1. Abdollahi-Arpanahi, R., Pakdel, A., and Zandi, M.B. 2012. From infinity locus genetic model with minor effects (infinitesimal model) to genomic selection. Modern Genetics Journal. 7: 105- 114.
2. Abdollahi-Arpanahi, R., Pakdel, A., Nejati-Javaremi, A., Moradi Shahrbabak, M., Morota, G., Valente, B.D., Kranis, A., Rosa, G.J.M., and Gianola, D. 2014. Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Journal of Animal Breeding and Genetics. 131: 183–193.
3. Alijani, S. 2010. Major genes detection in farm animals using statistical Bayesian and molecular methods. PHD Thesis. Tehran University, Karaj, Iran. 142 p.
4. Blasco, A. 2001. The Bayesian controversy in animal breeding. Journal of Animal Science. 79: 2023-2046.
5. Deimi Ghias Abadi, P., Alijani, S., Shodja Ghias, J., and Pirani, N. 2012. Comparison of tow restricted maximum likelihood (REML) and Bayesian statistical methods for estimating genetic parameter of some economically important traits in Fars native chickens. Research On Animal Production (Scientific and Research). 3: 1-13.
6. Dekkers, J.C.M., and Hospital, F. 2002. The use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics. 3: 22-32.
7. Eynard, S.E., Windig, J.J., Leroy, G., Van Binsbergen, R., and Calus, M. 2015. The effect of rare alleles on estimated genomic relationships from whole genome sequence data. BMC Genetics. 16: 24.
8. Gianola, D., and De los Campos, G. 2013. Genome-enable prediction of complex traits. University of Wisconsin-Medison, May 27th-31st, 2013.
9. Gianola, D., and Fernando, R.L. 1986. Bayesian methods in animal breeding theory. Journal of Animal Science. 63: 217-244.
10. Goddard, M. 2010. Introduction to Bayesian Statistics. University of Melbourne and Victorian Institute of Animal Science.Pp: 135-162.
11. Haile-Mariam, M., Nieuwhof, G., Beard, K., Konstatinov, K., and Hayes, B. 2013. Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations. Journal of Animal Breeding and Genetics. 130: 20–31.
12. Hayes, B.J. 2007. QTL Mapping, MAS and Genomic Selection. A short-course organized by Animal Breeding and Genetics, Department of Animal Science, Iowa State University. 118 pp.
13. Hayes, B., Bowman, P., Chamberlain, A., and Goddard, M. 2009. Invited review: Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science. 92: 433–443.
14. Hindorff, L.A., Sethupathy, P., Junkins, H.A., Ramos, E.M., Mehta, J.P, Collins, F.S., and Manolio, T.A. 2009. Potential etiologic and functional implications of genome-wide
association loci for human diseases and traits. Proceedings of the National Academy of Sciences of the United States of America .106: 9362–9367.
15. Huisman, A.E., and Brown, D.J., 2009. Genetic parameters for bodyweight, wool, and disease resistance and reproduction traits in Merino sheep. 3. Genetic relationships between ultrasound scan traits and other traits. Journal of Animal Production Science. 49: 283-288.
16. Jasori, M., Alijani, S., Pirany, N., Baghernejad, M., and Jafarzadeh, R. 2011. Estimation of genetic parameters of Holstein dairy cattle using Bayesian procedure. 4th Iranian Animal Science Congress. Tehran, Iran. Pp: 3022-3025.
17. Jensen, J., Su, G., and Madsen, P. 2012. Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle. BMC Genetics. 13: 44.
18. Kijas, J.W., Lenstra, J.A., Hayes, B., Boitard, S., Porto-Neto, L.R., San Cristobal, M., Servin, B., McCulloch, R., Whan, V., Gietzen, K., Paiva, S., Barendse, W., Ciani, E., Raadsma, H., McEwan, J., and Dalrymple, B. 2012. Other members of the International Sheep Genomics Consortium. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biology. 10: e1001258. 1-14.
19. Lee, S.H., DeCandia, T.R., Ripke, S., Yang, J., Sullivan, P.F., Goddard, M.E., Keller, M.C., Visscher, P.M., and Wray, N.R. 2012. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Journal of National Genetics. 44: 247–250.
20. Lee, S.H., Harold, D., Nyholt, D.R., Goddard, M.E., Zondervan, K.T., Williams, J., Montgomery G.W., Wray, N.R., and Visscher, P.M. 2013. Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer’s disease, multiple sclerosis and endometriosis. Journal of Human Molecular Genetics. 22: 832–841.
21. Lettre, G. 2011. Recent progress in the study of the genetics of height. Journal of Human Genetic. 129: 465–472.
22. Manolio, T.A., Collins, F.S., Cox, N.J., Goldstein, D.B., Hindorff, L.A., Hunter, D.J., McCarthy, M.I., Ramos, E.M., Cardon, L.R., Chakravarti, A., Cho, J.H., Guttmacher, A.E., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C.N., Slatkin, M., Valle, D., Whittemore, A.S., Boehnke, M., Clark, A.G., Eichler, E.E., Gibson, G., Haines, J.L., Mackay, T.F., McCarroll, S.A., and Visscher, P.M. 2009. Finding the missing heritability of com-plex diseases. Nature. 461: 747–753.
23. Mrode, R.A. 1996. Linear models for the prediction of animal breeding values. C.A.B. International. Pp: 187.
24. Ogawa, S., Matsuda1, H., Taniguchi, Y., Watanabe, T., Sugimoto, Y., and Iwaisaki, H. 2016. Estimated Genetic Variance Explained by Single Nucleotide Polymorphisms of Different Minor Allele Frequencies for Carcass Traits in Japanese Black Cattle. Journal of Biosciences and Medicines. 4: 89-97.
25. Park, J.H., Gail, M.H., Weinberg, C.R., Carroll, R.J., Chung, C.C., Wang, Z., Chanock, S.J., Fraumeni, J.F., and Chatterjee, N. 2011. Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proceedings of the National Academy of Sciences of the USA. 108: 18026–18031.
26. Pérez, P., and De los Campos, G. 2013. BGLR: a statistical package for whole genome regression and prediction. R package version, 1(0.2)
27. Pimentel, E.C.G., Erbe, M., Konig, S., and Simianer, H. 2011. Genome partitioning of genetic variation for milk production and composition traits in Holstein cattle.Journal of Frontiers Genetics. 2: 19.
28. Purcell, S., Neale, B., Todd-Brown, K., Thomas. L., Ferreira, M.A.R., Bender, D., Maller, J., Sklar, P., De Bakker, P.I.W., and Daly, M.J. 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Journal of Humen Genetics. 81: 559–575.
29. Rashedi-Dehsahraei, A., Fayazi, J., Vatankhah, M., and Beigi-Nasiri, M.T. 2013. Estimation of (Co) variance components and genetic parameters for growth traits in Lori-Bakhtiari lambs using a Bayesian approach via Gibbs sampling. Journal of Ruminant Research. 1: 109-128.
30. Solberg, T.R., Sonesson, A.K., Woolliamas, J.A., and Meuwissen, T.H. 2008. Genomic selection using different marker types and densities. Journal of Animal Science. 86: 2447-2454.
31. Uemoto, Y., Sasaki, S., Kojima, T., Sugimoto, Y., and Watanabe, T. 2015. Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle. BMC Genetics. 16: 134.
32. Van Raden, P.M. 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science. 91: 4414–23.
33. Villanuva, B., Pong-Wong, R., Fernandez, J., and Toro, M.A. 2005. Benefits from marker-assisted selection under an additive polygenic genetic model. Journal of Animal Science. 83: 1747-1752.
34. Watson, C.T., Disanto, G., Breden, F., Giovannoni, G., and Ramagopalan, S.V. 2012. Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs. Journal of Scientific Reports. 2: 770.
35. White, J.D., Allingham, P.G., Gorman, Ch.M., Emery, D.L., Hynd, P., Owens, J., Bell, A., Siddell, J., Harper, G., Hayes, B.J., Daetwyler, H.D., Usmar, J., Goddard, M.E., Henshall, J.M., Dominik, S., Brewer, H., van der Werf, J.H.J., Nicholas, F.W., Warner, R., Hofmyer, C., Longhurst, T., Fisher, T., Swan, P., Forage, R., and Oddy, V.H. 2012. Design and phenotyping procedures for recording wool, skin, parasite resistance, growth, carcass yield and quality traits of the Sheep GENOMICS mapping flock. Journal of Animal Production Science. 52: 157–171.
36. Witte, J.S., Visscher, P.M., and Wray, N.R. 2014. The contribution of genetic variants to disease depends on the ruler. Nature Reviews Genetics. 15: 765–776.
37. Wray, N.R. 2005. Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin. Res. Journal of Human Genetics. 8: 87–94.
38. Yang, J., Benyamin, B., McEvoy, B.P., Gordon, S., Henders, A.K., Nyholt, D.R., Madden, P.A., Heath, A.C., Martin, N.G., Montgomery, G.W., Goddard, M.E., and Visscher, P.W. 2010. Common SNPs explain a large proportion of the heritability for human height. Nature Reviews Genetics. 42: 565–569.
39. Yang, J., Manolio, T.A., Pasquale, L.R., Boerwinkle, E., Caporaso, N., Cunningham, J.M., de Andrade, M., Feenstra, B., Feingold, E., Hayes, M.G., Hill, W.G., Landi, M.T., Alonso, A., Letter, G., Lin, P., Ling, H., Lowe, W., Mathias, R.A., Melbye, M., Pugh, E., Cornelis, M.C., Weir, B.S., Goddard, B.S., and Vischer, P.M. 2011. Genome partitioning of genetic variation for complex traits using common SNPs. Nature Reviews Genetics. 43: 519–525.
40. Yang, J., Lee, H., Goddard, M., and Visscher, P. 2014. GCTA: A tool for genome‐wide complex trait analysis. Version 1.24, 28 July 2014. University of Queensland.
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