Breed4Food reviewed publications 2012-2016

  1. Calus M. P. L., Bouwman A. C., Schrooten C, Veerkamp R. F. Efficient genomic prediction based on whole-genome sequence data using split-and-merge Bayesian variable selection. Genet. Sel. Evol. 48:49.
  2. Calus M. P. L., J. Vandenplas, J. ten Napel, and R. F. Veerkamp. Validation of simultaneous deregression of cow and bull breeding values and derivation of appropriate weights. J Dairy Sci. 99:6403-6419.
  3. Alemu, S. W., M. P. L. Calus, W. M. Muir, K. Peeters, A. Vereijken, and P. Bijma. 2016. Genomic prediction of survival time in a population of brown laying hens showing cannibalistic behavior. Genetics Selection Evolution. 48:68.
  4. Heidaritabar, M., M. P. L. Calus, H. J. Megens, A. Vereijken, M. A. M. Groenen, and J. W. M. Bastiaansen. 2016. Accuracy of genomic prediction using imputed whole-genome sequence data in white layers. J Anim Breed Genet. 133:167-179.
  5. Hidalgo, A. M., J. W. M. Bastiaansen, M. S. Lopes, M. P. L. Calus, and D. J. de Koning. 2016. Accuracy of genomic prediction of purebreds for cross bred performance in pigs. J. Anim. Breed. Genet. doi:10.1111/jbg.12214.
  6. Pszczola, M. and M. P. L. Calus. 2016. Updating the reference population to achieve constant genomic prediction reliability across generations. Animal. 10:1018-1024.
  7. Sevillano C.A., J. Vandenplas, J.W.M. Bastiaansen, M.P.L. Calus. 2016. Empirical determination of breed-of-origin of alleles in three-breed cross pigs. Genet. Sel. Evol. 48:55.
  8. Vandenplas J, M.P.L. Calus, C.A. Sevillano, J.J. Windig, J.W.M. Bastiaansen. Assigning breed origin to alleles in crossbred animals. Genet. Sel. Evol. 2016;48:61.
  9. Wientjes YCJ, Bijma P, Veerkamp RF, Calus MPL: An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments. Genetics 2015.
  10. van Binsbergen R, Bink M, Calus MPL, van Eeuwijk FA, Hayes BJ, Hulsegge I, Veerkamp RF: Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle. Genetics Selection Evolution 2014, 46.
  11. Pszczola M, Veerkamp RF, de Haas Y, Wall E, Strabel T, Calus MPL: Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population. Animal 2013, 7(11):1759-1768.
  12. Pryce JE, Johnston J, Hayes BJ, Sahana G, Weigel KA, McParland S, Spurlock D, Krattenmacher N, Spelman RJ, Wall E, Calus MPL: Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations. Journal of Dairy Science 2014, 97(3):1799-1811.
  13. Hulsegge B, Calus MPL, Windig JJ, Hoving-Bolink AH, Maurice-van Eijndhoven MHT, Hiemstra SJ: Selection of SNP from 50K and 777K arrays to predict breed of origin in cattle. Journal of Animal Science 2013, 91(11):5128-5134.
  14. Heidaritabar M, Calus MPL, Vereijken A, Groenen MAM, Bastiaansen JWM: Accuracy of imputation using the most common sires as reference population in layer chickens. Bmc Genetics 2015, 16.
  15. de Haas Y, Pryce JE, Calus MPL, Wall E, Berry DP, Lovendahl P, Krattenmacher N, Miglior F, Weigel K, Spurlock D, Macdonald KA, Hulsegge B, Veerkamp RF: Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia. Journal of Dairy Science 2015, 98(9):6522-6534.
  16. Calus MPL, de Haas Y, Veerkamp RF: Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies. Journal of Dairy Science 2013, 96(10):6703-6715.
  17. Calus MPL, de Haas Y, Pszczola M, Veerkamp RF: Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal 2013, 7(2):183-191.
  18. Bouwman AC, Veerkamp RF: Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy. Bmc Genetics 2014, 15.
  19. Bouwman AC, Hickey JM, Calus MPL, Veerkamp RF: Imputation of non-genotyped individuals based on genotyped relatives: assessing the imputation accuracy of a real case scenario in dairy cattle. Genetics Selection Evolution 2014, 46.
  20. Calus MPL, Bouwman AC, Hickey JM, Veerkamp RF, Mulder HA: Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications. Animal 2014, 8(11):1743-1753.
  21. Calus MPL, Bijma R, Veerkamp RF: Evaluation of genomic selection for replacement strategies using selection index theory. Journal of Dairy Science 2015, 98(9):6499-6509.
  22. Daetwyler HD, Capitan A, Pausch H, Stothard P, Van Binsbergen R, Brondum RF, Liao XP, Djari A, Rodriguez SC, Grohs C, Esquerre D, Bouchez O, Rossignol MN, Klopp C, Rocha D, Fritz S, Eggen A, Bowman PJ, Coote D, Chamberlain AJ, Anderson C, VanTassell CP, Hulsegge I, Goddard ME, Guldbrandtsen B, Lund MS, Veerkamp RF, Boichard DA, Fries R, Hayes BJ: Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nature Genetics 2014, 46(8):858-865.
  23. Veerkamp RF, Tenghe AMM, Kaal L, Bouwman AC: Genetics and genomics of fertility in dairy cows. Cattle Practice 2015, 23:98-102.
  24. Veerkamp RF, Coffey MP, Berry DP, de Haas Y, Strandberg E, Bovenhuis H, Calus MPL, Wall E: Genome-wide associations for feed utilisation complex in primiparous Holstein-Friesian dairy cows from experimental research herds in four European countries. Animal 2012, 6(11):1738-1749.
  25. Hidalgo AM, Lopes MS, Harlizius B, Bastiaansen JWM: Genome-wide association study reveals regions associated with gestation length in two pig populations. Animal Genetics 2015:n/a-n/a.
  26. Bosse M, Lopes MS, Madsen O, Megens HJ, Crooijmans R, Frantz LAF, Harlizius B, Bastiaansen JWM, Groenen MAM: Artificial selection on introduced Asian haplotypes shaped the genetic architecture in European commercial pigs. Proceedings of the Royal Society B-Biological Sciences 2015, 282(1821).
  27. Bosse M, Megens HJ, Frantz LAF, Madsen O, Larson G, Paudel Y, Duijvesteijn N, Harlizius B, Hagemeijer Y, Crooijmans R, Groenen MAM: Genomic analysis reveals selection for Asian genes in European pigs following human-mediated introgression. Nature Communications 2014, 5.
  28. Hidalgo AM, Bastiaansen JWM, Harlizius B, Megens HJ, Madsen O, Crooijmans R, Groenen MAM: On the relationship between an Asian haplotype on chromosome 6 that reduces androstenone levels in boars and the differential expression of SULT2A1 in the testis. Bmc Genetics 2014, 15.
  29. Lopes MS, Bastiaansen JWM, Harlizius B, Knol EF, Bovenhuis H: A Genome-Wide Association Study Reveals Dominance Effects on Number of Teats in Pigs. Plos One 2014, 9(8).
  30. Sell-Kubiak E, Duijvesteijn N, Lopes MS, Janss LLG, Knol EF, Bijma P, Mulder HA: Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. Bmc Genomics 2015, 16.
  31. Sevillano CA, Lopes MS, Harlizius B, Hanenberg E, Knol EF, Bastiaansen JWM: Genome-wide association study using deregressed breeding values for cryptorchidism and scrotal/inguinal hernia in two pig lines. Genetics Selection Evolution 2015, 47.
  32. Verardo LL, Silva FF, Varona L, Resende MDV, Bastiaansen JWM, Lopes PS, Guimares SEF: Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs. Journal of Applied Genetics 2015, 56(1):123-132.
  33. Calus MPL: Right-hand-side updating for fast computing of genomic breeding values. Genetics Selection Evolution 2014, 46.
  34. Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM: Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking. Genetics 2013, 193(2):347-+.
  35. van Binsbergen R, Calus MPL, Bink M, van Eeuwijk FA, Schrooten C, Veerkamp RF: Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle. Genetics Selection Evolution 2015, 47.
  36. Zhang QQ, Calus MPL, Guldbrandtsen B, Lund MS, Sahana G: Estimation of inbreeding using pedigree, 50k SNP chip genotypes and full sequence data in three cattle breeds. Bmc Genetics 2015, 16.
  37. Napel Jt, H. A. Mulder, M. Lidauer, I. Stranden, E. Mäntysaari, M. H. Pool, and R. F. Veerkamp. Netherlands.: MiXBLUP, the Mixed-model Best Linear Unbiased Prediction software for PCs for large genetic evaluation systems. . Version 131, Wageningen, the 2014.
  38. Wientjes YCJ: Multi-population genomic prediction. PhD thesis Wageningen University 2016.
  39. Huang HY, Windig JJ, Vereijken A, Calus MPL: Genomic prediction based on data from three layer lines using non-linear regression models. Genetics Selection Evolution 2014, 46.
  40. Calus MPL, Huang HY, Vereijken A, Visscher J, ten Napel J, Windig JJ: Genomic prediction based on data from three layer lines: a comparison between linear methods. Genetics Selection Evolution 2014, 46.
  41. van den Berg S, Calus MPL, Meuwissen THE, Wientjes YCJ: Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP. Bmc Genetics 2015, 16.
  42. Wientjes YCJ, Veerkamp RF, Calus MPL: Using selection index theory to estimate consistency of multi-locus linkage disequilibrium across populations. Bmc Genetics 2015, 16.
  43. Wientjes YCJ, Veerkamp RF, Calus MPL: The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction. Genetics 2013, 193(2):621-+.
  44. Wientjes YCJ, Veerkamp RF, Bijma P, Bovenhuis H, Schrooten C, Calus MPL: Empirical and deterministic accuracies of across-population genomic prediction. Genetics Selection Evolution 2015, 47.
  45. Wientjes YCJ, Calus MPL, Goddard ME, Hayes BJ: Impact of QTL properties on the accuracy of multi-breed genomic prediction. Genetics Selection Evolution 2015, 47.
  46. Dadousis C, Veerkamp RF, Heringstad B, Pszczola M, Calus MPL: A comparison of principal component regression and genomic REML for genomic prediction across populations. Genetics Selection Evolution 2014, 46.
  47. Hidalgo AM, Bastiaansen JWM, Lopes MS, Harlizius B, Groenen MAM, de Koning D-J: Accuracy of Predicted Genomic Breeding Values in Purebred and Crossbred Pigs. G3-Genes Genomes Genetics 2015, 5(8):1575-1583.
  48. Veroneze R, Bastiaansen JWM, Knol EF, Guimaraes SEF, Silva FF, Harlizius B, Lopes MS, Lopes PS: Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations. Bmc Genetics 2014, 15.
  49. Veroneze R, Lopes MS, Hidalgo AM, Guimaraes SEF, Silva FF, Harlizius B, Lopes PS, Knol EF, van Arendonk JAM, Bastiaansen JWM: Accuracy of genome-enabled prediction exploring purebred and crossbred pig populations. Journal of Animal Science 2015, 93(10):4684-4691.
  50. Lopes MS, Bastiaansen JWM, Janss L, Knol EF, Bovenhuis H: Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data. G3-Genes Genomes Genetics 2015, 5(12):2629-2637.
  51. Lopes MS, Bastiaansen JWM, Janss L, Knol EF, Bovenhuis H: Genomic prediction of growth in pigs based on a model including additive and dominance effects. Journal of Animal Breeding and Genetics 2015:n/a-n/a.
  52. Amuzu-Aweh EN, Bijma P, Kinghorn BP, Vereijken A, Visscher J, van Arendonk JAM, Bovenhuis H: Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crosses. Heredity 2013, 111(6):530-538.
  53. Van Middelaar CE, Berentsen PBM, Dijkstra J, Van Arendonk JAM, De Boer IJM: Effect of feed-related farm characteristics on relative values of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain. Journal of Dairy Science 2015, 98(7):4889-4903.
  54. van Middelaar CE, Berentsen PBM, Dijkstra J, van Arendonk JAM, de Boer IJM: Methods to determine the relative value of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain. Journal of Dairy Science, 97(8):5191-5205.
  55. Zanten HHE, Mollenhorst H, Klootwijk CW, Middelaar CE, Boer IJM: Global food supply: land use efficiency of livestock systems. The International Journal of Life Cycle Assessment 2015:1-12.
  56. Pryce JE, Wales WJ, de Haas Y, Veerkamp RF, Hayes BJ: Genomic selection for feed efficiency in dairy cattle. Animal 2014, 8(1):1-10.
  57. Pryce JE, Gonzalez-Recio O, Thornhill JB, Marett LC, Wales WJ, Coffey MP, de Haas Y, Veerkamp RF, Hayes BJ: Short communication: Validation of genomic breeding value predictions for feed intake and feed efficiency traits. Journal of Dairy Science 2014, 97(1):537-542.
  58. Manzanilla-Pech CIV, Veerkamp RF, Tempelman RJ, van Pelt ML, Weigel KA, VandeHaar M, Lawlor TJ, Spurlock DM, Armentano LE, Staples CR, Hanigan M, De Haas Y: Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations-the Netherlands and United States. Journal of Dairy Science 2016, 99(1):443-457.
  59. Manzanilla C, Veerkamp RF, Calus MPL, Zom R, van Knegsel A, Pryce JE, De Haas Y: Genetic parameters across lactation for feed intake, fat-and protein-corrected milk, and liveweight in first-parity Holstein cattle. Journal of Dairy Science 2014, 97(9):5851-5862.
  60. Berry DP, Coffey MP, Pryce JE, de Haas Y, Lovendahl P, Krattenmacher N, Crowley JJ, Wang Z, Spurlock D, Weigel K, Macdonald K, Veerkamp RF: International genetic evaluations for feed intake in dairy cattle through the collation of data from multiple sources. Journal of Dairy Science 2014, 97(6):3894-3905.
  61. Pickering NK, Oddy VH, Basarab J, Cammack K, Hayes B, Hegarty RS, Lassen J, McEwan JC, Miller S, Pinares-Patino CS, de Haas Y: Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animal 2015, 9(9):1431-1440.
  62. Benis N, Schokker D, Suarez-Diez M, dos Santos V, Smidt H, Smits MA: Network analysis of temporal functionalities of the gut induced by perturbations in new-born piglets. Bmc Genomics 2015, 16.
  63. Schokker D, Zhang J, Vastenhouw SA, Heilig H, Smidt H, Rebel JMJ, Smits MA: Long-Lasting Effects of Early-Life Antibiotic Treatment and Routine Animal Handling on Gut Microbiota Composition and Immune System in Pigs. Plos One 2015, 10(2).
  64. Schokker D, Bannink A, Smits MA, Rebel JMJ: A mathematical model representing cellular immune development and response to Salmonella of chicken intestinal tissue. J Theor Biol 2013, 330:75-87.
  65. Schokker D, Zhang J, Zhang LL, Vastenhouw SA, Heilig H, Smidt H, Rebel JMJ, Smits MA: Early-Life Environmental Variation Affects Intestinal Microbiota and Immune Development in New-Born Piglets. Plos One 2014, 9(6).
  66. Schokker D, Veninga G, Vastenhouw SA, Bossers A, de Bree FM, Kaal-Lansbergen L, Rebel JMJ, Smits MA: Early life microbial colonization of the gut and intestinal development differ between genetically divergent broiler lines. Bmc Genomics 2015, 16.
  67. Ellen ED, Rodenburg TB, Albers GAA, Bolhuis JE, Camerlink I, Duijvesteijn N, Knol EF, Muir WM, Peeters K, Reimert I, Sell-Kubiak E, Van Arendonk JAM, Visscher J, Bijma P: The prospects of selection for social genetic effects to improve welfare and productivity in livestock. Frontiers in Genetics 2014, 5.
  68. van Pelt ML, Meuwissen THE, de Jong G, Veerkamp RF: Genetic analysis of longevity in Dutch dairy cattle using random regression. Journal of Dairy Science 2015, 98(6):4117-4130.
  69. de Hollander CA, Knol EF, Heuven HCM, van Grevenhof EM: Interval from last insemination to culling: II. Culling reasons from practise and the correlation with longevity. Livestock Science 2015, 181:25-30.
  70. de Koning DB, Damen E, Nieuwland MGB, van Grevenhof EM, Hazeleger W, Kemp B, Parmentier HK: Association of natural (auto-) antibodies in young gilts with osteochondrosis at slaughter. Livestock Science 2015, 176:152-160.
  71. de Koning DB, van Grevenhof EM, Laurenssen BFA, Hazeleger W, Kemp B: Associations of conformation and locomotive characteristics in growing gilts with osteochondrosis at slaughter. Journal of Animal Science 2015, 93(1):93-106.
  72. Orr N, Hill EW, Gu J, Govindarajan P, Conroy J, van Grevenhof EM, Ducro BJ, van Arendonk JAM, Knaap JH, van Weeren PR, MacHugh DE, Ennis S, Brama PAJ: Genome-wide association study of osteochondrosis in the tarsocrural joint of Dutch Warmblood horses identifies susceptibility loci on chromosomes 3 and 10. Animal Genetics 2013, 44(4):408-412.
  73. van Grevenhof EM, Knol EF, Heuven HCM: Interval from last insemination to culling: I. The genetic background in crossbred sows. Livestock Science 2015, 181:103-107.
  74. Mulder HA, Hill WG, Knol EF: Heritable Environmental Variance Causes Nonlinear Relationships Between Traits: Application to Birth Weight and Stillbirth of Pigs. Genetics 2015, 199(4):1255-U1584.
  75. Sell-Kubiak E, Bijma P, Knol EF, Mulder HA: Comparison of methods to study uniformity of traits: Application to birth weight in pigs. Journal of Animal Science 2015, 93(3):900-911.
  76. Sell-Kubiak E, Wang S, Knol EF, Mulder HA: Genetic analysis of within-litter variation in piglets’ birth weight using genomic or pedigree relationship matrices. Journal of Animal Science 2015, 93(4):1471-1480.
  77. Herrero-Medrano JM, Mathur PK, ten Napel J, Rashidi H, Alexandri P, Knol EF, Mulder HA: Estimation of genetic parameters and breeding values across challenged environments to select for robust pigs. Journal of Animal Science 2015, 93(4):1494-1502.
  78. Backus GBC, van den Broek E, van der Fels B, Heres L, Immink VM, Knol EF, Kornelis M, Mathur PK, van der Peet-Schwering C, van Riel JW, Snoek HM, de Smet A, Tacken GML, Valeeva NI, van Wagenberg CPA: Evaluation of producing and marketing entire male pigs. NJAS – Wageningen Journal of Life Sciences.
  79. de Campos CF, Lopes MS, Silva FFE, Veroneze R, Knol EF, Lopes PS, Guimaraes SEF: Genomic selection for boar taint compounds and carcass traits in a commercial pig population. Livestock Science 2015, 174:10-17.
  80. Brinker T, Ellen ED, Veerkamp RF, Bijma P: Predicting direct and indirect breeding values for survival time in laying hens using repeated measures. Genetics Selection Evolution 2015, 47.
  81. Calus MPL, Schrooten C, Veerkamp RF: Genomic prediction of breeding values using previously estimated SNP variances. Genetics Selection Evolution 2014, 46.
  82. Calus MPL, Vandenplas J, Ten Napel J: Ever-growing data sets pose (new) challenges to genomic prediction models. Journal of Animal Breeding and Genetics 2015, 132(6):407-408.
  83. de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL: Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding. Genetics 2013, 193(2):327-+.
  84. de Mol RM, Andre G, Bleumer EJB, van der Werf JTN, de Haas Y, van Reenen CG: Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows. Journal of Dairy Science 2013, 96(6):3703-3712.
  85. Engelsma KA, Veerkamp RF, Calus MPL, Windig JJ: Consequences for diversity when animals are prioritized for conservation of the whole genome or of one specific allele. Journal of Animal Breeding and Genetics 2014, 131(1):61-70.
  86. Eynard SE, Windig JJ, Leroy G, van Binsbergen R, Calus MPL: The effect of rare alleles on estimated genomic relationships from whole genome sequence data. Bmc Genetics 2015, 16.
  87. Gaspa G, Veerkamp RF, Calus MPL, Windig JJ: Assessment of genomic selection for introgression of polledness into Holstein Friesian cattle by simulation. Livestock Science 2015, 179:86-95.
  88. Hulsegge I, Woelders H, Smits M, Schokker D, Jiang L, Sorensen P: Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining. Physiological Genomics 2013, 45(10):400-406.
  89. Jarquin D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Perez P, Calus M, Burgueno J, de los Campos G: A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical and Applied Genetics 2014, 127(3):595-607.
  90. Maurice-Van Eijndhoven MHT, Bovenhuis H, Soyeurt H, Calus MPL: Differences in milk fat composition predicted by mid-infrared spectrometry among dairy cattle breeds in the Netherlands. Journal of Dairy Science 2013, 96(4):2570-2582.
  91. Maurice-Van Eijndhoven MHT, Bovenhuis H, Veerkamp RF, Calus MPL: Overlap in genomic variation associated with milk fat composition in Holstein Friesian and Dutch native dual-purpose breeds. Journal of Dairy Science 2015, 98(9):6510-6521.
  92. Maurice-Van Eijndhoven MHT, Soyeurt H, Dehareng F, Calus MPL: Validation of fatty acid predictions in milk using mid-infrared spectrometry across cattle breeds. Animal 2013, 7(2):348-354.
  93. Maurice-Van Eijndhoven MHT, Veerkamp RF, Soyeurt H, Calus MPL: Heritability of milk fat composition is considerably lower for Meuse-Rhine-Yssel compared to Holstein Friesian cattle. Livestock Science 2015, 180:58-64.
  94. Mulder HA, Crump RE, Calus MPL, Veerkamp RF: Unraveling the genetic architecture of environmental variance of somatic cell score using high-density single nucleotide polymorphism and cow data from experimental farms. Journal of Dairy Science 2013, 96(11):7306-7317.
  95. Mulder HA, Ronnegard L, Fikse WF, Veerkamp RF, Strandberg E: Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models. Genetics Selection Evolution 2013, 45.
  96. Ouweltjes W, Windig JJ, van Pelt ML, Calus MPL: Genotype by environment interaction for livability of dairy calves from first parity cows. Animal 2015, 9(10):1617-1623.
  97. te Pas MFW, Koopmans SJ, Kruijt L, Calus MPL, Smits MA: Plasma Proteome Profiles Associated with Diet-Induced Metabolic Syndrome and the Early Onset of Metabolic Syndrome in a Pig Model. Plos One 2013, 8(9).
  98. Tempelman RJ, Spurlock DM, Coffey M, Veerkamp RF, Armentano LE, Weigel KA, de Haas Y, Staples CR, Connor EE, Lu Y, VandeHaar MJ: Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries. Journal of Dairy Science 2015, 98(3):2013-2026.
  99. ten Napel J, Veerkamp RF: The Dutch national breeding programmes have developed to major globally operating companies. Journal of Animal Breeding and Genetics 2015, 132(3):205-206.
  100. Tenghe AMM, Bouwman AC, Berglund B, Strandberg E, Blom JY, Veerkamp RF: Estimating genetic parameters for fertility in dairy cows from in-line milk progesterone profiles. Journal of Dairy Science 2015, 98(8):5763-5773.
  101. Windig JJ, Hoving-Bolink RA, Veerkamp RF: Breeding for polledness in Holstein cattle. Livestock Science 2015, 179:96-101.