1. Abdollahi-Arpanahi, R. 2013. The Impact of allelic architecture of complex traits on genetic evaluations and evolutionary genetics. Ph.D. dissertation Universityof Tehran, Tehran.
2. Abdollahi-Arpanahi, R., A. Pakdel, A. Nejati-Javaremi and M.M. Shahrbabak. Comparison ofdifferent methods of genomic evolution in traits with different genetic architecture. Journal of Animal Production, 15: 65-77 (In Persian).
3. Aguilar, I., I. Misztal, D.L. Johnson, A. Legarra and S. Tsuruta. 2010. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93: 743-752. [
DOI:10.3168/jds.2009-2730]
4. Calus, M.P. 2009. Genomic breeding value prediction: methods and procedures. Animal, 4(2): 157-164. [
DOI:10.1017/S1751731109991352]
5. Chen, L, C. Li, M. Sargolzaei and F. Schenkel. 2014. Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction. PLoS ONE, 9(7): e101544. [
DOI:10.1371/journal.pone.0101544]
6. Cheng, H., D.J. Garrick and R.L. Fernando. 2016. JWAS: Julia implementation of whole-genome analyses software using univariate and multivariate Bayesian mixed effects model. Retrieved June 8, 2019 from http://QTL.rocks.
7. Colombani, C., A. Legarra, S. Fritz, F. Guillaume, P. Croiseau, V. Ducrocq and C. Robert-Granié. 2012. Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCp methods for genomic selection in French Holstein and Montbéliarde breeds. Journal of Dairy Science, 96: 575-91. [
DOI:10.3168/jds.2011-5225]
8. Daetwyler, H.D., K.E. Kemper, J.H. Vander Werf and B.J. Hayes. 2012. Components of the accuracy of genomic prediction in a multi-breed sheep population, Journal of Animal Science, 90(10): 3375-3384. [
DOI:10.2527/jas.2011-4557]
9. De los Campos, G., H. Naya, D. Gianola, J. Crossa, A. Legarra, E. Manfredi, K. Weigel and J.M. Cotes. 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics, 182(1): 375-385. [
DOI:10.1534/genetics.109.101501]
10. De los Campos, G., A.I. Vazquez, R. Fernando, Y.C. Klimentidis and D. Sorensen. 2013. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genetics, 9: e1003608. [
DOI:10.1371/journal.pgen.1003608]
11. Fernando, R.L., J.C. Dekkers and D.J. Garrick. 2014. A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genetics Selection Evolution, 46: 50. [
DOI:10.1186/1297-9686-46-50]
12. Gianola, D., G. DeLos Campos, W.G. Hill, E. Manfredi and R. Fernando. 2009. Additive genetic variability and the Bayesian alphabet. Genetics, 183: 347-363. [
DOI:10.1534/genetics.109.103952]
13. Goddard, M. 2009. Genomic selection: prediction of accuracy and maximization of long term response. Genetica, 136: 245-257. [
DOI:10.1007/s10709-008-9308-0]
14. Habier, D., R.L. Fernando, J.C. Dekkers. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177(4): 2389-2397. [
DOI:10.1534/genetics.107.081190]
15. Habier, D., J. Tetens, F.R. Seefried, P. Lichtner and G. Thaller. 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution, 42(1): 5. [
DOI:10.1186/1297-9686-42-5]
16. Habier, D., R.L. Fernando, K. Kizilkaya and D.J. Garrick. 2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics, 2: 186-194. [
DOI:10.1186/1471-2105-12-186]
17. Hayes, B.J., P.J. Bowman, A.J Chamberlain and M.E. Goddard. 2009. Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of DairyScience, 92: 433-443. [
DOI:10.3168/jds.2008-1646]
18. Hickey, J., S. Dreisigacker, J. Crossa, S. Hearne, R. Babu, B. Prasanna, M. Grondona, A. Zambelli, V. Windhausen, K. Mathews and G. Gorjanc. 2014. Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Science, 54: 1476-1488. [
DOI:10.2135/cropsci2013.03.0195]
19. Kang, H., L. Zhou, R. Mrode, Q. Zhang and J.F. Liu. 2016. Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait. Heredity, 119: 459-467. [
DOI:10.1038/hdy.2016.91]
20. Karaman, E., H. Cheng, M.Z. Firat, D.J. Garrick and R.L. Fernando. 2016. An upper bound for accuracy of prediction using GBLUP. PLoS ONE, 11(8): e0161054. [
DOI:10.1371/journal.pone.0161054]
21. Lee, J., H. Cheng, D. Garrick, B. Golden, J.C. Dekkers, K. Park, D. Lee and R. Fernando. 2017. Comparison of alternative approaches to single‑trait genomic prediction using genotyped and non‑genotyped Hanwoo beef cattle. Genetiscs Selection Evolution, 49: 2. [
DOI:10.1186/s12711-016-0279-9]
22. Legarra, A., I. Aguilar and I. Misztal. 2009. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92: 4656-63. [
DOI:10.3168/jds.2009-2061]
23. Meuwissen, T., B. Hayes and M. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819-29.
24. Moschopoulos, P.G. 1985. The distribution of the sum of independent gamma random variables. Annals of the Institute of Statistical Mathematics, 37(1): 541-544. [
DOI:10.1007/BF02481123]
25. Muir, W.M. 2007. Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics, 124: 342-355. [
DOI:10.1111/j.1439-0388.2007.00700.x]
26. Naderi, Y. 2018. The importance of genetic relationships and phenotypic record on genomic accuracy of simulated imputation data via animal models in presence of genotype × environment interactions. Research on Animal Production, 22: 119-130 (In Persian). [
DOI:10.29252/rap.9.22.119]
27. Nejati-Javaremi, A., C. Smith and J. Gibson. 1997. Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of animal science, 75: 1738-45. [
DOI:10.2527/1997.7571738x]
28. Park, T. and G. Casella. 2008. The Bayesian lasso. Journal of the American Statistical Association, 103: 681-6. [
DOI:10.1198/016214508000000337]
29. Pérez, P. and G. Delos Campos. 2014. Genome-wide regression and prediction with the BGLR statistical package. Genetics, 198: 483-495. [
DOI:10.1534/genetics.114.164442]
30. Saheb Alam, H., M. Gholizadeh, H. Hafezian and A. Farhadi. 2017. Comparison of Bayesian methods in the genomic evaluation with different genetic architecture. Research on Animal Production, 18: 177-186 (In Persian). [
DOI:10.29252/rap.8.18.177]
31. Sargolzaei, M. and F.S. Schenkel. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25: 680. [
DOI:10.1093/bioinformatics/btp045]
32. Toghiani, S., S.E. Aggrey and R. Rekaya. 2016. Multi-generational imputation of single nucleotide polymorphism marker genotypesand accuracy of genomic selection. Animal, 10: 1077-85. [
DOI:10.1017/S1751731115002906]
33. VanRaden, P.M., C.P. Van Tassell, G.R. Wiggans, T.S. Sonstegard, R.D. Schnabel, J.F. Taylor and F.S. Schenkel. 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science, 92: 16-24. [
DOI:10.3168/jds.2008-1514]
34. Wolc, A., J. Arango, P. Settar, J.E. Fulton, N.P. O'Sullivan and J.C. Dekkers. 2016. Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions. Journal of Animal Science, 7: 7. [
DOI:10.1186/s40104-016-0066-z]
35. Zhou, L., R. Mrode, S. Zhang, Q. Zhang, B. Li and J. Liu. 2018. Factors affecting GEBV accuracy with single-step Bayesian models. Heredity, 120: 100-109. [
DOI:10.1038/s41437-017-0010-9]