Optimum Contribution Selection Research Articles

Genetic improvement of small ruminant local breeds with nucleus and inbreeding control: A simulation study

In small ruminant local breeds of Southern Europe genetic selection is often constrained by small population sizes, poor animal identification, inadequate animal performance and pedigree recording, and organizational shortcomings. Under these conditions nucleus breeding schemes can offer practical and cost effective solutions. The paper investigated genetic gain in stochastically simulated dairy small ruminant nuclei of 100, 200 and 400 females, supporting commercial populations from 500 to 5000 females. In the nucleus, a young sire selection scheme was used, with optimum contribution selection on a dairy trait, at an annual inbreeding rate of 0.3%, corresponding to a generation inbreeding rate of 0.001. Sires, both selected and not-select as sires of sires, after 1 year of use in the nucleus were utilized in the commercial population for one, or alternatively, 2 years. Annual genetic gain ranged from a minimum of 0.073 SD with 100 females nucleus supporting a commercial population of 500 females, to a maximum of 0.138 SD with a 400 females nucleus supporting a commercial population of 5000 females. Negligible differences in genetic gain were observed between nuclei and corresponding commercial populations. When sires were used for only 1 year in the commercial population, we observed 7.7 years of genetic lag with the nucleus, that increased to 8.2 years when sires were used for 2 years. Results showed that there are opportunities for selection even in populations of a few hundreds of females. Considering a specific breeds, or a specific farming area, a cost benefit analysis should be carried out to orientate the choice of nucleus size and strategy of use of sires.

Selection with inbreeding control in simulated young bull schemes for local dairy cattle breeds

Local breeds are rarely subject to modern selection techniques; however, selection programs will be required if local breeds are to remain a viable livelihood option for farmers. Selection in small populations needs to take into account accurate inbreeding control. Optimum contribution selection (OCS) is efficient in controlling inbreeding and maximizes genetic gain. The current paper investigates genetic progress in simulated dairy cattle populations from 500 to 6,000 cows undergoing young bull selection schemes with OCS compared with truncation selection (TS) at an annual inbreeding rate of 0.003. Selection is carried out for a dairy trait with a base heritability of 0.3. A young bull selection scheme was used because of its simplicity in implementation. With TS, annual genetic gain from 0.111 standard deviation units with 500 cows increases rapidly to 0.145 standard deviation units with 4,000 cows. Then, genetic gain increases more slowly up to 6,000 cows. At the same inbreeding rate, OCS produces higher genetic progress than TS. Differences in genetic gain between OCS and TS vary from to 2 to 6.3%. Genetic gain is also improved by increasing the number of years that males can be used as sires of sires. When comparing OCS versus TS at different heritabilities, we observe an advantage of OCS only at high heritability, up to 8% with heritability of 0.9. By increasing the constraint on inbreeding, the difference of genetic gain between the 2 selection methods increases in favor of OCS, and the advantage at the inbreeding rate of 0.001 per generation is 6 times more than at the inbreeding rate of 0.003. Opportunities exist for selection even in dairy cattle populations of a few hundred females. In any case, selection in local breeds will most often require specific investments in infrastructure and manpower, including systems for accurate data recording and selection skills and the presence of artificial insemination and breeders organizations. A cost-benefit analysis is therefore advisable before considering the implementation of selection schemes in local dairy cattle breeds.

Indirect genetic effects and inbreeding: consequences of BLUP selection for socially affected traits on rate of inbreeding.

BackgroundSocial interactions often occur among living organisms, including aquatic animals. There is empirical evidence showing that social interactions may genetically affect phenotypes of individuals and their group mates. In this context, the heritable effect of an individual on the phenotype of another individual is known as an Indirect Genetic Effect (IGE). Selection for socially affected traits may increase response to artificial selection, but also affect rate of inbreeding.MethodsA simulation study was conducted to examine the effect of Best Linear Unbiased Prediction (BLUP) selection for socially affected traits on the rate of inbreeding. A base scenario without IGE and three alternative scenarios with different magnitudes of IGE were simulated. In each generation, 25 sires and 50 dams were mated, producing eight progeny per dam. The population was selected for 20 generations using BLUP. Individuals were randomly assigned to groups of eight members in each generation, with two families per group, each contributing four individuals. “Heritabilities” (for both direct and indirect genetic effects) were equal to 0.1, 0.3 or 0.5, and direct–indirect genetic correlations were −0.8, −0.4, 0, 0.4, or 0.8. The rate of inbreeding was calculated from generation 10 to 20.ResultsFor the base scenario, the rates of inbreeding were 4.09, 2.80 and 1.95% for “heritabilities” of 0.1, 0.3 and 0.5, respectively. Overall, rates of inbreeding for the three scenarios with IGE ranged from 2.21 to 5.76% and were greater than for the base scenarios. The results show that social interaction within groups of two families increases the resemblance between estimated breeding values of relatives, which, in turn, increases the rate of inbreeding.ConclusionBLUP selection for socially affected traits increased the rate of inbreeding. To maintain inbreeding at an acceptable rate, a selection algorithm that restricts the increase in mean kinship, such as optimum contribution selection, is required.

Consequences for diversity when animals are prioritized for conservation of the whole genome or of one specific allele.

When animals are selected for one specific allele, for example for inclusion in a gene bank, this may result in the loss of diversity in other parts of the genome. The aim of this study was to quantify the risk of losing diversity across the genome when targeting a single allele for conservation when storing animals in a gene bank. From a small Holstein population, genotyped for 54,001 SNP loci, animals were prioritized for a single allele while maximizing the genomewide diversity using optimal contribution selection. Selection for a single allele was done for five different target frequencies: (i) no restriction on a target frequency; (ii) target frequency = original frequency in population; (iii) target frequency = 0.50; (iv) target frequency of the major allele = 1 (fixation); and (v) target frequency of the major allele = 0 (elimination). To do this, optimal contribution selection was extended with an extra constraint on the allele frequency of the target SNP marker. Results showed that elimination or fixation of alleles can result in substantial losses in genetic diversity around the targeted locus and also across the rest of the genome, depending on the allele frequency and the target frequency. It was concluded that losses of genetic diversity around the target allele are the largest when the target frequency is very different from the current allele frequency.

The effect of genomic information on optimal contribution selection in livestock breeding programs

BackgroundLong-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity.MethodsThe effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences.ResultsOptimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data.ConclusionsThe use of genomic estimated breeding values increased genetic gain in optimal contribution selection. Genomic estimated breeding values were more accurate and showed more within-family variation, which led to higher genetic gains for the same restriction on inbreeding. Using genomic relationships to restrict inbreeding provided no additional gain, except in the case of very large full-sib families.

Genetic diversity of Guernsey population using pedigree data and gene-dropping simulations

The objectives of this study were to analyze the trend of within-breed genetic diversity and identify major causes leading to loss of genetic diversity in Guernsey breed in three countries. Pedigree files of Canadian (GCN), South African (GSA) and American (GUS) Guernsey populations containing 130 927, 18 593 and 1 851 624 records, respectively, were analyzed. Several parameters derived from the in-depth pedigree analyses were used to measure trends and current levels of genetic diversity. Pedigree completeness index of GCN, GSA and GUS populations, in the most recent year (2007), was 97%, 74% and 79%, respectively, considering four generations back in the analysis. The rate of inbreeding in each population was 0.19%, 0.16% and 0.17% between 2002 and 2007, respectively. For the same period, the estimated effective population size for GCN, GSA and GUS was 46, 57 and 46, respectively. The estimated percentage of genetic diversity lost within each population over the last four decades was 8%, 3% and 5%, respectively. The relative proportion of genetic diversity lost due to random genetic drift in the three populations was 93%, 91% and 86%, respectively. In conclusion, the results suggested that GCN and GUS have lost more genetic diversity than GSA over the past four decades, and this loss is gaining momentum due to increasing rates of inbreeding. Therefore, strategies such as optimum contribution selection and migration of genetic material are advised to increase effective population size, particularly in GCN and GUS.

Implementation of Optimum Contributions Selection in endangered local breeds: the case of the Menorca Horse population

Limiting the inbreeding rate (∆F) while maximizing genetic gain for any trait of economic interest is especially important in small populations of local breeds, like the Menorca Horse. In this breed, dressage performance is important for the profitability of the breed and should be accounted in the selection criterion. The aim of this study was to assess if a breeding programme aiming at improved dressage performance is feasible in such a small breed. To perform the analysis, animals that were currently available for breeding (between 3 and 20years) were used. Selection was based on the estimated breeding values for dressage obtained by BLUP. The pedigree and molecular coancestry between potential breeding horses was used (separately or in combination) to account for the restriction on ∆F. Results show that it is possible to avoid large increases in inbreeding while obtaining acceptable levels of genetic gain (i.e. a ∆F of 1% would imply a maximum loss in genetic gain of 2%). Thus, the Menorca Horse population is suitable for a management procedure which jointly optimizes the response to selection and the levels of variability and inbreeding (Optimal Contribution selection). Regarding the source of information used to calculate the relationships, molecular information would provide a greater range of solutions to increase genetic gain than using pedigree coancestry (gain was 1-4% higher for the same levels of restriction on the increase in inbreeding).

Optimum contribution selection for conserved populations with historic migration

BackgroundIn recent decades, local varieties of domesticated animal species have been frequently crossed with economically superior breeds which has resulted in considerable genetic contributions from migrants. Optimum contribution selection by maximizing gene diversity while constraining breeding values of the offspring or vice versa could eventually lead to the extinction of local breeds with historic migration because maximization of gene diversity or breeding values would be achieved by maximization of migrant contributions. Therefore, other objective functions are needed for these breeds.ResultsDifferent objective functions and side constraints were compared with respect to their ability to reduce migrant contributions, to increase the genome equivalents originating from native founders, and to conserve gene diversity. Additionally, a new method for monitoring the development of effective size for breeds with incomplete pedigree records was applied. Approaches were compared for Vorderwald cattle, Hinterwald cattle, and Limpurg cattle. Migrant contributions could be substantially decreased for these three breeds, but the potential to increase the native genome equivalents is limited.ConclusionsThe most promising approach was constraining migrant contributions while maximizing the conditional probability that two alleles randomly chosen from the offspring population are not identical by descent, given that both descend from native founders.

Genomic selection requires genomic control of inbreeding

BackgroundIn the past, pedigree relationships were used to control and monitor inbreeding because genomic relationships among selection candidates were not available until recently. The aim of this study was to understand the consequences for genetic variability across the genome when genomic information is used to estimate breeding values and in managing the inbreeding generated in the course of selection on genome-enhanced estimated breeding values.MethodsThese consequences were measured by genetic gain, pedigree- and genome-based rates of inbreeding, and local inbreeding across the genome. Breeding schemes were compared by simulating truncation selection or optimum contribution selection with a restriction on pedigree- or genome-based inbreeding, and with selection using estimated breeding values based on genome- or pedigree-based BLUP. Trait information was recorded on full-sibs of the candidates.ResultsWhen the information used to estimate breeding values and to constrain rates of inbreeding were either both pedigree-based or both genome-based, rates of genomic inbreeding were close to the desired values and the identical-by-descent profiles were reasonably uniform across the genome. However, with a pedigree-based inbreeding constraint and genome-based estimated breeding values, genomic rates of inbreeding were much higher than expected. With pedigree-instead of genome-based estimated breeding values, the impact of the largest QTL on the breeding values was much smaller, resulting in a more uniform genome-wide identical-by-descent profile but genomic rates of inbreeding were still higher than expected based on pedigree relationships, because they measure the inbreeding at a neutral locus not linked to any QTL. Neutral loci did not exist here, where there were 100 QTL on each chromosome. With a pedigree-based inbreeding constraint and genome-based estimated breeding values, genomic rates of inbreeding substantially exceeded the value of its constraint. In contrast, with a genome-based inbreeding constraint and genome-based estimated breeding values, marker frequencies changed, but this change was limited by the inbreeding constraint at the marker position.ConclusionsTo control inbreeding, it is necessary to account for it on the same basis as what is used to estimate breeding values, i.e. pedigree-based inbreeding control with traditional pedigree-based BLUP estimated breeding values and genome-based inbreeding control with genome-based estimated breeding values.

Efficient selection against categorically scored hip dysplasia in dogs is possible using best linear unbiased prediction and optimum contribution selection: a simulation study

Breeding to reduce the prevalence of categorically scored hip dysplasia (HD), based on phenotypic assessment of radiographic hip status, has had limited success. The aim of this study was to evaluate two selection strategies for improved hip status: truncation selection based on phenotypic record versus best linear unbiased prediction (BLUP), using stochastic simulation and selection scenarios resembling those in real dog populations. In addition, optimum contribution selection (OCS) was evaluated. Two traits were considered: HD (as a categorical trait with five classes and a heritability of 0.45 on the liability scale) and a continuous trait (with a heritability of 0.25) intended to represent other characteristics in the breeding goal. A population structure mimicking that in real dog populations was modelled. The categorical nature of HD caused a considerably lower genetic gain compared to simulating HD as a continuous trait. Genetic gain was larger for BLUP selection than for phenotypic selection in all scenarios. However, BLUP selection resulted in higher rates of inbreeding. By applying OCS, the rate of inbreeding was lowered to about the same level as phenotypic selection but with increased genetic improvement. For efficient selection against HD, use of BLUP breeding values should be prioritized. In small populations, BLUP should be used together with OCS or similar strategy to maintain genetic variation.

Optimal contribution selection applied to the Norwegian and the North‐Swedish cold‐blooded trotter – a feasibility study

The aim of this study was to examine how to apply optimal contribution selection (OCS) in the Norwegian and the North-Swedish cold-blooded trotter and give practical recommendations for the future. OCS was implemented using the software Gencont with overlapping generations and selected a few, but young sires, as these turn over the generations faster and thus is less related to the mare candidates. In addition, a number of Swedish sires were selected as they were less related to the selection candidates. We concluded that implementing OCS is feasible to select sires (there is no selection on mares), and we recommend the number of available sire candidates to be continuously updated because of amongst others deaths and geldings. In addition, only considering sire candidates with phenotype above average within a year class would allow selection candidates from many year classes to be included and circumvent current limitation on number of selection candidates in Gencont (approx. 3000). The results showed that mare candidates can well be those being mated the previous year. OCS will, dynamically, recruit young stallions and manage the culling or renewal of annual breeding permits for stallions that had been previously approved. For the annual mating proportion per sire, a constraint in accordance with the maximum that a sire can mate naturally is recommended.

There is room for selection in a small local pig breed when using optimum contribution selection: A simulation study1,2

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

A simple selection scheme to improve disease resistance and growth

A simple selection scheme is proposed that facilitates the selection for a growth trait (GROWTH) with heritability of 0.25 and one disease trait (SURVIVAL) with heritability of 0.1 or 0.4 using a two step selection procedure. First, the scheme applies phenotypic selection for SURVIVAL among all the 10,000 candidates per generation, assuming that Nsurvival = 2500, 5000 or 7500 survived a disease challenge test. Survivors were measured for GROWTH, and the (Ngenotyped = 1000 or 2000) highest ranked were DNA genotyped for parentage testing. Second, parents were selected for GROWTH on their BLUP breeding values using optimum contribution selection, which restricted the rates of inbreeding to 1% per generation. At this second selection step, no selection for SURVIVAL was possible, because all survivors had the same phenotype for this trait and the pedigree of the dead individuals were not known. Results show that selection for SURVIVAL was very efficient for schemes with high heritability of SURVIVAL (0.4) and positive genetic correlation between the traits (0.3), and more efficient with decreasing survival rate, because of the higher intensity of selection. However, for schemes with negative correlation between the traits (− 0.3), the heritability of 0.1 of SURVIVAL was too low to get positive genetic response for SURVIVAL. Genetic gain for GROWTH and SURVIVAL was not significantly improved by increasing Ngenotyped = 1000 to 2000 for the schemes used here, i.e. genotyping 1000 individuals allowed the best possible parents to be selected for breeding when using optimum contribution selection. In conclusion, this is an efficient and very simple selection scheme for aquaculture species which can be used for specific cases where a whole production system is limited by a disease that is not transmitted from parent to offspring. The scheme may be worth considering also for vertically transmitted diseases for which the breeding candidates or their gametes can be reliably disinfected and screened before being transferred to the nucleus and the industry.

Analyzing the effect of different approaches of penalized relationship in multistage selection schemes1

The aim of this study was to extend optimum contribution selection to more realistic breeding schemes with multistage selection. It seems that if the last selection stage accounts for the relationship of the selected animals, then previous selection stages also account for this relationship. An extreme example was considered here: the preselection of dairy bulls that enter a progeny testing scheme. First the penalty on the average relationship in selection step 1 is assumed the same as in step 2. Thereafter, situations with different penalties on the average relationship in the 2 selection steps were analyzed. The simulation started with the generation of prior EBV, which were sampled from a truncated normal distribution. Possible candidates for further progeny testing were selected and progeny test EBV were simulated, where the progeny test was based on 100 daughters per young bull. In situations with greater accuracy of prior EBV, high trait heritability and prior EBV were available for 2,000 bulls; the results were similar for both approaches, independent of family size. However, in a situation with low accuracy of prior EBV and low trait heritability it could be observed that with increasing penalty on the average relationship, correction for relationship in stage 1 yielded in a similar genetic level compared with selecting only for high prior EBV. If the number of bulls with prior EBV increased from 2,000 to 4,000, an increasing penalty on an average relationship gave an improved genetic level. A further improvement of the results with respect to genetic level and average relationship could be observed by increasing the penalty on an average relationship in selection step 1 above that in selection step 2. Overall, this study showed that it is beneficial to use a penalty on an average relationship already for the selection of bulls that enter the progeny test. In case optimum contribution was applied with a constraint on the average relationship in stage 2, this constraint may be translated into a penalty on the average relationship, and the current results suggested that the optimal penalty in selection stage 1 should be twice that of stage 2.

Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes

The aim of this study was to compare genetic gain for a traditional aquaculture sib breeding scheme with breeding values based on phenotypic data (TBLUP) with a breeding scheme with genome-wide (GW) breeding values. Both breeding schemes were closed nuclei with discrete generations modeled by stochastic simulation. Optimum contribution selection was applied to restrict pedigree-based inbreeding to either 0.5 or 1% per generation. There were 1,000 selection candidates and a sib test group of either 4,000 or 8,000 fish. The number of selected dams and sires to create full sib families in each generation was determined from the optimum contribution selection method. True breeding values for a trait were simulated by summing the number of each QTL allele and the true effect of each of the 1,000 simulated QTL. Breeding values in TBLUP were predicted from phenotypic and pedigree information, whereas genomic breeding values were computed from genetic markers whose effects were estimated using a genomic BLUP model. In generation 5, genetic gain was 70 and 74% greater for the GW scheme than for the TBLUP scheme for inbreeding rates of 0.5 and 1%. The reduction in genetic variance was, however, greater for the GW scheme than for the TBLUP scheme due to fixation of some QTL. As expected, accuracy of selection increased with increasing heritability (e.g., from 0.77 with a heritability of 0.2 to 0.87 with a heritability of 0.6 for GW, and from 0.53 and 0.58 for TBLUP in generation 5 with sib information only). When the trait was measured on the selection candidate compared with only on sibs and the heritability was 0.4, accuracy increased from 0.55 to 0.69 for TBLUP and from 0.83 to 0.86 for GW. The number of selected sires to get the desired rate of inbreeding was in general less in GW than in TBLUP and was 33 for GW and 83 for TBLUP (rate of inbreeding 1% and heritability 0.4). With truncation selection, genetic gain for the scheme with GW breeding values was nearly twice as large as a scheme with traditional BLUP breeding values. The results indicate that the benefits of applying GW breeding values compared with TBLUP are reduced when contributions are optimized. In conclusion, genetic gain in aquaculture breeding schemes with optimized contributions can increase by as much as 81% by applying genome-wide breeding values compared with traditional BLUP breeding values.

Optimum contribution selection using differential evolution

A program to determine optimum contribution selection using differential evolution was developed. The objective function to be optimized was composed by the expected merit of the future progeny and the coancestry among selected parents. Simulated and real datasets of populations with overlapping generations were used to validate and test the performance of the program. The program was computationally efficient and feasible for practical applications. The expected consequences of using the program, in contrast to empirical procedures to control inbreeding and/or to selection based exclusively on expected genetic merit, would be the improvement of the selection response and a more effective control of inbreeding.

Optimum contribution selection in large general tree breeding populations with an application to Scots pine

Development of selection methods that optimises selection differential subject to a constraint on the increase of inbreeding (or coancestry) in a population is an important part of breeding programmes. One such method that has received much attention in animal breeding is the optimum contribution (OC) dynamic selection method. We implemented the OC algorithm and applied it to a diallel progeny trial of Pinus sylvestris L. (Scots pine) focussing on two traits (total tree height and stem diameter). The OC method resulted in a higher increase in genetic gain (8-30%) compared to the genetic gain achieved using standard restricted selection method at the same level of coancestry constraint. Genetic merit obtained at two different levels of restriction on coancestry showed that the benefit of OC was highest when restriction was strict. At the same level of genetic merit, OC decreased coancestry with 56 and 39% for diameter and height, respectively, compared to the level of coancestry obtained using unrestricted truncation selection. Inclusion of a dominance term in the statistical model resulted in changes in contribution rank of trees with 7 and 13% for diameter and height, respectively, compared to results achieved by using a pure additive model. However, the genetic gain was higher for the pure additive model than for the model including dominance for both traits.

Optimal genetic contribution selection in Danish Holstein depends on pedigree quality

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.

Establishing a base population for a breeding program in aquaculture, from multiple subpopulations, differentiated by genetic drift: I. Effects of the number of subpopulations, heritability and mating strategies using optimum contribution selection

Guidelines for constructing base populations in fish were examined using a drift model. A large number of drift differentiated, wild subpopulations, with inbreeding Fwild (Fwild∈[0.05–0.20]), and a trait with heritability htot2 (htot2∈[0.10–0.30]), were assumed. A base population was generated by sampling 2N fish from each of nsub subpopulations (nsub∈[1–8]), and then mated to produce 160 or 240 families per generation. The matings were either at random, in a structured diallel-cross, or within subpopulations to produce generation 1. Optimal contribution selection on phenotype was applied from generation 1 to11, with a rate of inbreeding of 0.5% per generation. Genetic gain (Δg) improved with nsub, the improvement being largest when going from one to two subpopulations, with diminishing returns. Most of the potential improvement was obtained with nsub=4, and was well predicted by ΔgnsubΔg1≈(1−Fwild/nsub)(1−Fwild). Initial matings across subpopulations improved Δg from the second round of selection compared to within. Consequently the effect of the mating strategy were first observed 3 generation after decisions were made and persisted over subsequent generations, but with minimal effect on the accumulated additive genetic gain in generation 11. It was concluded that sampling from at least four subpopulations was beneficial and that mating across subpopulations should start as early as possible.

Establishing a base population for a breeding program in aquaculture, from multiple subpopulations, differentiated by genetic drift: II. Sensitivity to assumptions on the additive genetic relationships of base animals

The simulation study examined the effect in optimal contribution selection of specifying an additive genetic relationship matrix (A-matrix) deviating from the true A-matrix of the base population. A base population of 160 males and 160 females was sampled evenly from 4 or 8 randomly chosen, drift-differentiated, wild subpopulations. Wild fish had true inbreeding coefficient of Fwild, either 0.05 or 0.20, giving additive genetic relationships of 2Fwild within, and 0 between subpopulations. Selection based upon phenotypes was simulated on one continuous trait of heritability, hwild2, either 0.1 or 0.3. Base animals were mated either within subpopulations, or at random, so that the first round of selection occurred either on purebred or crossbred fish respectively. Optimum contributions was implemented from generation 1 for a desired rate of inbreeding of 0.5% per generation, using an assumed Fwild⁎ which varied between 0.0 and 0.3. Assuming Fwild⁎=0.0 compared to 0.3, resulted in steeper regressions of the fractional contributions of subpopulations on average breeding value, higher true inbreeding and consequently reduced additive genetic variance, albeit with larger genetic gain from the first round of selection. When the first round of selection followed random mating the regression of the fractional contributions of the subpopulations was also less steep. These effects persisted, but originated from the misspecification of Fwild⁎ and so will not accumulate further in the medium-to long-term. Consequently, assuming Fwild⁎ was less than the true value led to sub-optimal use of subpopulations with an increased risk of loss of alleles of direct and strategic relevance to the breeding program. In conclusion, assuming Fwild⁎≈0.0 should be avoided and Fwild⁎ should be estimated with procedures such as marker estimated kinships, QST estimates or empirical observations of clines.