Truncation Selection Research Articles

Mate selection in aquaculture breeding using differential evolution algorithm

An algorithm to perform mate selection in aquaculture breeding using a computational optimization procedure called “differential evolution” (DE) was applied under optimum contribution selection and mate selection scenarios, to assess its efficiency in maximizing the genetic merit while controlling inbreeding. Real aquaculture data sets with 8,782 Nile tilapias from five generations and 79,144 coho salmon from eight generations were used to optimize objective functions accounting for coancestry of parents and expected genetic merit and inbreeding of the future progeny. The mate selection results were compared with those from the realized scenario (real mates), truncation selection and optimum contribution selection method. Mate selection allowed reducing inbreeding up to 73% for Nile tilapia, compared with truncation selection, and up to 20% for coho salmon, compared with realized scenario. There was evidence that mate selection outperformed optimum contribution selection followed by minimum inbreeding mating in controlling inbreeding under the same expected genetic gain. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in aquaculture breeding.

A comparison of nonlinear mixed models and response to selection of tick-infestation on lambs.

Tick-borne fever (TBF) is stated as one of the main disease challenges in Norwegian sheep farming during the grazing season. TBF is caused by the bacterium Anaplasma phagocytophilum that is transmitted by the tick Ixodes ricinus. A sustainable strategy to control tick-infestation is to breed for genetically robust animals. In order to use selection to genetically improve traits we need reliable estimates of genetic parameters. The standard procedures for estimating variance components assume a Gaussian distribution of the data. However, tick-count data is a discrete variable and, thus, standard procedures using linear models may not be appropriate. Thus, the objectives of this study were twofold: 1) to compare four alternative non-linear models: Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial based on their goodness of fit for quantifying genetic variation, as well as heritability for tick-count and 2) to investigate potential response to selection against tick-count based on truncation selection given the estimated genetic parameters from the best fit model. Our results showed that zero-inflated Poisson was the most parsimonious model for the analysis of tick count data. The resulting estimates of variance components and high heritability (0.32) led us to conclude that genetic determinism is relevant on tick count. A reduction of the breeding values for tick-count by one sire-dam genetic standard deviation on the liability scale will reduce the number of tick counts below an average of 1. An appropriate breeding scheme could control tick-count and, as a consequence, probably reduce TBF in sheep.

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.

Mating strategies with genomic information reduce rates of inbreeding in animal breeding schemes without compromising genetic gain

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

Efficient Breeding by Genomic Mating.

Selection in breeding programs can be done by using phenotypes (phenotypic selection), pedigree relationship (breeding value selection) or molecular markers (marker assisted selection or genomic selection). All these methods are based on truncation selection, focusing on the best performance of parents before mating. In this article we proposed an approach to breeding, named genomic mating, which focuses on mating instead of truncation selection. Genomic mating uses information in a similar fashion to genomic selection but includes information on complementation of parents to be mated. Following the efficiency frontier surface, genomic mating uses concepts of estimated breeding values, risk (usefulness) and coefficient of ancestry to optimize mating between parents. We used a genetic algorithm to find solutions to this optimization problem and the results from our simulations comparing genomic selection, phenotypic selection and the mating approach indicate that current approach for breeding complex traits is more favorable than phenotypic and genomic selection. Genomic mating is similar to genomic selection in terms of estimating marker effects, but in genomic mating the genetic information and the estimated marker effects are used to decide which genotypes should be crossed to obtain the next breeding population.

Evolving gene banks: improving diverse populations of crop and exotic germplasm with optimal contribution selection.

We simulated pre-breeding in evolving gene banks - populations of exotic and crop types undergoing optimal contribution selection for long-term genetic gain and management of population genetic diversity. The founder population was based on crosses between elite crop varieties and exotic lines of field pea (Pisum sativum) from the primary genepool, and was subjected to 30 cycles of recurrent selection for an economic index composed of four traits with low heritability: black spot resistance, flowering time and stem strength (measured on single plants), and grain yield (measured on whole plots). We compared a small population with low selection pressure, a large population with high selection pressure, and a large population with moderate selection pressure. Single seed descent was compared with S0-derived recurrent selection. Optimal contribution selection achieved higher index and lower population coancestry than truncation selection, which reached a plateau in index improvement after 40 years in the large population with high selection pressure. With optimal contribution selection, index doubled in 38 years in the small population with low selection pressure and 27-28 years in the large population with moderate selection pressure. Single seed descent increased the rate of improvement in index per cycle but also increased cycle time.

Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?

Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new elitist black-box model, in which algorithms are required to base all decisions solely on (the relative performance of) a fixed number of the best search points sampled so far. Our elitist model thus combines features of the ranking-based and the memory-restricted black-box models with an enforced usage of truncation selection. We provide several examples for which the elitist black-box complexity is exponentially larger than that of the respective complexities in all previous black-box models, thus showing that the elitist black-box complexity can be much closer to the runtime of typical evolutionary algorithms. We also introduce the concept of p-Monte Carlo black-box complexity, which measures the time it takes to optimize a problem with failure probability at most p. Even for small p, the p-Monte Carlo black-box complexity of a function class [Formula: see text] can be smaller by an exponential factor than its typically regarded Las Vegas complexity (which measures the expected time it takes to optimize [Formula: see text]).

Genomic prediction of survival time in a population of brown laying hens showing cannibalistic behavior.

BackgroundMortality due to cannibalism causes both economic and welfare problems in laying hens. To limit mortality due to cannibalism, laying hens are often beak-trimmed, which is undesirable for animal welfare reasons. Genetic selection is an alternative strategy to increase survival and is more efficient by taking heritable variation that originates from social interactions into account, which are modelled as the so-called indirect genetic effects (IGE). Despite the considerable heritable variation in survival time due to IGE, genetic improvement of survival time in laying hens is still challenging because the detected heritable variation of the trait with IGE is still limited, ranging from 0.06 to 0.26, and individuals that are still alive at the end of the recording period are censored. Furthermore, survival time records are available late in life and only on females. To cope with these challenges, we tested the hypothesis that genomic prediction increases the accuracy of estimated breeding values (EBV) compared to parental average EBV, and increases response to selection for survival time compared to a traditional breeding scheme. We tested this hypothesis in two lines of brown layers with intact beaks, which show cannibalism, and also the hypothesis that the rate of inbreeding per year is lower for genomic selection than for the traditional breeding scheme.Results and discussionThe standard deviation of genomic prediction EBV for survival time was around 22 days for both lines, indicating good prospects for selection against mortality in laying hens with intact beaks. Genomic prediction increased the accuracy of the EBV by 35 and 32 % compared to the parent average EBV for the two lines. At the current reference population size, predicted response to selection was 91 % higher when using genomic selection than with the traditional breeding scheme, as a result of a shorter generation interval in males and greater accuracy of selection in females. The predicted rate of inbreeding per generation with truncation selection was substantially lower for genomic selection than for the traditional breeding scheme for both lines.ConclusionsGenomic selection for socially affected traits is a promising tool for the improvement of survival time in laying hens with intact beaks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0247-4) contains supplementary material, which is available to authorized users.

Mating structures for genomic selection breeding programs in aquaculture.

BackgroundIn traditional family-based aquaculture breeding, each sire is mated to two dams in order to separate the sire’s genetic effect from other family effects. Factorial mating involves more mates per sire and/or dam and result in more but smaller full- and/or half-sib families. For traits measured on sibs of selection candidates, factorial mating increases intensity of selection between families when selection is on traditional best linear unbiased prediction (BLUP) estimated breeding values (TRAD-EBV). However, selection on genome-wide estimated breeding values (GW-EBV), uses both within- and between-family effects and the advantage of factorial mating is less obvious. Our aim was to compare by computer simulation the impact of various factorial mating strategies for truncation selection on TRAD-EBV versus GW-EBV on rates of inbreeding, accuracy of selection and genetic gain for two traits, i.e. one measured on selection candidates (CAND-TRAIT) and one on their sibs (SIB-TRAIT).ResultsSire:dam mating ratios of 1:1, 2:2 or 10:10 were tested with 100, 200 or 1000 families produced from a constant number of parents (100 sires and 100 dams), and a mating ratio of 1:2 with 200 families produced from 100 sires and 200 dams. With GW-EBV, changing the mating ratio from 1:1 to 10:10 had a limited effect on genetic gain (less than 5 %) for both CAND-TRAIT and SIB-TRAIT, whereas with TRAD-EBV, selection intensity increased for SIB-TRAIT and genetic gain increased by 41 and 77 % for schemes with 3000 and 12,000 selection candidates, respectively. For both GW-EBV and TRAD-EBV, rates of inbreeding decreased by up to ~30 % when the mating ratio was changed from 1:1 to 10:10 for schemes with 3000 to 12,000 selection candidates. Similar results were found for alternative heritabilities of SIB-TRAIT and total number of tested sibs.ConclusionsChanging the sire:dam mating ratio from 1:1 to 10:10 increased genetic gain substantially with TRAD-EBV, mainly through increased selection intensity for the SIB-TRAIT, whereas with GW-EBV, it had a limited effect on genetic gain for both traits. Rates of inbreeding decreased for both selection methods.

Effect of manipulating recombination rates on response to selection in livestock breeding programs.

BackgroundIn this work, we performed simulations to explore the potential of manipulating recombination rates to increase response to selection in livestock breeding programs.MethodsWe carried out ten replicates of several scenarios that followed a common overall structure but differed in the average rate of recombination along the genome (expressed as the length of a chromosome in Morgan), the genetic architecture of the trait under selection, and the selection intensity under truncation selection (expressed as the proportion of males selected). Recombination rates were defined by simulating nine different chromosome lengths: 0.10, 0.25, 0.50, 1, 2, 5, 10, 15 and 20 Morgan, respectively. One Morgan was considered to be the typical chromosome length for current livestock species. The genetic architecture was defined by the number of quantitative trait variants (QTV) that affected the trait under selection. Either a large (10,000) or a small (1000 or 500) number of QTV was simulated. Finally, the proportions of males selected under truncation selection as sires for the next generation were equal to 1.2, 2.4, 5, or 10 %.ResultsIncreasing recombination rate increased the overall response to selection and decreased the loss of genetic variance. The difference in cumulative response between low and high recombination rates increased over generations. At low recombination rates, cumulative response to selection tended to asymptote sooner and the genetic variance was completely eroded. If the trait under selection was affected by few QTV, differences between low and high recombination rates still existed, but the selection limit was reached at all rates of recombination.ConclusionsHigher recombination rates can enhance the efficiency of breeding programs to turn genetic variation into response to selection. However, to increase response to selection significantly, the recombination rate would need to be increased 10- or 20-fold. The biological feasibility and consequences of such large increases in recombination rates are unknown.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0221-1) contains supplementary material, which is available to authorized users.

Truncation selection and payoff distributions applied to the replicator equation

The replicator equation has been frequently used in the theoretical literature to explain a diverse array of biological phenomena. However, it makes several simplifying assumptions, namely complete mixing, an infinite population, asexual reproduction, proportional selection, and mean payoffs. Here, we relax the conditions of mean payoffs and proportional selection by incorporating payoff distributions and truncation selection into extensions of the replicator equation and agent-based models. In truncation selection, replicators with fitnesses above a threshold survive. The reproduction rate is equal for all survivors and is sufficient to replace the replicators that did not survive. We distinguish between two types of truncation: independent and dependent with respect to the fitness threshold. If the payoff variances from all strategy pairing are the same, then we recover the replicator equation from the independent truncation equation. However, if all payoff variances are not equal, then any boundary fixed point can be made stable (or unstable) if only the fitness threshold is chosen appropriately. We observed transient and complex dynamics in our models, which are not observed in replicator equations incorporating the same games. We conclude that the assumptions of mean payoffs and proportional selection in the replicator equation significantly impact replicator dynamics.

OneMax in Black-Box Models with Several Restrictions

Black-box complexity studies lower bounds for the efficiency of general-purpose black-box optimization algorithms such as evolutionary algorithms and other search heuristics. Different models exist, each one being designed to analyze a different aspect of typical heuristics such as the memory size or the variation operators in use. While most of the previous works focus on one particular such aspect, we consider in this work how the combination of several algorithmic restrictions influence the black-box complexity of a problem. Our testbed are so-called OneMax functions which require to minimize the Hamming distance to an unknown target string $$z \in \{0,1\}^n$$zź{0,1}n. We analyze in particular the combined memory-restricted ranking-based black-box complexity of OneMax for different memory sizes. While its isolated (1+1) memory-restricted as well as its ranking-based black-box complexity for bit strings of length n is only of order $$n/\log n$$n/logn, the combined model does not allow for algorithms being faster than linear in n, as can easily be seen by standard information-theoretic considerations. Our main result is a matching upper bound. That is, we show that the (1+1) memory-restricted ranking-based black-box complexity of OneMax is linear. We also analyze its black-box complexity for memory sizes other than (1+1). Moreover, we show that these results apply to the (Monte Carlo) complexity of OneMax in the recently introduced elitist model [Doerr/Lengler GECCO 2015] that combines the above-mentioned memory-restrictions and ranking-based decisions with an enforced usage of truncation selection. Finally, we also provide improved lower bounds for the complexity of OneMax in the regarded models.

A Comparative Analysis of Selection Schemes in the Artificial Bee Colony Algorithm

The Artificial Bee Colony (ABC) algorithm is a popular swarm based algorithm inspired by the intelligent foraging behavior of honey bees. In the past, many swarm intelligence based techniques were introduced and proved their effective performance in solving various optimization problems. The exploitation of food sources is performed by onlooker bees in accordance with a proportional selection scheme that can be further modified to avoid such shortcomings as population diversity and premature convergence. In this paper, different selection schemes, namely, tournament selection, truncation selection, disruptive selection, linear dynamic scaling, linear ranking, sigma truncation, and exponential ranking have been used to analyze the performance of the ABC algorithm by testing on standard benchmark functions. From the simulation results, the schemes other than the standard ABC prove their efficient performance.

SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.

The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.

A simple strategy for managing many recessive disorders in a dairy cattle breeding program.

BackgroundHigh-density single nucleotide polymorphism genotypes have recently been used to identify a number of novel recessive mutations that adversely affect fertility in dairy cattle, as well as to track other conditions such as red coat color and polled. Most current methods for mate allocation fail to consider this information, and it will become increasingly difficult to manage matings as the number of recessive mutations to be accounted for increases.MethodsA modified version of a mating strategy that constrains inbreeding based on genomics (the Pryce method) was developed that also accounts for the economic effects of Mendelian disorders on overall economic merit (modified Pryce method) and compared with random mating, truncation selection, and the Pryce scheme. Several scenarios were considered, including scenarios with six hypothetical recessive alleles and 12 recessive alleles that are currently segregating in the US Holstein population.ResultsThe Pryce method and the modified Pryce method showed similar ability to reduce frequencies of recessive alleles, particularly for loci with frequencies greater than 0.30. The modified Pryce method outperformed the Pryce method for low-frequency alleles with small economic value. Cumulative genetic gain for the selection objective was slightly greater when using the Pryce method, but rates of inbreeding were similar across methods.ConclusionsThe proposed method reduces allele frequencies faster than other methods, and also can be used to maintain or increase the frequency of desirable recessives. It can be easily implemented in software for mate allocation, and the code used in this study is freely available as a reference implementation.

Options for converting a clone trial of Eucalyptus camaldulensis into a clonal seed orchard considering gain, fertility and effective clone number

We evaluated a clone trial comprised of 20 ramets each of 40 Eucalyptus camaldulensis clones for growth and fertility at 4 years of age. The clones differed significantly in growth with 65–100 % survival in 38 clones while two clones had low survival (40 and 10 %). Fecundity was high and fertility variation low, as indicated by the sibling coefficient value (Ψ = 1.4) with 55 % of clones contributing 80 % of the fruits. Thinning strategies were considered to convert the clone trial to a clonal seed orchard and enhance both gain and diversity in seed crop. When clones were selected based on growth without considering fertility, truncation selection (to retain clones with higher DBH than the trial mean value) would give 8 % gain, but 59 % reduction in effective population size (Ne). Gain and predicted diversity (Ne) was highest when ramets were selected in linear proportion to the breeding value of each clone. Mass selection with the same intercept (for DBH) as truncation selection would give a moderate 3 % gain, but over two times higher predicted Ne than truncation selection. When fertility of retained trees was considered after thinning, mass selection would yield 52 % effective contribution from the orchard trees compared to only 30 % contribution from truncation selection and linear thinning. Higher representation of superior clones in linear thinning and mass selection would lead to greater fertility variation and over 30 % reduction in effective clone number (Nc) from that predicted (Ne, assuming equal fertility among ramets) before thinning.

Reciprocal sign epistasis and truncation selection: When is recombination favorable in a pre-breeding program with a selfing species?

Since the dawn of agriculture, humans have applied artificial selection on traits of interest, regardless of their genetic architecture. Yet, still today, most models used to study and streamline this process overlook genetic interactions. In this study, we determined the conditions in which a target genotype can be fixed when truncation selection is applied on an epistatic trait. Previous studies have shown that reciprocal sign epistasis with two fitness peaks of unequal height involves multiple equilibrium states, i.e. below one critical parameter value, such as a critical recombination rate, one genotype may be fixed, and above it, another one may be fixed. Using a haploid bi-locus model, we identified which genotype would be fixed, and how quickly, in an infinite population selected for a phenotypic trait subject to reciprocal sign epistasis with unequal peak heights, depending on two criteria: the recombination rate and percentage of selected individuals. The critical parameter values at which bistability sets in, were also calculated. These results were complemented by stochastic simulations in finite populations. Our results confirmed that, in the case of fitness under reciprocal sign epistasis, high recombination rates induce blockage at the local optimum or attainment of an equilibrium state between the two peaks. However, if linkage disequilibrium is negative in the initial population, recombination is necessary to create the most favorable genotype. Therefore, in this case, reciprocal sign epistasis favors non-null recombination rates, particularly if selection is intense.

Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection.

Deterministic formulas accurately forecast the decline in predictive ability of genomic prediction with changing testers, target environments or traits and truncation selection. Genomic prediction of testcross performance (TP) was found to be a promising selection tool in hybrid breeding as long as the same tester and environments are used in the training and prediction set. In practice, however, selection targets often change in terms of testers, target environments or traits leading to a reduced predictive ability. Hence, it would be desirable to estimate for given training data the expected decline in the predictive ability of genomic prediction under such settings by deterministic formulas that require only quantitative genetic parameters available from the breeding program. Here, we derived formulas for forecasting the predictive ability under different selection targets in the training and prediction set and applied these to predict the TP of lines based on line per se or testcross evaluations. On the basis of two experiments with maize, we validated our approach in four scenarios characterized by different selection targets. Forecasted and empirically observed predictive abilities obtained by cross-validation generally agreed well, with deviations between -0.06 and 0.01 only. Applying the prediction model to a different tester and/or year reduced the predictive ability by not more than 18%. Accounting additionally for truncation selection in our formulas indicated a substantial reduction in predictive ability in the prediction set, amounting, e.g., to 53% for a selected fraction α = 10%. In conclusion, our deterministic formulas enable forecasting the predictive abilities of new selection targets with sufficient precision and could be used to calculate parameters required for optimizing the allocation of resources in multi-stage genomic selection.

Genetic parameters for breech cover, wrinkle and wool coverage scores and their implications for Merino sheep breeding programs and flock management

There is an increased impetus within the Australian sheep industry to pursue a genetic solution to increase the bare area around the breech and reduce the degree of breech wrinkles in order to reduce the incidence of flystrike and the need for surgical mulesing. Breeding programs seeking either a genetic increase in bare skin area around the breech or a decrease in wrinkling require accurate estimates of genetic parameters for breech cover, wrinkle and wool coverage traits as well as the phenotypic and genetic correlations between these traits. Genetic parameters for breech cover, breech wrinkle, body wrinkle, neck wrinkle and wool coverage were estimated from a Merino genetic resource flock at various ages from marking (breech cover and breech wrinkle), to yearling and adult (all traits). The breech cover and wool coverage traits were lowly heritable (h2<0.16), while the various wrinkle traits were moderately heritable (h2 range 0.26–0.34). While all traits will respond to selection, genetic improvement of breech cover and wool coverage are likely to be slower and potentially more difficult due to their low heritability estimates. The various breech cover and wrinkle traits are moderately correlated, phenotypically and genetically, both across age assessments (marking, yearling and adult) and with each other. The phenotypic and genetic correlations were medium to high, particularly between the yearling and adult expressions of these traits. It is likely that selection at yearling age, 10–13 months, may be sufficient for genetic improvement programs seeking to decrease the extent of wool coverage around the breech or the degree of wrinkling on the breech or body of sheep. Such genetic improvement programs would have no impact on wool coverage due to negligible phenotypic and genetic correlations between this trait and the breech cover and wrinkle traits. There is potential for the phenotypic variation in breech cover and wrinkle traits to be exploited by commercial sheep producers to support various on-farm decisions including truncation selection to keep or cull individuals with high breech cover or wrinkle scores, mating allocation as well as potential flock segmentation into ‘easy care’ versus normal management mobs.

Artificial selection with traditional or genomic relationships: consequences in coancestry and genetic diversity.

Estimated breeding values (EBVs) are traditionally obtained from pedigree information. However, EBVs from high-density genotypes can have higher accuracy than EBVs from pedigree information. At the same time, it has been shown that EBVs from genomic data lead to lower increases in inbreeding compared with traditional selection based on genealogies. Here we evaluate the performance with BLUP selection based on genealogical coancestry with three different genome-based coancestry estimates: (1) an estimate based on shared segments of homozygosity, (2) an approach based on SNP-by-SNP count corrected by allelic frequencies, and (3) the identity by state methodology. We evaluate the effect of different population sizes, different number of genomic markers, and several heritability values for a quantitative trait. The performance of the different measures of coancestry in BLUP is evaluated in the true breeding values after truncation selection and also in terms of coancestry and diversity maintained. Accordingly, cross-performances were also carried out, that is, how prediction based on genealogical records impacts the three other measures of coancestry and inbreeding, and viceversa. Our results show that the genetic gains are very similar for all four coancestries, but the genomic-based methods are superior to using genealogical coancestries in terms of maintaining diversity measured as observed heterozygosity. Furthermore, the measure of coancestry based on shared segments of the genome seems to provide slightly better results on some scenarios, and the increase in inbreeding and loss in diversity is only slightly larger than the other genomic selection methods in those scenarios. Our results shed light on genomic selection vs. traditional genealogical-based BLUP and make the case to manage the population variability using genomic information to preserve the future success of selection programmes.