Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program.

BACKGROUND: Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden's method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden's method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship. RESULTS: We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant. CONCLUSIONS: Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.

Recombination rates in pigs differ between breeds, sexes and individuals, and are associated with the RNF212, SYCP2, PRDM7, MEI1 and MSH4 loci.

BACKGROUND: Recombination is a fundamental part of mammalian meiosis that leads to the exchange of large segments of DNA between homologous chromosomes and is therefore an important driver of genetic diversity in populations. In breeding populations, understanding recombination is of particular interest because it can break up unfavourable linkage phases between alleles and produce novel combinations of alleles that could be exploited in selection. In this study, we used dense single nucleotide polymorphism (SNP) genotype data and pedigree information to analyse individual and sex-specific variation and genetic architecture of recombination rates within and between five commercially selected pig breeds. RESULTS: In agreement with previous studies, recombination rates were higher in females than in males for all breeds and for all chromosomes, except 1 and 13, for which male rates were slightly higher. Total recombination rate differed between breeds but the pattern of recombination along the chromosomes was well conserved across breeds for the same sex. The autosomal linkage maps spanned a total length of 1731 to 1887 cM for males and of 2231 to 2515 cM for females. Estimates of heritability for individual autosomal crossover count ranged from 0.04 to 0.07 for males and from 0.08 to 0.11 for females. Fourteen genomic regions were found to be associated with individual autosomal crossover count. Of these, four were close to or within candidate genes that have previously been associated with individual recombination rates in pigs and other mammals, namely RNF212, SYCP2 and MSH4. Two of the identified regions included the PRDM7 and MEI1 genes, which are known to be involved in meiosis but have not been previously associated with variation in individual recombination rates. CONCLUSIONS: This study shows that genetic variation in autosomal recombination rate persists in domesticated species under strong selection, with differences between closely-related breeds and marked differences between the sexes. Our findings support results from other studies, i.e., that individual crossover counts are associated with the RNF212, SYCP2 and MSH4 genes in pig. In addition, we have found two novel candidate genes associated with the trait, namely PRDM7 and MEI1.

Pre-selection against a lethal recessive allele in breeding schemes with optimum-contribution selection or truncation selection.

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 [Formula: see text] was predicted and controlled using pedigree relationships (POCS), (2) OCS in which [Formula: see text] 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.

Domesticated Animal Biobanking: Land of Opportunity.

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.

Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs.

BACKGROUND: Phenotypic 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. RESULTS: Using 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. CONCLUSIONS: The use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.

Correction to: Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments.

After publication of this work [1], we noticed that there was an error: the formula to calculate the standard error of the estimated correlation.

Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Pedigree relationships to control inbreeding in optimum-contribution selection realise more genetic gain than genomic relationships.

BACKGROUND: We 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. METHODS: We 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. RESULTS: POCS 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. CONCLUSIONS: POCS 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.

Balanced selection on purebred and crossbred performance increases gain in crossbreds.

BACKGROUND: Genomic selection can be applied to select purebreds for crossbred performance (CP). The average performance of crossbreds can be considered as the summation of two components, i.e. the breed average (BA) of the parental breeds and heterosis (H) present in crossbreds. Selection of pure breeds for CP based on genomic estimated breeding values for crossbred performance (GEBV-C) or for purebred performance (GEBV-P) may differ in their ability to exploit BA and H and can affect the merit of crossbreds in both the short and long term. Selection based on GEBV-C is beneficial for CP, because H in crossbreds is efficiently exploited, whereas selection on GEBV-P results in more genetic progress in pure breeds, which increases the BA component of CP. To investigate the outcome of selection on GEBV-C and GEBV-P in both the short and long term, a two-way crossbreeding program was simulated to test the following hypotheses: (1) does selection on GEBV-P result in higher long-term CP compared to selection on GEBV-C and (2) does selection on a combination of GEBV-P and GEBV-C lead to more long-term gain in CP than selection on either separately. METHODS: We investigated the performance of crossbreds in a two-way crossbreeding program across 40 generations and considered different criteria to select purebred parents that ranged from selection on purebred performance to selection for CP with different weights on genomic evaluations based on purebred and CP. These criteria were compared under three genetic models to investigate the effects of the amount of dominance variance, absence of over-dominance, and the structure of the reference population on CP, both in the short and long term. RESULTS AND CONCLUSIONS: Although beneficial in the short to medium term, genomic selection in pure breeds on a criterion that specifically targets CP was inferior to selection for purebred performance in the long term. A selection criterion that maximizes a combination of short- and long-term responses in CP, should improve the components that define crossbred merit (i.e., BA and H) simultaneously.

Response to Selection in Finite Locus Models with Nonadditive Effects.

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.

Upweighting rare favourable alleles increases long-term genetic gain in genomic selection programs.

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.

Most of the long-term genetic gain from optimum-contribution selection can be realised with restrictions imposed during optimisation.

BACKGROUND: We 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. METHODS: We 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. RESULTS: OCS 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. CONCLUSIONS: Most 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.

Allele frequency changes due to hitch-hiking in genomic selection programs.

BACKGROUND: Genomic selection makes it possible to reduce pedigree-based inbreeding over best linear unbiased prediction (BLUP) by increasing emphasis on own rather than family information. However, pedigree inbreeding might not accurately reflect loss of genetic variation and the true level of inbreeding due to changes in allele frequencies and hitch-hiking. This study aimed at understanding the impact of using long-term genomic selection on changes in allele frequencies, genetic variation and level of inbreeding. METHODS: Selection was performed in simulated scenarios with a population of 400 animals for 25 consecutive generations. Six genetic models were considered with different heritabilities and numbers of QTL (quantitative trait loci) affecting the trait. Four selection criteria were used, including selection on own phenotype and on estimated breeding values (EBV) derived using phenotype-BLUP, genomic BLUP and Bayesian Lasso. Changes in allele frequencies at QTL, markers and linked neutral loci were investigated for the different selection criteria and different scenarios, along with the loss of favourable alleles and the rate of inbreeding measured by pedigree and runs of homozygosity. RESULTS: For each selection criterion, hitch-hiking in the vicinity of the QTL appeared more extensive when accuracy of selection was higher and the number of QTL was lower. When inbreeding was measured by pedigree information, selection on genomic BLUP EBV resulted in lower levels of inbreeding than selection on phenotype BLUP EBV, but this did not always apply when inbreeding was measured by runs of homozygosity. Compared to genomic BLUP, selection on EBV from Bayesian Lasso led to less genetic drift, reduced loss of favourable alleles and more effectively controlled the rate of both pedigree and genomic inbreeding in all simulated scenarios. In addition, selection on EBV from Bayesian Lasso showed a higher selection differential for mendelian sampling terms than selection on genomic BLUP EBV. CONCLUSIONS: Neutral variation can be shaped to a great extent by the hitch-hiking effects associated with selection, rather than just by genetic drift. When implementing long-term genomic selection, strategies for genomic control of inbreeding are essential, due to a considerable hitch-hiking effect, regardless of the method that is used for prediction of EBV.

Indirect genetic effects contribute substantially to heritable variation in aggression-related traits in group-housed mink (Neovison vison).

BACKGROUND: Since the recommendations on group housing of mink (Neovison vison) were adopted by the Council of Europe in 1999, it has become common in mink production in Europe. Group housing is advantageous from a production perspective, but can lead to aggression between animals and thus raises a welfare issue. Bite marks on the animals are an indicator of this aggressive behaviour and thus selection against frequency of bite marks should reduce aggression and improve animal welfare. Bite marks on one individual reflect the aggression of its group members, which means that the number of bite marks carried by one individual depends on the behaviour of other individuals and that it may have a genetic basis. Thus, for a successful breeding strategy it could be crucial to consider both direct (DGE) and indirect (IGE) genetic effects on this trait. However, to date no study has investigated the genetic basis of bite marks in mink. RESULT AND DISCUSSION: A model that included DGE and IGE fitted the data significantly better than a model with DGE only, and IGE contributed a substantial proportion of the heritable variation available for response to selection. In the model with IGE, the total heritable variation expressed as the proportion of phenotypic variance (T2) was six times greater than classical heritability (h2). For instance, for total bite marks, T2 was equal to 0.61, while h2 was equal to 0.10. The genetic correlation between direct and indirect effects ranged from 0.55 for neck bite marks to 0.99 for tail bite marks. This positive correlation suggests that mink have a tendency to fight in a reciprocal way (giving and receiving bites) and thus, a genotype that confers a tendency to bite other individuals can also cause its bearer to receive more bites. CONCLUSION: Both direct and indirect genetic effects contribute to variation in number of bite marks in group-housed mink. Thus, a genetic selection design that includes both direct genetic and indirect genetic effects could reduce the frequency of bite marks and probably aggression behaviour in group-housed mink.

Genetic architecture of individual meiotic crossover rate and distribution in Atlantic Salmon.

Meiotic recombination through chromosomal crossovers ensures proper segregation of homologous chromosomes during meiosis, while also breaking down linkage disequilibrium and shuffling alleles at loci located on the same chromosome. Rates of recombination can vary between species, but also between and within individuals, sex and chromosomes within species. Indeed, the Atlantic salmon genome is known to have clear sex differences in recombination with female biased heterochiasmy and markedly different landscapes of crossovers between males and females. In male meiosis, crossovers occur strictly in the telomeric regions, whereas in female meiosis crossovers tend to occur closer to the centromeres. However, little is known about the genetic control of these patterns and how this differs at the individual level. Here, we investigate genetic variation in individual measures of recombination in > 5000 large full-sib families of a Norwegian Atlantic salmon breeding population with high-density SNP genotypes. We show that females had 1.6 × higher crossover counts (CC) than males, with autosomal linkage maps spanning a total of 2174 cM in females and 1483 cM in males. However, because of the extreme telomeric bias of male crossovers, female recombination is much more important for generation of new haplotypes with 8 × higher intra-chromosomal genetic shuffling than males. CC was heritable in females (h2 = 0.11) and males (h2 = 0.10), and shuffling was also heritable in both sex but with a lower heritability in females (h2 = 0.06) than in males (h2 = 0.11). Inter-sex genetic correlations for both traits were close to zero, suggesting that rates and distribution of crossovers are genetically distinct traits in males and females, and that there is a potential for independent genetic change in both sexes in the Atlantic Salmon. Together, these findings give novel insights into the genetic architecture of recombination in salmonids and contribute to a better understanding of how rates and distribution of recombination may evolve in eukaryotes more broadly.

Genetic diversity and historical demography of underutilised goat breeds in North-Western Europe.

In the last decade, several studies aimed at dissecting the genetic architecture of local small ruminant breeds to discover which variations are involved in the process of adaptation to environmental conditions, a topic that has acquired priority due to climate change. Considering that traditional breeds are a reservoir of such important genetic variation, improving the current knowledge about their genetic diversity and origin is the first step forward in designing sound conservation guidelines. The genetic composition of North-Western European archetypical goat breeds is still poorly exploited. In this study we aimed to fill this gap investigating goat breeds across Ireland and Scandinavia, including also some other potential continental sources of introgression. The PCA and Admixture analyses suggest a well-defined cluster that includes Norwegian and Swedish breeds, while the crossbred Danish landrace is far apart, and there appears to be a close relationship between the Irish and Saanen goats. In addition, both graph representation of historical relationships among populations and f4-ratio statistics suggest a certain degree of gene flow between the Norse and Atlantic landraces. Furthermore, we identify signs of ancient admixture events of Scandinavian origin in the Irish and in the Icelandic goats. The time when these migrations, and consequently the introgression, of Scandinavian-like alleles occurred, can be traced back to the Viking colonisation of these two isles during the Viking Age (793-1066 CE). The demographic analysis indicates a complicated history of these traditional breeds with signatures of bottleneck, inbreeding and crossbreeding with the improved breeds. Despite these recent demographic changes and the historical genetic background shaped by centuries of human-mediated gene flow, most of them maintained their genetic identity, becoming an irreplaceable genetic resource as well as a cultural heritage.

Structural gene variants in the porcine mannose-binding lectin 1 (MBL1) gene are associated with low serum MBL-A concentrations.

Mannose-binding lectin (MBL) is a collagenous lectin that kills a wide range of pathogenic microbes through complement activation. The MBL1 and MBL2 genes encode MBL-A and MBL-C, respectively. MBL deficiency in humans is associated with higher susceptibility to viral as well as bacterial infections. A number of single nucleotide polymorphisms (SNP) have been identified in the collagen-like domain of the human MBL gene, of which several are strongly associated with decreased concentrations of MBL in serum. In this study, we have identified a number of SNPs in the porcine MBL-A gene. Sequence comparisons identified a total of 14 SNPs, eight of which were found in exons and six in introns. Four of the eight exon-located SNPs were non-synonymous. Sequence data from several Duroc and Landrace pigs identified four different haplotypes. One haplotype was found in Duroc pigs only, and three haplotypes were found in the Landrace pigs. One of the identified haplotypes was associated with low concentration of MBL-A in serum. The concentration of MBL-A in serum was further assessed in a large number of Duroc and Landrace boars to address its correlation with disease frequency. The MBL-A concentration in Duroc boars showed one single population, whereas Landrace boars showed four distinct populations for MBL-A concentration. The Landrace boars were finally assessed for disease incidence, and the association with the concentration of MBL-A in serum was investigated. No association between MBL and disease incidence was found in this study.

Cross-Breeding Is Inevitable to Conserve the Highly Inbred Population of Puffin Hunter: The Norwegian Lundehund.

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.

Genetic rescue of an endangered domestic animal through outcrossing with closely related breeds: A case study of the Norwegian Lundehund.

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.