اثر استفاده از اطلاعات ژنومی گاوهای ماده در جمعیت مرجع، روی صحت ارزش‌های اصلاحی ‏ژنومی برآوردشده در گاوهای هلشتاین ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، پردیس بین‌المللی ارس دانشگاه تهران، جلفا، ایران

2 استاد، گروه علوم دامی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران

3 استادیار، گروه علوم دامی، دانشگاه گوئلف کانادا

چکیده

ارزیابی‌های دقیق ژنومی به یک جمعیت مرجع بزرگ با اطلاعات عملکردی قابل اعتماد (مانند ارزش‌های اصلاحی پیش‌بینی‌شده) بستگی دارد. هدف از این مطالعه، شناسایی مناسب‌ترین جمعیت مرجع برای پیش‌بینی ارزش اصلاحی ژنومی حیوانات در برنامه‌های اصلاح‌نژاد گاو شیری هلشتاین ایران می‌باشد. ابتدا با استفاده از شبیه‌سازی ژنوم و مطابق با روند اصلاح‌نژاد گاو شیری در ایران (هسته اصلاح‌نژادی باز با تبادل ژنی بین هسته و جمعیت تجاری) جمعیت‌های مورد نیاز شبیه‌سازی شدند. دو سطح وراثت‌پذیری متوسط (3/0) و پایین (05/0) به‌طور مستقل در نظر گرفته شد. در تمام مراحل، فرایند شبیه‌سازی 10 بار تکرار شد و نتایج مورد بررسی قرار گرفت. در این مطالعه، گاوهای ماده جهت تعیین ژنوتیپ، بر اساس چهار سناریوی انتخاب تصادفی، افراد دو کران بالا و پایین توزیع ارزش فنوتیپی، افراد با بالاترین ارزش فنوتیپی و افراد با بالاترین ارزش اصلاحی در تعداد مختلف انتخاب و به جمعیت مرجع افزوده شدند. با روش تک‌مرحله‌ای بهترین پیش‌بینی نااُریب خطی (Single Step BLUP)، برای افراد جمعیت آزمون ارزش اصلاحی ژنومی پیش‌بینی شد. برای تمام سناریوهای گفته‌شده، صحت و ضریب نااُریبی پیش‌بینی ارزش اصلاحی برآورد شد. نتایج نشان داد زمانی که حیوانات ماده با بیشترین و کمترین ارزش فنوتیپی (سناریوی دوم تعیین ماده‌ها) انتخاب شدند، نسبت به سایر سناریوها، صحت پیش‌بینی ارزش اصلاحی بیشتر بود. تعیین ماده‌ها با ارزش فنوتیپی بالا (سناریوی سوم انتخاب ماده‌ها)، کمترین اُریبی را به بار آورد. استفاده از نرهای وارداتی دارای ژنوتیپ و استفاده از آنها به تنهایی به عنوان جمعیت مرجع، کمترین صحت و بیشترین اُریبی را نشان داد. ترکیب نرها با ماده‌ها نسبت به سناریوهای صرفاً نرها یا صرفاً ماده‌ها، افزایش صحت و کاهش اُریبی را به همراه داشت، بنابراین با احتساب هزینه‌های اقتصادی، تعیین ژنوتیپ، استفاده از گاوهای ماده در جمعیت مرجع (2000 رأس گاو ماده ژنوتیپ شده)، مطابق با سناریوی دوم انتخاب ماده‌ها، بهترین راهبرد پیشنهادی جهت تشکیل جمعیت مرجع و ارزیابی ژنومی با کمترین هزینه، در ایران می‌باشد.  

کلیدواژه‌ها


عنوان مقاله [English]

The effect of using cow genomic information in reference population on the accuracy ‎of genomic estimated breeding values in Iranian Holstein cattle

نویسندگان [English]

  • Mohammad Reza Mansourian 1
  • Seyed Reza Miraei Ashtiani 2
  • Ardeshir Nejati Javaremi 2
  • Mahdi Sargolzaei 3
1 Ph.D. Cnadidate, Aras International Campus, University of Tehran, Jolfa, Iran‎
2 Professor, Department of Animal Science, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
3 Assistant Professor, Department of Animal Science, University of Guelph, Canada
چکیده [English]

Accurate genomic evaluation depend on large reference population with reliable performance information such as predicted breeding value (PBVs). The aim of this study was to identify the most appropriate reference population to predict the genomic breeding value for Iran Holstein dairy breeding programs. Phenotypes and genotypes were simulated based on the dairy cattle Iran population program (open breeding nucleus with gene flows between the nucleus and the commercial population). Medium (0.3) and low (0.05) heritability levels were considered independently. All simulations were performed with 10 replications and the results were evaluated. In the first study, female cows were selected for genotyping in four scenarios: random selection, individuals with upper and lower extremities of phenotypic value, highest phenotypic value and highest breeding value with maximum accuracy; and these females are added to the reference population. Single Step BLUP (SSBLUP) was used to predict the genomic breeding value for individuals in the population. The accuracy and unbiased coefficient of predicted breeding value were investigated. The results showed that when female animals with the highest and lowest phenotypic values were selected (the second scenario of determining females), the highest accuracy of prediction of breeding value was observed compared to other scenarios. Determination of substances with high phenotypic value (third scenario of female selection) showed the least bias. The use of imported males with genotype and their use alone as a reference population showed the least accuracy and the most bias. The combination of males and females showed an increase in accuracy and a decrease in bias compared to the scenarios for males or females alone. However, in relation to the size of the population similar to females, no improvement in the prediction of the breeding value was observed. Therefore, in terms of economic conditions (genotyping costs), the use of only female cows in the reference population (2000 females genotyped), according to the second scenario of female selection, is the best strategy to form a reference population and genomic evaluation at the lowest cost, in Iran.

کلیدواژه‌ها [English]

  • Genomic breeding value
  • Reference population
  • Simulation
  • SSBLUP‎
  1.  Abdollahi-Arpanahi, R., Pakdel, A., Nejati-Javaremi, A. & Moradi Shahbabak, M. (2014). Comparison of genomic evaluation methods in complex traits with different geneticarchitecture.Iranian Journal of Animal Sceince, 15(1), 65-67. (In Persian)
  2. Ardebili, R.A., Shadparvar, A.A. & Shakalgorabi, S.J. (2016). Comparison of genetic gain for milk production in Holstein cows in Iran resulted from progeny test and genomic selection and effect of number of young bulls on it. Animal Production Research, 5(2), 37-45. (In Persian)
  3. Baneh, H., Nejati-Javaremi, A., Rahimi-Mianji, GH. & Honarvar, M. (2017). Genomic evaluation of threshold traits with different genetic architecture using Bayesian approaches. Research on Animal Production, 8 (6). (In Persian)
  4. Bohmanova, J., Sargolzaei, M. & Schenkel, F.S. (2010). Characteristics of linkage disequilibrium in North American Holsteins. BMC Genomics, 11, 421.
  5. Dehnavi, E., Mahyari, S.A., Schenkel, F.S. & Sargolzaei, M. (2018). The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population. Journal of Dairy Science, 101(6), 5166-5176.
  6. Ding, X., Zhang, Z., Li, X., Wang, S., Wu, X., Sun, D. & Zhang, S. (2013). Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. Journal of Dairy Science, 96(8), 5315-5323.
  7. Gao, H., Su, G., Janss, L., Zhang, Y. & Lund, M. S. (2013). Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population. Journal of Dairy Science, 96(7), 4678-4687
  8. Ghaderi Zefrei, M., Mohaghegh Dolatabadi, M., Bahreini, M. & Haghparvar, R. (2012). The Weakness of dairy cattle breeding structure in Iran, who is to blame?.Proceedings of 5th Iranian Congress of Animal Science. Isfahan University of Technology, Animal Breeding Section: pp381-385. (in Persian)
  9. Goddard, M.E. & Hayes, B.J. (2007). Genomic selection. Journal of Animal Breeding and Genetics,24(6), 323-330.
  10. Goddard, M.E. & Hayes, B.J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Revew of Genetics, 10, 381-391.
  11. Harlizius, B., Wijk, R. & Merks, J. (2004). Genomics for food safety and sustainable animal production. Journal of Biotechnology,113, 33-42.
  12. Harris, B.L. & Johnson, D.L. (2010). Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. Journal of Dairy Science,93(3), 1243-1252.
  13. Hayes, B.J., Bowman, P.J., Chamberlain, A.J. & Goddard, M.E. (2009). Invited review:   Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 92, 433-443.
  14. Jenko, J., Wiggans, G.R., Cooper, T.A., Eaglen, S.A.E., Luff, W.D.L., Bichard, M., Pong-Wong, R. & Woolliams, J.A. (2017). Cow genotyping strategies for genomic selection in a small dairy cattle population. Journal of Dairy Science, 100(1), 439-452.
  15. Jimenez-Montero, J.A., Gonzalez-Recio, O. & Alenda, R. (2012). Genotyping strategies for genomic selection in small dairy cattle populations. Animal, 6, 1216-1224.
  16. Karimi, D,. Tahmoorespoor, M., Dadpasand, M., Aslami nejad, A., Sandolund, M., (2015). Effect of enlarging the number of reference population cows and imputed markers on reliability of genomic prediction in Jersey breed. Iranian Journal of Animal Science Reaserch, 6(3), 270-278. (In Persian)
  17. Koivula, M., Strandén, I., Aamand, G. P. & Mäntysaari, E. A. (2014). Effect of cow reference group on validation accuracy of genomic evaluation. In: Proceedings of the 10th World Congress on Genetics Applied to Livestock Production, Vancouver, BC, Canada (pp. 17-22).
  18. Koivula, M., Strandén, I., Aamand, G. P. & Mäntysaari, E. A. (2016). Effect of cow reference group on validation reliability of genomic evaluation. Animal, 10(6), 1061-1066.
  19. Koivula, M., Strandén, I., Aamand, G.P. & Mäntysaari, E.A. (2018). Reducing bias in the dairy cattle single step genomic evaluation by ignoring bulls without progeny. Journal of Animal Breeding and Genetics, 135(2), 107-115.
  20. Legarra, A., Aguikar, I. & Misztal, I. (2009). A relationship matrix including full pedigree and gegenomic information. Journal of Dairy Science, 92(9), 4656-4663.
  21. Lillehammer, M., Meuwissen, T.H. & Sonesson, A.K. (2011). A comparison of dairy cattle breeding designs that use genomic selection. Journal of Dairy Science, 94, 493-500.
  22. Lund, M. S., van den Berg, I., Ma, P., Brøndum, R. F. & Su, G. (2016). Review: How to improve genomic predictions in small dairy cattle populations. Animal, 10(6), 1042-1049.
  23. Mahmoudi, N., Ayatollahi Mehrgerdi, A., Honarvar, M. & Esmailizadeh kashkooeiyeh, A. (2015). Study of QTL effects distribution on accuracy of genomic breeding values estimated using Bayesian method. Iranian Journal of Animal Science Research, 7(3), 356-363.(In Persian)
  24. Mc Hugh, N., Meuwissen, T.H., Cromie, A.R. & Sonesson, A.K. (2011). Use of female information in dairy cattle genomic breeding programs. Journal of Dairy Science, 94, 4109-4118.
  25. Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829.
  26. Nejati Javaremi, A., Smith, C. & Gibson, J. (1997). Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of Animal Science, 75, 1738-1745.
  27. Patry, C. & Ducrocq, V. (2011). Evidence of biases in genetic evaluations due to genomic preselection in dairy cattle. Journal of Dairy Science, 94, 1011-1020.
  28. Pérez, P. & de Los Campos, G. (2014). Genome-wide regression and prediction with the BGLR statistical package. Genetics, 198(2), 483-495.
  29. Plieschke, L., Edel, C., Pimentel, E.C., Emmerling, R., Bennewitz, J. & Götz, K.U. (2016). Systematic genotyping of groups of cows to improve genomic estimated breeding values of selection candidates. Genetics Selection Evolution, 48(1), 73.
  30. Pryce, J., Hayes, B. & Goddard, M. (2012a). Genomics-what does the future hold for dairy farmers? Proceeding of 38th  ICAR Conference, Cork, Ireland.
  31. Pryce, J.E., Arias, J., Bowman, P.J., Davis, S.R., Macdonald, K.A., Waghorn, G.C., Wales, W.J., Williams, Y.J., Spelman, R.J. & Hayes, B.J. (2012b). Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. Journal of Dairy Science, 95(4), 2108-2119.
  32. Pryce, J.E. & Daetwyler, H.D. (2012). Designing dairy cattle breeding schemes under genomic selection: a review of international research. Animal Production Science, 52(3), 107-114.
  33. Pszczola, M. & Calus, M. P. (2016). Updating the reference population to achieve constant genomic prediction reliability across generations. Animal, 10(6), 1018-1024.
  34. Rensing, S., Alkhoder, H., Kubitz, C., Schierenbeck, S. & Segelke, D. (2017). Cow reference population–benefit for genomic evaluation system and farmers. Interbull Bulletin, (51). Tallinn, Estonia.
  35. Sargolzaei, M. & Schenkel, F. (2009). QMSim: A large-scale genome simulator for livestock. Bioinformatics, 25, 680-681.
  36. Schaeffer, L. R. (2006). Strategy for applying genome-wide selection in dairy cattle. J Animal Breeding and Genetics, 123(4), 218-223.
  37. Su, G., Ma, P., Nielsen, U.S., Aamand, G.P., Wiggans, G., Guldbrandtsen, B. & Lund, M.S. (2016). Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey. Animal, 10(6), 1067-1075.
  38. Teymourian, M., Shariati, M.M. & Aslami nejad, A.A. (2014). Comparison of methods for the implementation of genomic selection in Holstein. Research on Animal Production, 7(14), 198-203. (In Persian)
  39. Turral, H., Burke, J. & Faurès, J. (2011). Climate Change, Water and Food Security. FAO Water Reports, Food and Agriculture Organization of the United Nations, Rome.
  40. VanRaden, P.M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91, 4414-4423.
  41. VanRaden, P.M., Van Tassell, C.P., Wiggans, G.R., Sonstegard, T.S., Schnabel, R.D., Taylor, J.F. & Schenkel, F.S. (2009). Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science, 92(1), 16-24.
  42. VanRaden, P.M. & Sullivan, P.G. (2010). International genomic evaluation methods for dairy cattle. Genet Select Evol, 42, 7.
  43. Varkoohi, S., (2014). A review of genomic selection methods in animal breeding. Proceedings of First National Conference on Agriculture, Environment and Food Security. University of Jiroft, Jiroft, Iran. (In Persian).
  44. Wiggans, G. R., Cole, J. B., Hubbard, S. M. & Sonstegard, T. S. (2017). Genomic selection in dairy cattle: the USDA experience. Annual Review of Animal Biosciences, 5, 309-327.
  45. Wiggans, G.R., Cooper, T.A., Van Raden, P.M. & Silva, M.V. (2010). Increased reliability of genetic evaluations for dairy cattle in the United States from use of genomic information. Proceeding of 9th World Congress of Genetics Applied in Livestock Production Leipzig, Germany. Aug, 2010