دوره 9، شماره 22 - ( زمستان 97 1397 )                   جلد 9 شماره 22 صفحات 130-119 | برگشت به فهرست نسخه ها


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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. rap. 9(22), 119-130. doi:10.29252/rap.9.22.119
URL: http://rap.sanru.ac.ir/article-1-945-fa.html
نادری یوسف. اهمیت خویشاوندی ژنتیکی و رکورد فنوتیپی بر صحت ژنومی داده‌های جانهی شبیه‌ سازی شده با استفاده از مدل های حیوانی در حضور اثرات متقابل ژنوتیپ و محیط پژوهشهاي توليدات دامي 1397; 9 (22) :130-119 10.29252/rap.9.22.119

URL: http://rap.sanru.ac.ir/article-1-945-fa.html


استادیار، گروه علوم دامی، دانشگاه آزاد اسلامی، واحد آستارا، آستارا، ایران
چکیده:   (3305 مشاهده)
هدف این تحقیق بررسی نقش ارتباط خویشاوندی بین جمعیت مرجع و تأیید با نسبت‌های مختلف رکوردهای فنوتیپی جمعیت مرجع بر صحت پیش‌بینی‌های ژنومی مدل‌های حیوانی مختلف با استفاده از شبیه‌سازی داده‌های ژنومی جانهی بود.  بدین منظور، چهار سناریو متفاوت برای سطوح مختلف عدم تعادل پیوستگی (بالا و پایینLD=) و جایگاه ­های صفات کمی (90 و 300) با تراکم K15 شبیه‌سازی شد. بعد از شبیه‌‌سازی جمعیت‌­ها، به طور تصادفی اقدام به حذف 50 و 95 درصد نشانگرها نموده و در مرحله بعد نشانگرهای حذف شده جانهی شدند. در ادامه، از طریق کد نویسی در نرم افزار R، شبیه‌سازی جهت ایجاد خویشاوندی ژنتیکی بین صفات در سه محیط با وراثت­‌پذیری‌های مختلف برای داده‌های اصلی و جانهی انجام شد. دامنه صحت جانهی بین 737/0-941/0 بود. LD فاکتور اصلی مؤثر بر صحت جانهی بود. با افزایش سطح LD و وراثت ­پذیری، صحت پیش­ بینی ژنومی افزایش یافت. زمانی که حیوانات بدون رکورد فنوتیپی محیط دوم، با استفاده از اطلاعات ژنومی خویشاوندان شان در محیط سوم ارزیابی شدند مقدار صحت پیش ­بینی ژنومی بالا بود. هنگامی که حیوانات با 75 درصد رکورد فنوتیپی در محیط دوم، با استفاده از اطلاعات ژنومی خویشاوندان شان در محیط اول و سوم ارزیابی شدند بالاترین مقدار صحت پیش­­ بینی ژنومی مشاهده شد. نتایج نشان داد که جانهی پنل­‌های با تراکم خیلی پایین به K15 همیشه راه کار مناسبی جهت پیش­‌بینی ژنوتیپ‌ها نمی‌باشد. به طور کلی، استفاده هم­زمان از اطلاعات خویشاوندان و افزایش تعداد رکورد فنوتیپی در جمعیت مرجع، صحت پیش­ بینی ژنومی مدل­‌های حیوانی مختلف را در حضور اثرات متقابل ژنوتیپ و محیط افزایش داد.
متن کامل [PDF 367 kb]   (1315 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: ژنتیک و اصلاح نژاد دام
دریافت: 1397/4/15 | ویرایش نهایی: 1398/9/30 | پذیرش: 1397/7/2 | انتشار: 1397/11/6

فهرست منابع
1. Aguilar, I., I. Misztal, A. Legarra and S. Tsuruta. 2011. Efficient computation of the genomic relationship matrix and other matrices used in singlestep evaluation. Journal of Animal Breeding and Genetics, 128: 422-428. [DOI:10.1111/j.1439-0388.2010.00912.x]
2. Baneh, H., A. Nejati-Javaremi, G. Rahimi-Mianji and M. Honarvar. 2017. Genomic evaluation of threshold traits with different genetic architecture using bayesian approaches. Research on Animal Production, 8: 149-154 (In Persian). [DOI:10.29252/rap.8.15.149]
3. Beerda, B., W. Ouweltjes, L. Šebek, J. Windig and R. Veerkamp. 2007. Effects of genotype by environment interactions on milk yield, energy balance, and protein balance. Journal of Dairy Science, 90: 219-228. [DOI:10.3168/jds.S0022-0302(07)72623-1]
4. Bohlouli, M., S. Alijani, A.N. Javaremi, S. König and T. Yin. 2017. Genomic prediction by considering genotype× environment interaction using different genomic architectures. Annals of Animal Science, 17: 683-701. [DOI:10.1515/aoas-2016-0086]
5. Bohlouli, M., J. Shodja, S. Alijani and N. Pirany. 2014. Interaction between genotype and geographical region for milk production traits of Iranian Holstein dairy cattle. Livestock Science, 169: 1-9. [DOI:10.1016/j.livsci.2014.08.010]
6. Boison, S., H.H.d.R. Neves, A.P. O'Brien, Y.T. Utsunomiya, R. Carvalheiro, M. da Silva, J. Sölkner and J.F. Garcia. 2014. Imputation of non-genotyped individuals using genotyped progeny in Nellore, a Bos indicus cattle breed. Livestock Science, 166: 176-189. [DOI:10.1016/j.livsci.2014.05.033]
7. Browning, S.R. 2008. Missing data imputation and haplotype phase inference for genome-wide association studies. Human genetics, 124: 439-450. [DOI:10.1007/s00439-008-0568-7]
8. Calus, M., Y. De Haas, M. Pszczola and R. Veerkamp. 2013. Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal, 7: 183-191. [DOI:10.1017/S1751731112001450]
9. Calus, M., A. De Roos and R. Veerkamp. 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics, 178: 553-561. [DOI:10.1534/genetics.107.080838]
10. Carvalheiro, R., S.A. Boison, H.H. Neves, M. Sargolzaei, F.S. Schenkel, Y.T. Utsunomiya, A.M.P. O'Brien, J. Sölkner, J.C. McEwan and C.P. Van Tassell. 2014. Accuracy of genotype imputation in Nelore cattle. Genetics Selection Evolution, 46: 69. [DOI:10.1186/s12711-014-0069-1]
11. Ceron-Munoz, M., H. Tonhati, C. Costa, D. Rojas-Sarmiento and D.E. Echeverri. 2004. Factors that cause genotype by environment interaction and use of a multiple-trait herd-cluster model for milk yield of Holstein cattle from Brazil and Colombia. Journal of Dairy Science, 87: 2687-2692. [DOI:10.3168/jds.S0022-0302(04)73395-0]
12. 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: e101544. [DOI:10.1371/journal.pone.0101544]
13. Costa, C., R. Blake, E. Pollak, P. Oltenacu, R. Quaas and S. Searle. 2000. Genetic analysis of Holstein cattle populations in Brazil and the United States. Journal of Dairy Science, 83: 2963-2974. [DOI:10.3168/jds.S0022-0302(00)75196-4]
14. Daetwyler, H.D., M.P. Calus, R. Pong-Wong, G. de los Campos and J.M. Hickey. 2013. Genomic prediction in animals and plants: simulation of data, validation, reporting and benchmarking. Genetics, 193: 347-365. [DOI:10.1534/genetics.112.147983]
15. Daetwyler, H.D., G.R. Wiggans, B.J. Hayes, J.A. Woolliams and M.E. Goddard. 2011. Imputation of missing genotypes from sparse to high density using long-range phasing. Genetics, 189: 317-327. [DOI:10.1534/genetics.111.128082]
16. De Jong, G. 1995. Phenotypic plasticity as a product of selection in a variable environment. The American Naturalist, 145: 493-512. [DOI:10.1086/285752]
17. De Jong, G. and P. Bijma. 2002. Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livestock Production Science, 78: 195-214. [DOI:10.1016/S0301-6226(02)00096-9]
18. De Roos, A., and M. Goddard. 2009. Reliability of genomic predictions across multiple populations. Genetics, 183:1545-1553. [DOI:10.1534/genetics.109.104935]
19. Falconer, D. and T. Mackay. 1996. Introduction to quantitative genetics. Longman, Harlow, UK. Introduction to quantitative genetics. 4th ed. Longman, Harlow, UK., 218 pp.
20. Felipe, V.P., H. Okut, D. Gianola, M.A. Silva and G.J. Rosa. 2014. Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data. BMC genetics, 15: 149. [DOI:10.1186/s12863-014-0149-9]
21. Fikse, W., R. Rekaya and K. Weigel. 2003. Genotype× environment interaction for milk production in Guernsey cattle. Journal of Dairy Science, 86: 1821-1827. [DOI:10.3168/jds.S0022-0302(03)73768-0]
22. Guo, G., F. Zhao, Y. Wang, Y. Zhang, L. Du and G. Su. 2014. Comparison of single-trait and multiple-trait genomic prediction models. BMC genetics, 15: 30. [DOI:10.1186/1471-2156-15-30]
23. Haile-Mariam, M., J. Pryce, C. Schrooten and B. Hayes. 2015. Including overseas performance information in genomic evaluations of Australian dairy cattle. Journal of Dairy Science, 98: 3443-3459. [DOI:10.3168/jds.2014-8785]
24. Hammami, H., B. Rekik, C. Bastin, H. Soyeurt, J. Bormann, J. Stoll and N. Gengler. 2009. Environmental sensitivity for milk yield in Luxembourg and Tunisian Holsteins by herd management level. Journal of Dairy Science, 92: 4604-4612. [DOI:10.3168/jds.2008-1513]
25. Hayashi, T. and H. Iwata. 2013. A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC bioinformatics, 14: 34. [DOI:10.1186/1471-2105-14-34]
26. Hayes, B.J., P.J. Bowman, A.J. Chamberlain, K. Savin, C.P. Van Tassell, T.S. Sonstegard and M.E. Goddard. 2009. A validated genome wide association study to breed cattle adapted to an environment altered by climate change. PloS one, 4: e6676. [DOI:10.1371/journal.pone.0006676]
27. Hayes, B.J., H.D. Daetwyler and M.E. Goddard. 2016. Models for genome× environment interaction: Examples in livestock. Crop Science, 56: 2251-2259. [DOI:10.2135/cropsci2015.07.0451]
28. Heidaritabar, M., M.P. Calus, A. Vereijken, M.A. Groenen and J.W. Bastiaansen. 2015. Accuracy of imputation using the most common sires as reference population in layer chickens. BMC genetics, 16: 101. [DOI:10.1186/s12863-015-0253-5]
29. Hickey, J.M., J. Crossa, R. Babu and G. de los Campos. 2012. Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Crop Science, 52: 654-663. [DOI:10.2135/cropsci2011.07.0358]
30. Horan, B., P. Dillon, P. Faverdin, L. Delaby, F. Buckley and M. Rath. 2005. The interaction of strain of Holstein-Friesian cows and pasture-based feed systems on milk yield, body weight, and body condition score. Journal of Dairy Science, 88: 1231-1243. [DOI:10.3168/jds.S0022-0302(05)72790-9]
31. Ke, X., S. Hunt, W. Tapper, R. Lawrence, G. Stavrides, J. Ghori, P. Whittaker, A. Collins, A.P. Morris, and D. Bentley. 2004. The impact of SNP density on fine-scale patterns of linkage disequilibrium. Human Molecular Genetics, 13: 577-588. [DOI:10.1093/hmg/ddh060]
32. Khatkar, M.S., G. Moser, B.J. Hayes and H.W. Raadsma. 2012. Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC genomics, 13: 538. [DOI:10.1186/1471-2164-13-538]
33. Kolver, E., J. Roche, M. De Veth, P. Thorne and A. Napper. 2002. Total mixed ration versus pasture diets: Evidence of a genotype x diet interaction. New Zealand Societyy of Animal Production, 62: 246-251.
34. König, S., G. Dietl, I. Raeder and H. Swalve. 2005. Genetic relationships for dairy performance between large-scale and small-scale farm conditions. Journal of Dairy Science, 88: 4087-4096. [DOI:10.3168/jds.S0022-0302(05)73093-9]
35. Lillehammer, M., J. Ødegård and T.H. Meuwissen. 2007. Random regression models for detection of gene by environment interaction. Genetics Selection Evolution, 39: 105. [DOI:10.1186/1297-9686-39-2-105]
36. Lund, M.S., A.P. De Roos, A.G. De Vries, T. Druet, V. Ducrocq, S. Fritz, F. Guillaume, B. Guldbrandtsen, Z. Liu and R. Reents. 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics Selection Evolution, 43: 43. [DOI:10.1186/1297-9686-43-43]
37. Lund, M.S., G. Su, L. Janss, B. Guldbrandtsen and R.F. Brøndum. 2014. Genomic evaluation of cattle in a multi-breed context. Livestock Science, 166: 101-110. [DOI:10.1016/j.livsci.2014.05.008]
38. Macdonald, K., L. McNaughton, G. Verkerk, J. Penno, L. Burton, D. Berry, P. Gore, J. Lancaster and C. Holmes. 2007. A comparison of three strains of Holstein-Friesian cows grazed on pasture: growth, development, and puberty. Journal of Dairy Science, 90: 3993-4003. [DOI:10.3168/jds.2007-0119]
39. McCarthy, S., D. Berry, P. Dillon, M. Rath and B. Horan. 2007. Influence of Holstein-Friesian strain and feed system on body weight and body condition score lactation profiles. Journal of Dairy Science, 90: 1859-1869. [DOI:10.3168/jds.2006-501]
40. Meuwissen, T. and M. Goddard. 2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics, 185: 623-631. [DOI:10.1534/genetics.110.116590]
41. Misztal, I., S. Tsuruta, T. Strabel, B. Auvray, T. Druet and D. Lee. 2002. BLUPF90 and related programs (BGF90). In: Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, 1-3 pp., August. 19-23 Montpellier France,
42. Muir, W. 2007. Comparison of genomic and traditional BLUPestimated 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]
43. Mulder, H., M. Calus, T. Druet and C. Schrooten. 2012. Imputation of genotypes with lowdensity chips and its effect on reliability of direct genomic values in Dutch Holstein cattle. Journal of Dairy Science, 95: 876-889. [DOI:10.3168/jds.2011-4490]
44. Mulder, H.A. 2007. Methods to optimize livestock breeding programs with genotype by environment interaction and genetic heterogeneity of environmental variance.
45. Nguyen, T.T., P.J. Bowman, M. Haile-Mariam, J.E. Pryce and B.J. Hayes. 2016. Genomic selection for tolerance to heat stress in Australian dairy cattle. Journal of Dairy Science, 99: 2849-2862. [DOI:10.3168/jds.2015-9685]
46. Ogawa, S., H. Matsuda, Y. Taniguchi, T. Watanabe, A. Takasuga, Y. Sugimoto and H. Iwaisaki. 2016. Accuracy of imputation of single nucleotide polymorphism marker genotypes from lowdensity panels in Japanese Black cattle. Animal Science Journal, 87: 3-12. [DOI:10.1111/asj.12393]
47. Pausch, H., I.M. MacLeod, R. Fries, R. Emmerling, P.J. Bowman, H.D. Daetwyler and M.E. Goddard. 2017. Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle. Genetics Selection Evolution, 49: 24. [DOI:10.1186/s12711-017-0301-x]
48. Pryce, J.E., B.L. Nielsen, R.F. Veerkamp and G. Simm. 1999. Genotype and feeding system effects and interactions for health and fertility traits in dairy cattle. Livestock Production Science, 57: 193-201. [DOI:10.1016/S0301-6226(98)00180-8]
49. Sargolzaei, M., J. Chesnais and F. Schenkel. 2011. FImpute-An efficient imputation algorithm for dairy cattle populations. Journal of Dairy Science, 94: 421.
50. Sargolzaei, M. and F.S. Schenkel. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25: 680-681. [DOI:10.1093/bioinformatics/btp045]
51. Solberg, T., A. Sonesson and J. Woolliams. 2008. Genomic selection using different marker types and densities. Journal of Animal Science, 86: 2447-2454. [DOI:10.2527/jas.2007-0010]
52. Sun, X., R. Fernando and J. Dekkers. 2016. Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction. Genetics Selection Evolution, 48: 77. [DOI:10.1186/s12711-016-0255-4]
53. Toghiani, S., S. Aggrey and R. Rekaya. 2016. Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection. animal, 10: 1077-1085. [DOI:10.1017/S1751731115002906]
54. VanRaden, P.M., J.R. O'Connell, G.R. Wiggans and K.A. Weigel. 2011. Genomic evaluations with many more genotypes. Genetics Selection Evolution, 43: 10. [DOI:10.1186/1297-9686-43-10]
55. VanRaden, P.M. and P.G. Sullivan. 2010. International genomic evaluation methods for dairy cattle. Genetics Selection Evolution, 42: 7. [DOI:10.1186/1297-9686-42-7]
56. Veerkamp, R., G. Simm and J. Oldham. 1995. Genotype by environment interactions: experience from Langhill. BSAP Occasional Publication, 19: 59-66. [DOI:10.1017/S0263967X00031803]
57. Ventura, R.V., S.P. Miller, K.G. Dodds, B. Auvray, M. Lee, M. Bixley, S.M. Clarke and J.C. McEwan. 2016. Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population. Genetics Selection Evolution, 48: 71. [DOI:10.1186/s12711-016-0244-7]
58. Wang, Q., Y. Yu, J. Yuan, X. Zhang, H. Huang, F. Li and J. Xiang. 2017. Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei. BMC genetics, 18: 45. [DOI:10.1186/s12863-017-0507-5]
59. Weigel, K., G. de Los Campos, A. Vazquez, G. Rosa, D. Gianola and C. Van Tassell. 2010. Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle. Journal of Dairy Science, 93: 5423-5435. [DOI:10.3168/jds.2010-3149]
60. Wientjes, Y.C., M.P. Calus, M.E. Goddard, and B.J. Hayes. 2015. Impact of QTL properties on the accuracy of multi-breed genomic prediction. Genetics Selection Evolution, 47: 42. [DOI:10.1186/s12711-015-0124-6]
61. Yin, T., E. Pimentel, U.K.v. Borstel and S. König. 2014. Strategy for the simulation and analysis of longitudinal phenotypic and genomic data in the context of a temperature× humiditydependent covariate. Journal of Dairy Science, 97: 2444-2454. [DOI:10.3168/jds.2013-7143]
62. Zhang, Z. and T. Druet. 2010. Marker imputation with low-density marker panels in Dutch Holstein cattle. Journal of Dairy Science, 93: 5487-5494. [DOI:10.3168/jds.2010-3501]

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