1. Boison, S.A., Utsunomiya, A.T.H., Santos, D.J.A., Neves, H.H.R., Carvalheiro, R. and Mészáros, G. 2017. Accuracy of genomic predictions in Gyr (Bosindicus) dairy cattle. J. Dairy Sci., 100, 1–12.
2. Chang, L., Toghiani, S., Ling, A., Aggrey, S.E. and Rekaya, R. 2018. High density marker panels, SNPs prioritizing and accuracy of genomic selection. BMC Genetics, 19:4.
3. Calus, M.P.L. 2010. Genomic breeding value prediction: methods and procedures. Animal., 4: 157-164.
4. Chen, L., Li, C., Sargolzaei, M. and Schenkel, F. 2014. Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction. PLoS One., 9, e101544.
5. Clark, S.A., Hickey, J.M., Daetwyler, H.D. and van der Werf, J.H. 2012. The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet. Select. Evol., 44: 4.
6. De Los Campos, G., Hickey, J.M., Pong-Wong, R., Daetwyler, H.D. and Calus, M.P, 2013. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327-345.
7. Georges, M., Charlier, C.and Hayes, B.2019. Harnessing genomic information for livestock improvement. Nat. Rev. Genet., 20: 135-156.
8. Gray, K.A., Cassady, J.P., Huang, Y. and Maltecca, C. 2012. Effectiveness of genomic prediction on milk flow traits in dairy cattle. Genet. Sel. Evol., 44:24.
9. Hayes, B.J., Bowman, P.J., Chamberlain, A.J. and Goddard, M.E. 2009. Invited review: Genomic selection in dairy cattle: progress and challenges. J. Dairy. Sci., 92: 433-443.
10. Larmer, S., Sargolzaei, M., Brito, L., Ventura, R. and Schenkel, F. 2017. Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy. BMC Genet., 18:120.
11. Lopes, M. S., Bovenhuis, H., Hidalgo, A. M., Arendonk, J. A., Knol, E. F. and Bastiaansen, J. W. 2017. Genomic selection for crossbred performance accounting for breed-specific effects. Genet. Sel. Evol., 49:51.
12. Meuwissen, T.H., Hayes, B.J. and Goddard, M.E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics., 157: 1819-1829.
13. Moghaddar, N., Gore, K. P., Daetwyler, H. D., Hayes, B. J. and van der Werf, J. H. J., 2015. Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction. Genet. Sel. Evol., 47: 97.
14. Momen, M., Ayatollahi Mehrgardi, A., Amiri Roudbar, M., Kranis, A., Mercuri Pinto, R., Valente, B. D., Morota, G., Rosa, G. J. M. and Gianola, D. 2018. Including phenotypic causal networks in Genome-Wide Association studies using mixed effects structural equation models. Front. Genet., 9:455.
15. Mrode, R., Ojango, J. M. K., Okeyo, A. M. and Mwacharo, J. M. 2019. Genomic selection and use of molecular tools in breeding programs for indigenous and crossbred cattle in developing countries: Current status and future prospects. Front. Genet., 9:694.
16. Oliveira Junior, G. A., Chud, T. C. S., Ventura, R.V., Garrick, D. J., Cole, J. B., Munari, D. P., Ferraz, J. B. S., Mullart, E., DeNise, S. and Smith, S. 2017. Genotype imputation in a tropical crossbred dairy cattle population. J. Dairy Science., 100: 1–12.
17. Sargolzaei, M. and Schenkel, F. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics., 25, 680-681.
18. Sargolzaei, M., Chesnais, J.P. and Schenkel, F.S. 2014. A new approach for efficient genotype imputation using information from relatives. BMC Genomics., 15: 478.
19. Schaeffer, L.R. 2006. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet., 123: 218-223.
20. Schrooten, C., Dassonneville, R., Ducrocq, V., Brondum, R., Lund, M. and Chen, J. 2014. Error rate for imputation from the Illumina BovineSNP50 chip to the Illumina BovineHD chip. Genet Sel Evol., 46:10.
21. Silva, R. M. O., Fragomeni, B. O., Lourenco, D. A. L., Magalhães, A. F. B., Irano, N. and Carvalheiro, R. 2016. Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population. J. Anim. Sci., 94, 3613–3623.
22. Van Binsbergen, R., Bink, M.C., Calus, M.P., Van Eeuwijk, F.A., Hayes, B.j., Hulsegge, I. and Veerkamp, R.F. 2014. Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle. Genet. Sel. Evol., 46:41.
23. VanRaden, P.M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci., 91: 4414-4423.
24. Villumsen, T.M., Janss, L. and Lund, M.S. 2009. The importance of haplotype length and heritability using genomic selection in dairy cattle. J. Anim. Breed. Genet. 126,3-13.
25. Wang, Q., Yu, Y., Yuan, J., Zhang, X., Huang, H., Li, F. and Xiang, J. 2017. Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei. BMC Genetics, 18:45.
26. Weigel, K. A., Van Tassell, C. P., O’Connell, J. R., VanRaden, P. M. and Wiggans, G. R. 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. J. Dairy Sci., 93, 2229–2238.
27. Zhang, Z., and Druet, T. 2010. Marker imputation with low-density marker panels in Dutch Holstein cattle. J. Dairy Sci., 93(11), 5487-5494.