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We compared the effects of using three genomic relationship matrices and a pedigree based relationship matrix for optimum contribution selection (OCS) with a constraint on number of sires selected, on genetic gain, kinship and additive genetic variance. This was done by simulating a genomically selected small dairy cattle population. The cattle population underwent genomic selection for 11 generations. In total, 6,000 females and 2,500 males were genotyped per generation. We compared a pedigree-based matrix as well as VanRaden’smethod 1 to construct genomic relationship matrices using either allele frequencies of the base population, of the current generation, or allele frequencies among all genotyped animals. The pedigree-based OCS resulted in higher kinship and more loss of genetic variance than using genomic OCS based on base population frequencies. Thus, genomic OCS with genomic relationship matrices using base population frequencies are preferable compared with pedigreebased OCS.

ADAM software is a stochastic simulation tool intended to model a variety of selective breeding schemes for animals and plants. ADAM can be used to generate genetic data to test statistical tools and the implemented algorithms can evaluate breeding schemes by tracing genetic change under different selective breeding scenarios. In practice, crossbreeding has been used extensively in beef cattle, pig, and poultry production systems, and the use of crossbreeding isincreasing in dairy cattle breeding system due to changes in the dairy market. The aim of this paper was to present a new crossbreeding feature of the ADAM software. The software was updated to include multiple purebred populations and crossbred populations in animals with different genetic backgrounds considering both additive and dominance effects. Our simulated results showed that, by using the new features in ADAM, breeding organizations can optimizetheir design of crossbreeding schemes and, thereby, optimize profitability.

Meiotic recombination is an important evolutionary mechanism that breaks up linkages between loci and creates novel haplotypes for selection to act upon. Understanding the genetic control of variation in recombination rates is therefore of great interest in both natural and domestic breeding populations. In this study, we used pedigree information and medium-density (∼50K) genotyped data in a large cattle (Bos taurus) breeding population in Norway (Norwegian Red cattle) to investigate recombination rate variation between sexes and individual animals. Sex-specific linkage mapping showed higher rates in males than in females (total genetic length of autosomes = 2,492.9 cM in males and 2,308.9 cM in females). However, distribution of recombination along the genome showed little variation between males and females compared with that in other species. The heritability of autosomal crossover count was low but significant in both sexes (h2 = 0.04 and 0.09 in males and females, respectively). We identified 2 loci associated with variation in individual crossover counts in female, one close to the candidate gene CEP55 and one close to both MLH3 and NEK9. All 3 genes have been associated with recombination rates in other cattle breeds. Our study contributes to the understanding of how recombination rates are controlled and how they may vary between closely related breeds as well as between species.

The Norwegian coastal goat is a national and endangered breed. Coastal goat populations are mainly divided with a large mainland and two small island populations. The objective of this study is to describe genetic diversity in the feral Skorpa island population and its relationship to the mainland coastal goat population (Selje) using the Norwegian milk goat population as a reference. Analyses were based on 96 samples genotyped by the CaprineSNP50 Beadchip from three populations; 7 Skorpa (SK), 37 Selje (SE) and 52 Norwegian milk goats (MG). The SK population had significantly less genetic variation and higher levels of inbreeding than the two other populations. It was more distant from the two mainland populations than they were from each other. The marginal contribution of the SK population to genetic diversity was small. Means of introducing genetic diversity into the SK population should be considered if the population is prioritized for conservation.

The Nordic countries are committed to conserving farm animal genetic diversity. National reports on phenotypic and genetic characterization point towards a lack of characterization for many Nordic ...

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.

International audience; AbstractBackgroundWe tested the premise that optimum-contribution selection with pedigree relationships to control inbreeding (POCS) realises at least as much true genetic gain as optimum-contribution selection with genomic relationships (GOCS) at the same rate of true inbreeding.MethodsWe used stochastic simulation to estimate rates of true genetic gain realised by POCS and GOCS at a 0.01 rate of true inbreeding in three breeding schemes with best linear unbiased predictions of breeding values based on pedigree (PBLUP) and genomic (GBLUP) information. The three breeding schemes differed in number of matings and litter size. Selection was for a single trait with a heritability of 0.2. The trait was controlled by 7702 biallelic quantitative-trait loci (QTL) that were distributed across a 30-M genome. The genome contained 54,218 biallelic markers that were used in GOCS and GBLUP. A total of 6012 identity-by-descent loci were placed across the genome in base populations. Unique alleles at these loci were used to calculate rates of true inbreeding. Breeding schemes were run for 10 discrete generations. Selection candidates were genotyped and phenotyped before selection.ResultsPOCS realised more true genetic gain than GOCS at a 0.01 rate of true inbreeding in all combinations of breeding scheme and prediction method. POCS realised 14 to 33% more true genetic gain than GOCS with PBLUP in the three breeding schemes. It realised 1.5 to 5.7% more true genetic gain than GOCS with GBLUP.ConclusionsPOCS realised more true genetic gain than GOCS because it managed expected genetic drift without restricting selection at QTL. By contrast, GOCS penalised changes in allele frequencies at markers that were generated by genetic drift and selection. Because these marker alleles were in linkage disequilibrium with QTL alleles, GOCS restricted changes in allele frequencies at QTL. This provides little incentive to use GOCS and highlights that we have more to learn before we can control inbreeding using genomic relationships in selective-breeding schemes. Until we can do so, POCS remains a worthy method of optimum-contribution selection because it realises more true genetic gain than GOCS at the same rate of true inbreeding.

The aim of this study was to evaluate genetic diversity within and between lines at the Norwegian live poultry gene bank as well as assessing the conservation value in an international context. Eight lines including the national breed, Jærhøns, were genotyped with the 600K Affymetrix® Axiom® Chicken Genotyping Array. The white egg layers were generally more inbred than the brown layers. Comparative analyses were carried out with 72 international populations of different origins. The lines that were last bred for commercial production in Norway, Norbrid, are clearly separated from the rest of the international set and more closely related to the current commercial lines. The brown egg layer Norbrid 7 has the highest relative contribution to genetic diversity. The Norwegian genebank lines are of conservation value in a national and international perspective, as they all add genetic diversity to the global set.

BackgroundPhenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships.ResultsUsing genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes.ConclusionsThe use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

BackgroundThe increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment.MethodsA pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only.ResultsSeveral indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds.ConclusionsThe G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment.A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only.Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds.The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

Displacement and admixture are threatening the survival and genetic integrity of the European dark bee, Apis mellifera mellifera. Studies on the phenotype-genotype map and genotype by environment interactions in honey bees are demonstrating that variation at subspecies level exists and is worth conserving. SNP-based tools for monitoring genetic integrity in bees have been developed, but are not yet widely used by European dark bee breeders. We used a panel of ancestry informative SNP markers to assess the level of admixture in Nordic dark bee breeding stocks. We found that bee breeders falsely classified admixed stocks based on morphometry as purebred and vice versa. Even though most Nordic A. m. mellifera breeding stocks have low proportions of C-lineage ancestry, we recommend to incorporate genotyping in Nordic dark bee breeding programmes to ensure that minimal genetic diversity is lost, while the genetic integrity of the subspecies is maintained. We are greatly indebted to the participating bee breeders for allowing us to include their samples in this study and for providing the samples. We sincerely thank Dr. Stefan Fuchs for providing the morphological reference data. This project would not have been possible without the help of João Costa from the Genomics Unit of the Instituto Gulbenkian de Ciência, Portugal, who carried out the genotyping for us. We thank the two anonymous reviewers who helped greatly to improve the manuscript. This research was partly funded by the Norwegian Agriculture Agency 17/3472. info:eu-repo/semantics/publishedVersion

To improve genetic gain of breeding programs for village poultry production, breeding schemes with observations obtained in village production systems using individual (VIO) and group recording (VGO) were examined under different levels of genotype-by-environment-interactions (GxE). GxE was modeled by varying the correlation between traits measured in the breeding station and village environments for bodyweight (r g_BW ) and egg production (r g_EP ). Relative and absolute genetic gains obtained from VIO and VGO were used for comparison between the schemes. Results showed that village observations significantly improved genetic gains compared to the scheme without birds tested in the village. The improvement was only slightly larger with individual observations than with group observations. Higher r g_BW and r g_EP led to lower relative genetic gain, but a higher absolute gain of VIO and VGO. It is recommended to apply a breeding scheme using group recording of village performance when strong GxE in breeding for village poultry is expected.

Genetic rescue, outcrossing with individuals from a related population, is used to augment genetic diversity in populations threatened by severe inbreeding and extinction. The endangered Norwegian Lundehund dog underwent at least two severe bottlenecks in the 1940s and 1960s that each left only five inbred dogs, and the approximately 1500 dogs remaining world-wide today appear to descend from only two individuals. The Lundehund has a high prevalence of a gastrointestinal disease, to which all remaining dogs may be predisposed. Outcrossing is currently performed with three Nordic Spitz breeds: Norwegian Buhund, Icelandic Sheepdog, and Norrbottenspets. Examination of single nucleotide polymorphism (SNP) genotypes based on 165K loci in 48 dogs from the four breeds revealed substantially lower genetic diversity for the Lundehund (HE 0.035) than for other breeds (HE 0.209–0.284). Analyses of genetic structure with > 15K linkage disequilibrium-pruned SNPs showed four distinct genetic clusters. Pairwise FST values between Lundehund and the candidate breeds were highest for Icelandic Sheepdog, followed by Buhund and Norrbottenspets. We assessed the presence of outlier loci among candidate breeds and examined flanking genome regions (1 megabase) for genes under possible selection to identify potential adaptive differences among breeds; outliers were observed in flanking regions of genes associated with key functions including the immune system, metabolism, cognition and physical development. We suggest crossbreeding with multiple breeds as the best strategy to increase genetic diversity for the Lundehund and to reduce the incidence of health problems. For this project, the three candidate breeds were first selected based on phenotypes and then subjected to genetic investigation. Because phenotypes are often paramount for domestic breed owners, such a strategy could provide a helpful approach for genetic rescue and restoration of other domestic populations at risk, by ensuring the involvement of owners, breeders and managers at the start of the project. © 2017 Stronen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The Norwegian Lundehund is a highly endangered native dog breed. Low fertility and high frequency predisposition to intestinal disorder imply inbreeding depression. We assessed the genetic diversity of the Lundehund population from pedigree data and evaluated the potential of optimal contribution selection and cross-breeding in the long-term management of the Lundehund population. The current Norwegian Lundehund population is highly inbred and has lost 38.8% of the genetic diversity in the base population. Effective population size estimates varied between 13 and 82 depending on the method used. Optimal contribution selection alone facilitates no improvement in the current situation in the Lundehund due to the extremely high relatedness of the whole population. Addition of (replacement with) 10 breeding candidates of foreign breed to 30 Lundehund breeders reduced the parental additive genetic relationship by 40–42% (48–53%). Immediate actions are needed to increase the genetic diversity in the current Lundehund population. The only option to secure the conservation of this rare breed is to introduce individuals from foreign breeds as breeding candidates. © 2017 Kettunen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This investigation presents results from a genetic characterization of 5 Danish dog breeds genotyped on the CanineHD BeadChip microarray with 170,000 SNP. The breeds investigated were 1) Danish Spitz (DS; n=8), 2) Danish-Swedish Farm Dog (DSF; n=18), 3) Broholmer (BR; n=22), 4) Old Danish Pointing Dog (ODP; n=24), and 5) Greenland Dog (GD; n=23). The aims of the investigation were to characterize the genetic profile of the abovementioned dog breeds by quantifying the genetic differentiation among them and the degree of genetic homogeneity within breeds. The genetic profile was determined by means of principal component analysis (PCA) and through a Bayesian clustering method. Both the PCA and the Bayesian clustering method revealed a clear genetic separation of the 5 breeds. The level of genetic variation within the breeds varied. The expected heterozygosity (HE) as well as the degree of polymorphism (P%) ranked the dog breeds in the order DS>DSF>BR>ODP>GD. Interestingly, the breed with a tenfold higher census population size compared to the other breeds, the Greenland Dog, had the lowest within-breed genetic variation, emphasizing that census size is a poor predictor of genetic variation. The observed differences in variation among and within dog breeds may be related to factors such as genetic drift, founder effects, genetic admixture, and population bottlenecks. We further examined whether the observed genetic patterns in the 5 dog breeds can be used to design breeding strategies for the preservation of the genetic pool of these dog breeds.

Heritability is a central element in quantitative genetics. New molecular markers to assess genetic variance and heritability are continually under development. The availability of molecular single nucleotide polymorphism (SNP) markers can be applied for estimation of variance components and heritability on population, where relationship information is unknown. In this study, we evaluated the capabilities of two Bayesian genomic models to estimate heritability in simulated populations. The populations comprised different family structures of either no or a limited number of relatives, a single quantitative trait, and with one of two densities of SNP markers. All individuals were both genotyped and phenotyped. Results illustrated that the two models were capable of estimating heritability, when true heritability was 0.15 or higher and populations had a sample size of 400 or higher. For heritabilities of 0.05, all models had difficulties in estimating the true heritability. The two Bayesian models were compared with a restricted maximum likelihood (REML) approach using a genomic relationship matrix. The comparison showed that the Bayesian approaches performed equally well as the REML approach. Differences in family structure were in general not found to influence the estimation of the heritability. For the sample sizes used in this study, a 10-fold increase of SNP density did not improve precision estimates compared with set-ups with a less dense distribution of SNPs. The methods used in this study showed that it was possible to estimate heritabilities on the basis of SNPs in animals with direct measurements. This conclusion is valuable in cases when quantitative traits are either difficult or expensive to measure.

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.

Article, In the past decade, biobanking has fuelled great scientific advances in the human medical sector. Well-established domesticated animal biobanks and integrated networks likewise harbour immense potential for great scientific advances with broad societal impacts, which are currently not being fully realised. Political and scientific leaders as well as journals and ethics committees should help to ensure that we are well equipped to meet future demands in livestock production, animal models, and veterinary care of companion animals., published version, http://purl.org/eprint/status/PeerReviewed

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.

Social interactions among individuals are abundant, both in wild and in domestic populations. With social interactions, the genes of an individual may affect the trait values of other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. Most IGE models assume that individuals interact equally to all group mates irrespective of relatedness. Kin selection theory, however, predicts that an individual will interact differently with family members versus non-family members. Here, we investigate kin- and sex-specific non-genetic social interactions in group-housed mink. Furthermore, we investigated whether systematic non-genetic interactions between kin or individuals of the same sex influence the estimates of genetic parameters. As a second objective, we clarify the relationship between estimates of the traditional IGE model and a family-based IGE model proposed in a previous study. Our results indicate that male siblings in mink show different non-genetic interactions than female siblings in mink and that this may impact the estimation of genetic parameters. Moreover, we have shown how estimates from a family-based IGE model can be translated to the ordinary direct-indirect model and vice versa. We find no evidence for genetic differences in interactions among related versus unrelated mink.

Surveys among wild marine fish have revealed occurrence of viral haemorrhagic septicaemia virus (VHSV) infections in a high number of diverse fish species. In marine aquaculture of rainbow trout, preying on invading wild fish might thus be a risk factor for introduction and adaptation of VHSV and subsequent disease outbreaks. Our objective was to determine whether an oral transmission route for VHSV in rainbow trout exists. Juvenile trout were infected through oral, waterborne and cohabitation transmission routes, using a recombinant virus strain harbouring Renilla luciferase as reporter gene. Viral replication in stomach and kidney tissue was detected through bioluminescence activity of luciferase and qRT-PCR. Replication was detected in both tissues, irrespective of transmission route. Replication patterns, however, differed among transmission routes. In trout infected through oral transmission, replication was detected in the stomach prior to kidney tissue. In trout infected through waterborne or cohabitation transmission, replication was detected in kidney prior to stomach or in both tissues simultaneously. We demonstrate the existence of an oral transmission route for VHSV in rainbow trout. This implies that preying on invading infected wild fish is a risk factor for introduction of VHSV into marine cultures of rainbow trout.

An experiment was conducted to investigate the effect of short-term selection for 4 week breast weight (4wk BRW), and to estimate genetic parameters of body weight, and carcass traits. A selection (S) line and control (C) line was randomly selected from a base population. Data were collected over two consecutive hatches for four generations. A total of 1 135 records from 156 sires and 218 dams were used to estimate the genetic parameters. The genetic improvement of 4wk BRW was 3.5, 2.7 and 0.6 g in generation 2, 3 and 4, respectively. The estimated heritability by using pedigree information was 0.35±0.06. There were a significant difference for BW, and carcass weights but not for carcass percent components between lines (P

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.

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.

An elimination programme was carried out to remove the dominant Rendement Napole mutation (RN(-) ) from Danish Hampshire pigs. We reasoned that during and after the elimination of the RN(-) allele, genetic gain of production traits decreased while rate of inbreeding in the population increased compared to the period prior to elimination. The hypothesis was tested by estimating the genetic gain in seven production traits and measuring the rate of inbreeding in the population prior to and during the elimination period. Genetic gain was reduced for quantitative traits daily gain(30-100 kg) and feed conversion ratio, while gain for ultimate-pH, lean meat percentage and slaughter loss were increased slightly. There were no changes in genetic gain for daily gain(birth-30 kg) and conformation. RN polymorphism affected several of the quantitative traits. The RN(-) mutation had a dominant effect on the traits daily gain(birth-30 kg) , daily gain(30-100 kg) , slaughter loss, lean meat percentage and ultimate-pH. It exhibited overdominance for feed conversion ratio and additive effect for conformation. Rate of inbreeding decreased during the elimination of RN(-) . Our findings indicate that the consequences of the elimination programme were not as serious as were feared and that a carefully designed preselection strategy may avoid unacceptable loss of genetic gain and excessive loss of genetic variation.

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.

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.

Genetic parameters for carcass traits and ultrasonic scanning measurements were estimated for Danish Texel and Shropshire, the most common sheep breeds in Denmark. Data used in this study were collected from 1990 to 2005 by the Danish Agricultural Advisory Service. A multivariate animal model was used for estimation of (co)variance components for muscle depth (MD), fat depth (FD), carcass conformation score (FORM) and carcass fatness (FAT). Heritabilities for MD were 0.29 and 0.28 for Texel and Shropshire, respectively. Diverging heritabilities were found for FD - 0.39 for Texel and 0.12 for Shropshire. Carcass conformation was highly heritable, 0.45 for Texel and 0.36 for Shropshire. The heritability for FAT was 0.11 for Texel and 0.19 for Shropshire. Genetic correlations between MD and FORM, and FD and FAT were positive and favourable. It was concluded that ultrasound measures on live animals are good predictors for final carcass classification, and can be useful in a breeding programme where carcass traits are included

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.

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.

Summary Genetic contributions were first formalized in 1958 by James and McBride (Journal of Genetics, 56, 55–62) and have since been shown to provide a unifying framework for theories of gain and inbreeding. As such they have underpinned the development of methods that provide the most effective combination of maximizing gain whilst managing inbreeding and loss of genetic variation. It is shown how this optimum contribution technology can be developed from theory and adapted to provide practical selection protocols for a wide variety of situations including overlapping generations and multistage selection. The natural development of the theory to incorporate genomic selection and genomic control of inbreeding is also shown.