Theo H E Meuwissen

Norwegian Institute of Food, Fisheries and Aquaculture Research, Tromsø, Troms, Norway

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Publications (173)358.76 Total impact

  • P. Stratz, K. Wimmers, T.H.E. Meuwissen, J. Bennewitz
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    ABSTRACT: The aim of this study was to study the population structure, to characterize the LD structure and to define core regions based on low recombination rates among SNP pairs in the genome of Piétrain pigs using data from the PorcineSNP60 BeadChip. This breed is a European sire line and was strongly selected for lean meat content during the last decades. The data were used to map signatures of selection using the REHH test. In the first step, selection signatures were searched genome-wide using only core haplotypes having a frequency above 0.25. In the second step, the results from the selection signature analysis were matched with the results from the recently conducted genome-wide association study for economical relevant traits to investigate putative overlaps of chromosomal regions. A small subdivision of the population with regard to the geographical origin of the individuals was observed. The extent of LD was determined genome-wide using r2 values for SNP pairs with a distance ≤5 Mb and was on average 0.34. This comparable low r2 value indicates a high genetic diversity in the Piétrain population. Six REHH values having a p-value < 0.001 were genome-wide detected. These were located on SSC1, 2, 6 and 17. Three positional candidate genes with potential biological roles were suggested, called LOC100626459, LOC100626014 and MIR1. The results imply that for genome-wide analysis especially in this population, a higher marker density and higher sample sizes are required. For a number of nine SNPs, which were successfully annotated to core regions, the REHH test was applied. However, no selection signatures were found for those regions (p-value < 0.1).
    Journal of Animal Breeding and Genetics 07/2014; · 1.65 Impact Factor
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    ABSTRACT: The main aim of this study was to compare accuracies of imputation and genomic predictions based on single and joint reference populations for Norwegian Red (NRF) and a composite breed (DFS) consisting of Danish Red, Finnish Ayrshire, and Swedish Red. The single nucleotide polymorphism (SNP) data for NRF consisted of 2 data sets: one including 25,000 markers (NRF25K) and the other including 50,000 markers (NRF50K). The NRF25K data set had 2,572 bulls, and the NRF50K data set had 1,128 bulls. Four hundred forty-two bulls were genotyped in both data sets (double-genotyped bulls). The DFS data set (DSF50K) included 50,000 markers of 13,472 individuals, of which around 4,700 were progeny-tested bulls. The NRF25K data set was imputed to 50,000 density using the software Beagle. The average error rate for the imputation of NRF25K decreased slightly from 0.023 to 0.021, and the correlation between observed and imputed genotypes changed from 0.935 to 0.936 when comparing the NRF50K reference and the NRF50K-DFS50K joint reference imputations. A genomic BLUP (GBLUP) model and a Bayesian 4-component mixture model were used to predict genomic breeding values for the NRF and DFS bulls based on the single and joint NRF and DFS reference populations. In the multiple population predictions, accuracies of genomic breeding values increased for the 3 production traits (milk, fat, and protein yields) for both NRF and DFS. Accuracies increased by 6 and 1.3 percentage points, on average, for the NRF and DFS bulls, respectively, using the GBLUP model, and by 9.3 and 1.3 percentage points, on average, using the Bayesian 4-component mixture model. However, accuracies for health or reproduction traits did not increase from the multiple population predictions. Among the 3 DFS populations, Swedish Red gained most in accuracies from the multiple population predictions, presumably because Swedish Red has a closer genetic relationship with NRF than Danish Red and Finnish Ayrshire. The Bayesian 4-component mixture model performed better than the GBLUP model for most production traits for both NRF and DFS, whereas no advantage was found for health or reproduction traits. In general, combining NRF and DFS reference populations was useful in genomic predictions for both the NRF and DFS bulls.
    Journal of Dairy Science 05/2014; · 2.57 Impact Factor
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    ABSTRACT: A simulation study was conducted to investigate the potential advantage of two-step selection for dissemination of genetic gains in salmon production through a system with a nucleus breeding population, a multiplier tier and a grow-out tier. Results demonstrated that profit (measured in the grow-out tier in generation 8) can be substantially increased through production and dissemination of specialised stocks suited for e.g. specific production environments or markets. Truncation selection alternatives in two steps with varying selection proportions were compared to random sampling of parents in both dissemination steps: from the nucleus to the multiplier and from the multiplier to the grow-out tier. Strategies where truncation selection was used in one step and random sampling of parents in the other step were also tested. The selection alternatives with truncation selection in both steps gave on average between 31% and 26% higher profit than random selection. The selection alternative with an extremely low truncation selection proportion in two steps would on average give 2% higher profit than the selection alternative with extremely low truncation selection proportion from the nucleus to the multiplier (1st step), and a normally low truncation selection proportion from the multiplier to the grow-out (2nd step). However, the former alternative yielded five times fewer eggs. The study also showed that one step of truncation selection and one of random sampling of parents, irrespective of the order, would give on average about 19% higher profit compared to random selection in two steps. The effect of the correlation between the nucleus/multiplier breeding goal and the breeding objective of the grow-out was that profit was highest when the correlation was high. With a negative genetic correlation between the traits, profit was still high if the trait with the highest heritability (i.e. the trait measured on candidate itself) had the highest economic value. It was concluded that selection of specialised stocks for specific breeding objectives in two steps from the nucleus via the multiplier and to the grow-out could increase profit by 24%. Specific breeding objectives would also give more flexibility for a final product when the grow-out producers could ask for unique trait-combinations for their fish.
    Aquaculture. 01/2014; s 422–423:78–83.
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    Marie Lillehammer, Theo H Meuwissen, Anna K Sonesson
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    ABSTRACT: Genomic selection can increase genetic gain within aquaculture breeding programs, but the high costs related to high-density genotyping of a large number of individuals would make the breeding program expensive. In this study, a low-cost method using low-density genotyping of pre-selected candidates and their sibs was evaluated by stochastic simulation. A breeding scheme with selection for two traits, one measured on candidates and one on sibs was simulated. Genomic breeding values were estimated within families and combined with conventional family breeding values for candidates that were pre-selected based on conventional BLUP breeding values. This strategy was compared with a conventional breeding scheme and a full genomic selection program for which genomic breeding values were estimated across the whole population. The effects of marker density, level of pre-selection and number of sibs tested and genotyped for the sib-trait were studied. Within-family genomic breeding values increased genetic gain by 15% and reduced rate of inbreeding by 15%. Genetic gain was robust to a reduction in marker density, with only moderate reductions, even for very low densities. Pre-selection of candidates down to approximately 10% of the candidates before genotyping also had minor effects on genetic gain, but depended somewhat on marker density. The number of test-individuals, i.e. individuals tested for the sib-trait, affected genetic gain, but the fraction of the test-individuals genotyped only affected the relative contribution of each trait to genetic gain. A combination of genomic within-family breeding values, based on low-density genotyping, and conventional BLUP family breeding values was shown to be a possible low marker density implementation of genomic selection for species with large full-sib families for which the costs of genotyping must be kept low without compromising the effect of genomic selection on genetic gain.
    Genetics Selection Evolution 10/2013; 45(1):39. · 3.49 Impact Factor
  • B S Dagnachew, T H E Meuwissen, T Adnøy
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    ABSTRACT: The usual practice today is that milk component phenotypes are predicted using Fourier transform infrared (FTIR) spectra and they are then, together with pedigree information, used in BLUP for calculation of individual estimated breeding values. Here, this is referred to as the indirect prediction (IP) approach. An alternative approach-a direct prediction (DP) method-is proposed, where genetic analyses are directly conducted on the milk FTIR spectral variables. Breeding values of all derived milk traits (protein, fat, fatty acid composition, and coagulation properties, among others) can then be predicted as traits correlated only to the genetic information of the spectra. For the DP, no need exists to predict the phenotypes before calculating breeding values for each of the traits-the genetic analysis is done once for the spectra, and is applicable to all traits derived from the spectra. The aim of the study was to compare the effects of DP and IP of milk composition and quality traits on prediction error variance (PEV) and genetic gain. A data set containing 27,927 milk FTIR spectral observations and milk composition phenotypes (fat, lactose, and protein) belonging to 14,869 goats of 271 herds was used for training and evaluating models. Partial least squares regression was used for calibrating prediction models for fat, protein, and lactose percentages. Restricted maximum likelihood was used to estimate variance components of the spectral variables after principal components analysis was applied to reduce the spectral dimension. Estimated breeding values were predicted for fat, lactose, and protein percentages using DP and IP methods. The DP approach reduced the mean PEV by 3.73, 4.07, and 7.04% for fat, lactose, and protein percentages, respectively, compared with the IP method. Given the reduction in PEV, relative genetic gains were 2.99, 2.78, and 4.85% for fat, lactose, and protein percentages, respectively. We concluded that more accurate estimated breeding values could be found using genetic components of milk FTIR spectra compared with single-trait animal model analyses on phenotypes predicted from the spectra separately. The potential and application is not only limited to milk FTIR spectra, but could also be extended to any spectroscopy techniques implemented in other species and for other traits.
    Journal of Dairy Science 07/2013; · 2.57 Impact Factor
  • H F Olsen, T Meuwissen, G Klemetsdal
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    ABSTRACT: 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.
    Journal of Animal Breeding and Genetics 06/2013; 130(3):170-7. · 1.65 Impact Factor
  • M Lillehammer, T H E Meuwissen, A K Sonesson
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    ABSTRACT: The objective of this study was to compare different implementations of genomic selection to a conventional maternal pig breeding scheme, when selection was based partly on production traits and partly on maternal traits. A nucleus pig breeding population with size and structure similar to Norwegian Landrace was simulated where equal weight was used for maternal and production traits. To genotype the boars at the boar station and base the final selection of boars on genomic breeding values increased total genetic gain by 13 % and reduced the rate of inbreeding by 40 %, without significantly affecting the relative contribution of each trait to total genetic gain. In order to increase the size of the reference population and thereby accuracy of selection, female sibs in the selected litters can also be genotyped to increase genetic gain for maternal traits more than for production traits, thereby resulting in an increased relative contribution of maternal traits to total genetic gain. Genotyping 2400 females each year increased the relative contribution of maternal traits to total genetic gain from 16 % to 32 %. Performing pre-selection of males by allowing genotyping of 2 males per litter and allowing for selection across and within litters prior to the boar test, increased genetic gain by 5-11 %, compared to genotyping the boars at the boar station, without significant effects on the relative contribution of each trait to total genetic gain. Genotyping more animals consequently increased genetic gain. Genotyping females to build a larger reference base for maternal traits gave similar genetic gain as genotyping the same amount of additional males, but with a lower rate of inbreeding and a higher contribution of maternal traits to total genetic gain. In conclusion, genotyping females should be prioritized before genotyping more males than the tested boars if the breeding goal is to increase maternal traits specifically over production traits or genomic selection is used as a tool to reduce the rate of inbreeding.
    Journal of Animal Science 05/2013; · 2.09 Impact Factor
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    Jørgen Ødegård, Theo H E Meuwissen
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    ABSTRACT: In classical pedigree-based analysis, additive genetic variance is estimated from between-family variation, which requires the existence of larger phenotyped and pedigreed populations involving numerous families (parents). However, estimation is often complicated by confounding of genetic and environmental family effects, with the latter typically occurring among full-sibs. For this reason, genetic variance is often inferred based on covariance among more distant relatives, which reduces the power of the analysis. This simulation study shows that genome-wide identity-by-descent sharing among close relatives can be used to quantify additive genetic variance solely from within-family variation using data on extremely small family samples. Identity-by-descent relationships among full-sibs were simulated assuming a genome size similar to that of humans (effective number of loci ~80). Genetic variance was estimated from phenotypic data assuming that genomic identity-by-descent relationships could be accurately re-created using information from genome-wide markers. The results were compared with standard pedigree-based genetic analysis. For a polygenic trait and a given number of phenotypes, the most accurate estimates of genetic variance were based on data from a single large full-sib family only. Compared with classical pedigree-based analysis, the proposed method is more robust to selection among parents and for confounding of environmental and genetic effects. Furthermore, in some cases, satisfactory results can be achieved even with less ideal data structures, i.e., for selectively genotyped data and for traits for which the genetic variance is largely under the control of a few major genes. Estimation of genetic variance using genomic identity-by-descent relationships is especially useful for studies aiming at estimating additive genetic variance of highly fecund species, using data from small populations with limited pedigree information and/or few available parents, i.e., parents originating from non-pedigreed or even wild populations.
    Genetics Selection Evolution 05/2012; 44:16. · 3.49 Impact Factor
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    ABSTRACT: The risk of long-term unequal contribution of mating pairs to the gene pool is that deleterious recessive genes can be expressed. Such consequences could be alleviated by appropriately designing and optimizing breeding schemes i.e. by improving selection and mating procedures. We studied the effect of mating designs, random, minimum coancestry and minimum covariance of ancestral contributions on rate of inbreeding and genetic gain for schemes with different information sources, i.e. sib test or own performance records, different genetic evaluation methods, i.e. BLUP or genomic selection, and different family structures, i.e. factorial or pair-wise. Results showed that substantial differences in rates of inbreeding due to mating design were present under schemes with a pair-wise family structure, for which minimum coancestry turned out to be more effective to generate lower rates of inbreeding. Specifically, substantial reductions in rates of inbreeding were observed in schemes using sib test records and BLUP evaluation. However, with a factorial family structure, differences in rates of inbreeding due mating designs were minor. Moreover, non-random mating had only a small effect in breeding schemes that used genomic evaluation, regardless of the information source. It was concluded that minimum coancestry remains an efficient mating design when BLUP is used for genetic evaluation or when the size of the population is small, whereas the effect of non-random mating is smaller in schemes using genomic evaluation.
    Genetics Selection Evolution 04/2012; 44:11. · 3.49 Impact Factor
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    Naveen K Kadri, Patrick D Koks, Theo H E Meuwissen
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    ABSTRACT: A newly recognized type of genetic variation, Copy Number Variation (CNV), is detected in mammalian genomes, e.g. the cattle genome. This form of variation can potentially cause phenotypic variation. Our objective was to determine whether dense SNP (single nucleotide polymorphisms) panels can capture the genetic variation due to a simple bi-allelic CNV, with the prospect of including the effect of such structural variations into genomic predictions. A deletion type CNV on bovine chromosome 6 was predicted from its neighboring SNP with a multiple regression model. Our dataset consisted of CNV genotypes of 1,682 cows, along with 100 surrounding SNP genotypes. A prediction model was fitted considering 10 to 100 surrounding SNP and the accuracy obtained directly from the model was confirmed by cross-validation. The accuracy of prediction increased with an increasing number of SNP in the model and the predicted accuracies were similar to those obtained by cross-validation. A substantial increase in accuracy was observed when the number of SNP increased from 10 to 50 but thereafter the increase was smaller, reaching the highest accuracy (0.94) with 100 surrounding SNP. Thus, we conclude that the genotype of a deletion type CNV and its putative QTL effect can be predicted with a maximum accuracy of 0.94 from surrounding SNP. This high prediction accuracy suggests that genetic variation due to simple deletion CNV is well captured by dense SNP panels. Since genomic selection relies on the availability of a dense marker panel with markers in close linkage disequilibrium to the QTL in order to predict their genetic values, we also discuss opportunities for genomic selection to predict the effects of CNV by dense SNP panels, when CNV cause variation in quantitative traits.
    Genetics Selection Evolution 03/2012; 44:7. · 3.49 Impact Factor
  • M E Goddard, B J Hayes, T H E Meuwissen
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    ABSTRACT: Estimated breeding values (EBVs) using data from genetic markers can be predicted using a genomic relationship matrix, derived from animal's genotypes, and best linear unbiased prediction. However, if the accuracy of the EBVs is calculated in the usual manner (from the inverse element of the coefficient matrix), it is likely to be overestimated owing to sampling errors in elements of the genomic relationship matrix. We show here that the correct accuracy can be obtained by regressing the relationship matrix towards the pedigree relationship matrix so that it is an unbiased estimate of the relationships at the QTL controlling the trait. This method shows how the accuracy increases as the number of markers used increases because the regression coefficient (of genomic relationship towards pedigree relationship) increases. We also present a deterministic method for predicting the accuracy of such genomic EBVs before data on individual animals are collected. This method estimates the proportion of genetic variance explained by the markers, which is equal to the regression coefficient described above, and the accuracy with which marker effects are estimated. The latter depends on the variance in relationship between pairs of animals, which equals the mean linkage disequilibrium over all pairs of loci. The theory was validated using simulated data and data on fat concentration in the milk of Holstein cattle.
    Journal of Animal Breeding and Genetics 12/2011; 128(6):409-21. · 1.65 Impact Factor
  • T H E Meuwissen, T Luan, J A Woolliams
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    ABSTRACT: Previous proposals for a unified approach for amalgamating information from animals with or without genotypes have combined the numerator relationship matrix A with the genomic relationship G estimated from the markers. These approaches have resulted in biased genomic EBV (GEBV), and methodology was developed to overcome these problems. Firstly, a relationship matrix, G(FG) , based on linkage analysis was derived using the same base population as A, which (i) utilizes the genomic information on the same scale as the pedigree information and (ii) permits the regression coefficients used to propagate the genomic data from the genotyped to ungenotyped individuals to be calculated in the light of the genomic information, rather than ignoring it. Secondly, the elements of G were regressed back towards their expected values in the A matrix to allow for their estimation errors. These developments were combined in a methodology LDLAb and tested on simulated populations where either parents were phenotyped and offspring genotyped or vice versa. The LDLAb method was demonstrated to be a unified approach that maximized accuracy of GEBV compared to previous methodologies and removed the bias in the GEBV. Although LDLAb is computationally much more demanding than MLAC, it demonstrates how to make best use the marker information and also shows the computational problems that need to be solved in the future to make best use of the marker data.
    Journal of Animal Breeding and Genetics 12/2011; 128(6):429-39. · 1.65 Impact Factor
  • I Cervantes, T H E Meuwissen
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    ABSTRACT: The preservation of the maximum genetic diversity in a population is one of the main objectives within a breed conservation programme. We applied the maximum variance total (MVT) method to a unique population in order to maximize the total genetic variance. The function maximization was performed by the annealing algorithm. We have selected the parents and the mating scheme at the same time simply maximizing the total genetic variance (a mate selection problem). The scenario was compared with a scenario of full-sib lines, a MVT scenario with a rate of inbreeding restriction, and with a minimum coancestry selection scenario. The MVT method produces sublines in a population attaining a similar scheme as the full-sib sublining that agrees with other authors that the maximum genetic diversity in a population (the lowest overall coancestry) is attained in the long term by subdividing it in as many isolated groups as possible. The application of a restriction on the rate of inbreeding jointly with the MVT method avoids the consequences of inbreeding depression and maintains the effective size at an acceptable minimum. The scenario of minimum coancestry selection gave higher effective size values, but a lower total genetic variance. A maximization of the total genetic variance ensures more genetic variation for extreme traits, which could be useful in case the population needs to adapt to a new environment/production system.
    Journal of Animal Breeding and Genetics 12/2011; 128(6):465-72. · 1.65 Impact Factor
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    Xijiang Yu, Theo H E Meuwissen
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    ABSTRACT: Genome-wide breeding value (GWEBV) estimation methods can be classified based on the prior distribution assumptions of marker effects. Genome-wide BLUP methods assume a normal prior distribution for all markers with a constant variance, and are computationally fast. In Bayesian methods, more flexible prior distributions of SNP effects are applied that allow for very large SNP effects although most are small or even zero, but these prior distributions are often also computationally demanding as they rely on Monte Carlo Markov chain sampling. In this study, we adopted the Pareto principle to weight available marker loci, i.e., we consider that x% of the loci explain (100 - x)% of the total genetic variance. Assuming this principle, it is also possible to define the variances of the prior distribution of the 'big' and 'small' SNP. The relatively few large SNP explain a large proportion of the genetic variance and the majority of the SNP show small effects and explain a minor proportion of the genetic variance. We name this method MixP, where the prior distribution is a mixture of two normal distributions, i.e. one with a big variance and one with a small variance. Simulation results, using a real Norwegian Red cattle pedigree, show that MixP is at least as accurate as the other methods in all studied cases. This method also reduces the hyper-parameters of the prior distribution from 2 (proportion and variance of SNP with big effects) to 1 (proportion of SNP with big effects), assuming the overall genetic variance is known. The mixture of normal distribution prior made it possible to solve the equations iteratively, which greatly reduced computation loads by two orders of magnitude. In the era of marker density reaching million(s) and whole-genome sequence data, MixP provides a computationally feasible Bayesian method of analysis.
    Genetics Selection Evolution 11/2011; 43:35. · 3.49 Impact Factor
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    J Fernández, T H E Meuwissen, M A Toro, A Mäki-Tanila
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    ABSTRACT: Many local breeds of farm animals have small populations and, consequently, are highly endangered. The correct genetic management of such populations is crucial for their survival. Managing an animal population involves two steps: first, the individuals who will be permitted to leave descendants are to be chosen and the number offspring they will be permitted to produce has to be determined; second, the mating scheme has to be identified. Strategies dealing with the first step are directed towards the maximisation of effective population size and, therefore, act jointly on the reduction in the loss of genetic variation and in the increase of inbreeding. In this paper, the most relevant methods are summarised, including the so-called 'Optimum Contribution' methodology (contributions are proportional to the coancestry of each individual with the rest), which has been shown to be the best. Typically, this method is applied to pedigree information, but molecular marker data can be used to complete or replace the genealogy. When the population is subjected to explicit selection on any trait, the above methodology can be used by balancing the response to selection and the increase in coancestry/inbreeding. Different mating strategies also exist. Some of the mating schemes try to reduce the level of inbreeding in the short term by preventing mating between relatives. Others involve regular (circular) schemes that imply higher levels of inbreeding within populations in the short term, but demonstrate better performance in the long term. In addition, other tools such as cryopreservation and reproductive techniques aid in the management of small populations. In the future, genomic marker panels may replace the pedigree information in measuring the coancestry. The paper also includes the results of several experiments and field studies on the effectiveness and on the consequences of the use of the different strategies.
    animal 09/2011; 5(11):1684-98. · 1.65 Impact Factor
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    ABSTRACT: Genetic variation is vital for the populations to adapt to varying environments and to respond to artificial selection; therefore, any conservation and development scheme should start from assessing the state of variation in the population. There are several marker-based and pedigree-based parameters to describe genetic variation. The most suitable ones are rate of inbreeding and effective population size, because they are not dependent on the amount of pedigree records. The acceptable level for effective population size can be considered from different angles leading to a conclusion that it should be at least 50 to 100. The estimates for the effective population size can be computed from the genealogical records or from demographic and marker information when pedigree data are not available. Marker information could also be used for paternity analysis and for estimation of coancestries. The sufficient accuracy in marker-based parameters would require typing thousands of markers. Across breeds, diversity is an important source of variation to rescue problematic populations and to introgress new variants. Consideration of adaptive variation brings new aspects to the estimation of the variation between populations.
    animal 09/2011; 5(11):1669-83. · 1.65 Impact Factor
  • M Lillehammer, T H E Meuwissen, A K Sonesson
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    ABSTRACT: The aim of this study was to compare alternative designs for implementation of genomic selection to improve maternal traits in pigs, with a conventional breeding scheme and a progeny testing scheme. The comparison was done through stochastic simulation of a pig population. It was assumed that selection was performed based on a trait that could be measured on females after the first litter, with a heritability of 0.1. Genomic selection increased genetic gain and reduced the rate of inbreeding, compared with conventional selection without progeny testing. Progeny testing could also increase genetic gain and decrease the rate of inbreeding, but because of the increased generation interval, the increase in annual genetic gain was only 7%. When genomic selection was applied, genetic gain was increased by 23 to 91%, depending on which and how many animals were genotyped. Genotyping dams in addition to the male selection candidates gave increased accuracy of the genomic breeding values, increased genetic gain, and decreased rate of inbreeding. To genotype 2 or 3 males from each litter, in order to perform within-litter selection, increased genetic gain 8 to 12%, compared with schemes with the same number of genotyped females but only 1 male candidate per litter. Comparing schemes with the same total number of genotyped animals revealed that genotyping more females caused a greater increase in genetic gain than genotyping more males because greater accuracy of selection was more advantageous than increasing the number of male selection candidates. When more than 1 male per litter was genotyped, and thereby included as selection candidates, rate of inbreeding increased because of coselection of full sibs. The conclusion is that genomic selection can increase genetic gain for traits that are measured on females, which includes several traits with economic importance in maternal pig breeds. Genotyping females is essential to obtain a high accuracy of selection.
    Journal of Animal Science 08/2011; 89(12):3908-16. · 2.09 Impact Factor
  • N Mc Hugh, T H E Meuwissen, A R Cromie, A K Sonesson
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    ABSTRACT: Genomic selection has the potential to increase the accuracy of selection and, therefore, genetic gain, as well as reducing the rate of inbreeding, yet few studies have evaluated the potential benefit of the contribution of females in genomic selection programs. The objective of this study was to determine the effect on genetic gain, accuracy of selection, generation interval, and inbreeding, of including female genotypes in a genomic selection breeding program. A population of approximately 3,500 females and 500 males born annually was simulated and split into an elite and commercial tier representation of the Irish national herd. Several alternative breeding schemes were evaluated to quantify the potential benefit of female genomic information within dairy breeding schemes. Results showed that the inclusion of female phenotypic and genomic information can lead to a 3-fold increase in the rate of genetic gain compared with a traditional BLUP breeding program and decrease the generation interval of the males by 3.8 yr, while maintaining a reasonable rate of inbreeding. The accuracy of the selected males was increased by 73% in the final 3 yr of the genomic schemes compared with the traditional BLUP scheme. The results of this study have several implications for national breeding schemes. Although an investment in genotyping a large population of animals is required, these costs can be offset by the greater genetic gain achievable through the increased accuracy of selection and decreased generation intervals associated with genomic selection.
    Journal of Dairy Science 08/2011; 94(8):4109-18. · 2.57 Impact Factor
  • D Hinrichs, T H E Meuwissen
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    ABSTRACT: 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.
    Journal of Animal Science 06/2011; 89(11):3426-32. · 2.09 Impact Factor
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    ABSTRACT: In aquaculture breeding, resistance against infectious diseases is commonly assessed as time until death under exposure to a pathogen. For some diseases, a fraction of the individuals may appear as "cured" (non-susceptible), and the resulting survival time may thus be a result of two confounded underlying traits, i.e., endurance (individual hazard) and susceptibility (whether at risk or not), which may be accounted for by fitting a cure survival model. We applied a cure model to survival data of Pacific white shrimp (Penaeus vannamei) challenged with the Taura syndrome virus, which is one of the major pathogens of Panaeid shrimp species. In total, 15,261 individuals of 513 full-sib families from three generations were challenge-tested in 21 separate tests (tanks). All challenge-tests were run until mortality naturally ceased. Time-until-event data were analyzed with a mixed cure survival model using Gibbs sampling, treating susceptibility and endurance as separate genetic traits. Overall mortality at the end of test was 28%, while 38% of the population was considered susceptible to the disease. The estimated underlying heritability was high for susceptibility (0.41 ± 0.07), but low for endurance (0.07 ± 0.03). Furthermore, endurance and susceptibility were distinct genetic traits (rg = 0.22 ± 0.25). Estimated breeding values for endurance and susceptibility were only moderately correlated (0.50), while estimated breeding values from classical models for analysis of challenge-test survival (ignoring the cured fraction) were closely correlated with estimated breeding values for susceptibility, but less correlated with estimated breeding values for endurance. For Taura syndrome resistance, endurance and susceptibility are apparently distinct genetic traits. However, genetic evaluation of susceptibility based on the cure model showed clear associations with standard genetic evaluations that ignore the cure fraction for these data. Using the current testing design, genetic variation in observed survival time and absolute survival at the end of test were most likely dominated by genetic variation in susceptibility. If the aim is to reduce susceptibility, earlier termination of the challenge-test or back-truncation of the follow-up period should be avoided, as this may shift focus of selection towards endurance rather than susceptibility.
    Genetics Selection Evolution 03/2011; 43:14. · 3.49 Impact Factor

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Institutions

  • 2009–2012
    • Norwegian Institute of Food, Fisheries and Aquaculture Research
      Tromsø, Troms, Norway
  • 2005–2012
    • Norwegian University of Life Sciences (UMB)
      • Department of Animal and Aquacultural Sciences (IHA)
      Ås, Akershus Fylke, Norway
  • 2011
    • Complutense University of Madrid
      • Departamento de Producción Animal
      Madrid, Madrid, Spain
    • TEAGASC - The Agriculture and Food Development Authority
      • Grange Animal & Grassland Research and Innovation Centre
      Carlow, Leinster, Ireland
    • Norsvin SA
      Hamar, Hedmark county, Norway
  • 2007–2011
    • University of Melbourne
      • • Department of Agriculture and Food Systems
      • • Melbourne’s School of Land and Environment (MSLE)
      Melbourne, Victoria, Australia
  • 2005–2011
    • Christian-Albrechts-Universität zu Kiel
      • Institute of Animal Breeding and Husbandry
      Kiel, Schleswig-Holstein, Germany
  • 2010
    • Hohenheim University
      • Institute of Animal Husbandry and Animal Breeding
      Stuttgart, Baden-Wuerttemberg, Germany
  • 2008–2010
    • Life University
      Marietta, Georgia, United States
    • Department of Environment and Primary Industries
      Melbourne, Victoria, Australia
  • 2004–2008
    • Wageningen University
      • Animal Breeding and Genomics Centre
      Wageningen, Provincie Gelderland, Netherlands
  • 2006
    • French National Institute for Agricultural Research
      Lutetia Parisorum, Île-de-France, France
  • 2002
    • Irish Cattle Breeding Federation
      Dublin, Leinster, Ireland
  • 1995–2001
    • Merck Animal Health Netherlands
      Boksmeer, North Brabant, Netherlands
  • 2000
    • University of Georgia
      • Department of Animal and Dairy Science
      Athens, GA, United States
  • 1994
    • University of Guelph
      • Department of Animal and Poultry Science
      Guelph, Ontario, Canada
  • 1992–1994
    • Sociaal en Cultureel Planbureau
      's-Gravenhage, South Holland, Netherlands