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In Northern European countries, a great variety of Red cattle populations exists which can be broadly categorized in two groups: specialized dairy and dual-purpose breeds. Collaboration between these breeds (i.e. the exchange of sires across breeds) can be beneficial but is limited so far. The aim of this study was to demonstrate and evaluate consequences of collaboration between Red breeds using stochastic simulations. Two breeding lines (dairy type and dual purpose) were simulated. As a special aspect of this study, differences in genetic levels of breeding traits (milk production, beef production, mastitis resistance, fertility, feed efficiency) have been taken into account. Various scenarios were investigated where across-breed selection was either restricted or allowed and with different correlations between breeding goals in the two lines. The results of this study were influenced by the different genetic levels in breeding traits only in the first years of simulation. In the long run, the breed differences did not affect the degree of collaboration between lines. When the correlation between breeding goals was close to unity, the selection of external bulls was highly beneficial in terms of genetic gain and total monetary gain. Additionally, the lowest rate of inbreeding was found in that case. With decreasing correlations between environments, degree of cooperation between lines rapidly terminated and lines operated individually. In last years of simulation, cooperation was only found when the correlation between breeding goals was close to unity. From a long-term perspective, the exchange of breeding sires across lines also caused negative effects. In the dual-purpose line, deterioration of genetic gain in mastitis resistance and fertility was observed. Additionally, breeding lines genetically converged, which decreased genetic diversity. Collectively, short-term benefits and long-term negative effects have to be reconciled if collaboration between Red breeds in Northern Europe is to be pursued.

Both small dairy cattle populations and dairy cattle populations with a low level of linkage disequilibrium (LD) suffer from low reliability of genomic prediction. In this study, we investigated whether adding more genotyped cows to the reference population influences the rate of genetic gain and rate of inbreeding by affecting the reliability. A standard breeding program with a large reference population and high LD, which mimicked a breeding program for Danish Holstein population, was simulated as a reference. A Danish Jersey population with a small reference population and high LD and a Red Dairy Cattle population with a large reference population and low LD were also simulated. Two additional breeding programs were simulated for Danish Jersey and Red Dairy Cattle populations, where 2,000 additional genotyped cows were included in the population for genomic selection. All 5 simulated breeding programs were initiated by a founder population to generate LD resembling the real LD pattern, followed by a 20-yr conventional progeny-testing scheme with 1,000 or 10,000 genotyped progeny-tested bulls and a 10-yr genomic selection scheme with or without 2,000 additional genotyped cows. Evaluation criteria were annual monetary genetic gain and rate of true inbreeding. Our results showed that adding more genotyped cows to the reference in dairy cattle populations has the potential to increase genetic gain and reduce the rate of inbreeding, regardless of reference population size and level of LD. However, it is still not possible to reach the same genetic gain as in the simulated Danish Holstein population with either a small reference population or low LD. Our results also showed that in a small reference population with high LD, it is difficult to manage inbreeding because of lower accuracy compared with the simulated Danish Holstein population and a smaller number of relevant families to select from. Therefore, breeding strategies need to be chosen to match population size and structure. The rate of true inbreeding is always underestimated by pedigree inbreeding and even more in genomic breeding programs, indicating that some forms of genome-wide inbreeding, instead of pedigree-based inbreeding, should be used to monitor inbreeding when genomic selection is implemented.

Background We tested the hypothesis that breeding schemes with a pre-selection step, in which carriers of a lethal recessive allele (LRA) were culled, and with optimum-contribution selection (OCS) reduce the frequency of a LRA, control rate of inbreeding, and realise as much genetic gain as breeding schemes without a pre-selection step. Methods We used stochastic simulation to estimate true genetic gain realised at a 0.01 rate of true inbreeding (ΔFtrue) by breeding schemes that combined one of four pre-selection strategies with one of three selection strategies. The four pre-selection strategies were: (1) no carriers culled, (2) male carriers culled, (3) female carriers culled, and (4) all carriers culled. Carrier-status was known prior to selection. The three selection strategies were: (1) OCS in which $$\Delta {\text{F}}_{{{\text{true}}}}$$ Δ F true was predicted and controlled using pedigree relationships (POCS), (2) OCS in which $$\Delta {\text{F}}_{{{\text{true}}}}$$ Δ F true was predicted and controlled using genomic relationships (GOCS), and (3) truncation selection of parents. All combinations of pre-selection strategies and selection strategies were tested for three starting frequencies of the LRA (0.05, 0.10, and 0.15) and two linkage statuses with the locus that has the LRA being on a chromosome with or without loci affecting the breeding goal trait. The breeding schemes were simulated for 10 discrete generations (t = 1, …, 10). In all breeding schemes, ΔFtrue was calibrated to be 0.01 per generation in generations t = 4, …, 10. Each breeding scheme was replicated 100 times. Results We found no significant difference in true genetic gain from generations t = 4, …, 10 between breeding schemes with or without pre-selection within selection strategy. POCS and GOCS schemes realised similar true genetic gains from generations t = 4, …, 10. POCS and GOCS schemes realised 12% more true genetic gain from generations t = 4, …, 10 than truncation selection schemes. Conclusions We advocate for OCS schemes with pre-selection against the LRA that cause animal suffering and high costs. At LRA frequencies of 0.10 or lower, OCS schemes in which male carriers are culled reduce the frequency of LRA, control rate of inbreeding, and realise no significant reduction in true genetic gain compared to OCS schemes without pre-selection against LRA.

Conventional wheat-breeding programs involve crossing parental lines and subsequent selfing of the offspring for several generations to obtain inbred lines. Such a breeding program takes more than 8 years to develop a variety. Although wheat-breeding programs have been running for many years, genetic gain has been limited. However, the use of genomic information as selection criterion can increase selection accuracy and that would contribute to increased genetic gain. The main objective of this study was to quantify the increase in genetic gain by implementing genomic selection in traditional wheat-breeding programs. In addition, we investigated the effect of genetic correlation between different traits on genetic gain. A stochastic simulation was used to evaluate wheat-breeding programs that run simultaneously for 25 years with phenotypic or genomic selection. Genetic gain and genetic variance of wheat-breeding program based on phenotypes was compared to the one with genomic selection. Genetic gain from the wheat-breeding program based on genomic estimated breeding values (GEBVs) has tripled compared to phenotypic selection. Genomic selection is a promising strategy for improving genetic gain in wheat-breeding programs.

Selective genotyping of phenotypically superior animals may lead to bias and less accurate genomic breeding values (GEBV). Performing selective genotyping based on phenotypes measured in the breeding environment (B) is not necessarily a good strategy when the aim of a breeding program is to improve animals’ performance in the commercial environment (C). Our simulation study compared different genotyping strategies for selection candidates and for fish in C in a breeding program for rainbow trout in the presence of genotype-by-environment interactions when the program had limited genotyping resources and unregistered pedigrees of individuals. For the reference population, selective genotyping of top and bottom individuals in C based on phenotypes measured in C led to the highest genetic gains, followed by random genotyping and then selective genotyping of top individuals in C. For selection candidates, selective genotyping of top individuals in B based on phenotypes measured in B led to the highest genetic gains, followed by selective genotyping of top and bottom individuals and then random genotyping. Selective genotyping led to bias in predicting GEBV. However, in scenarios that used selective genotyping of top fish in B and random genotyping of fish in C, predictions of GEBV were unbiased, with genetic correlations of 0.2 and 0.5 between traits measured in B and C. Estimates of variance components were sensitive to genotyping strategy, with an overestimation of the variance with selective genotyping of top and bottom fish and an underestimation of the variance with selective genotyping of top fish. Unbiased estimates of variance components were obtained when fish in B and C were genotyped at random. In conclusion, we recommend phenotypic genotyping of top and bottom fish in C and top fish in B for the purpose of selecting breeding animals and random genotyping of individuals in B and C for the purpose of estimating variance components when a genomic breeding program for rainbow trout aims to improve animals’ performance in C.

Organic dairy production differs from conventional dairy production in many aspects. However, breeding programs for the 2 production systems are the same in most countries. Breeding goals (BG) might be different for the 2 production systems and genotype × environment interaction may exist between organic and conventional dairy production, both of which have an effect on genetic gain in different breeding strategies. Other aspects also need to be considered, such as the application of multiple ovulation and embryo transfer (MOET), which is not allowed in organic dairy production. The general aim of this research was to assess different environment-specific breeding strategies for organic dairy production. The specific aim was to study differences in BG weights and include the effect of genotype × environment interaction, MOET, and the selection of breeding bulls from the conventional environment. Different scenarios were simulated. In the current scenario, the present-day situation for dairy production in Denmark was emulated as much as possible. The BG was based on a conventional dairy production system, MOET was applied in both environments, and conventional bulls could be selected as breeding bulls in the organic environment. Four alternative scenarios were simulated, all with a specific organic BG in the organic breeding program but differences in the usage of MOET and the selection of conventional bulls as breeding bulls. Implementation of a specific BG in organic dairy production slightly increased genetic gain in the aggregate genotype compared with the breeding program that is currently implemented in organic dairy production. Not using embryo transfer or only selecting breeding bulls from the organic environment decreased genetic gain in the aggregate genotype by as much as 24%. However, the use of embryo transfer is debatable because this is not allowed according to current regulations for organic dairy production. Assessing genetic gain on trait levels showed that a significant increase for functional traits was possible compared with the current breeding program in the organic environment without a decrease in genetic gain in the aggregate genotype. This difference on trait level was even more present when selection of conventional bulls as breeding bulls in the organic environment was not possible. This finding is very relevant when breeding for the desired cow in organic dairy production.

Background The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. Results VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct the H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from H-AM under random genotyping have the expected values. Predicted breeding values from H-AM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions. Conclusions We conclude that VC estimates from H-AM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of H-AM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation.

Background After the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno1: phenotypes from only the conventional phase (1–20 years); (2) Pheno1 + 2: phenotypes from both the conventional phase and GS phase (1–35 years); (3) Pheno2: phenotypes from only the GS phase (21–35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base). Results In general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno2), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno2) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated. Conclusions The results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

The overall aim of this research was to characterize the preferences of Danish dairy farmers for improvements in breeding goal traits. The specific aims were (1) to investigate the presence of heterogeneity in farmers’ preferences by means of cluster analysis, and (2) to associate these clusters with herd characteristics and production systems (organic or conventional). We established a web-based survey to characterize the preferences of farmers for improvements in 10 traits, by means of pairwise rankings. We also collected a considerable number of herd characteristics. Overall, 106 organic farmers and 290 conventional farmers answered the survey, all with Holstein cows. The most preferred trait improvement was cow fertility, and the least preferred was calving difficulty. By means of cluster analysis, we identified 4 distinct clusters of farmers and named them according to the trait improvements that were most preferred: Health and Fertility, Production and Udder Health, Survival, and Fertility and Production. Some herd characteristics differed between clusters; for example, farmers in the Survival cluster had twice the percentage of dead cows in their herds compared with the other clusters, and farmers that gave the highest ranking to cow and heifer fertility had the lowest conception rate in their herds. This finding suggests that farmers prefer to improve traits that are more problematic in their herd. The proportion of organic and conventional farmers also differed between clusters; we found a higher proportion of organic farmers in the production-based clusters. When we analyzed organic and conventional data separately, we found that organic farmers ranked production traits higher than conventional farmers. The herds of organic farmers had lower milk yields and lower disease incidences, which might explain the high ranking of milk production and the low ranking of disease traits. This study shows that heterogeneity exists in farmers’ preferences for improvements in breeding goal traits, that organic and conventional farmers differ in their preferences, and that herd characteristics can be linked to different farmer clusters. The results of this study could be used for the future development of breeding goals in Danish Holstein cows and for the development of customized total merit indices based on farmer preferences.

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (rg). The break-even correlation (rb) refers to the specific rg where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if rg is higher than rb, whereas 2 environment-specific BP have higher genetic gain if rg is lower than rb. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find rb when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on rb and genetic gain; and (4) to find rb from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The rb was 0.65 and not different from rb with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on rb was not large. A different size in 2 environments decreased rb by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high rg. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when rg between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.

Background A breeding program for commercial broiler chicken that is carried out under strict biosecure conditions can show reduced genetic gain due to genotype by environment interactions (G × E) between bio-secure (B) and commercial production (C) environments. Accuracy of phenotype-based best linear unbiased prediction of breeding values of selection candidates using sib-testing in C is low. Genomic prediction based on dense genetic markers may improve accuracy of selection. Stochastic simulation was used to explore the benefits of genomic selection in breeding schemes for broiler chicken that include birds in both B and C for assessment of phenotype. Results When genetic correlations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{g}$$\end{document}rg) between traits measured in B and C were equal to 0.5 and 0.7, breeding schemes with 15, 30 and 45% of birds assessed in C resulted in higher genetic gain for performance in C compared to those without birds in C. The optimal proportion of birds phenotyped in C for genetic gain was 30%. When the proportion of birds in C was optimal and genotyping effort was limited, allocating 30% of the genotyping effort to birds in C was also the optimal genotyping strategy for genetic gain. When \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{g}$$\end{document}rg was equal to 0.9, genetic gain for performance in C was not improved with birds in C compared to schemes without birds in C. Increasing the heritability of traits assessed in C increased genetic gain significantly. Rates of inbreeding decreased when the proportion of birds in C increased because of a lower selection intensity among birds retained in B and a reduction in the probability of co-selecting close relatives. Conclusions If G × E interactions (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{g}$$\end{document}rg of 0.5 and 0.7) are strong, a genomic selection scheme in which 30% of the birds hatched are phenotyped in C has larger genetic gain for performance in C compared to phenotyping all birds in B. Rates of inbreeding decreased as the proportion of birds moved to C increased from 15 to 45%.

Making decisions on plant breeding programs require plant breeders to be able to test different breeding strategies by taking into account all the crucial factors affecting crop genetic improvement. Due to the complexity of the decisions, computer simulation serves as an important tool for researchers and plant breeders. This paper describes ADAM-plant, which is a computer software that models breeding schemes for self-pollinated and cross-pollinated crop plants using stochastic simulation. The program simulates a population of plants and traces the genetic changes in the population under different breeding scenarios. It takes into account different population structures, genomic models, selection (strategies and units) and crossing strategies. It also covers important features e.g., allowing users to perform genomic selection (GS) and speed breeding, simulate genotype-by-environment interactions using multiple trait approach, simulate parallel breeding cycles and consider plot sizes. In addition, the software can be used to simulate datasets produced from very complex breeding program in order to test new statistical methodology to analyze such data. As an example, three wheat-breeding strategies were simulated in the current study: (1) phenotypic selection, (2) GS, and (3) speed breeding with genomic information. The results indicate that the genetic gain can be doubled by GS compared to phenotypic selection and genetic gain can be further increased considerably by speed breeding. In conclusion, ADAM-plant is an important tool for comparing strategies for plant breeding and for estimating the effects of allocation of different resources to the breeding program. In the current study, it was used to compare different methodologies for utilizing genomic information in cereal breeding programs for selection of best-fit breeding strategy as per available resources.

In Denmark, Finland, and Sweden, the Nordic Total Merit index is used as the breeding selection tool for both organic and conventional dairy farmers based on common economic models for conventional dairy farming. Organic farming is based on the principles of organic agriculture (POA) defined by the International Federation of Organic Agriculture Movements. These principles are not set up with an economic point of view, and therefore it may be questionable to use a breeding goal (BG) for organic dairy production based on economic models. In addition to economics and the principles of organic agriculture, it is important to look at farmers' preferences for improving BG traits when setting up a BG for organic farming. The aim of this research was to set up, simulate, and compare long-term effects of different BG for organic and conventional dairy production systems based on economic models, farmers' preferences, and POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency. The BG based on economic models and on farmers' preferences were taken from previous studies. The other BG were desired gains indices, set up by means of a questionnaire about relatedness between the POA and BG traits. Each BG was simulated in the stochastic simulation program ADAM. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, caused favorable genetic gain in all 12 traits included in this study compared with 6 traits for the other BG. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, were very different from BG for organic and conventional production based on economic models and farmers' preferences in both simulated genetic change and correlations between BG. The BG that was created based on the principles of organic agriculture could be used as a specific index for organic dairy farming in Denmark, but this index was economically not very sustainable. Hence, an intermediate breeding goal could be developed by breeding companies to address both economics and the principles of organic agriculture.

The aim of this study was to assess the impact of varying features of an open multiple ovulation and embryo transfer (MOET) nucleus used in a genomic dairy cattle breeding scheme on both genetic gain and inbreeding rates. The Viking Red breeding scheme served as a case study to design scenarios that were stochastically simulated. We analyzed the number of AI sires used in the breeding population, the number of flushed heifers, the number of flushings per heifer and the genotyping capacity allocated to young females. The results supported that setting up a MOET program in a genomic dairy cattle scheme increases genetic gain without increasing inbreeding rates when the MOET nucleus size and the number of AI sires in service are large enough. Secondly, it was shown that increasing the number of genotyped heifers could not compensate the loss in genetic gain caused by closing the MOET nucleus. On the contrary, when extending the flushing capacity of the MOET program, increasing the number of flushings per heifer had a greater impact on genetic gain, but also on inbreeding rates, than increasing the number of flushed females. So, when a constraint applies on the flushing capacity in an open MOET scheme and the achieved inbreeding rate permits it, it seems more relevant to increase the number of flushings per heifer than the number of flushed heifers. Results also indicated that the number of genotypings allocated to females had to be sufficient to get maximal returns from the MOET scheme. In this case study, little extra genetic gain could be obtained by extending the MOET scheme size with the initial genotyping strategy (800 genotyped females). Indeed, the genotyping capacity should permit to genotype all heifers produced in the MOET scheme to discriminate the best heifers within families, and should be also sufficient to identify the best heifers outside the MOET nucleus.

Dairy farmers can increase the number of dairy heifer calves born in their herd by using sexed semen. They can reduce the number of both dairy bull and heifer calves by using beef semen. Long before sexed semen became commercially available, it was believed that it would provide opportunities for increasing genetic level in both herds and populations. In this study, we studied the potential for increasing the genetic level of a herd by using beef semen in combination with sexed semen. We tested the hypothesis that the potential of increasing the genetic level and the overall net return would depend on herd management. To test this hypothesis, we simulated 7 scenarios using beef semen and sexed semen in 5 herds at different management levels. We combined the results of 2 stochastic simulation models, SimHerd and ADAM. SimHerd simulated the effects of the scenarios and management levels on economic outcomes (i.e., operational return) and on technical outcomes such as the parity distribution of the dams of heifer calves, but it disregarded genetic progress. The ADAM model quantified genetic level by using the dams' parity distributions and the frequency of sexed and beef semen to estimate genetic return per year. We calculated the annual net return per slot as the sum of the operational return and the genetic return, divided by the total number of slots. Net return increased up to €18 per slot when using sexed semen in 75% genetically superior heifers and beef semen in 70% genetically inferior, multiparous cows. The assumed reliability of selection was 0.84. These findings were for a herd with overall high management for reproductive performance, longevity, and calf survival. The same breeding strategy reduced net return by €55 per slot when management levels were average. The main reason for the large reduction in net return was the heifer shortage that arose in this scenario. Our hypothesis that the potential for beef semen to increase genetic level would be herd-specific was supported. None of the scenarios were profitable under Danish circumstances when the value of the increased genetic level was not included. A comparable improvement in genetic level could be realized by selectively selling dairy heifer calves rather than using beef semen.

Our aim was to investigate whether four fertility traits were different in herds using the activity tag Heatime for reproduction management compared within other herds. The genetic parameters were estimated using animal models. The fertility traits were number of inseminations (NINS), interval from first to last insemination (IFL), age at first insemination (AFI), and age at first calving (AFC) in Danish Holstein heifers. The means of the traits were all significantly different between the two groups of herds. Heritabilities for NINS and IFL were lower than 1%, while heritabilities for AFI and AFC were 12–29%. Heritabilities were all lower in Heatime herds than in reference herds. The genetic correlations between traits in different environments were high for NINS, AFI, and AFC (>0.88), while the correlation for IFL was 0.68 but with a higher SE (0.313). Thus, IFL seems not to be the same trait in Heatime and reference herds.

The main purpose of this study was to evaluate whether the population structure in Danish Jersey (DJ) known from the history of the breed also is reflected in its genomic structure. This is done by comparing the linkage disequilibrium and persistence of phase for subgroups of Jersey animals with high proportions of Danish (DNK) or United States (USJ) origin. Furthermore, it is investigated whether a model explicitly incorporating breed origin of animals, inferred either through the known pedigree or from SNP marker data, leads to improved genomic predictions compared with a model ignoring breed origin. The study of the population structure incorporated 1,730 genotyped Jersey animals. In total 39,542 SNP markers were included in the analysis. The 1,079 genotyped bulls with de-regressed proof for udder health were used in the analysis for the predictions of the genomic breeding values. A range of random regressions models that included the breed origin were analyzed and compared with a basic genomic model that assumes a homogeneous breed structure. The main finding in this study is that the importation of germplasm from the USJ population is readily reflected in the genomes of modern DJ animals. First, linkage disequilibrium in the group of admixed DJ animals is lower compared with the groups of the original DNK and USJ animals. Second, persistence of linkage disequilibrium phase is not conserved for longer marker distances between animals with mainly Danish or United States origin. Third, the STRUCTURE analysis could retrieve genomic-based breed proportions in alignment to the pedigree-based breed proportions. However, including this population structure in a random regression prediction model did not clearly improve the reliabilities of the genomic predictions compared with a basic genomic model.

2012-5158 ABSTRACT. We reasoned that marginal returns from genomic selection diminish as the proportion of genotyped selection candidates increases and breeding values (BV) based on a priori information are used to choose candidates that are genotyped. We tested this premise by stochastic simulation of breeding schemes that resembled those used for pigs. We estimated rates of genetic gain and inbreeding realized by genomic selection in breeding schemes where candidates were phenotyped before genotyping and 0 to 100% of the candidates were genotyped based on predicted BV. Genotyping was allocated to male and female candidates at ratios of 100:0, 75:25, 50:50, 25:75, and 0:100. For genotyped candidates, a direct-genomic value (DGV) was sampled with reliabilities 0.10, 0.50, and 0.90. Ten sires and 300 dams with the largest BV after genotyping were selected at each generation. Selection was for a single trait with heritability 0.20. We found that the marginal returns did diminish as genotyping proportion was increased, while the rate at which the returns diminished slowed as DGV became more reliable. With DGV reliability 0.10, genotyping as little as 5% of the selection candidates realized 86% of the additional genetic gain and 67% of the reduction in inbreeding that was realized by genotyping 100% of the candidates. All of the genetic gain and reduction in inbreeding was realized by genotyping 40 and 50% of the candidates. When the reliability was increased to 0.90, genotyping 20% of the candidates was required to realize 76% of the genetic gain and 85% of the reduction in inbreeding. Genotyping 50% of the selection candidates with DGV reliability 0.90 realized 91% of the genetic gain and 94% of the reduction in inbreeding. Regardless of DGV reliability, returns at small genotyping proportions of 0.5 to 10% were maximized when only male candidates were genotyped. At the large genotyping proportions of 20 to 50%, returns were maximized by genotyping both males and females. Our fi ndings indicate that, provided a priori information is available, only 5 to 20% of the selection candidates need to be genotyped to realize most of the benefi ts from genomic selection. At these genotyping proportions, it is best to target males in schemes when selection intensity for males is greater than females. Our fi ndings should benefi t breeders because they suggest that large investments in genotyping are not required to reap most of the benefi ts from genomic selection.

Background In pig breeding, selection is usually carried out in purebred populations, although the final goal is to improve crossbred performance. Genomic selection can be used to select purebred parental lines for crossbred performance. Dominance is the likely genetic basis of heterosis and explicitly including dominance in the genomic selection model may be an advantage when selecting purebreds for crossbred performance. Our objectives were two-fold: (1) to compare the predictive ability of genomic prediction models with additive or additive plus dominance effects, when the validation criterion is crossbred performance; and (2) to compare the use of two pure line reference populations to a single combined reference population. Methods We used data on litter size in the first parity from two pure pig lines (Landrace and Yorkshire) and their reciprocal crosses. Training was performed (1) separately on pure Landrace (2085) and Yorkshire (2145) sows and (2) the two combined pure lines (4230), which were genotyped for 38 k single nucleotide polymorphisms (SNPs). Prediction accuracy was measured as the correlation between genomic estimated breeding values (GEBV) of pure line boars and mean corrected crossbred-progeny performance, divided by the average accuracy of mean-progeny performance. We evaluated a model with additive effects only (MA) and a model with both additive and dominance effects (MAD). Two types of GEBV were computed: GEBV for purebred performance (GEBV) based on either the MA or MAD models, and GEBV for crossbred performance (GEBV-C) based on the MAD. GEBV-C were calculated based on SNP allele frequencies of genotyped animals in the opposite line. Results Compared to MA, MAD improved prediction accuracy for both lines. For MAD, GEBV-C improved prediction accuracy compared to GEBV. For Landrace (Yorkshire) boars, prediction accuracies were equal to 0.11 (0.32) for GEBV based on MA, and 0.13 (0.34) and 0.14 (0.36) for GEBV and GEBV-C based on MAD, respectively. Combining animals from both lines into a single reference population yielded higher accuracies than training on each pure line separately. In conclusion, the use of a dominance model increased the accuracy of genomic predictions of crossbred performance based on purebred data. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0220-2) contains supplementary material, which is available to authorized users.

Under the finite-locus model in the absence of mutation, the additive genetic variation is expected to decrease when directional selection is acting on a population, according to quantitative-genetic theory. However, some theoretical studies of selection suggest that the level of additive variance can be sustained or even increased when nonadditive genetic effects are present. We tested the hypothesis that finite-locus models with both additive and nonadditive genetic effects maintain more additive genetic variance (VA) and realize larger medium- to long-term genetic gains than models with only additive effects when the trait under selection is subject to truncation selection. Four genetic models that included additive, dominance, and additive-by-additive epistatic effects were simulated. The simulated genome for individuals consisted of 25 chromosomes, each with a length of 1 M. One hundred bi-allelic QTL, 4 on each chromosome, were considered. In each generation, 100 sires and 100 dams were mated, producing 5 progeny per mating. The population was selected for a single trait (h2 = 0.1) for 100 discrete generations with selection on phenotype or BLUP-EBV. VA decreased with directional truncation selection even in presence of nonadditive genetic effects. Nonadditive effects influenced long-term response to selection and among genetic models additive gene action had highest response to selection. In addition, in all genetic models, BLUP-EBV resulted in a greater fixation of favorable and unfavorable alleles and higher response than phenotypic selection. In conclusion, for the schemes we simulated, the presence of nonadditive genetic effects had little effect in changes of additive variance and VA decreased by directional selection.

The aim of this study was to characterize preferences of farmers for breeding goal traits with Danish Red (DR) or Danish Jersey (DJ) cows. A breed-specific survey was established to characterize farmers’ preferences for improvements in 10 traits, by means of pairwise rankings using the online software 1000Minds. These pairwise rankings were based on equal economic worth of trait improvements. The DR survey was filled in by 87 farmers and the DJ survey by 76 farmers. Both DR and DJ farmers gave the highest preference to improvements in mastitis, and the lowest to calving difficulty. By means of a cluster analysis, three distinct clusters of farmers were identified per breed. Comparisons of herd characteristics between clusters suggest that farmers choose to improve traits that are problematic in their herds. This study shows that heterogeneity exists in farmers’ preferences for trait improvements and that herd characteristics influence these preferences in DR and DJ.

We tested the hypothesis that mating strategies with genomic information realise lower rates of inbreeding (∆F) than with pedigree information without compromising rates of genetic gain (∆G). We used stochastic simulation to compare ∆F and ∆G realised by two mating strategies with pedigree and genomic information in five breeding schemes. The two mating strategies were minimum-coancestry mating (MC) and minimising the covariance between ancestral genetic contributions (MCAC). We also simulated random mating (RAND) as a reference point. Generations were discrete. Animals were truncation-selected for a single trait that was controlled by 2000 quantitative trait loci, and the trait was observed for all selection candidates before selection. The criterion for selection was genomic-breeding values predicted by a ridge-regression model. Our results showed that MC and MCAC with genomic information realised 6% to 22% less ∆F than MC and MCAC with pedigree information without compromising ∆G across breeding schemes. MC and MCAC realised similar ∆F and ∆G. In turn, MC and MCAC with genomic information realised 28% to 44% less ∆F and up to 14% higher ∆G than RAND. These results indicated that MC and MCAC with genomic information are more effective than with pedigree information in controlling rates of inbreeding. This implies that genomic information should be applied to more than just prediction of breeding values in breeding schemes with truncation selection.

The aim of this study was to characterize preferences of farmers with Red Danish Cattle (RDC) or Jersey herds. A breed-specific survey was established to characterize preferences of farmers for improvements in ten traits, by means of pairwise rankings using the online software 1000Minds. These pairwise rankings were based on equal economic worth of trait improvements. The RDC-survey was filled in by 87 farmers and the Jersey-survey by 76 farmers. Both RDC and Jersey herds had the highest preference for improvements in mastitis, followed by milk production. The lowest preference was given to calving difficulty. By means of a cluster analysis distinct clusters of farmers were identified and named according to the trait improvements that werethe most preferred per cluster. RDC herds were divided into three different clusters: Robustness, Production & Health, and Production & Fertility. The Jersey herds were divided into the clusters Production & Fertility, Production & Robustness, and Survival. For RDC herds we found that the cluster Production & Fertility had the highest percentage of organic farmers (50%), the lowest percentage of crossbreeding and the lowest yields and herd size. The cluster Robustness had the highest yields and the highest prevalence of udder disorders, and these farmers ranked production traits very low and mastitis very high. For Jersey herds, the percentage of dead cows was highest for the cluster Survival. This cluster also ranked mastitis the highest compared to the other clusters, and contained herds with the highest prevalence of udder disorders. These results suggest that farmers choose to improve traits that are more problematic in their herds. This study shows that heterogeneity exists in farmers’ preferences for trait improvements and that some herd characteristics can be linked to the different farmer clusters. The results from this study give a strong basis for setting up customized indexes or breeding goals for the different farmer clusters, which might increase the uptake of genetic merit.

Today, almost all reference populations consist of progeny tested bulls. However, older progeny tested bulls do not have reliable estimated breeding values (EBV) for new traits. Thus, to be able to select for these new traits, it is necessary to build a reference population. We used a deterministic prediction model to test the hypothesis that the value of cows in reference populations depends on the availability of phenotypic records. To test the hypothesis, we investigated different strategies of building a reference population for a new functional trait over a 10-year period. The trait was either recorded on a large scale (30 000 cows per year) or on a small scale (2000 cows per year). For large-scale recording, we compared four scenarios where the reference population consisted of 30 sires; 30 sires and 170 test bulls; 30 sires and 2000 cows; or 30 sires, 2000 cows and 170 test bulls in the first year with measurements of the new functional trait. In addition to varying the make-up of the reference population, we also varied the heritability of the trait (h2 = 0.05 v. 0.15). The results showed that a reference population of test bulls, cows and sires results in the highest accuracy of the direct genomic values (DGV) for a new functional trait, regardless of its heritability. For small-scale recording, we compared two scenarios where the reference population consisted of the 2000 cows with phenotypic records or the 30 sires of these cows in the first year with measurements of the new functional trait. The results showed that a reference population of cows results in the highest accuracy of the DGV whether the heritability is 0.05 or 0.15, because variation is lost when phenotypic data on cows are summarized in EBV of their sires. The main conclusions from this study are: (i) the fewer phenotypic records, the larger effect of including cows in the reference population; (ii) for small-scale recording, the accuracy of the DGV will continue to increase for several years, whereas the increases in the accuracy of the DGV quickly decrease with large-scale recording; (iii) it is possible to achieve accuracies of the DGV that enable selection for new functional traits recorded on a large scale within 3 years from commencement of recording; and (iv) a higher heritability benefits a reference population of cows more than a reference population of bulls.

Selection progress must be carefully balanced against the conservation of genetic variation in small populations of local breeds. Well-defined breeding programs with specified selection traits are rare in local pig breeds. Given the small population size, the focus is often on the management of genetic diversity. However, in local breeds, optimum contribution selection can be applied to control the rate of inbreeding and to avoid reduced performance in traits with high market value. The aim of this study was to assess the extent to which a breeding program aiming for improved product quality in a small local breed would be feasible. We used stochastic simulations to compare 25 scenarios. The scenarios differed in size of population, selection intensity of boars, type of selection (random selection, truncation selection based on BLUP breeding values, or optimum contribution selection based on BLUP breeding values), and heritability of the selection trait. It was assumed that the local breed is used in an extensive system for a high-meat-quality market. The simulations showed that in the smallest population (300 female reproducers), inbreeding increased by 0.8% when selection was performed at random. With optimum contribution selection, genetic progress can be achieved that is almost as great as that with truncation selection based on BLUP breeding values (0.2 to 0.5 vs. 0.3 to 0.5 genetic SD, P < 0.05), but at a considerably decreased rate of inbreeding (0.7 to 1.2 vs. 2.3 to 5.7%, P < 0.01). This confirmation of the potential utilization of OCS even in small populations is important in the context of sustainable management and the use of animal genetic resources.

The aim of this study was to test whether the use of X-semen in a dairy cattle population using genomic selection (GS) and multiple ovulation and embryo transfer (MOET) increases the selection intensity on cow dams and thereby the genetic gain in the entire population. Also, the dynamics of using different types of sexed semen (X, Y or conventional) in the nucleus were investigated. The stochastic simulation study partly supported the hypothesis as the genetic gain in the entire population was elevated when X-semen was used in the production population as GS exploited the higher selection intensity among heifers with great accuracy. However, when MOET was applied, the effect was considerably diminished as was the exchange of females between the nucleus and the production population, thus causing modest genetic profit from using X-sorted semen in the production population. In addition, the effect of using sexed semen on the genetic gain was very small compared with the effect of MOET and highly dependent on whether cow dams or bull dams were inseminated with sexed semen and on what type of semen that was used for the bull dams. The rate of inbreeding was seldom affected by the use of sexed semen. However, when all young bull candidates were born following MOET, the results showed that the use of Y-semen in the breeding nucleus tended to decrease the rate of inbreeding as it enabled GS to increase within-family selection. This implies that the benefit from using sexed semen in a modern dairy cattle breeding scheme applying both GS and MOET may be found in its beneficial effect on the rate of inbreeding.

We tested the following hypotheses: (i) breeding schemes with genomic selection are superior to breeding schemes without genomic selection regarding annual genetic gain of the aggregate genotype (ΔG(AG) ), annual genetic gain of the functional traits and rate of inbreeding per generation (ΔF), (ii) a positive interaction exists between the use of genotypic information and a short generation interval on ΔG(AG) and (iii) the inclusion of an indicator trait in the selection index will only result in a negligible increase in ΔG(AG) if genotypic information about the breeding goal trait is known. We examined four breeding schemes with or without genomic selection and with or without intensive use of young bulls using pseudo-genomic stochastic simulations. The breeding goal consisted of a milk production trait and a functional trait. The two breeding schemes with genomic selection resulted in higher ΔG(AG) , greater contributions of the functional trait to ΔG(AG) and lower ΔF than the two breeding schemes without genomic selection. Thus, the use of genotypic information may lead to more sustainable breeding schemes. In addition, a short generation interval increases the effect of using genotypic information on ΔG(AG) . Hence, a breeding scheme with genomic selection and with intensive use of young bulls (a turbo scheme) seems to offer the greatest potential. The third hypothesis was disproved as inclusion of genomically enhanced breeding values (GEBV) for an indicator trait in the selection index increased ΔG(AG) in the turbo scheme. Moreover, it increased the contribution of the functional trait to ΔG(AG) , and it decreased ΔF. Thus, indicator traits may still be profitable to use even when GEBV for the breeding goal traits are available.

Using stochastic simulation, the effect of using sexed semen to cow dams (CD) in a dairy cattle breeding scheme, with or without use of multiple ovulation and embryo transfer (MOET) to bull dams (BD), on annual genetic gain at the population level was examined. Three levels of sexed semen were combined with three levels of MOET: no sexed semen, sexed semen to the best CD and sexed semen to all heifers, combined with no MOET, MOET on all BD and MOET randomly on 20% of the BD. In total, nine scenarios were compared. The simulated population was monitored for 30 years and included 450 herds with 100 cows each. Each year 50 young bulls (YB), 10 active sires and 215 BD were selected on best linear unbiased prediction estimated breeding values by truncation selection across the simulated population, and the YB were tested within the population. Use of sexed semen alone gave a positive increase in the annual genetic gain of 2.1% when used on the best CD and 2.7% when used on all heifers, but only the latter was statistically significant. The increased annual genetic gain was caused by a larger contribution from the CD to the BD. Use of sexed semen together with MOET on BD increased the annual genetic gain by 1.8-2.5% compared with schemes without sexed semen and MOET on all BD. Performing MOET on all BD enables selection of offspring with high Mendelian deviations, which increase the annual genetic gain. Use of sexed semen decreased the genetic lag between the sires and the CD by 12-14% when used on the best CD and by 6% when used to all heifers. The decrease in the genetic lag is caused by the increased selection intensity of the cow dams.

Summary Genetic parameters were estimated for protein yield (PY), clinical mastitis (CM), somatic cell score, number of inseminations (NI) and days from calving to first insemination (CFI) in first-parity Swedish Red cows by series of tri-variate linear animal models. The heritability of PY was moderate (0.34 ± 0.004), and the heritabilities of the functional traits were all low (0.014 ± 0.001–0.14 ± 0.004). The genetic correlation between CM and CFI (0.38 ± 0.05) was stronger than the correlation between CM and NI (0.05 ± 0.06), perhaps because CM and CFI usually are observed in early lactation when the cow is likely to be in negative energy balance, whereas NI generally is recorded when the cow is not in negative energy balance any more. The genetic correlation between NI and CFI was very close to zero (−0.002 ± 0.05), indicating that these two fertility traits have different genetic backgrounds. All genetic correlations between PY and the functional traits were moderate and unfavourable, ranging from 0.22 ± 0.02 to 0.47 ± 0.03. In addition, the effect of including genetic and phenotypic correlations between the trait groups milk production, udder health and female fertility on the accuracy of the selection index was quantified for a heifer, a cow and a proven bull. The difference between the accuracy obtained by multi-trait and single-trait evaluations was largest for the cow (0.012) and small for the heifer and the bull (0.006 and 0.004) because the phenotype of the cow for one trait could assist in predicting the Mendelian sampling term for a correlated trait.

Pedigree information of 852,443 registered Holstein cows and bulls, collected by the Animal Breeding Center of Iran from 1971 to 2007, was used to calculate inbreeding coefficients and their effect on production, reproduction, somatic cell count, calving ease, and longevity traits. The average inbreeding coefficient for the entire population was 2.90%, ranging from zero to 47.03%. The rates of inbreeding from 1989 to 2007 were 0.22 and 0.15% per year for females and males, respectively. The rates were higher after 2000, being 0.31 and 0.21% per year for females and males, respectively. Inbreeding had a deleterious effect on most traits. For the first 3 lactations, the inbreeding depression per 1% increase in inbreeding was -18.72, -16.19, and -27.38 kg for milk yield, -0.443, -0.367, and -0.690 kg for fat yield, and -0.476, -0.425, and -0.66 kg for protein yield, respectively. For all reproductive traits, the observed undesirable effect of inbreeding was not significant, except for the calving interval (0.53 d per 1% increase in inbreeding) in the third parity and age at first calving (0.45 d per 1% increase in inbreeding). Calving ease in heifers and cows was significantly influenced by the inbreeding of the dam, indicating that highly inbred cows had a higher incidence of difficult calvings. The estimate of inbreeding depression for somatic cell score was low and significant only for the third lactation. However, animals with high inbreeding coefficient tended to have higher somatic cell scores than animals with low inbreeding coefficients. For type traits, the influence of inbreeding was significant only for stature, chest width, body depth, size, rear udder height, suspensory ligament, udder depth, and front and rear teat placement. Cows with high levels of inbreeding coefficient were at higher relative risk of being culled. Udgivelsesdato: July Pedigree information of 852,443 registered Holstein cows and bulls, collected by the Animal Breeding Center of Iran from 1971 to 2007, was used to calculate inbreeding coefficients and their effect on production, reproduction, somatic cell count, calving ease, and longevity traits. The average inbreeding coefficient for the entire population was 2.90%, ranging from zero to 47.03%. The rates of inbreeding from 1989 to 2007 were 0.22 and 0.15% per year for females and males, respectively. The rates were higher after 2000, being 0.31 and 0.21% per year for females and males, respectively. Inbreeding had a deleterious effect on most traits. For the first 3 lactations, the inbreeding depression per 1% increase in inbreeding was -18.72, -16.19, and -27.38 kg for milk yield, -0.443, -0.367, and -0.690 kg for fat yield, and -0.476, -0.425, and -0.66 kg for protein yield, respectively. For all reproductive traits, the observed undesirable effect of inbreeding was not significant, except for the calving interval (0.53 d per 1% increase in inbreeding) in the third parity and age at first calving (0.45 d per 1% increase in inbreeding). Calving ease in heifers and cows was significantly influenced by the inbreeding of the dam, indicating that highly inbred cows had a higher incidence of difficult calvings. The estimate of inbreeding depression for somatic cell score was low and significant only for the third lactation. However, animals with high inbreeding coefficient tended to have higher somatic cell scores than animals with low inbreeding coefficients. For type traits, the influence of inbreeding was significant only for stature, chest width, body depth, size, rear udder height, suspensory ligament, udder depth, and front and rear teat placement. Cows with high levels of inbreeding coefficient were at higher relative risk of being culled.

We used computer simulations to investigate to what extent true inbreeding, i.e. identity-by-descent, is affected by the use of marker-assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h(2) = 0.04 vs. 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (alpha = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome consisted of two chromosomes of 100 cM each in addition to a polygenic component. On chromosome 1, a biallelic QTL as well as 4 markers were simulated in linkage disequilibrium. Chromosome 2 was selectively neutral. The results showed that, while reducing pedigree estimated inbreeding, MAS and GAS did not always reduce true inbreeding at the QTL relative to BLUP. MAS and GAS differs from BLUP by increasing the weight on Mendelian sampling terms and thereby lowering inbreeding, while increasing the fixation rate of the favourable QTL allele and thereby increasing inbreeding. The total outcome in terms of inbreeding at the QTL depends on the balance between these two effects. In addition, as a result of hitchhiking, MAS results in extra inbreeding in the region surrounding QTL, which could affect the overall genomic inbreeding.

We reasoned that mating animals by minimising the covariance between ancestral contributions (MCAC mating) will generate less inbreeding and at least as much genetic gain as minimum-coancestry mating in breeding schemes where the animals are truncation-selected. We tested this hypothesis by stochastic simulation and compared the mating criteria in hierarchical and factorial breeding schemes, where the animals were selected based on breeding values predicted by animal-model BLUP. Random mating was included as a reference-mating criterion. We found that MCAC mating generated 4% to 8% less inbreeding than minimum-coancestry mating in the hierarchical and factorial breeding schemes without any loss in genetic gain. Moreover, it generated upto 28% less inbreeding and about 3% more genetic gain than random mating. The benefits of MCAC mating over minimum-coancestry mating are worthwhile because they can be achieved without extra costs or practical constraints. MCAC mating merely uses pedigree information to pair the animals more appropriately and is clearly a worthy alternative to minimum-coancestry mating and probably any other mating criterion. We believe, therefore, that MCAC mating should be used in breeding schemes where pedigree information is available. Udgivelsesdato: October We reasoned that mating animals by minimising the covariance between ancestral contributions (MCAC mating) will generate less inbreeding and at least as much genetic gain as minimum-coancestry mating in breeding schemes where the animals are truncation-selected. We tested this hypothesis by stochastic simulation and compared the mating criteria in hierarchical and factorial breeding schemes, where the animals were selected based on breeding values predicted by animal-model BLUP. Random mating was included as a reference-mating criterion. We found that MCAC mating generated 4% to 8% less inbreeding than minimum-coancestry mating in the hierarchical and factorial breeding schemes without any loss in genetic gain. Moreover, it generated upto 28% less inbreeding and about 3% more genetic gain than random mating. The benefits of MCAC mating over minimum-coancestry mating are worthwhile because they can be achieved without extra costs or practical constraints. MCAC mating merely uses pedigree information to pair the animals more appropriately and is clearly a worthy alternative to minimum-coancestry mating and probably any other mating criterion. We believe, therefore, that MCAC mating should be used in breeding schemes where pedigree information is available.

The aim of this study was to assess the importance of pedigree depth when performing optimal contribution selection as implemented in the software program EVA. This was done by applying optimal genetic contribution in the breeding program of the major Danish Dairy breed Danish Holstein. In the analyses earlier breeding decisions were considered by including all AI waiting- and young bulls and contract matings. Twenty potential sires, 2169 potential dams, 1421 AI-bulls and 754 contract matings plus pedigree animals were included. Results showed that the outcome was very dependent on quality of pedigree, also for information going more than 25 years (5-7 generations) back. The analyses showed that EVA works satisfactorily as a management tool for planning of breeding schemes with respect to contributions of sires of sons at population level in maximising the genetic gain, while controlling the increase in future inbreeding. The more weight put on the average additive genetic relationship in next generation relative to genetic merit, the lower the average merit of the matings, and the lower average additive genetic relationship among the chosen matings and the present breeding animals. Furthermore more weight on average additive genetic relationship gives a more diverse use of sires of sons. Given the potential sires and dams the average additive genetic relationship among the selected matings and the present breeding animals can be reduced from 0.1621 to 0.1495 at the cost of 0.7 genetic S.D. units at the total merit index. This reduction was obtained when selection was only on reduction of average relationship compared to selection only on genetic merit. Optimal genetic contribution selection is a promising tool for managing breeding schemes for populations facing inbreeding problems, such as Danish Holstein and other dairy breeds. However sufficient pedigree information is a necessity. The aim of this study was to assess the importance of pedigree depth when performing optimal contribution selection as implemented in the software program EVA. This was done by applying optimal genetic contribution in the breeding program of the major Danish Dairy breed Danish Holstein. In the analyses earlier breeding decisions were considered by including all AI waiting- and young bulls and contract matings. Twenty potential sires, 2169 potential dams, 1421 AI-bulls and 754 contract matings plus pedigree animals were included. Results showed that the outcome was very dependent on quality of pedigree, also for information going more than 25 years (5-7 generations) back. The analyses showed that EVA works satisfactorily as a management tool for planning of breeding schemes with respect to contributions of sires of sons at population level in maximising the genetic gain, while controlling the increase in future inbreeding. The more weight put on the average additive genetic relationship in next generation relative to genetic merit, the lower the average merit of the matings, and the lower average additive genetic relationship among the chosen matings and the present breeding animals. Furthermore more weight on average additive genetic relationship gives a more diverse use of sires of sons. Given the potential sires and dams the average additive genetic relationship among the selected matings and the present breeding animals can be reduced from 0.1621 to 0.1495 at the cost of 0.7 genetic S.D. units at the total merit index. This reduction was obtained when selection was only on reduction of average relationship compared to selection only on genetic merit. Optimal genetic contribution selection is a promising tool for managing breeding schemes for populations facing inbreeding problems, such as Danish Holstein and other dairy breeds. However sufficient pedigree information is a necessity.

The objective of this study was to analyze the development of inbreeding and estimate inbreeding depression in the Danish populations of 3 major meat type sheep breeds. The pedigrees contained 29,336 Texel, 22,838 Shropshire, and 11,487 Oxford Down. The rate of inbreeding was approximately 1% per generation for all breeds, but the rate of increase in co-ancestry was somewhat lower (0.45 to 0.71), indicating that more inbreeding has been accumulating than would be expected if mating was at random. Inbreeding depression for birth weight, ADG from birth until 2 mo, and litter size was estimated for all 3 breeds using a minimum of 15,000 records per trait and breed. All traits showed depression due to inbreeding of the animal itself. For most combinations of trait and breed, there was also a significant reduction of the phenotype due to inbreeding in the dam. The size of inbreeding depression was 1.2 to 2.6% of the mean, resulting in an increase in the inbreeding coefficient of the individual of 0.10, and estimates were similar for similar increases in maternal inbreeding. The rate of inbreeding in these breeds needs to be reduced in the future to avoid a further decline in birth weight, ADG, and litter size.

BackgroundWe tested the hypothesis that optimum-contribution selection (OCS) with restrictions imposed during optimisation realises most of the long-term genetic gain realised by OCS without restrictions.MethodsWe used stochastic simulation to estimate long-term rates of genetic gain realised by breeding schemes that applied OCS without and with restrictions imposed during optimisation, where long-term refers to generations 23 to 25 (approximately). Six restrictions were imposed. Five of these removed solutions from the solution space. The sixth removed records of selection decisions made at earlier selection times. We also simulated a conventional breeding scheme with truncation selection as a reference point. Generations overlapped, selection was for a single trait, and the trait was observed for all selection candidates prior to selection.ResultsOCS with restrictions realised 67 to 99% of the additional gain realised by OCS without restrictions, where additional gain was the difference in the long-term rates of genetic gain realised by OCS without restrictions and our reference point with truncation selection. The only exceptions were those restrictions that removed all solutions near the optimum solution from the solution space and the restriction that removed records of selection decisions made at earlier selection times. Imposing these restrictions realised only −12 to 46% of the additional gain.ConclusionsMost of the long-term genetic gain realised by OCS without restrictions can be realised by OCS with restrictions imposed during optimisation, provided the restrictions do not remove all solutions near the optimum from the solution space and do not remove records of earlier selection decisions. In breeding schemes where OCS cannot be applied optimally because of biological and logistical restrictions, OCS with restrictions provides a useful alternative. Not only does it realise most of the long-term genetic gain, OCS with restrictions enables OCS to be tailored to individual breeding schemes.

The objective of the present study was to examine whether genomic selection of females interacts with the use of reproductive technologies (RT) to increase annual monetary genetic gain (AMGG). This was tested using a factorial design with 3 factors: genomic selection of females (0 or 2,000 genotyped heifers per year), RT (0 or 50 donors selected at 14mo of age for producing 10 offspring), and 2 reliabilities of genomic prediction. In addition, different strategies for use of RT and how strategies interact with the reliability of genomic prediction were investigated using stochastic simulation by varying (1) number of donors (25, 50, 100, 200), (2) number of calves born per donor (10 or 20), (3) age of donor (2 or 14mo), and (4) number of sires (25, 50, 100, 200). In total, 72 different breeding schemes were investigated. The profitability of the different breeding strategies was evaluated by deterministic simulation by varying the costs of a born calf with reproductive technologies at levels of €500, €1,000, and €1,500. The results confirm our hypothesis that combining genomic selection of females with use of RT increases AMGG more than in a reference scheme without genomic selection in females. When the reliability of genomic prediction is high, the effect on rate of inbreeding (ΔF) is small. The study also demonstrates favorable interaction effects between the components of the breeder’s equation (selection intensity, selection accuracy, generation interval) for the bull dam donor path, leading to higher AMGG. Increasing the donor program and number of born calves to achieve higher AMGG is associated with the undesirable effect of increased ΔF. This can be alleviated, however, by increasing the numbers of sires without compromising AMGG remarkably. For the major part of the investigated donor schemes, the investment in RT is profitable in dairy cattle populations, even at high levels of costs for RT.

Background The short-term impact of using different genomic prediction (GP) models in genomic selection has been intensively studied, but their long-term impact is poorly understood. Furthermore, long-term genetic gain of genomic selection is expected to improve by using Jannink’s weighting (JW) method, in which rare favourable marker alleles are upweighted in the selection criterion. In this paper, we extend the JW method by including an additional parameter to decrease the emphasis on rare favourable alleles over the time horizon, with the purpose of further improving the long-term genetic gain. We call this new method dynamic weighting (DW). The paper explores the long-term impact of different GP models with or without weighting methods. Methods Different selection criteria were tested by simulating a population of 500 animals with truncation selection of five males and 50 females. Selection criteria included unweighted and weighted genomic estimated breeding values using the JW or DW methods, for which ridge regression (RR) and Bayesian lasso (BL) were used to estimate marker effects. The impacts of these selection criteria were compared under three genetic architectures, i.e. varying numbers of QTL for the trait and for two time horizons of 15 (TH15) or 40 (TH40) generations. Results For unweighted GP, BL resulted in up to 21.4% higher long-term genetic gain and 23.5% lower rate of inbreeding under TH40 than RR. For weighted GP, DW resulted in 1.3 to 5.5% higher long-term gain compared to unweighted GP. JW, however, showed a 6.8% lower long-term genetic gain relative to unweighted GP when BL was used to estimate the marker effects. Under TH40, both DW and JW obtained significantly higher genetic gain than unweighted GP. With DW, the long-term genetic gain was increased by up to 30.8% relative to unweighted GP, and also increased by 8% relative to JW, although at the expense of a lower short-term gain. Conclusions Irrespective of the number of QTL simulated, BL is superior to RR in maintaining genetic variance and therefore results in higher long-term genetic gain. Moreover, DW is a promising method with which high long-term genetic gain can be expected within a fixed time frame. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0101-0) contains supplementary material, which is available to authorized users.

The purpose of this study was to monitor current and predict future rates of inbreeding in the Danish dairy breeds. Calves born from 1999 until 2003 and registered as Danish Holstein (1,883,983), Danish Jersey (336,966), or Danish Red (261,047) were reference populations. Average complete generation equivalent was approximately 7. For calves born in 2003, average inbreeding was 3.9, 3.4, and 1.4% for Holstein, Jersey, and Danish Red, respectively. In recent years, effective population sizes were 49, 53, and 47, respectively. Based on coancestry statistics, future effective population sizes will be 43, 42, and 51, respectively. The effective number of founders, effective number of ancestors, and effective number of founder genomes were calculated. These measures of genetic diversity were all low for Holstein and Jersey and somewhat larger for Danish Red. The most important ancestors of Danish Holstein were Elevation (13.8%), Chief (10.9%), and Bell (8.5%). The most important ancestor of Danish Red was Momentum (9.4%), a Red Holstein-Friesian. The most important ancestor for Danish Jersey was FYN Lemvig (12.1%) with a large number of progeny in the reference population. The results of this study indicate the necessity for active management of the rate of inbreeding in the future.