyping of males and females provided intermediate accuracy results regardless of the female selection strategy. Therefore, assuming that genotyping rams is interesting, the incorporation of genotyped females would be beneficial and worthwhile. The benefits of genotyping individuals from various generations were highlighted, although it was also possible to gain prediction accuracy when historic individuals were not considered. Greater genotyped population sizes resulted in more accuracy, even if the increase seems to reach a plateau.     

Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP

Single-step genomic BLUP (ssGBLUP) is a method for genomic prediction that integrates matrices of pedigree (A) and genomic (G) relationships into a single unified additive relationship matrix whose inverse is incorporated into a set of mixed model equations (MME) to compute genomic predictions. Pedigree information in dairy cattle is often incomplete. Missing pedigree potentially causes biases and inflation in genomic estimated breeding values (GEBV) obtained with ssGBLUP. Three major issues are associated with missing pedigree in ssGBLUP, namely biased predictions by selection, missing inbreeding in pedigree relationships, and incompatibility between G and A in level and scale. These issues can be solved using a proper model for unknown-parent groups (UPG). The theory behind the use of UPG is well established for pedigree BLUP, but not for ssGBLUP. This study reviews the development of the UPG model in pedigree BLUP, the properties of UPG models in ssGBLUP, and the effect of UPG on genetic trends and genomic predictions. Similarities and differences between UPG and metafounder (MF) models, a generalized UPG model, are also reviewed. A UPG model (QP) derived using a transformation of the MME has a good convergence behavior. However, with insufficient data, the QP model may yield biased genetic trends and may underestimate UPG. The QP model can be altered by removing the genomic relationships linking GEBV and UPG effects from MME. This altered QP model exhibits less bias in genetic trends and less inflation in genomic predictions than the QP model, especially with large data sets. Recently, a new model, which encapsulates the UPG equations into the pedigree relationships for genotyped animals, was proposed in simulated purebred populations. The MF model is a comprehensive solution to the missing pedigree issue. This model can be a choice for multibreed or crossbred evaluations if the data set allows the estimation of a reasonable relationship matrix for MF. Missing pedigree influences genetic trends, but its effect on the predictability of genetic merit for genotyped animals should be negligible when many proven bulls are genotyped. The SNP effects can be back-solved using GEBV from older genotyped animals, and these predicted SNP effects can be used to calculate GEBV for young-genotyped animals with missing parents.     

Deterministic models of breeding scheme designs that incorporate genomic selection

A deterministic model to calculate rates of genetic gain and inbreeding was used to compare a range of breeding scheme designs under genomic selection (GS) for a population of 140,000 cows. For most schemes it was assumed that the reliability of genomic breeding values (GEBV) was 0.6 across 4 pathways of selection. In addition, the effect of varying reliability on the ranking of schemes was also investigated. The schemes considered included intense selection in male pathways and genotyping of 1,000 young bulls (GS-Y). This scheme was extended to include selection in females and to include a “worldwide” scheme similar to GS-Y, but 6 times as large and assuming genotypes were freely exchanged between 6 countries. An additional worldwide scheme was modeled where GEBV were available through international genetic evaluations without exchange of genotypes. Finally, a closed nucleus herd that used juvenile in vitro embryo transfer in heifers was modeled so that the generation interval in female pathways was reduced to 1 or 2 yr. When the breeding schemes were compared using a GEBV reliability of 0.6, the rates of genetic gain were between 59 and 130% greater than the rate of genetic gain achieved in progeny testing. This was mainly through reducing the generation interval and increasing selection intensity. Genomic selection of females resulted in a 50% higher rate of genetic gain compared with restricting GS to young bulls only. The annual rates of inbreeding were, in general, 60% lower than with progeny testing, because more sires of bulls and sires of cows were selected, thus increasing the effective population size. The exception was in nucleus breeding schemes that had very short generation intervals, resulting in higher rates of both gain and inbreeding. It is likely that breeding companies will move rapidly to alter their breeding schemes to make use of genomic selection because benefits to the breeding companies and to the industry are considerable.     

Efficient methods to compute genomic predictions

Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.     

Short communication: The role of genotypes from animals without phenotypes in single-step genomic evaluations

In a 2-step genomic system, genotypes of animals without phenotypes do not influence genomic prediction of other animals, but that might not be the case in single-step systems. We investigated the effects of including genotypes from culled bulls on the reliability of genomic predictions from single-step evaluations. Four scenarios with a constant amount of phenotypic information and increasing numbers of genotypes from culled bulls were simulated and compared with respect to prediction reliability. With increasing numbers of genotyped culled bulls, there was a corresponding increase in prediction reliability. For instance, in our simulation scenario the reliability for selection candidates was twice as large when all culled bulls from the last 4 generations were included in the analysis. Single-step evaluations imply the imputation of all nongenotyped animals in the pedigree. We showed that this imputation was increasingly more accurate as increasingly more genotypic information from the culled bulls was taken into account. This resulted in higher prediction reliabilities. The extent of the benefit from including genotypes from culled bulls might be more relevant for small populations with low levels of reliabilities.