T.H.E. Meuwissen

Norwegian University of Life Sciences (UMB), Aas, Akershus county, Norway

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Publications (238)412.62 Total impact

  • Source
    M L van Pelt · T H E Meuwissen · G de Jong · R F Veerkamp
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    ABSTRACT: Longevity, productive life, or lifespan of dairy cattle is an important trait for dairy farmers, and it is defined as the time from first calving to the last test date for milk production. Methods for genetic evaluations need to account for censored data; that is, records from cows that are still alive. The aim of this study was to investigate whether these methods also need to take account of survival being genetically a different trait across the entire lifespan of a cow. The data set comprised 112,000 cows with a total of 3,964,449 observations for survival per month from first calving until 72 mo in productive life. A random regression model with second-order Legendre polynomials was fitted for the additive genetic effect. Alternative parameterizations were (1) different trait definitions for the length of time interval for survival after first calving (1, 3, 6, and 12 mo); (2) linear or threshold model; and (3) differing the order of the Legendre polynomial. The partial derivatives of a profit function were used to transform variance components on the survival scale to those for lifespan. Survival rates were higher in early life than later in life (99 vs. 95%). When survival was defined over 12-mo intervals survival curves were smooth compared with curves when 1-, 3-, or 6-mo intervals were used. Heritabilities in each interval were very low and ranged from 0.002 to 0.031, but the heritability for lifespan over the entire period of 72 mo after first calving ranged from 0.115 to 0.149. Genetic correlations between time intervals ranged from 0.25 to 1.00. Genetic parameters and breeding values for the genetic effect were more sensitive to the trait definition than to whether a linear or threshold model was used or to the order of Legendre polynomial used. Cumulative survival up to the first 6 mo predicted lifespan with an accuracy of only 0.79 to 0.85; that is, reliability of breeding value with many daughters in the first 6 mo can be, at most, 0.62 to 0.72, and changes of breeding values are still expected when daughters are getting older. Therefore, an improved model for genetic evaluation should treat survival as different traits during the lifespan by splitting lifespan in time intervals of 6 mo or less to avoid overestimated reliabilities and changes in breeding values when daughters are getting older. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
    Journal of Dairy Science 04/2015; 98(6). DOI:10.3168/jds.2014-9090 · 2.55 Impact Factor
  • J.A. Woolliams · P. Berg · B.S. Dagnachew · T.H.E. Meuwissen
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    ABSTRACT: Genetic contributions were first formalized in 1958 by James and McBride (Journal of Genetics, 56, 55-62) and have since been shown to provide a unifying framework for theories of gain and inbreeding. As such they have underpinned the development of methods that provide the most effective combination of maximizing gain whilst managing inbreeding and loss of genetic variation. It is shown how this optimum contribution technology can be developed from theory and adapted to provide practical selection protocols for a wide variety of situations including overlapping generations and multistage selection. The natural development of the theory to incorporate genomic selection and genomic control of inbreeding is also shown. © 2015 Blackwell Verlag GmbH.
    Journal of Animal Breeding and Genetics 04/2015; 132(2). DOI:10.1111/jbg.12148 · 2.06 Impact Factor
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    ABSTRACT: The term functionality in animal breeding is used for traits that increase the efficiency of production by lowering the input cost, such as animal health and leg weakness related to longevity. The main objective of the study was to investigate the impact of genomic information, in a multivariate variance component analysis, on some of these traits. In addition, the effect of the inclusion was studied by testing the model's prediction ability based on best linear unbiased estimates for fixed and random effects. The material in this study consists of phenotypes from 76 683 animals, of which 4933 animals are genotyped. The heritabilities for front leg conformation, stayability, osteochondrosis and arched back, estimated using the traditional pedigree, were found to be between 0.12 and 0.29. When using the combined genomic and pedigree relationship matrix, the heritabilities were between 0.14 and 0.36. The results show that the combined relationship matrix can be used for the estimation of (co)variance components, and that the predictive ability of the model in this study marginally increases with the inclusion of genomic information. © 2015 Blackwell Verlag GmbH.
    Journal of Animal Breeding and Genetics 03/2015; DOI:10.1111/jbg.12156 · 2.06 Impact Factor
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    ABSTRACT: Reliability of genomic selection (GS) models was tested in an admixed population of Atlantic salmon, originating from crossing of several wild subpopulations. The models included ordinary genomic BLUP models (GBLUP), using genome-wide SNP markers of varying densities (1-220 k), a genomic identity-by-descent model (IBD-GS), using linkage analysis of sparse genome-wide markers, as well as a classical pedigree-based model. Reliabilities of the models were compared through 5-fold cross-validation. The traits studied were salmon lice (Lepeophtheirus salmonis) resistance (LR), measured as (log) density on the skin and fillet color (FC), with respective estimated heritabilities of 0.14 and 0.43. All genomic models outperformed the classical pedigree-based model, for both traits and at all marker densities. However, the relative improvement differed considerably between traits, models and marker densities. For the highly heritable FC, the IBD-GS had similar reliability as GBLUP at high marker densities (>22 k). In contrast, for the lowly heritable LR, IBD-GS was clearly inferior to GBLUP, irrespective of marker density. Hence, GBLUP was robust to marker density for the lowly heritable LR, but sensitive to marker density for the highly heritable FC. We hypothesize that this phenomenon may be explained by historical admixture of different founder populations, expected to reduce short-range lice density (LD) and induce long-range LD. The relative importance of LD/relationship information is expected to decrease/increase with increasing heritability of the trait. Still, using the ordinary GBLUP, the typical long-range LD of an admixed population may be effectively captured by sparse markers, while efficient utilization of relationship information may require denser markers (e.g., 22 k or more).
    Frontiers in Genetics 11/2014; 5:402. DOI:10.3389/fgene.2014.00402
  • Theo H. E. Meuwissen · Anna K Sonesson · Jrgen degrd
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    ABSTRACT: Abstract Text: In the literature an abundance of genomic relationship matrices have been described which mainly differ in the age of the relationships that they trace. Marker based relationship matrices (G) generally trace very old relationships, since the marker mutations occurred. Pedigree (A) and linkage analysis relationship matrices (GLA) trace relationships since pedigree recording started, i.e. since the founder population. Genomic selection (GS) is based on three sources of information: a) pedigree relationships (A); b) linkage analysis (LA) information and c)linkage disequilibrium (LD) information, where LD is defined between alleles in the founder population. LD due to cosegregation of alleles in the known pedigree is denoted LA information. The described relationship matrices follow the same pattern, i.e. A, GLA and G, respectively. Keywords:genomic selection genomic relationships genetic modelling whole genome sequence data
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    ABSTRACT: Abstract Text: Purebred Duroc and Yorkshire sows were crossed with Göttingen minipig boars to obtain two separate F2 intercross resource populations (n=287 and 279 respectively). Several obesity, metabolic and slaughter measurements were recorded from birth to slaughter (220±45 days). In addition, body composition was determined at about two months of age (64±11 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined LDLA approach was used to perform genome-wide linkage and association analysis for body fat traits. Subsequently bioinformatic analysis was performed to identify genes in close proximity of chromosomal positions where statistically significant QTLs were identified. Several important genes previously linked to obesity (e.g. BBS4, CHRNA2, DLK1), along with other novel genes were identified, that together provide novel insights that may further the current understanding of the molecular mechanisms underlying human obesity. Keywords: LDLA, Pig model, QTL mapping
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • Xijiang Yu · Theo H. E. Meuwissen · Matthew Baranski · Anna K Sonesson
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    ABSTRACT: Abstract Text: Atlantic cod families from year 2009 of the Norwegian national cod breeding program were challenged for viral nervous necrosis and vibriosis. Mortality was recorded. Around 1600 offspring and their parents were genotyped at 10,913 SNP loci, covering 2,285 scaffolds/contigs in the reference genome, which accounts for ~71.3% of total sequence length. Genomic enabled breeding values (GEBV) were estimated. A 10-folds cross-validation shows that the correlations of the survival states and corresponding GEBV were 0.085 for vibriosis and 0.55 for VNN. Whole genome resequencing of 111 parents was performed to an approximately 12x coverage per individual. Variant calling in the sequence of a subset of parents showed that all 12K SNP array SNPs were called and had matching genotypes. Imputation with Beagle and LDMIP software will enable inference of sequence data for all the challenge tested fish and the resulting improvement in accuracy will be investigated. Keywords: Atlantic cod Disease Sequence
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    Kahsay G Nirea · Anna K Sonesson · Marie Lillehammer · Theo H. E. Meuwissen
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    ABSTRACT: Abstract Text: A higher accuracy of prediction was obtained for within family genomic selection as compared to the conventional selection method. The accuracy of selection was higher when the family structure was 10x10 followed by 1x10 and 2x2 specifically for within family genomic selection. This was in accordance with the number of full sibs and half sibs produced which increased relationships within or across test and candidate sibs. In all scenarios, accuracy of selection increased as family size increases but the increase was moderate when family size was beyond 40-50 individuals per family. In addition, the benefit would be higher if more full sibs and half sibs are available in the test and candidate group. Keywords: Aquaculture Within-Family Genomic selection Breeding Genetics
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • John A. Woolliams · Kahsay G Nirea · Theo H. E. Meuwissen
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    ABSTRACT: Abstract Text: Predicting gain for optimum contribution selection is associated with two issues, the first concerned with inter-generational dependence of the contributions, and the second concerned with dynamic desirability. By ignoring the latter, which is valid when the accuracy of candidates approaches 1, a formula for ΔG(T, ΔF, α) can be obtained, where ΔG(T, ΔF, α) is the maximum gain possible with T candidates per generation, rate of inbreeding ΔF, and degree of coancestry α. Simulation showed predictions were reasonable, although further validation is required. The developed theory made testable predictions that the importance of mating designs depended only on their impact on α as accuracy approaches 1, and simulations also validated this prediction. Mating designs that affect α retain impact because they affect both the variance of the Mendelian sampling term and the relationship between squared contributions and ΔF. Keywords: Rate of Gain, Rate of Inbreeding, Optimum Contributions, Genomic Selection, Mating Design.
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • Binyam S. Dagnachew · Theo H. E. Meuwissen
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    ABSTRACT: Abstract Text: A novel iterative algorithm, Gencont2, for calculating optimum genetic contributions was developed. It was validated by comparing it with a previous program, Gencont, on three datasets obtained from practical breeding programs of three species (cattle, pig and sheep). The numbers of selection candidates were 2,929, 3,907 and 6,875 for the pig, cattle and sheep datasets respectively. In most cases, both algorithms select the same candidates and gave very similar results in genetic gain. In cases, when there were differences in number of animals to select, the extra selected candidates had contributions within the range of 0.006–0.08%. The correlations between assigned contributions were very close to 1; however, Gencont2 considerably decreased the computation time by 90% to 95% (13 to 22 times faster) compared to Gencont. This fast iterative algorithm makes the practical implementation of OC selection feasible in large scale breeding programs. Keywords: Inbreeding optimum genetic contributions genetic gain
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • Jrgen degrd · Thomas Moen · N. Santi · S. A. Korsvoll · S. Kjglum · Theo H. E. Meuwissen
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    ABSTRACT: Abstract Text: Reliability of genomic selection (GS) models was tested in an admixed population of Atlantic salmon, originating from crossing of several wild subpopulations. The GS models included ordinary genomic BLUP models (IBS-GS), using varying marker densities (1 to 220K) and a genomic IBD model (IBD-GS) using genomic relationships estimated through linkage analysis of sparse markers (ignoring LD). The models were compared based on 5-fold cross-validation. The traits studied were log density of salmon lice on skin (logDL) and fillet color (FC), with respective estimated heritabilities of 0.14 and 0.43. IBD-GS and IBS-GS (220K) had similar reliabilities’ for FC, while IBS-GS was superior for logDL. The IBS-GS model was remarkable robust to marker density, especially for logDL, and outperformed pedigree-based models at all densities, which may be explained by admixture of the population, introducing long-range LD. Increasing SNP densities beyond 22K had limited effect for both traits. Keywords: Atlantic salmon, genomic selection, admixture
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • Kristine Hov Martinsen · Jrgen degrd · Dan Olsen · Theo H. E. Meuwissen
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    ABSTRACT: Abstract Text: Data were recorded on 7,434 Norwegian Landrace boars born from 2008 to 2013. Data was provided from the boar test station of the Norwegian Pig Breeders’ Association. Feed consumption in the test period was registered together with carcass traits assessed by computer tomography. The data were analyzed with an animal model with lean meat (kg) and fat (kg) included as covariates through fixed and random regressions. The results showed significant genetic variation in the animals’ efficiency to deposit lean meat and fat, and indicated that there was a greater genetic variation in efficiency to deposit fat compared with lean meat. The genetic correlation between the two efficiency traits was high (0.72), indicating that these two factors are distinct, albeit correlated genetic traits. Keywords: Feed efficiency Genetics Lean meat
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    ABSTRACT: Abstract Text: The availability of whole-genome sequence data (WGS data) on large number of livestock’s provides new opportunity for genomic selection. We investigated how much accuracy is gained by using WGS data in diverged cattle populations, using simulation. Relative performance of genomic BLUP and a Bayesian (BayesP) method with a mixture of normal distributions were compared. WGS data increased accuracy (3-7%) of within population predictions for moderate – lowly heritable traits. The advantage of WGS data (18-24%) was more pronounced with reference populations (RP) combined across breeds and when using BayesP. Extending the RP to multiple-breeds resulted in a 10-22% increase in accuracy with WGS data. BayesP outperformed GBLUP at 45 QTL/M, although in real data both methods have been shown to perform quite similar. Genomic predictions in numerically minor cattle populations would benefit from a combination of WGS data, multi-breed RP, and Bayesian estimation methods. Keywords: genomic prediction; whole-genome sequence; multi-breed
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    ABSTRACT: Abstract Text: Traditionally, rates of inbreeding and effective population sizes have been estimated by the use of pedigree data. Here, inbreeding coefficients were estimated from runs of homozygosity in 322 Norwegian Red bulls born between 1982 and 2002. Further, inbreeding rates were estimated by regressing the natural logarithm of (1-FROH) on year of birth, resulting in an inbreeding rate per generation of 0.303 % and a corresponding effective population size of 165 individuals. This resembles the estimates made by the industry in 2011 based on pedigree information, giving an inbreeding rate of 0.26 % and an effective population size of 194. These results suggests that these two parameters can be estimated by the use of genomic data only, with possible application also to wild and/or endangered populations. Keywords: Effective population size (Ne) Rate of inbreeding (ΔF) Runs of Homozygosity (ROH) Norwegian Red cattle
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    ABSTRACT: Abstract Text: Genomic prediction from dense SNP genotypes is widely used to predict breeding values for livestock, and crop breeding. In many cases the most accurate methods are Bayesian, usually implemented via Markov Chain Monte Carlo (MCMC) scheme but this is computationally expensive. To retain the advantages of the Bayesian methods, with greatly reduced computation time, an efficient Expectation-Maximisation algorithm termed emBayesR is proposed. emBayesR retains the BayesR model’s prior assumption for SNP effects, of four normal distributions with increasing variance. To improve the accuracy of genomic prediction compared to other non-MCMC approaches, emBayesR estimates the effect of each SNP while allowing for the error associated with estimation of all other SNP effects. Compared with BayesR, emBayesR reduced computational time up to 8 fold while maintaining similar prediction accuracy on both simulated data, and real 800K dairy data. Keywords: Genomic prediction Expectation-Maximisation algorithm Markov Chain Monte Carlo
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • Tu Luan · Xijiang Yu · Theo H. E. Meuwissen
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    ABSTRACT: Abstract Text: The aim of this research was to investigate the effect of prioritized genotyping cows to improve the accuracy of genomic selection. In the study, TBVs, genotypic and phenotypic data of 326 target bulls, 4,138 training bulls and 5,000 prioritized genotyping cows were simulated based on a real pedigree of dairy cattle. The heritability was 0.8 for bulls and 0.2 for cows. The bulls were 54K genotyped, and cows were 10K genotyped. The GEBVs of target bulls were predicted with training bulls only, and with 1,000, 2,000, 3,000, 4,000 and 5,000 cows included, using GBLUP method. Both weighted and unweighted analyses were carried out. The accuracy was the correlation between GEBVs and TBVs. The results showed that including cows may help to improve the accuracy of the GEBV prediction when reference animals were weighted. When animals were unweighted, including cows didn’t improve the accuracy. Keywords: genomic selection prioritizing cow
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
  • Marie Lillehammer · Theo H. E. Meuwissen · Anna K Sonesson
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    ABSTRACT: Abstract Text: Potential benefits of genotyping production animals with phenotypes for a trait not routinely measured on close relatives of the selection candidates were studied by stochastic simulations. The population structure was similar to a typical pig population structure. The trait under investigation had low heritability, was measured late in life on production animals only and was negatively correlated to other traits in the breeding goal. Under such unfavorable conditions, genotyping production animals could not prevent this trait to get negative genetic gain or reduce the drop in genetic level significantly unless the economic weight of the trait in the nucleus was at least 50 % of the breeding goal. The genotyping had however some impact on the rate of inbreeding. If the traits were uncorrelated traits genetic gain increased for the trait under investigation and the effect of genotyping animals with phenotypes increased. Keywords: Genomic selection Reference population Pig
    10th World Congress on Genetics Applied to Livestock Production; 08/2014
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    ABSTRACT: Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P<0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167-0.198) and BLUP (0.105-0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.Heredity advance online publication, 30 July 2014; doi:10.1038/hdy.2014.55.
    Heredity 07/2014; 113(6). DOI:10.1038/hdy.2014.55 · 3.80 Impact Factor
  • 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; 131(6). DOI:10.1111/jbg.12107 · 2.06 Impact Factor
  • Source
    L Zhou · B Heringstad · G Su · B Guldbrandtsen · T H E Meuwissen · M Svendsen · H Grove · U S Nielsen · M S Lund
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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; 97(7). DOI:10.3168/jds.2013-7580 · 2.55 Impact Factor

Publication Stats

7k Citations
412.62 Total Impact Points

Institutions

  • 2005–2014
    • Norwegian University of Life Sciences (UMB)
      • Department of Animal and Aquacultural Sciences (IHA)
      Aas, Akershus county, Norway
    • Life University
      Marietta, Georgia, United States
  • 2002–2010
    • University of Melbourne
      • Department of Agriculture and Food Systems
      Melbourne, Victoria, Australia
  • 1988–2008
    • Wageningen University
      • Animal Breeding and Genomics Centre
      Wageningen, Provincie Gelderland, Netherlands
  • 1995–2001
    • Merck Animal Health Netherlands
      Boksmeer, North Brabant, Netherlands
  • 1998
    • University of Nebraska at Lincoln
      Lincoln, Nebraska, United States
  • 1994
    • University of Guelph
      • Department of Animal and Poultry Science
      Guelph, Ontario, Canada
  • 1993
    • Sociaal en Cultureel Planbureau
      's-Gravenhage, South Holland, Netherlands