Genomic Enhanced Breeding Values Research Articles

Genetic and genomic evaluation of age at first calving in Italian Holsteins

Age at first calving (AFC) represents the non-productive period of around 2 yr in Holstein cows and thus it has a relevant impact on the cost of rearing replacements in the dairy herd. In the present study we aimed at evaluating genetic and genomic aspects of AFC in the Italian Holstein population. Data of 4,206,218 heifers with first calving between 1996 and 2020 were used. Age at first calving averaged 26.09 ± 3.07 mo and decreased across years. Heritability was estimated using a linear animal model which included the fixed effects of herd-year-season of birth and classes of gestation length, and the random animal additive genetic effect fitted to a pedigree-based relationship matrix. Estimated breeding values (EBV) and genomically enhanced breeding values (GEBV) were obtained, and they were standardized to mean 100 and standard deviation 5, where animals above the mean are those contributing to reduce AFC. Heritability estimates of AFC ranged from 0.031 to 0.045. The trend of sires' GEBV was favorable and indicated a reduced AFC across years. Approximate genetic correlations between GEBV of AFC and GEBV of other economically important traits were calculated on a subset of genotyped females born after 2015. Moderate favorable associations of AFC with production traits (0.39 to 0.51), udder depth (0.40), interval from first to last insemination in heifer (-0.43), and longevity (0.34) were assessed. Overall, the greatest lifetime productive performances and most favorable days open in first lactation were observed when heifers calved at 22-23 mo. On the other hand, progeny of sires with GEBV of AFC above the mean yielded more milk, fat, and protein in first lactation, and had shorter days open than progeny of sires with GEBV of AFC below the mean. Results suggested that breeding strategies to improve AFC should be pursued, also considering genetic correlations between AFC and traits which are already part of the Italian Holstein breeding objective. The inclusion of AFC in an aggregate index is expected to contribute to enhance farm income.

100 Pedigree Effective Population Size Affects Genomic Prediction Accuracy in Katahdin Sheep

Abstract Katahdin sheep are a composite hair breed of medium-size, relatively prolific, with potential to exhibit resistance to gastrointestinal parasites. Recently, genomic predictions of breeding values were introduced in Katahdins. Prediction accuracy depends on the genetic variation present, and selection using genomically-enhanced breeding values may reduce diversity. We aimed to characterize the current genetic variability in Katahdins utilizing their extensive pedigree. The National Sheep Improvement Program provided the pedigree of 92,030 sheep born from 1984-2019. Two reference populations were defined for animals born between 2017-2019: all Katahdins (n = 23,494), and Katahdins with at least three generations of known Katahdin ancestry (n = 9,327). Using ENDOG and PopReport, effective population sizes (Ne) were estimated. We then considered the effect of Ne on the size of the genomic reference population needed to achieve certain levels of prediction accuracy. Weaning weight, a lowly heritable trait for this breed (h2 = 0.13), was evaluated. Based on individual increase in inbreeding, inbreeding coefficient of animal adjusted for equivalent complete generations, the estimated Ne for the full pedigree, reference 1, and reference 2 were 92, 104, and 87, respectively. When inbreeding coefficients were regressed or log-regressed on birth date and adjusted for generation interval, the Ne estimates were 53 and 49 for reference 1, and 76 and 67 for reference 2, respectively. For our average Ne = 75 with 3,960 effective chromosomal segments, the accuracy of prediction for weaning weight was estimated for genomic reference population sizes (N) of 10k, 15k, 20k, and 25k. Starting at N = 10k, the prediction accuracy was 0.58, and as N increased by 5k increments the accuracy increased to 0.65, 0.70, and 0.74. Conversely, with our smallest Ne = 49 with 2,548 effective chromosomal segments, the prediction accuracy was 0.64, 0.71, 0.75, and 0.78 for N = 10k, 15k, 20k, and 25k, respectively. Estimated Ne varied depending on the method utilized, and tended to be larger for reference 1 than 2. However, this was not the case for Ne based on regression. Inbreeding coefficients were greater in reference 2 than 1, and the generation interval was shorter in reference 2 (2.84 years) than in 1 (2.97 years). These combined differences led to larger Ne estimates for reference 2 than 1, reflecting the impact of different criteria used to obtain this statistic. The generally small, estimated Ne benefitted genomic prediction due to a greater shared number of effective chromosomal segments. Investing in larger genomic reference populations will only marginally increase prediction accuracy even for lowly heritable traits. Selection using genomically-enhanced breeding values will accelerate genetic gains, potentially reducing the Ne of Katahdins and thereby increasing prediction accuracy. Yet, long-term gains may be at risk due to the loss of genetic diversity.

Using Pedigree and Genomic Data toward Better Management of Inbreeding in Italian Dairy Sheep and Goat Breeds.

Simple SummaryThe inbreeding coefficient is relevant for managing livestock and safeguarding biodiversity, especially in small populations. Small ruminant breeders mainly rely on pedigree information, but genomics is increasingly gaining ground as a tool to face possible pedigree inaccuracies. This study investigates pedigree-based (FPED) and genomic (FROH and FGRM) inbreeding in a representative number of Italian sheep and goat populations. Indeed, even though it has been widely studied in cattle, there is still little knowledge about the relationship between these coefficients in small ruminants, which are characterized by a different population structure, often with unconnected farms. Mean inbreeding values were low, FROH being the highest, with breed differences due to different managements; the correlation between FPED and FROH was the strongest and directly related to pedigree depth. Moreover, we estimated FPED from FROH using a linear regression model. Since massive genotyping is not affordable to small ruminant breeders, it is important to understand the distinction and relationship between differently calculated inbreeding coefficients, also in view of the introduction of genomic enhanced breeding values. Our study highlights the importance of accurate pedigree information and, especially if not obtainable, of genotyping animals. Correct data contribute to mitigate inbreeding depression and loss of genetic variability.The inbreeding coefficient is an important parameter for livestock management. Small ruminant breeders and associations mainly rely on pedigree information, but genomic tools are gaining relevance, overcoming possible pedigree inconsistencies. This study investigates the relationship between pedigree-based and genomic inbreeding in two goat and four sheep dairy breeds. Pedigree and genomic data (medium-density SNPchip) were obtained for 3107 goats and 2511 sheep. We estimated pedigree depth (number of fully traced generations, FullGen) and inbreeding (FPED), as well as two genomic inbreeding indexes, using runs of the homozygosity (FROH) and genomic relationship matrix (FGRM). The correlation between the inbreeding coefficients was assessed. A linear regression model (LRM) was fitted for estimating FPED from FROH. After quality control on genomic data, we retained 5085 animals. Mean inbreeding values were low, with higher FROH than FPED and FGRM. Breed differences can partially depend on different managements. The correlation between FPED and FROH was the highest and directly related to pedigree depth. The best LRM was chosen for FullGen ≥4 and ≥6 for goats and sheep, respectively; after excluding animals with extreme residuals, a new refined regression equation was calculated. Since massive genotyping is not affordable to small ruminant breeders, it is important to understand the distinction and relationship between differently calculated inbreeding coefficients, also in view of the introduction of genomic enhanced breeding values. Our study highlights the importance of accurate pedigree information and, especially if not obtainable, of calculating genomic-based inbreeding coefficients. A better estimation of animals’ relatedness contributes to improve animal breeding and conservation.

Single- and multiple-breed genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats

Conformation traits are functional traits known to affect longevity, production efficiency, and profitability of dairy goats. However, genetic progress for these traits is expected to be slower than for milk production traits due to the limited number of herds participating in type classification programs, and often lower heritability estimates. Genomic selection substantially accelerates the rate of genetic progress in many species and industries, especially for lowly heritable, difficult, or expensive to measure traits. Therefore, the main objectives of this study were (1) to evaluate the potential benefits of the implementation of single-step genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats, and (2) to investigate the effect of the use of single- and multiple-breed training populations. The phenotypes used in this study were linear conformation scores, on a 1-to-9 scale, for 8 traits (i.e., body capacity, dairy character, fore udder, feet and legs, general appearance, rear udder, medial suspensory ligament, and teats) of 5,158 Alpine and 2,342 Saanen does. Genotypes were available for 833 Alpine and 874 Saanen animals. Averaged across all traits, the use of multiple-breed analyses increased validation accuracy for Saanen, and reduced bias of genomically enhanced breeding values (GEBV) for both Alpine and Saanen compared with single-breed analyses. Little benefit was observed from the use of GEBV relative to pedigree-based EBV in terms of validation accuracy and bias, possibly due to limitations in the validation design, but substantial gains of 0.14 to 0.21 (32-50%) were observed in the theoretical accuracy of validation animals when averaged across traits for single- and multiple-breed analyses. Across the whole genotyped population, average gains in theoretical accuracy for GEBV compared with EBV across all traits ranged from 0.15 to 0.17 (32-37%) for Alpine and 0.17 to 0.19 (40-41%) for Saanen, depending on the model used. The largest gains were observed for does without classification records (0.19-0.22 or 50-55%) and bucks without daughter classification records (0.20-0.27 or 57-82%), which have the least information contributing to their traditional EBV. The use of multiple-breed rather than single-breed models was most beneficial for the Saanen breed, which had fewer phenotypic records available for the analyses. These results suggest that the implementation of genomic selection could increase the accuracy of breeding values for conformation traits in Canadian dairy goats.

Association of genomically enhanced and parent average breeding values with cow performance in Nordic dairy cattle

This study compared the abilities of virgin heifer genomically enhanced breeding values (GEBV) and parent average breeding values (PA) to predict future cow performance. To increase confidence in genomic technology among farmers, a clear demonstration of the relationship between genomic predictions and future phenotypes is needed. We analyzed 12 different traits in first parity, including production, conformation, fertility, and other functional traits. Phenotype data were obtained from national milk recording schemes and breeding values from the Nordic Cattle Genetic Evaluation. Direct genomic breeding values were calculated using genomic BLUP and combined with traditional breeding values, using bivariate blending. The data covered 14,862 Red Dairy Cattle, 17,145 Holstein, and 7,330 Jersey genotyped virgin heifers born between 2013 and 2015 in Denmark, Finland, and Sweden. Phenotypes adjusted for systematic environmental effects were used as measures of cow performance. Two methods were used to compared virgin heifer GEBV and PA regarding their ability to predict future cow performance: (1) correlations between breeding values and adjusted phenotypes, (2) ranking cows into 4 quartiles for their virgin heifer GEBV or PA, and calculating actual cow performance for each quartile. We showed that virgin heifer GEBV predicted cow performance significantly better than PA for the vast majority of analyzed traits. The correlations with adjusted phenotypes were 38 to 136% higher for GEBV than for PA in Red Dairy Cattle, 42 to 194% higher for GEBV in Holstein, and 11 to 78% higher for GEBV in Jersey. The relative change between GEBV bottom and top quartiles compared with that between PA bottom and top quartiles ranged from 9 to 261% for RDC, 42 to 138% for Holstein, and 4 to 90% for Jersey. Hence, farmers in Denmark, Finland, and Sweden can have confidence in using genomic technology on their herds.

Asymptotic response to four-path selection due to index and single trait selection according to genomically enhanced breeding values

Formulas were developed for calculating the asymptotic response from index and single trait selection for differential selection among male and female parents and of parents to produce male versus female replacements (four-path programs). The formulas were then used to study the effects on selection response in several scenarios. Index selection based on genomically enhanced breeding value (GEBV) achieved substantial genetic improvement even for low-heritability traits when their relative economic weights are greater than those of the other moderate-heritability index traits in spite of positive or negative genetic correlation between the two index traits. Index selection overwhelmed single trait selection with regard to selection response, particularly when genetic correlation was negative between the two index traits. When the reduction in genetic (co)variance after selection was ignored, the initial genetic response overestimated the asymptotic response and the overestimation was more remarkable for a moderately heritable trait than for a lowly heritable trait. The overestimation for the moderately heritable trait was more conspicuous in single trait selection than in index selection. The underestimation of the initial response during generation 0 due to the index selection was found for the lowly heritable traits when the lowly heritable traits had negative genetic correlation with the other moderately heritable traits and when economic weights of the lowly heritable traits were substantially small as compared to the other moderately heritable traits. The accounting percentage from a selection path from dams to breed sons for the total response per generation increased approximately from 30% to 39% when the selection percentage decreased from 10% to 1%. Applying selection based on GEBV in a selection path from dams to breed sons would make a great contribution to genetic improvement. Equilibrium correlated response to an unselected trait due to index selection became negative when initial genetic correlation during generation 0 between aggregate genotype and an unselected trait was negative. Consequently, we need to construct reference population for all the traits connected with the breeding goal in order to enable the genomic evaluations of all the traits connected with the breeding goal.

Genomics for the advancement of livestock production: A South African perspective

Most of the growth of human populations worldwide will be in developing countries, including South Africa. Natural resources are under immense pressure and animal scientists are faced with the challenges for increased efficiency and long-term sustainability of livestock production. Since the completion of the Human Genome Project, animal genomes have been mapped with genomics, enabling new opportunities for application in farm animal species. The use of microsatellite markers has made significant contributions to the insight in genetic characterisation of indigenous and local developed breeds in most farm species in South Africa and Africa. The single nucleotide polymorphic (SNP) marker discovery and development of commercial SNP arrays made genomic selection possible and genomic enhanced breeding values (GEBVs) are used widely in the First World. In South Africa, genomic programmes for beef and dairy cattle were established in 2015 and 2016, with the focus on building training populations for genomic selection. The SA Bonsmara breed was the first to receive GEBV. The availability of hard-to-measure phenotypes is limited, and these are the traits that hold the most potential for genomic selection and answering to the challenges of methane (CH 4 ) emissions and higher efficiency. Genome editing, which involves zinc-finger nucleases (ZFNs), transcription-activators such as endonucleases (TALEN) and RNA-programmable genome editor (CRISPR/CAS9), includes the most recent technology for application in precision genetics. Welfare and ethical concerns will be an important consideration in the acceptability of genome editing to consumers. Applications that benefit the animals are more acceptable to the public. The use of genome editing to produce polled cattle is one of the first applications with a direct welfare impact as it nullifies the need for painful dehorning. In this paper, genomic technology is reviewed with the focus on the most recent research trends and commercial application of genomics towards the genetic improvement of livestock with specific reference to South Africa. Keywords: Genetic diversity, genomic selection, gene editing, microsatellite markers, SNP

Effects of preselection of genotyped animals on reliability and bias of genomic prediction in dairy cattle.

ObjectiveModels for genomic selection assume that the reference population is an unselected population. However, in practice, genotyped individuals, such as progeny-tested bulls, are highly selected, and the reference population is created after preselection. In dairy cattle, the intensity of selection is higher in males than in females, suggesting that cows can be added to the reference population with less bias and loss of accuracy. The objective is to develop formulas applied to any genomic prediction studies or practice with preselected animals as reference population.MethodsWe developed formulas for calculating the reliability and bias of genomically enhanced breeding values (GEBV) in the reference population where individuals are preselected on estimated breeding values. Based on the formulas presented, deterministic simulation was conducted by varying heritability, preselection percentage, and the reference population size.ResultsThe number of bulls equal to a cow regarding the reliability of GEBV was expressed through a simple formula for the reference population consisting of preselected animals. The bull population was vastly superior to the cow population regarding the reliability of GEBV for low-heritability traits. However, the superiority of reliability from the bull reference population over the cow population decreased as heritability increased. Bias was greater for bulls than cows. Bias and reduction in reliability of GEBV due to preselection was alleviated by expanding reference population.ConclusionCows are easier in expanding reference population size compared with bulls and alleviate bias and reduction in reliability of GEBV of bulls which are highly preselected than cows by expanding the cow reference population.

Efficiency of genomic selection to improve meat quality in pigs using ZPLAN+

The phenotype or genomic enhanced breeding value (GEBV) of ultrasound intramuscular fat (UIMF) was used as the target trait to improve meat quality. The ZPLAN+ software was employed to calculate and compare the genetic gain and accuracy of each selection scenario. The first scenario reflected the current conventional selection program in which the selection index is composed of average daily gain (ADG), feed conversion ratio (FCR) and ultrasound backfat (UBF). In the second scenario, UIMF was added into the basic selection index as an indicator trait for meat quality. In scenario 3, UIMF was also incorporated into index; however, the GEBV was used instead of phenotype. In scenario 4 and 5, selection was based strictly on the GEBV, and UIMF was included in scenario 5. The results showed that the accuracies of scenario 3, 4 and 5, in which GEBV information was used, increased with increasing accuracy of the GEBV. Moreover, the trends of scenario 4 and 5 changed more rapidly relative to scenario 3. The addition of UIMF to the selection index had a positive effect on the genetic gain of ADG and FCR, but a negative effect on UBF. The addition of UIMF to the selection index led to improvement of other traits and to the overall meat quality, especially when genomic selection was applied.

Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.

Effect of genotyped cows in the reference population on the genomic evaluation of Holstein cattle

This study evaluated the dependence of reliability and prediction bias on the prediction method, the contribution of including animals (bulls or cows), and the genetic relatedness, when including genotyped cows in the progeny-tested bull reference population. We performed genomic evaluation using a Japanese Holstein population, and assessed the accuracy of genomic enhanced breeding value (GEBV) for three production traits and 13 linear conformation traits. A total of 4564 animals for production traits and 4172 animals for conformation traits were genotyped using Illumina BovineSNP50 array. Single- and multi-step methods were compared for predicting GEBV in genotyped bull-only and genotyped bull-cow reference populations. No large differences in realized reliability and regression coefficient were found between the two reference populations; however, a slight difference was found between the two methods for production traits. The accuracy of GEBV determined by single-step method increased slightly when genotyped cows were included in the bull reference population, but decreased slightly by multi-step method. A validation study was used to evaluate the accuracy of GEBV when 800 additional genotyped bulls (POPbull) or cows (POPcow) were included in the base reference population composed of 2000 genotyped bulls. The realized reliabilities of POPbull were higher than those of POPcow for all traits. For the gain of realized reliability over the base reference population, the average ratios of POPbull gain to POPcow gain for production traits and conformation traits were 2.6 and 7.2, respectively, and the ratios depended on heritabilities of the traits. For regression coefficient, no large differences were found between the results for POPbull and POPcow. Another validation study was performed to investigate the effect of genetic relatedness between cows and bulls in the reference and test populations. The effect of genetic relationship among bulls in the reference population was also assessed. The results showed that it is important to account for relatedness among bulls in the reference population. Our studies indicate that the prediction method, the contribution ratio of including animals, and genetic relatedness could affect the prediction accuracy in genomic evaluation of Holstein cattle, when including genotyped cows in the reference population.

Relevance of Additive and Nonadditive Genetic Relatedness for Genomic Prediction in Rice Population under Recurrent Selection Breeding

In genomic recurrent selection programs of self-pollinated crops, additive genetic effects (breeding values) are effectively relevant for selection of superior progenies as new parents. However, considering additive and nonadditive genetic effects can improve the prediction of genome-enhanced breeding values (GEBV) of progenies, for quantitative traits. In this study, we assessed the magnitude of additive and nonadditive genetic variances for eight key traits in a rice population under recurrent selection, using marker-based relationship matrices. We then assessed the goodness-to-fit, bias, stability and accuracy of prediction for breeding values and total (additive plus nonadditive) genetic values, in five genomic best linear unbiased prediction (GBLUP) models, ignoring or not nonadditive genetic effects. The models were compared using 6174 single nucleotide polymorphisms (SNP) markers from 174 S1:3 progenies evaluated in field yield trial. We found dominance effects accounting for a substantial proportion of the total genetic variance for the key traits in rice, especially for days to flowering. In average of the traits, the component of variance additive, dominance, and epistatic contributed to about 34%, 14% and 9% for phenotypic variance. Additive genomic models, ignoring nonadditive genetic effects, showed better fit to the data and lower bias, in addition to greater stability and accuracy for predict GEBV of progenies. These results improve our understanding of the genetic architecture of the key traits in rice, evaluated in early-generation testing. Clearly, this study highlighted the advantages of additive models using genome-wide information, for genomic prediction applied to recurrent selection in a self-pollinated crop

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.

GWAS in practical cattle breeding in Czech Republic, single step method, genetic progress

Development of genetic evaluation of animals is permanent process. It was going from estimated breeding value (EBV) calculated by CC-test, across a BLUP – animal model and RR-TDM, to the genomic enhanced breeding value (GEBV) using genetic markers. Methods of genetic evaluation become a part of marketing strategies of insemination companies. Therefore all countries and association of breeders seek to be compatible with others. Now we are in a period of massive global implementation of genomic evaluation, which combines traditional BLUP with huge quantity of genetic SNP markers. Multi-step procedures are now usual in practice, which work with deregressed proofs. Development of methods attained to the single-step procedure (ssGBLUP) which overcomes some difficulties of previous methods, improves reliabilities of evaluation and compares all animals, genotyped and ungenotyped, in entire nation-wide population. Genomic evaluation influence above all young genotyped animals. 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(2014) Single-step genomic evaluation for linear type traits of Holstein cows in Czech Republic. Anim. Sci. Papers and Reports vol. 32, pp. 201-208.

Comparison of genomic breeding values of Holstein in Czech Republic

Genomic evaluation by single-step procedure allows efficient implanting of genetic SNP markers into complicated procedure of random regression test-day-model of milk traits. Test-day records and pedigree files on nation-wide scale are combined with genomic relationship into common evaluation of both genotyped and ungenotyped animals. Due to strong import of foreign sperm into small national populations is the reliability of evaluation of young animals low. This is particularly seen in evaluation of young bulls, which frequently have both parents foreign. Genomic evaluation helps and notably improves reliability of evaluation. ssGBLUP procedure is advantageous especially for small populations. Domestic genomic evaluation of young animals has medium to high correlation with foreign Interbull values. Interbull conversion of values of bulls according MACE, which works with progeny tested bulls, is more reliable than conversion according GMACE procedure, which works with genomic evaluation of young animals. Keywords: genomic breeding value, ssGBLUP, test day model, MACE, GMACE References Bauer, J. et al. (2014) Approximation of the reliability of single-step genomic breeding values for dairy cattle in the Czech Republic. Anim. Sci. Papers and Reports, 32, pp. 301-306. Bauer, J., Přibyl, J. and Vostrý, L. (2015) Contribution of domestic production records and Interbull EBV on approximate reliabilities of single-step genomic breeding values in dairy cattle. Czech J. Anim. Sci., 60, 263-267. Christensen, O.F. and Lund, M.S. (2010) Genomic prediction when some animals are not genotyped. Genet.Sel.Evol. 42, pp. 2. Forni, S., Aguilar I. and Misztal, I. (2011) Different genomic relationship matrices for single step analysis using phenotypic, pedigree and genomic information. Genet. Sel. Evol., 43, pp. 1. Legarra A., Aguilar I. and Misztal, I. (2009) A relationship matrix including full pedigree and genomic information. J. Dairy Sci., 92, pp. 4656-4663. Meuwissen, T.H.E., Hayes, B.J. and Goddard, M.E. (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, pp. 1819–1829. Misztal, I., Legarra A. and Aguilar, I. (2009) Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci., 92, pp. 4648–4655. Pesek, P., Přibyl, J. and Vostrý, L . (2015) Genetic variances of SNP loci for milk yield in dairy cattle. J. Appl. Genet., 56, pp. 339-347. Plemdat, (2015) Descriptions of Breeding values Evaluation. Retrieved on 10 th June 2015 From www.plemdat.cz. Přibyl, J. et al. (2014) Domestic and Interbull information in the single step genomic evaluation of Holstein milk production. Czech J. Anim. Sci., 59, pp. 409-415. Přibyl, J. et al. (2015) Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values. Animal, 9, pp. 1635-1642. Vitezica, Z.G. et al. (2011) Bias in genomic predictions for populations under selection. Genet. Res. (Camb), 93, pp. 357-366. Zavadilova, L., Jamrozik, J. and Schaeffer, L.R. (2005a) Genetic parameters for test- day model with random regressions for production traits of Czech Holstein cattle. Czech J. Anim. Sci., 50, pp. 142-154. Zavadilova, L., Němcova, E. and Wolf, J. (2005b) Definition of subgroups for fixed regression in the test-day animal model for milk production of Holstein cattle in the Czech Republic. Czech J. Anim. Sci., 50, pp. 7-13. Zavadilova, L. et al. (2014) Single-step genomic evaluation for linear type traits of Holstein cows in Czech Republic. Anim. Sci. Papers and Reports vol. 32, pp. 201-208.

Optimization of Swine Breeding Programs Using Genomic Selection with ZPLAN.

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

Single-step genomic evaluation using multitrait random regression model and test-day data

The objectives of this study were to evaluate the feasibility of use of the test-day (TD) single-step genomic BLUP (ssGBLUP) using phenotypic records of Nordic Red Dairy cows. The critical point in ssGBLUP is how genomically derived relationships (G) are integrated with population-based pedigree relationships (A) into a combined relationship matrix (H). Therefore, we also tested how different weights for genomic and pedigree relationships affect ssGBLUP, validation reliability, and validation regression coefficients. Deregressed proofs for 305-d milk, protein, and fat yields were used for a posteriori validation. The results showed that the use of phenotypic TD records in ssGBLUP is feasible. Moreover, the TD ssGBLUP model gave considerably higher validation reliabilities and validation regression coefficients than the TD model without genomic information. No significant differences were found in validation reliability between the different TD ssGBLUP models according to bootstrap confidence intervals. However, the degree of inflation in genomic enhanced breeding values is affected by the method used in construction of the H matrix. The results showed that ssGBLUP provides a good alternative to the currently used multi-step approach but there is a great need to find the best option to combine pedigree and genomic information in the genomic matrix.

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP – Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.

Application of genomic evaluation to dairy cattle in New Zealand

A hybrid method of estimating genomically enhanced breeding values (GEBV) that uses features of a single-step approach and traditional national estimated breeding value (EBV), based on pedigree, as the starting point was developed and assessed. The method uses deregressed EBV from the traditional EBV estimation procedures and a pedigree matrix that is augmented by a genomic relationship matrix based on a Euclidean distance (EDM) in a Gaussian kernel. Genotypes were obtained using the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). The pedigree information is restricted to animals with genotypes, as well as all of their ancestors, resulting in a system of equations that is considerably smaller than that of the national evaluation. Genome-wide association studies were used to identify regions of the genome that had high association with the traits in the New Zealand national breeding objective. The EDM were created for each selected region. The amount of variance accounted for by each region was estimated, and the variances, relative to the EDM excluding these areas, were used to create trait-specific weighted EDM that were used for the evaluations. The evaluation is done on the restricted data set and is a reversal of the deregression procedure using the augmented pedigree-based relationship matrix. The method was tested using data on 7,204 progeny-tested sires, in which the daughter records for the 3 youngest cohorts (i.e., validation data; n=1,410) were excluded. Evaluations were done for the 7 traits (3 production, 4 nonproduction) in the New Zealand national breeding objective. Regression of the traditional EBV based on daughter information on the GEBV for validation sires had an average slope of 0.99 (range 0.93 to 1.06); the average correlation between the EBV was 0.79 (range 0.56 to 0.84). The average slope and correlation for the previously used blended GEBV were 0.64 and 0.61, respectively. The method was applied to all genotyped animals that included approximately 13,000 genotyped males and 47,000 genotyped females and their ancestors. The correlation between the traditional EBV and GEBV of the proven bulls was 0.99; the correlations obtained from the previously used blended GEBV were 0.88 (no inflation adjustment) and 0.94 (following inflation adjustment). The values for the unproven bulls were 0.95 (parent average EBV and GEBV), 0.56 (blended GEBV, no inflation adjustment), and 0.88 (blended GEBV, following inflation adjustment). The use of trait-specific weighted EDM provided a 1 to 2% improvement in the accuracy of the genomic evaluations of the production traits and no improvement in the nonproduction traits. The hybrid method of estimating GEBV was found to be considerably less biased and generally more accurate than the previously used blended genomic breeding value.

Domestic and Interbull information in the single step genomic evaluation of Holstein milk production

Estimated breeding values and genomic enhanced breeding values for milk production of young genotyped Holstein bulls were predicted using a conventional animal model, ridge regression genomic prediction procedure, genomic best linear unbiased prediction, single-step genomic best linear unbiased prediction, and one-step blending procedures. For prediction, the nation-wide database of domestic Czech production records was combined with deregressed proofs from Interbull files through 2008, which had been transformed by multiple across country evaluation to reflect domestic production conditions. 1259 genotyped bulls had already been proven in 2008. Analyses were run that used Interbull values only for these genotyped bulls and used Interbull values for all available sires. Predictions were validated by comparing correlations of breeding value predictions with estimated breeding values and daughter-yield-deviations after progeny test in 2012 of 140 young genotyped bulls and their associated reliabilities. Combining domestic data with Interbull estimated breeding values improved prediction of both estimated breeding values and genomic enhanced breeding values. Prediction by animal model (traditional estimated breeding values) using only the domestic database had 0.29 validated reliability of prediction; whereas combining the nation-wide domestic database with all available deregressed proofs for genotyped and non-genotyped sires from Interbull resulted in reliability of 0.34, compared to 0.36 when using Interbull data only. The highest reliabilities were for predictions from the single-step genomic best linear unbiased prediction procedure using combined data, or with all available deregressed proofs from Interbull only (one-step blending approach), which reached validated reliabilities for genomic enhanced breeding values predictions 0.53 and 0.54, respectively. &amp;nbsp;