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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Opportunity Maps for Sustainable Use of Natural Capital
Knegt, B. de; Hoek, Dirk Jan van der; Veerkamp, Clara - \ 2019
In: Atlas of Ecosystem Services / Schröter, Matthias, Bonn, Aletta, Klotz, Stefan, Seppelt, Ralf, Baessler, Cornelia, Springer - ISBN 9783319962283 - p. 365 - 372.
The Dutch government has the ambition to make its policies more “nature-inclusive”. Nature-inclusive policy recognises the wide range of services provided by ecosystems and biodiversity, aiming for sustainable use of these services. Hence, an important objective of the Dutch government is to more explicitly address these benefits and the effects of interventions on natural capital in decision-making processes. Our study contributes to this objective by identifying areas with opportunities for sustainable use of natural capital. It helps policymakers and other stakeholders to focus their policies and to set priorities.We developed a method for making opportunity maps that identify potential areas to use natural capital in a sustainable way. This method was applied to three cases: sustainable food production, flood safety improvement, and sustainable drinking water production
Imputation to whole-genome sequence using multiple pig populations and its use in genome-wide association studies
Berg, Sanne van den; Vandenplas, Jérémie ; Eeuwijk, Fred A. van; Bouwman, Aniek C. ; Lopes, Marcos S. ; Veerkamp, Roel F. - \ 2019
Genetics, Selection, Evolution 51 (2019)1. - ISSN 0999-193X - 1 p.

BACKGROUND: Use of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS. METHODS: Phenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line). Imputed 660 K and 80 K genotypes for the LW-line and DL-line, respectively, were imputed to iWGS using Beagle v.4.1. Since only 32 LW-line and 12 DL-line boars were sequenced, 142 animals from eight commercial lines were added. GWAS were performed for each line using the 80 K and 660 K SNPs, the genotype scores of iWGS SNPs that had an imputation accuracy (Beagle R2) higher than 0.6, and the dosage scores of all iWGS SNPs. RESULTS: For the DL-line (LW-line), imputation of 80 K genotypes to iWGS resulted in an average Beagle R2 of 0.39 (0.49). After quality control, 2.5 × 106 (3.5 × 106) SNPs had a Beagle R2 higher than 0.6, resulting in an average Beagle R2 of 0.83 (0.93). Compared to the 80 K and 660 K genotypes, using iWGS led to the identification of 48.9 and 64.4% more QTL regions, for the DL-line and LW-line, respectively, and the most significant SNPs in the QTL regions explained a higher proportion of phenotypic variance. Using dosage instead of genotype scores improved the identification of QTL, because the model accounted for uncertainty of imputation, and all SNPs were used in the analysis. CONCLUSIONS: Imputation to WGS using the multi-line reference population resulted in relatively poor imputation, especially when imputing from 80 K (DL-line). In spite of the poor imputation accuracies, using iWGS instead of a lower density SNP chip increased the number of detected QTL and the estimated proportion of phenotypic variance explained by these QTL, especially when dosage scores were used instead of genotype scores. Thus, iWGS, even with poor imputation accuracy, can be used to identify possible interesting regions for fine mapping.

An equation to predict accuracy of multi-breed genomic prediction model with multiple random effects
Raymond, Biaty ; Bouwman, A.C. ; Wientjes, Y.C.J. ; Schrooten, C. ; Houwing-Duistermaat, Jeanine J. ; Veerkamp, R.F. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science. - Wageningen : Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 121 - 121.
Reliability of breeding values for dry matter intake by adding data from additional research farms
Schopen, Ghyslaine ; Haas, Y. de; Vosman, J.J. ; Jong, G. de; Veerkamp, R.F. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science. - Wageningen : Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 451 - 451.
QTL detection in a breeding pedigree of diploid potato
Korontzis, G. ; Malosetti, Marcos ; Mulder, H.A. ; Maliepaard, C.A. ; Víquez Zamora, A.M. ; Veerkamp, R.F. ; Eeuwijk, F.A. van - \ 2018
Towards field specific phosphate applications norms with machine learning
Mollenhorst, H. ; Haan, M.H.A. de; Oenema, J. ; Hoving, A.H. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 341 - 341.
Boosted regression trees to predict pneumonia, growth and meat percentage of slaughter pigs
Mollenhorst, H. ; Greef, K.H. de; Ducro, B.J. ; Hulsegge, B. ; Hoving, A.H. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science Wageningen : Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 437 - 437.
In-line milk progesterone profiles to estimate genetic parameters for atypical fertility in cows
Binsbergen, R. van; Bouwman, A.C. ; Veerkamp, R.F. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science Wageningen : Wageningen Academic Publishers (Book of abstracts 24) - ISBN 9789086863235 - p. 285 - 285.
Development of resilience indicators using deviations in milk yield from the lactation curve
Poppe, H.W.M. ; Mulder, H.A. ; Veerkamp, R.F. - \ 2018
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science. - Wageningen Academic Publishers (Book of abstracts ) - ISBN 9789086863235 - p. 268 - 268.
Towards field specific phosphate applications norms with machine learning
Mollenhorst, H. ; Haan, M.H.A. de; Oenema, J. ; Hoving, A.H. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2018
- 1 p.
Using Data Lake Stack in Animal Sciences
Schokker, D. ; Athanasiadis, I.N. ; Visser, B. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2018
- 1 p.
Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest
Alsahaf, Ahmad ; Azzopardi, George ; Ducro, Bart ; Hanenberg, Egiel ; Veerkamp, Roel F. ; Petkov, Nicolai - \ 2018
Journal of Animal Science 96 (2018)12. - ISSN 0021-8812 - p. 4935 - 4943.
Breeding - Grouping strategies - Machine learning - Pigs - Random forest - Regression

The weight of a pig and the rate of its growth are key elements in pig production. In particular, predicting future growth is extremely useful, since it can help in determining feed costs, pen space requirements, and the age at which a pig reaches a desired slaughter weight. However, making these predictions is challenging, due to the natural variation in how individual pigs grow, and the different causes of this variation. In this paper, we used machine learning, namely random forest (RF) regression, for predicting the age at which the slaughter weight of 120 kg is reached. Additionally, we used the variable importance score from RF to quantify the importance of different types of input data for that prediction. Data of 32,979 purebred Large White pigs were provided by Topigs Norsvin, consisting of phenotypic data, estimated breeding values (EBVs), along with pedigree and pedigree-genetic relationships. Moreover, we presented a 2-step data reduction procedure, based on random projections (RPs) and principal component analysis (PCA), to extract features from the pedigree and genetic similarity matrices for use as inputs in the prediction models. Our results showed that relevant phenotypic features were the most effective in predicting the output (age at 120 kg), explaining approximately 62% of its variance (i.e., R2 = 0.62). Estimated breeding value, pedigree, or pedigree-genetic features interchangeably explain 2% of additional variance when added to the phenotypic features, while explaining, respectively, 38%, 39%, and 34% of the variance when used separately.

Correction to: Heritabilities and genetic correlations for honey yield, gentleness, calmness and swarming behaviour in Austrian honey bees
Brascamp, Evert W. ; Willam, Alfons ; Boigenzahn, Christian ; Bijma, Piter ; Veerkamp, Roel F. - \ 2018
Apidologie 49 (2018)4. - ISSN 0044-8435 - p. 462 - 463.

In the paper, we computed the phenotypic variances of traits ignoring that the worker effect is in fact the colony mean, which has consequences for the estimates of heritabilities.

Genomic prediction across populations, using pre-selected markers and differential weight models
Raymond, Biaty ; Bouwman, A.C. ; Schrooten, Chris ; Houwing-Duistermaat, Jeanine J. ; Veerkamp, R.F. - \ 2018
In: Proceedings of the World Congress on Genetics Applied to Livestock Production - 6 p.
Genomic prediction (GP) in numerically small breeds is limited due to the requirement for a large reference set. Across breed prediction has not been very successful either. Our objective was to test alternative models for across breed and multi-breed GP in a small Jersey population, utilizing prior information on marker causality. We used data on 596 Jersey bulls from new Zealand and 5503 Holstein bulls from the Netherlands, all of which had deregressed proofs for stature. Two sets of genotype data were used, one containing 357 potential causal markers identified from a multi-breed meta-GWAS on stature (top markers), while the other contained 48,912 markers on the custom 50k chip, excluding the top markers. We used models in which only one GRM (either top markers, 50k, or top plus 50k markers combined) was fitted, and models in which two GRMs (both the top and 50k) were fitted simultaneously, however with different variance components to weight the GRMs differently. Moreover, we estimated the genetic correlation(s) between the breeds (for each GRM) using a multi-trait GP model, which implicitly weights the contribution of one breed’s information to another. Across breed, we observed low accuracies of GP when the 50k markers were fitted alone (0.06) or when the top markers were added to 50k (0.15). Higher accuracy was obtained when only the top markers were fitted (0.21), whereas the highest accuracy was obtained when fitting 50k and top markers simultaneously as two independent GRMs (0.25). Multi-breed prediction outperformed both within and across breed prediction with accuracies ranging from 0.34 to 0.45, with the same trend as in across breed prediction. Based on our results, the best approach for across and multi-breed GP is to fit models that are able to isolate and differentially weight the most important markers for the trait. Keywords: Across breed genomic prediction, marker pre-selection, multi-trait model, sequence data.
Genomic prediction of feed intake using predictor traits
Manzanilla Pech, C.I.V. ; Veerkamp, R.F. ; Haas, Y. de; Calus, M.P.L. ; Napel, J. ten - \ 2018
In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. - - 4 p.
Genomic prediction of feed intake using predictor traits A total of 77,640 weekly records on dry matter intake (DMI), 64,443 on fat and protein corrected milk (FPCM) and 73,415 on live weight (LW) were analysed from 3,188 Dutch dairy cows in 6,820 lactations (first to third lactation) from 1980 to 2015. The objective of this study was to compare the accuracies of the genomic estimated breeding values (GEBV) for DMI, with or without predictor traits included (FPCM and LW) with a single step method (SS-GBLUP). Accuracies of GEBV for DMI was0.36 when FPCM and LW were included as reference traits, 0.37 when DMI was the only reference trait, and 0.38 when all 3 traits (DMI, FPCM and LW) were included as reference traits. When only using predictor traits in the reference population, the accuracies of estimated GEBV for DMI, were lower than in the scenarios using DMI as LW and FPCM can only explain 53% of the variation in DMI. Moreover, there was very little benefit of adding information on predictor traits to the reference population when DMI was already included on the same animals. However, in the absence of DMI records, having records on FPCM and LW from different lactations is a good way to obtain GEBV with a relatively good accuracy.
Genomic prediction for numerically small breeds, using models with pre-selected and differentially weighted markers
Raymond, Biaty ; Bouwman, Aniek C. ; Wientjes, Yvonne C.J. ; Schrooten, Chris ; Houwing-Duistermaat, Jeanine ; Veerkamp, Roel F. - \ 2018
Genetics, Selection, Evolution 50 (2018)1. - ISSN 0999-193X

Background: Genomic prediction (GP) accuracy in numerically small breeds is limited by the small size of the reference population. Our objective was to test a multi-breed multiple genomic relationship matrices (GRM) GP model (MBMG) that weighs pre-selected markers separately, uses the remaining markers to explain the remaining genetic variance that can be explained by markers, and weighs information of breeds in the reference population by their genetic correlation with the validation breed. Methods: Genotype and phenotype data were used on 595 Jersey bulls from New Zealand and 5503 Holstein bulls from the Netherlands, all with deregressed proofs for stature. Different sets of markers were used, containing either pre-selected markers from a meta-genome-wide association analysis on stature, remaining markers or both. We implemented a multi-breed bivariate GREML model in which we fitted either a single multi-breed GRM (MBSG), or two distinct multi-breed GRM (MBMG), one made with pre-selected markers and the other with remaining markers. Accuracies of predicting stature for Jersey individuals using the multi-breed models (Holstein and Jersey combined reference population) was compared to those obtained using either the Jersey (within-breed) or Holstein (across-breed) reference population. All the models were subsequently fitted in the analysis of simulated phenotypes, with a simulated genetic correlation between breeds of 1, 0.5, and 0.25. Results: The MBMG model always gave better prediction accuracies for stature compared to MBSG, within-, and across-breed GP models. For example, with MBSG, accuracies obtained by fitting 48,912 unselected markers (0.43), 357 pre-selected markers (0.38) or a combination of both (0.43), were lower than accuracies obtained by fitting pre-selected and unselected markers in separate GRM in MBMG (0.49). This improvement was further confirmed by results from a simulation study, with MBMG performing on average 23% better than MBSG with all markers fitted. Conclusions: With the MBMG model, it is possible to use information from numerically large breeds to improve prediction accuracy of numerically small breeds. The superiority of MBMG is mainly due to its ability to use information on pre-selected markers, explain the remaining genetic variance and weigh information from a different breed by the genetic correlation between breeds.

Heidevegetaties, biotoop van het jaar in 2018 en 2019
Veerkamp, M. ; Brouwer, E. ; Ozinga, W.A. - \ 2018
Coolia 61 (2018)3. - ISSN 0929-7839 - p. 119 - 130.
Early prediction of phenotypic survival to the second lactation in Dutch and Flemish Holstein heifers using genomic and phenotypic data
Heide, E.E.M. van der; Veerkamp, R.F. ; Kamphuis, C. ; Ducro, B.J. - \ 2018
In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. - - 6 p.
Due to uncertainty about survival and future performance of replacement heifers, many farmers rear a surplus of heifers. By predicting survival at an early age, uncertainty about heifer survival could be reduced, and fewer replacement heifers would be needed. A dataset of 1907 Holstein heifers born between 2012 and 2013 with 50 genomic breeding values (GEBV) and various phenotypic variables was used to predict survival to second lactation, at two moments in life; at birth, and at age of 18 months. While it was not possible to reliably predict survival outcome of individual heifers, the surviving heifers ranked higher on average than non-surviving heifers at birth (0.87 (SD = 0.047) vs 0.84 (SD =0.059), and at 18 months (0.89 (SD =0.066) vs 0.85 (SD = 0.080). The best prediction of survival in both cases was obtained by combining phenotypic information and gEBV, demonstrating the potential for farmers to combine both information sources to predict the probability of survival for their replacement heifer management. Keywords: phenotypic prediction, dairy cattle, survival
Value of the Dutch Holstein Friesian germplasm collection to increase genetic variability and improve genetic merit
Doekes, H.P. ; Veerkamp, R.F. ; Bijma, P. ; Hiemstra, S.J. ; Windig, J. - \ 2018
Journal of Dairy Science 101 (2018)11. - ISSN 0022-0302 - p. 10022 - 10033.
conservation - dairy cow - gene bank collection - genetic diversity - genetic improvement

National gene bank collections for Holstein Friesian (HF) dairy cattle were set up in the 1990s. In this study, we assessed the value of bulls from the Dutch HF germplasm collection, also known as cryobank bulls, to increase genetic variability and improve genetic merit in the current bull population (bulls born in 2010–2015). Genetic variability was defined as 1 minus the mean genomic similarity (SIMSNP) or as 1 minus the mean pedigree-based kinship (fPED). Genetic merit was defined as the mean estimated breeding value for the total merit index or for 1 of 3 subindices (yield, fertility, and udder health). Using optimal contribution selection, we minimized relatedness (maximized variability) or maximized genetic merit at restricted levels of relatedness. We compared breeding schemes with only bulls from 2010 to 2015 with schemes in which cryobank bulls were also included. When we minimized relatedness, inclusion of genotyped cryobank bulls decreased mean SIMSNP by 0.7% and inclusion of both genotyped and nongenotyped cryobank bulls decreased mean fPED by 2.6% (in absolute terms). When we maximized merit at restricted levels of relatedness, inclusion of cryobank bulls provided additional merit at any level of mean SIMSNP or mean fPED except for the total merit index at high levels of mean SIMSNP. Additional merit from cryobank bulls depended on (1) the relative emphasis on genetic variability and (2) the selection criterion. Additional merit was higher when more emphasis was put on genetic variability. For fertility, for example, it was 1.74 SD at a mean SIMSNP restriction of 64.5% and 0.37 SD at a mean SIMSNP restriction of 67.5%. Additional merit was low to nonexistent for the total merit index and higher for the subindices, especially for fertility. At a mean SIMSNP of 64.5%, for example, it was 0.60 SD for the total merit index and 1.74 SD for fertility. In conclusion, Dutch HF cryobank bulls can be used to increase genetic variability and improve genetic merit in the current population, although their value is very limited when selecting for the current total merit index. Anticipating changes in the breeding goal in the future, the germplasm collection is a valuable resource for commercial breeding populations.

Genomic selection and inbreeding and kinship in Dutch-Flemish Holstein Friesian cattle
Doekes, H.P. ; Veerkamp, R.F. ; Bijma, P. ; Hiemstra, S.J. ; Ursinus, W.W. ; Beek, S. van der; Windig, J.J. - \ 2018
In: Proceedings of the 11th World Congress on Genetics Applied to Livestock Production. - - 5 p.
Since 2009, genomic selection (GS) has been widely applied in Holstein Friesian (HF) breeding programs. In this study, we evaluated how the introduction of GS in the Dutch-Flemish HF breeding program has affected inbreeding and kinship trends, using both pedigree-based and genomic measures. Rates of inbreeding and kinship for artificial insemination (AI) bulls increased with the introduction of GS, from 0.1-0.7% in 2003-2009 to 1.6-2.5% in 2009-2015. Rates of inbreeding and kinship for cows also increased with GS, although they were lower than for AI-bulls (i.e. 0.79-1.14% in 2009-2017). Levels of identical by state (IBS), which include relatedness due to both recent and distant common ancestors, increased faster than levels of identical by descent (IBD), which include only recent inbreeding and kinship. Accumulation of inbreeding varied substantially across the genome over time, with specific regions showing a striking increase in inbreeding since the introduction of GS. These findings emphasize the need for efficient genomic management of inbreeding in GS-schemes.
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