Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    We will mail you new results for this query: keywords==Accuracy
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An automated positioning system for monitoring chickens' location : Accuracy and registration success in a free-range area
Stadig, Lisanne M. ; Ampe, Bart ; Rodenburg, Bas ; Reubens, Bert ; Maselyne, Jarissa ; Zhuang, Shaojie ; Criel, Johan ; Tuyttens, Frank A.M. - \ 2018
Applied Animal Behaviour Science 201 (2018). - ISSN 0168-1591 - p. 31 - 39.
Accuracy - Outdoor - Poultry - Signal reception - Ultra-wideband - Vegetation
Free-range use in chickens is often suboptimal, and the full potential of outdoor access for chicken welfare may not be achieved. Many studies use visual observations of free-range use, imposing several limitations. An automated system capable of continuously monitoring the location of multiple individual birds over a long time period has the potential to increase the amount and accuracy of the gathered data. Therefore, the aim of this study was to test a newly developed Ultra-Wideband system for monitoring the position of chickens with free-range access. This system consists of active tags (attached to the chickens) that send signals to anchors positioned at fixed locations in the field; the tags' position can be calculated using the time of arrival of their signal. The effects of vegetation type, precipitation, tags being mounted on a chicken, tag height, angle and orientation, coverage by A-frames or mobile chicken houses, and proximity of other tags on accuracy of the registered positions (distance between the registered and the true position of the tag) and on registration success (percentage of registrations where a position could be calculated) were assessed. Overall, the median error was 0.29 m, which was below the aim of 0.5 m, and the mean percentage of successful registered positions was 68%. None of the variables had a clear effect on the accuracy of the positions. Errors were generally larger in certain areas of the experimental field, which may be due to the asymmetrical setup of the anchors. The percentage of successful registrations was negatively affected by shelter type, with lower percentages in dense vegetation (short rotation coppice willows; SRCW) than on grassland, possibly due to malfunctioning of two anchors close to the SRCW plots. Rain and placing the tags underneath a wooden A-frame, but not placing them in a mobile house, resulted in a lower percentage of successful registrations. The tag being mounted on a chicken, height and angle of the tag and proximity of other tags had no negative effect on the percentage of successful registrations. Placing more (functioning) anchors may contribute to better accuracy and registration success. Alternatively, the bias resulting from the variables that had a negative effect on registration success could be corrected for when using the system in its current setup. Overall, this system shows great promise for monitoring chickens' free-range use.
Benefits of dominance over additive models for the estimation of average effects in the presence of dominance
Duenk, Pascal ; Calus, Mario P.L. ; Wientjes, Yvonne C.J. ; Bijma, Piter - \ 2017
G3 : Genes Genomes Genetics 7 (2017)10. - ISSN 2160-1836 - p. 3405 - 3414.
Accuracy - Average effect - Dominance - Hardy-Weinberg equilibrium - Root mean squared error
In quantitative genetics, the average effect at a single locus can be estimated by an additive (A) model, or an additive plus dominance (AD) model. In the presence of dominance, the AD-model is expected to be more accurate, because the A-model falsely assumes that residuals are independent and identically distributed. Our objective was to investigate the accuracy of an estimated average effect (â) in the presence of dominance, using either a single locus A-model or AD-model. Estimation was based on a finite sample from a large population in Hardy-Weinberg equilibrium (HWE), and the root mean squared error of α was calculated for several broad-sense heritabilities, sample sizes, and sizes of the dominance effect. Results show that with the A-model, both sampling deviations of genotype frequencies from HWE frequencies and sampling deviations of allele frequencies contributed to the error. With the AD-model, only sampling deviations of allele frequencies contributed to the error, provided that all three genotype classes were sampled. In the presence of dominance, the root mean squared error of â with the AD-model was always smaller than with the A-model, even when the heritability was less than one. Remarkably, in the absence of dominance, there was no disadvantage of fitting dominance. In conclusion, the AD-model yields more accurate estimates of average effects from a finite sample, because it is more robust against sampling deviations from HWE frequencies than the A-model. Genetic models that include dominance, therefore, yield higher accuracies of estimated average effects than purely additive models when dominance is present.
Estimating variance components and breeding values for number of oocytes and number of embryos in dairy cattle using a single-step genomic evaluation
Cornelissen, M.A.M.C. ; Mullaart, E. ; Linde, C. Van der; Mulder, H.A. - \ 2017
Journal of Dairy Science 100 (2017)6. - ISSN 0022-0302 - p. 4698 - 4705.
Accuracy - Genomic breeding value - Multiple ovulation and embryo transfer - Ovum pick-up

Reproductive technologies such as multiple ovulation and embryo transfer (MOET) and ovum pick-up (OPU) accelerate genetic improvement in dairy breeding schemes. To enhance the efficiency of embryo production, breeding values for traits such as number of oocytes (NoO) and number of MOET embryos (NoM) can help in selection of donors with high MOET or OPU efficiency. The aim of this study was therefore to estimate variance components and (genomic) breeding values for NoO and NoM based on Dutch Holstein data. Furthermore, a 10-fold cross-validation was carried out to assess the accuracy of pedigree and genomic breeding values for NoO and NoM. For NoO, 40,734 OPU sessions between 1993 and 2015 were analyzed. These OPU sessions originated from 2,543 donors, from which 1,144 were genotyped. For NoM, 35,695 sessions between 1994 and 2015 were analyzed. These MOET sessions originated from 13,868 donors, from which 3,716 were genotyped. Analyses were done using only pedigree information and using a single-step genomic BLUP (ssGBLUP) approach combining genomic information and pedigree information. Heritabilities were very similar based on pedigree information or based on ssGBLUP [i.e., 0.32 (standard error = 0.03) for NoO and 0.21 (standard error = 0.01) for NoM with pedigree, 0.31 (standard error = 0.03) for NoO, and 0.22 (standard error = 0.01) for NoM with ssGBLUP]. For animals without their own information as mimicked in the cross-validation, the accuracy of pedigree-based breeding values was 0.46 for NoO and NoM. The accuracies of genomic breeding values from ssGBLUP were 0.54 for NoO and 0.52 for NoM. These results show that including genomic information increases the accuracies. These moderate accuracies in combination with a large genetic variance show good opportunities for selection of potential bull dams.

Genomic selection improves response to selection in resilience by exploiting genotype by environment interactions
Mulder, Herman - \ 2016
Frontiers in Genetics Livestock Genomics 7 (2016)OCT. - ISSN 1664-8021
Accuracy - Breeding programs - Genomic selection - Genotype by environment interaction - Reaction norm model - Resilience - Response to selection

Genotype by environment interactions (GxE) are very common in livestock and hamper genetic improvement. On the other hand, GxE is a source of genetic variation: genetic variation in response to environment, e.g., environmental perturbations such as heat stress or disease. In livestock breeding, there is tendency to ignore GxE because of increased complexity of models for genetic evaluations and lack of accuracy in extreme environments. GxE, however, creates opportunities to increase resilience of animals toward environmental perturbations. The main aim of the paper is to investigate to which extent GxE can be exploited with traditional and genomic selection methods. Furthermore, we investigated the benefit of reaction norm (RN) models compared to conventional methods ignoring GxE. The questions were addressed with selection index theory. GxE was modeled according to a linear RN model in which the environmental gradient is the contemporary group mean. Economic values were based on linear and non-linear profit equations. Accuracies of environment-specific (G)EBV were highest in intermediate environments and lowest in extreme environments. RN models had higher accuracies of (G)EBV in extreme environments than conventional models ignoring GxE. Genomic selection always resulted in higher response to selection in all environments than sib or progeny testing schemes. The increase in response was with genomic selection between 9 and 140% compared to sib testing and between 11 and 114% compared to progeny testing when the reference population consisted of 1 million animals across all environments. When the aim was to decrease environmental sensitivity, the response in slope of the RN model with genomic selection was between 1.09 and 319 times larger than with sib or progeny testing and in the right direction in contrast to sib and progeny testing that still increased environmental sensitivity. This shows that genomic selection with large reference populations offers great opportunities to exploit GxE to increase resilience of animals.

Accounting for genetic architecture in single- and multipopulation genomic prediction using weights from genomewide association studies in pigs
Veroneze, R. ; Lopes, P.S. ; Lopes, M.S. ; Hidalgo, A.M. ; Guimarães, S.E.F. ; Harlizius, B. ; Knol, E.F. ; Arendonk, J.A.M. van; Silva, F.F. ; Bastiaansen, J.W.M. - \ 2016
Journal of Animal Breeding and Genetics 133 (2016)3. - ISSN 0931-2668 - p. 187 - 196.
Accuracy - Genetic variance - Genomic relationship - Single nucleotide polymorphisms

We studied the effect of including GWAS results on the accuracy of single- and multipopulation genomic predictions. Phenotypes (backfat thickness) and genotypes of animals from two sire lines (SL1, n = 1146 and SL3, n = 1264) were used in the analyses. First, GWAS were conducted for each line and for a combined data set (both lines together) to estimate the genetic variance explained by each SNP. These estimates were used to build matrices of weights (D), which was incorporated into a GBLUP method. Single population evaluated with traditional GBLUP had accuracies of 0.30 for SL1 and 0.31 for SL3. When weights were employed in GBLUP, the accuracies for both lines increased (0.32 for SL1 and 0.34 for SL3). When a multipopulation reference set was used in GBLUP, the accuracies were higher (0.36 for SL1 and 0.32 for SL3) than in single-population prediction. In addition, putting together the multipopulation reference set and the weights from the combined GWAS provided even higher accuracies (0.37 for SL1, and 0.34 for SL3). The use of multipopulation predictions and weights estimated from a combined GWAS increased the accuracy of genomic predictions.

Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP
Berg, S. van den; Calus, M.P.L. ; Meuwissen, T.H.E. ; Wientjes, Y.C.J. - \ 2015
BMC Genetics 16 (2015)1. - ISSN 1471-2156 - 12 p.
Accuracy - Across population - Bayesian variable selection - GBLUP - Genomic prediction - Number of independent chromosome segments

Background: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). Results: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M e ) across the populations. Conclusion: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M e . Across populations, M e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.

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