بررسی تغییر نسبت واریانس پلی‌ژنی باقیمانده بر توانایی پیش بینی ارزیابی ژنومی نتاج آمیخته

Aguilar, I., Fernandez, E. N., Blasco, A., Ravagnolo, O., & Legarra, A. (2020). Effects of ignoring inbreeding in model‐based accuracy for BLUP and SSGBLUP. Journal of animal breeding and genetics, 137(4), 356-364.

Alvarenga, A. B., Veroneze, R., Oliveira, H. R., Marques, D. B., Lopes, P. S., Silva, F. F., & Brito, L. F. (2020). Comparing alternative single-step GBLUP approaches and training population designs for genomic evaluation of crossbred animals. Frontiers in Genetics, 263.

Brown, D., Swan, A., Boerner, V., Li, L., Gurman, P., McMillan, A., Van der Werf, J., Chandler, H., Tier, B., & Banks, R. (2018). Single-step genetic evaluations in the Australian sheep industry. Proceedings of the world congress on genetics applied to livestock production,

Esfandyari, H., Sørensen, A. C., & Bijma, P. (2015). Maximizing crossbred performance through purebred genomic selection. Genetics Selection Evolution, 47(1), 1-16.

Falconer, D. S. (1996). Introduction to quantitative genetics. Pearson Education India.

Fragomeni, B. O., Lourenco, D. A., Masuda, Y., Legarra, A., & Misztal, I. (2017). Incorporation of causative quantitative trait nucleotides in single-step GBLUP. Genetics Selection Evolution, 49(1), 1-11.

Hollifield, M. K., Bermann, M., Lourenco, D., & Misztal, I. (2022). Impact of blending the genomic relationship matrix with different levels of pedigree relationships or the identity matrix on genetic evaluations. JDS communications, 3(5), 343-347.

Kluska, S., Masuda, Y., Ferraz, J. B. S., Tsuruta, S., Eler, J. P., Baldi, F., & Lourenco, D. (2021). Metafounders May Reduce Bias in Composite Cattle Genomic Predictions. Frontiers in Genetics, 1440.

Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of dairy science, 92(9), 4656-4663.

Legarra, A., Christensen, O. F., Aguilar, I., & Misztal, I. (2014). Single Step, a general approach for genomic selection. Livestock Science, 166, 54-65.

Liu, Z., Goddard, M., Reinhardt, F., & Reents, R. (2014). A single-step genomic model with direct estimation of marker effects. Journal of dairy science, 97(9), 5833-5850.

Lourenco, D., Legarra, A., Tsuruta, S., Masuda, Y., Aguilar, I., & Misztal, I. (2020). Single-step genomic evaluations from theory to practice: using SNP chips and sequence data in BLUPF90. Genes, 11(7), 790.

Lourenco, D., Tsuruta, S., Fragomeni, B., Chen, C., Herring, W., & Misztal, I. (2016). Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices. Journal of animal science, 94(3), 909-919.

Masuda, Y., VanRaden, P. M., Tsuruta, S., Lourenco, D. A., & Misztal, I. (2022). Invited review: Unknown-parent groups and metafounders in single-step genomic BLUP. Journal of dairy science, 105(2), 923-939.

Meyer, K., Tier, B., & Swan, A. (2018). Estimates of genetic trend for single-step genomic evaluations. Genetics Selection Evolution, 50, 1-11.

Misztal, I. (2017). Studies on inflation of GEBV in single-step GBLUP for type. Interbull Bulletin(51).

Misztal, I., Stein, Y., & Lourenco, D. (2022). Genomic evaluation with multibreed and crossbred data. JDS communications.

Misztal, I., Tsuruta, S., Lourenco, D., Aguilar, I., Legarra, A., & Vitezica, Z. (2014). Manual for BLUPF90 family of programs. Athens: University of Georgia, 199.

Neves, H. H., Carvalheiro, R., & Queiroz, S. A. (2012). A comparison of statistical methods for genomic selection in a mice population. BMC genetics, 13(1), 1-17.

Pahlavan, R., Abdollahi-Arpanahi, R., Afrazandeh, M., Nazari, B. M., & Kazemi, A. (2023). Scaling factor assessment in Single-Step GBLUP evaluations for small genotyped populations: a case study on Iranian Holstein cattle. Livestock Science, 105287.

Sargolzaei, M., & Schenkel, F. S. (2009). QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25(5), 680-681.

Tsuruta, S., Lourenco, D., Masuda, Y., Misztal, I., & Lawlor, T. (2019). Controlling bias in genomic breeding values for young genotyped bulls. Journal of dairy science, 102(11), 9956-9970.

Van Grevenhof, I. E., & Van der Werf, J. H. (2015). Design of reference populations for genomic selection in crossbreeding programs. Genetics Selection Evolution, 47(1), 1-9.

VanRaden, P., Tooker, M., Chud, T., Norman, H., Megonigal Jr, J., Haagen, I., & Wiggans, G. (2020). Genomic predictions for crossbred dairy cattle. Journal of dairy science, 103(2), 1620-1631.

VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of dairy science, 91(11), 4414-4423.

Vitezica, Z., Aguilar, I., Misztal, I., & Legarra, A. (2011). Bias in genomic predictions for populations under selection. Genetics Research, 93(5), 357-366.

Wientjes, Y. C., Bijma, P., Vandenplas, J., & Calus, M. P. (2017). Multi-population genomic relationships for estimating current genetic variances within and genetic correlations between populations. Genetics, 207(2), 503-515.