Abdollahi‐Arpanahi, R., Nejati‐Javaremi, A., Pakdel, A., Moradi‐Shahrbabak, M., Morota, G., Valente, B.D., Kranis, A., Rosa, G.J.M. and Gianola, D. (2014). Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. Journal of animal breeding and genetics, 131(2): pp.123-133.
Besnier, F., Wahlberg, P., Ronnegard, L., Ek, W., Andersson, L., Siegel, P.B. and Carlborg, O. (2011). Fine mapping and replication of QTL in outbred chicken advanced intercross lines. Genetics Selection Evolution, 43:3.
Calus, M.P., Huang, H., Vereijken, A., Visscher, J., ten Napel, J. and Windig, J.J. (2014). Genomic prediction based on data from three layer lines: a comparison between linear methods. Genetics Selection Evolution, 46(1): p.57.
Calus, M.P., Huang, H., Vereijken, A., Visscher, J., ten Napel, J. and Windig, J.J. (2014). Genomic prediction based on data from three layer lines: a comparison between linear methods. Genetics Selection Evolution, 46(1): p.57.
Emrani, H., Torshizi, R.V., Masoudi, A.A. and Ehsani, A. (2017). Identification of new loci for body weight traits in F2 chicken population using genome-wide association study. Livestock Science, 206: pp.125-131.
Gu, X.R., Feng, C.G., Ma, L., Song, C., Wang, Y.Q., Da, Y., Li, H., Chen, K., Ye, S., Ge, C., Hu, X. and Li, N. (2011). Genome-wide association study of body weight in chicken F2 resource population. PLoS One, 6(7): e21872.
Havenstein, G.B., Ferket, P.R., Scheideler, S.E. and Larson, B.T. (1994). Growth, livability and feed conversion of 1957 vs.1991 broilers when fed "typical" 1957 and 1991 broiler diets. Poultry Science, 73: 1785- 1794.
Hayes, B.J., Visscher, P.M. and Goddard, M.E. (2009). Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res. 91: 47–60.
Jin, C.F., Chen, Y.J., Yang, Z.Q., Shi, K. and Chen, C.K. (2015). A genome-wide association study of growth trait-related single nucleotide polymorphisms in Chinese Yancheng chickens. Genet. Mol. Res, 14(4): pp.15783-15792.
Kang, H.M., Sul. J.H., Service, S.K., Zaitlen, N.A., Kong, S.Y., Freimer, N.B., Sabatti, C. and Eskin, E. (2010). Variance component model to account for sample structure in genome-wide association studies. Nature Genetics, 42(4): 348-54.
Koivula, M., Strandén, I., Pösö, J., Aamand, G.P. and Mäntysaari, E.A. (2012). Single step genomic evaluations for the Nordic Red Dairy cattle test day data. Interbull Bulletin, (46).
Leahy, G.M.C. (2007). Farm-Animal Welfare, Legislation, and Trade. Law and contemporary problems.
Ledur, M.C., Navarro, N. and Perez-Enciso, M. (2009). Large-scale SNP genotyping incrosses between outbred lines: how useful is it? Heredity, 105: 173–182.
Leeson, S. (2007). Metabolic challenges past, present, and future. Journal of Applied Poultry Research, 16: 121-125.
Li, B., Zhang, N., Wang, Y.G., George, A.W., Reverter, A. and Li, Y. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in genetics, 9: p.237.
Li, M., Liu, X., Bradbury, P., Yu, J., Zhang, Y.M., Todhunter, R.J., Buckler, E.S. and Zhang, Z. (2014). Enrichment of statistical power for genome-wide association studies. BMC biology, 12(1): p.73.
Liu, R., Sun, Y., Zhao, G., Wang, H., Zheng, M., Li, P. Liu, L. and Wen, J. (2015). Identification of loci and genes for growth related traits from a genome-wide association study in a slow- × fast-growing broiler chicken cross. Genes & Genomics, 37: 829-836.
Liu, T., Luo, C., Ma, J., Wang, Y., Shu, D., Su, G. and Qu, H. (2020). High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers. Frontiers in Genetics, 11: p.108.
Liu, T., Qu, H., Luo, C., Shu, D., Wang, J., Lund, M.S. and Su, G. (2014). Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC genetics, 15(1): p.110.
Moser, G., Tier, B., Crump, R.E., Khatkar, M.S. and Raadsma, H.W. (2009). A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers. Genetics Selection Evolution, 41(1): p.56.
Ni, G., Cavero, D., Fangmann, A., Erbe, M. and Simianer, H. (2017). Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture. Genetics Selection Evolution, 49(1): p.8.
Oviedo-Rondon, E.O., Ferket, P.R. and Havenstein, G.B. (2006). Understanding long bone development in broilers and turkeys. Avian and Poultry Biology Reviews, 17: 77-88.
Price, A.L., Zaitlen, N.A., Reich, D. and Patterson, N. (2010). New approachesto population stratification in genome-wide association studies. Nature Reviews Genetics, 11: 459-63.
Rauw, W.M., Kanis, E., Noordhuizen-Stassen, E.N. and Grommers, F.J. (1998). Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science, 56: 15-33.
Shariatmadari, F. (2012). Plans of feeding broiler chickens. World's Poultry Science Journal, 68: 21-30.
Sharma, A., Lee, J.S., Dang, C.G., Sudrajad, P., Cheol Kim, H., Yeon, S, H., Kang, H. S., and Lee, S.H. (2015). Stories and Challenges of Genome Wide Association Studies in Livestock — A Review.Asian Australas. J. Anim.Sci. 28(10): 1371-1379.
Villanueva, B., Pong-Wong, R., Fernandez, J. and Toro, M.A. (2005). Benefits from marker-assisted selection under an additive polygenic genetic model. J. Anim. Sci. 83: 1747-1752.
Wahlberg, P., Carlborg, O., Foglio, M., Tordoir, X., Syvänen, A.C., Lathrop, M., Gut, G., Siegel, P.B. and Andersson, L. (2009). Genetic analysis of an F2 intercross between two chicken lines divergently selected for body-weight. BMC Genomics, 10: 248.
Weir, B.S. (1996). Genetic Data Analysis II, 2nd edition. Sinauer Associates.Sunderland, MA, USA.
Weller, J.I. (2016). Genomic selection in animals. John Wiley & Sons Incorporated.
Yanfa, S., Zhao, G., Liu, R., Zheng, M., Hu, Y., Wu, D., Zhang, L., Li, P. and Wen, L. (2013). The identification of 14 new genes for meat quality traits in chicken using a genome-wide association study. BMC Genomics, 14: 458.
Zhang, J., Wang, J., Li, Q., Wang, Q., Wen, J. and Zhao, G. (2020). Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens. Animals, 10(3): p.419.
Zhang, Q. and Xiao, X. (2015). Genome sequence–independent identification of RNA editing sites. Nature methods, 12(4): p.347.
Zhang, Z., Ersoz, E., Lai, C.Q., Todhunter, R.J., Tiwari, H.K., Gore, M.A., Bradbury, P.J., Yu, J., Arnett, D.K., Ordovas, J.M. and Buckler, E.S. (2010). Mixed linear model approach adapted for genome-wide association studies. Nature Genetics, 42(4): 355-360.
)