Volume 9, Issue 22 (12-2018)                   rap 2018, 9(22): 119-130 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Naderi Y. 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. 2018; 9 (22) :119-130
URL: http://rap.sanru.ac.ir/article-1-945-en.html
Islamic Azad University, Astara Branch
Abstract:   (598 Views)
The objective of this study was to investigate the role of genetic relationships between training and validation set with considering different ratio of phenotypic records of training set on accuracy of genomic prediction via animal models containing genotype × environment interactions in simulated imputation data. For this purpose, four different scenarios using 15k density containing different levels of linkage disequilibrium (LD= low and high) and quantitative trait loci (90 and 300) was simulated. After simulating the population, the markers randomly masked with 50 and 95 percentage of missing rate for each scenario; afterwards, hidden markers were imputed. In the following, using coding in R software, the simulation was done to create the genetic relationships between the traits in three environments with different heritability’s in original and imputed data. The accuracy of imputation ranged from 0.737 to 0.941. LD was main factor on accuracy of imputation. Accuracy of genomic prediction generally increased when LD and heritability was increased. When non-phenotyped animals for the second environment were estimated using information of their relatives in the third environment, accuracy of genomic predictions was generally high. The accuracy of genomic prediction was the highest, when 75% of the second environment animals had phenotypic records and evaluated using information of their relatives in the first and third environments. The results showed that genotype imputation of very low density to 15k panel is not always helpful for genotypes prediction. Generally, simultaneous usage of relatives information and increasing phenotypic records of validation set increase the accuracy of genomic predictions using animal models in presence of genotype × environment interactions.


Full-Text [PDF 367 kb]   (214 Downloads)    
Type of Study: Research | Subject: ژنتیک و اصلاح نژاد دام
Received: 2018/07/6 | Revised: 2019/12/21 | Accepted: 2018/09/24 | Published: 2019/01/26

References
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]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


© 2020 All Rights Reserved | Research On Animal Production(Scientific and Research)

Designed & Developed by : Yektaweb