Daetwyler, H. D., Calus, M. P., Pong-Wong, R., de los Campos, G., and Hickey,
J. M.: Genomic prediction in animals and plants: simulation of data, validation,
reporting, and benchmarking, Genetics, 193, 347–365, 2013.
de los Campos, G., Vazquez, A. I., Fernando, R., Klimentidis, Y. C., and
Sorensen, D.: Prediction of complex human traits using the genomic best linear
unbiased predictor, PLoS Genet., 9, e1003608, https://doi.org/10.1371/journal.pgen.1003608, 2013.
Devlin, B. and Risch, N.: A comparison of linkage disequilibrium measures for
fine-scale mapping, Genomics, 29, 311–322, 1995.
Goddard, M.: Genomic selection: prediction of accuracy and maximisation of long
term response, Genetica, 136, 245–257, 2009.
Hayes, B., Bowman, P., Chamberlain, A., and Goddard, M.: Invited review: Genomic
selection in dairy cattle: Progress and challenges, J. Dairy Sci., 92, 433–443, 2009.
Hedrick, P. W.: Gametic disequilibrium measures: proceed with caution, Genetics,
117, 331–341, 1987.
Hill, W., Goddard, M. E., and Visscher, P. M.: Data and theory point to mainly
additive genetic variance for complex traits, PLoS Genet., 4, e1000008,
https://doi.org/10.1371/journal.pgen.1000008, 2008.
Khatkar, M. S., Nicholas, F. W., Collins, A. R., Zenger, K. R., Cavanagh, J. A.,
Barris, W., Schnabel, R. D., Taylor, J. F., and MacLeod, I. M., Hayes, B. J.,
and Goddard, M. E.: The effects of demography and long-term selection on the
accuracy of genomic prediction with sequence data, Genetics, 198, 1671–1684, 2014.
Matukumalli, L. K., Lawley, C. T., Schnabel, R. D., Taylor, J. F., Allan, M. F.,
Heaton, M. P., O'connell, J., Moore, S. S., Smith, T. P., and Sonstegard, T. S.:
Development and characterization of a high density SNP genotyping assay for
cattle, PloS One, 4, e5350, https://doi.org/10.1371/journal.pone.0005350, 2009.
Meuwissen, T. and Goddard, M.: Accurate prediction of genetic values for complex
traits by whole-genome resequencing, Genetics, 185, 623–631, 2010.
Muir, W.: Comparison of genomic and traditional BLUP-estimated breeding value
accuracy and selection response under alternative trait and genomic parameters,
J. Anim. Breed. Genet., 124, 342–355, 2007.
R Core Team: R: a language and environment for statistical computing, R Foundation
for Statistical Computing, Vienna, Austria, available at:
http://cran.r-project.org,
last access: 20 June 2016.
Solberg, T., Sonesson, A., Woolliams, J., and Meuwissen, T.: Genomic selection
using different marker types and densities, J. Anim. Sci., 86, 2447–2454, 2008.
Speed, D., Hemani, G., Johnson, M. R., and Balding, D. J.: Improved heritability
estimation from genome-wide SNPs, Am. J. Hum. Genet., 91, 1011–1021, 2012.
Sun, X., Fernando, R. L., Garrick, D. J., and Dekkers, J.: Improved accuracy
of genomic prediction for traits with rare QTL by fitting haplotypes, Animal
Industry Report, 661, 86–88, 2015.
Technow, F.: R Package hypred: Simulation of Genomic Data in Applied Genetics,
University of Hohenheim, Institute of Plant Breeding, Seed Science and Population
Genetics, Stuttgart, Germany, 2011.
Uemoto, Y., Sasaki, S., Kojima, T., Sugimoto, Y., and Watanabe, T.: Impact of
QTL minor allele frequency on genomic evaluation using real genotype data and
simulated phenotypes in Japanese Black cattle, BMC Genet., 16, 134–148, 2015.
VanRaden, P. M.: Efficient methods to compute genomic predictions, J. Dairy Sci.,
91, 4414–4423, 2008.
VanRaden, P. M., Van Tassell, C., Wiggans, G., Sonstegard, T., Schnabel, R.,
Taylor, J., and Schenkel, F.: Invited review: Reliability of genomic predictions
for North American Holstein bulls, J. Dairy Sci., 92, 16–24, 2009.
Wientjes, Y. C., Calus, M. P., Goddard, M. E., and Hayes, B. J.: Impact of QTL
properties on the accuracy of multi-breed genomic prediction, Genet. Sel. Evol.,
47, 42–58, 2015.
Wray, N. R.: Allele frequencies and the
r2 measure of linkage disequilibrium:
impact on design and interpretation of association studies, Twin Res. Hum. Genet.,
8, 87–94, 2005.
Yan, J., Shah, T., Warburton, M. L., Buckler, E. S., McMullen, M. D., and Crouch,
J.: Genetic characterization and linkage disequilibrium estimation of a global
maize collection using SNP markers, PloS One, 4, e8451, https://doi.org/10.1371/journal.pone.0008451, 2009.