بررسی اثر متقابل ژنوتیپ و محیط با جانهی داده ژنومی شبیه سازی شده با استفاده از مدل های حیوانی مختلف

1.     Aguilar I, Misztal I, Legarra A and Tsuruta S (2011) Efficient computation of the genomic relationship matrix and other matrices used in single‐step evaluation. Journal of Animal Breeding and Genetics 128: 422-428.

2.     Bohlouli M, Alijani S, Javaremi AN, König S and Yin T (2017) Genomic prediction by considering genotype× environment interaction using different genomic architectures. Annals of Animal Science 17: 683-701.

3.      Bohlouli M, Shodja J, Alijani S and Eghbal A (2013) The relationship between temperature-humidity index and test-day milk yield of Iranian Holstein dairy cattle using random regression model. Livestock Science 157: 414-420.

4.     Browning SR (2008) Missing data imputation and haplotype phase inference for genome-wide association studies. Human genetics 124: 439-450.

5.     Calus M, De Haas Y, Pszczola M and Veerkamp R (2013) Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal 7: 183-191.

6.     Calus MP and Veerkamp RF (2011) Accuracy of multi-trait genomic selection using different methods. Genetics Selection Evolution 43: 26.

7.     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.

8.     Clark SA, Hickey JM and Van der Werf JH (2011) Different models of genetic variation and their effect on genomic evaluation. Genetics Selection Evolution 43: 18.

9.     Falconer D and Mackay T (1996) Introduction to quantitative genetics. Longman, Harlow, UK. Introduction to quantitative genetics 4^th ed Longman, Harlow, UK. pp. 183-184.

10.   Felipe VP, Okut H, Gianola D, Silva MA and Rosa GJ (2014) Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data. BMC genetics 15: 149.

11.   Goddard ME and Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Reviews Genetics 10: 381-391.

12.   Haile-Mariam M, Pryce J, Schrooten C and Hayes B (2015) Including overseas performance information in genomic evaluations of Australian dairy cattle. Journal of dairy science 98: 3443-3459.

13.   Hammami H, Rekik B, Bastin C, Soyeurt H, Bormann J, Stoll J and Gengler N (2009) Environmental sensitivity for milk yield in Luxembourg and Tunisian Holsteins by herd management level. Journal of dairy science 92: 4604-4612.

14.   Hayashi T and Iwata H (2013) A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC bioinformatics 14: 34.

15.   Hayes BJ, Daetwyler HD and Goddard ME (2016) Models for genome× environment interaction: Examples in livestock. Crop Science 56: 2251-2259.

16.   Ke X, Hunt S, Tapper W, Lawrence R, Stavrides G, Ghori J, Whittaker P, Collins A, Morris AP and Bentley D (2004) The impact of SNP density on fine-scale patterns of linkage disequilibrium. Human Molecular Genetics 13: 577-588.

17.   Lillehammer M, Ødegård J and Meuwissen TH (2007) Random regression models for detection of gene by environment interaction. Genetics Selection Evolution 39: 105.

18.   Muir W (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.

19.   Mulder H, Calus M, Druet T and Schrooten C (2012) Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. Journal of dairy science 95: 876-889.