The accuracy of genomic predictions for milk related traits in Najdi cattle breed

Hosseini-Vardanjani, Sayed Mahdi; Shariati, Mohammad Mahdi; Moradi Shahrebabak, Hossein; Tahmoorespur, Mojtaba

doi:10.22092/asj.2018.115893.1549

Document Type : Research Paper

Authors

¹ PhD student of animal breeding and genetics, Department of Animal Science, University of Mashhad, Iran

² Assistant Professor of animal breeding and genetics, Department of Animal Science, University of Mashhad, Iran

³ assistant professor/Department of Animal Sciences, University College of Agriculture and Natural Resources, University of Tehran

⁴ Professor of animal breeding and genetics, Department of Animal Science, University of Mashhad, Iran

https://doi.org/10.22092/asj.2018.115893.1549

Abstract

The objective of this study was to evaluate the performance of genomic selection for milk yield, fat percent and protein percent in Najdi cattle breed from station and Agmari herds using different statistical models. Traditional estimated breeding values obtained with a random regression model using pedigree and phenotypic information for each trait from 1990 to 2016 were used as response variables. Predictability was evaluated by using a 10 replicated Training-Testing scheme and considering four models including genomic best linear unbiased prediction scaled with observed allele frequency (GBLUP), and allele frequency of 0.5 (G05BLUP), BayesA and BayesB. The results showed that GBLUP had better performance than G05BLUP for milk yield (0.411 vs 0.385), but performance of G05BLUP was better for fat percent (0.257 vs 0.302) and protein percent (0.363 vs 0.388). The accuracy of breeding values of milk yield decrease to 0.371 and 0.353, and accuracy of fat percent increased to 0.329 and 0.314 with BayesA and BayesB, respectively. Bayesian methods had same accuracy as GBLUPs for protein percent. Across traits and methods BayesA with 0.14 for protein percent, and G05BLUP with 0.71 for milk yield had smallest and highest bias as deviate from 1, respectively. Accuracy of prediction using Agmariherds in addition station herd increased 0.01 to 0.09 depending on method and trait, but also bias were more than before. In conclusion, accuracy of genomic prediction of milk traits in Najdi cattle breed are moderate but suitable considering small size of population which makes genomic selection feasible in this breed.

Keywords

20.1001.1.25886436.1398.32.122.8.3

References

حسینی وردنجانی، س. م.، شریعتی، م. م.، و نعیمی پور، ح. (1396). تأثیر مقیاس ماتریس روابط خویشاوندی ژنگانی بر برآورد مؤلفه ‏های واریانس و درستی پیش‌بینی ارزش ‏های اصلاحی. مجله علوم دامی ایران، دوره 48، شماره 2، ص ص. 206-197.

نظری، م.، بیگی نصیری، م. ت.، و فیاضی، ج. (1386). ارزیابی قابلیت‏های ژنتیکی و فنوتیپی صفات تولید شیر، چربی و درصد چربی گاوهای شیری نجدی با استفاده از مدل حیوانی تک و دو صفتی. مجله دامپزشکی ایران، دوره سوم، شماره 4، ص ص. 79-73.

Aguilar, I., Misztal, I., Johnson, D., Legarra, A., Tsuruta, S. and Lawlor, T. (2010). Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science, 93(2), 743-752.

Browning, B. L. and Browning, S. R. (2009). A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. The American Journal of Human Genetics, 84(2), 210-223.

Calus, M. (2010). Genomic breeding value prediction: methods and procedures. Animal, 4(02), 157-164.

Clark, S. A., Hickey, J. M. and Van der Werf, J. H. (2011). Different models of genetic variation and their effect on genomic evaluation. Genetics Selection Evolution, 43(1), 18.

Coster, A., Bastiaansen, J. W., Calus, M. P., van Arendonk, J. A. and Bovenhuis, H. (2010). Sensitivity of methods for estimating breeding values using genetic markers to the number of QTL and distribution of QTL variance. Genetics Selection Evolution, 42(1), 9.

Daetwyler, H. D., Pong-Wong, R., Villanueva, B. and Woolliams, J. A. (2010). The impact of genetic architecture on genome-wide evaluation methods. Genetics, 185(3), 1021-1031.

de los Campos, G. Genome enabled prediction methods: Laboratory. University of Alabama at Birmingham, Birmingham, Alabama http:// www-personal. une. edu. au/~ jvanderw/ GdlCHandouts. pdf.

Forni, S., Aguilar, I. and Misztal, I. (2011). Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution, 43(1), 1.

Gianola, D., de los Campos, G., Hill, W. G., Manfredi, E. and Fernando, R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics, 183(1), 347-363.

Guo, G., Zhao, F., Wang, Y., Zhang, Y., Du, L. and Su, G. (2014). Comparison of single-trait and multiple-trait genomic prediction models. BMC genetics, 15(1), 30.

Gao, H., Christensen, O. F., Madsen, P., Nielsen, U. S., Zhang, Y., Lund, M. S. and Su, G. (2012). Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics Selection Evolution, 44(1), 8.

Habier, D., Fernando, R. L., Kizilkaya, K. and Garrick, D. J. (2011). Extension of the Bayesian alphabet for genomic selection. BMC bioinformatics, 12(1), 1.

Hayes, B., and Goddard, M. E. (2001). The distribution of the effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution, 33(3), 209-230.

Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K. and Goddard, M. E. (2009). Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 41(1), 51.

Hayes, B. J., Bowman, P. J., Chamberlain, A. and Goddard, M. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of dairy science, 92(2), 433-443.

Hoze, C., Fritz, S., Phocas, F., Boichard, D., Ducrocq, V. and Croiseau, P. (2014). Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population. Journal of dairy science, 97(6), 3918-3929.

Li, Z. and Sillanpää, M. J. (2012). Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theoretical and Applied Genetics, 125(3), 419-435.

Lourenco, D., Misztal, I., Tsuruta, S., Aguilar, I., Ezra, E., Ron, M., Shirak, A. and Weller, J. (2014). Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of dairy science, 97(3), 1742-1752.

Lund, M. S., Su, G., Janss, L., Guldbrandtsen, B. and Brøndum, R. F. (2014). Genomic evaluation of cattle in a multi-breed context. Livestock Science, 166, 101-110.

Madsen, P., Sørensen, P., Su, G., Damgaard, L. H., Thomsen, H. and Labouriau, R. (2006). DMU-a package for analyzing multivariate mixed models. Paper presented at the 8th World Congress on Genetics Applied to Livestock Production.

Nejati-Javaremi, A., Smith, C. and Gibson, J. (1997). Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of animal science, 75(7), 1738-1745.

Pérez, P. and de Los Campos, G. (2014). Genome-wide regression & prediction with the BGLR statistical package. Genetics, 114.164442.

Price, A. L., Zaitlen, N. A., Reich, D. and Patterson, N. (2010). New approaches to population stratification in genome-wide association studies. Nature reviews genetics, 11(7), 459.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D. and Daly, M. J. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81(3), 559-575.

Schaeffer, L. (2006). Strategy for applying genome‐wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123(4), 218-223.

Su, G., Brøndum, R. F., Ma, P., Guldbrandtsen, B., Aamand, G. P. and Lund, M. S. (2012). Comparison of genomic predictions using medium-density (∼ 54,000) and high-density (∼ 777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations. Journal of dairy science, 95(8), 4657-4665.

Tiezzi, F. and Maltecca, C. (2015). Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix. Genetics Selection Evolution, 47(1), 24.

VanRaden, P. (2008). Efficient methods to compute genomic predictions. Journal of dairy science, 91(11), 4414-4423.

VanRaden, P., Van Tassell, C., Wiggans, G., Sonstegard, T., Schnabel, R., Taylor, J. and Schenkel, F. (2009). Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of dairy science, 92(1), 16-24.

Research Journal of Livestock Science

The accuracy of genomic predictions for milk related traits in Najdi cattle breed

References

References

Volume 32, Issue 122
June 2019
Pages 93-104

The accuracy of genomic predictions for milk related traits in Najdi cattle breed

References

References

Volume 32, Issue 122June 2019Pages 93-104

Volume 32, Issue 122
June 2019
Pages 93-104