The Effect of Reference Population Factors on the Quality of Genomic Selection in Holstein Cattle of Iran

پژوهشهای علوم دامی ایران

Article 9, Volume 7, Issue 1 - Serial Number 21, March 2015, Pages 96-103   

Document Type: Genetics & breeding

DOI: 10.22067/ijasr.v7i1.31269

Authors

mohammad teimurian^{* 1}; Ali Asghar Aslaminejad²; Mohammad Mahdi Shariati²

¹Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad

²Department of Animal Sciences, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction: Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The success of genomic selection depends on the potential to predict genomic breeding values (GEBVs) with high accuracy. Genomic selection relies on relationships between individuals to accurately predict genetic value. Accuracy of genomic prediction is highly dependent on the size and type of the reference population (RP) used to estimate marker effects. For small populations, including information from other populations could improve this reliability. A usual strategy is to pool data from other populations.
Materials and Methods: Genome consists of 3 chromosomes each 100cM including 7500 markers with 0.04 cM space and 75 random distributed QTL were simulated. Genomic estimated breeding values of Iranian Holstein cattle were predicted using BayesB based on several reference dataset. Simulation was used to establish Iranian Holstein population and compare the accuracies of GEBVs under a range of different sizes and types of RP. The importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values and effect of pooling data from other populations were also examined to construct the best RP for genomic selection in Iran.
Results and Discussion: The relationship between the animals in the test and reference data sets had high effect on the accuracy of genomic predictions. The increase of accuracy of GEBV by adding bulls in the RP was more than of adding dams indicating a direct relationship between the accuracy of predictions and the number of animals of reference population. whatever the relative relationship between the reference population was reduced by selecting animals, the accuracy is also reduced that in addition to showing the importance of the relationship between the two populations, suggest that Estimates should be repeated over time. The extent of linkage disequilibrium was similar in the Iranian and foreign Holstein populations and linkage disequilibrium between the two populations was very consistent and using the joint versus the Iranian reference dataset increased accuracy of genomic prediction.
Conclusion: The makeup of reference data sets is an important factor for the design of genomic evaluation systems to enable additional genetic gain from genomic selection at the lowest cost. An animal’s relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. Our results suggest that the most accurate genomic predictions are achieved when data of dams and other population are combined by data of sires in RP.

Keywords

Genomic Selection; Iranian Holstein; Reference population; simulation

References

1- Browning, B. L., and S. R. Browning. 2009. A unified approach to genotype imputation and haplotype phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84:210–223.

2- Calus, M.P.L., 2010. Genomic breeding value prediction: methods and procedures. animal 4:157–164.

3- Clark S. A., J.M. Hickey, H.D. Daetwyler, andJ.H.J. Van der Werf. 2012. The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet Sel Evol, 44:4-12

4- de Roos, A. P. W., B. J. Hayes and M. E. Goddard. 2009. Reliability of Genomic Predictions Across Multiple Populations. Genetics 183: 1545–1553

5- Gianola, D., G. D. L. Campos, W.G. Hill, E. Manfredi, and R. Fernando. 2009. Additive Genetic Variability and the Bayesian Alphabet. Genetics 183:347–363.

6- Goddard, M. E., and B. J. Hayes. 2007. Genomic selection. J. Anim.Breed. Genet. 124:323–330.

7- Habier, D., R. L. Fernando and J. C. M. Dekkers. 2007. The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values.

8- Habier, D., J.Tetens, F.R. Seefried, P. Lichtner, and G. Thaller. 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol. 42:5-17

9- Hayes, B. J., P. J. Bowman, A. J. Chamberlain, and M. E. Goddard. 2009. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92:433–443.

10- Hayes, B. J., P. M. Visscher, and M. E. Goddard. 2009. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. (Camb.) 91:47–60.

11- Hayes, B., P. Bowman, A. Chamberlain, K.Verbyla, andM. Goddard, 2009. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Gene. Sel. Evol. 41:51.

12- Lund M.S., A.P.W. De Roos, A.G. De Vries, T. Druet, V. Ducrocq, S. Fritz, F. Guillaume, B. Guldbrandtsen, Z.T. Liu, R. Reents, C. Schrooten, F. Seefried, andG.S. Su. 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genet Sel Evol, 43:43-54

13- Mc Hugh N., T.H.E. Meuwissen, A.R. Cromie, and A.K. Sonesson.2011. Use of female information in dairy cattle genomic breeding programs. J Dairy Sci, 94:4109–4118.

14- Meuwissen, T. H. E., B. J. Hayes, and M. E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829.

15- Muir B, B. Van Doormaal, and G. Kistemaker. 2010. International genomic cooperation –North American perspective. Interbull Bull, 41:71–76.

16- Qanbari S., E. C. G. Pimentel, J. Tetens, G. Thaller, P. Lichtner, A. R. Sharifi, and H. Simianer. 2010. The pattern of linkage disequilibrium in German Holstein cattle. Anim Genet, 41:346–356.

17- Samuel A. C., J. M. Hickey, H. D. Daetwyler, and J. H.J. van der Werf. The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. 2012. Genet Sel Evol. 44:4-13

18- Sargolzaei, M., and F. S. Schenkel. 2009. QMSim: A large-scale genome simulator for livestock. Bioinformatics 25:680–681.

19- Sargolzaei, M., F. S. Schenkel, J. B. Jansen, and L. R. Schaeffer. 2008. Extent of linkage disequilibrium in Holstein cattle in North America. J. Dairy Sci. 91:2106–2117.

20- Schenkel F.S., M. Sargolzaei, G. Kistemaker, G.B. Jansen, P. Sullivan, B.J. Van Doormaal, P.M. VanRaden, and G.R. Wiggans. 2009. Reliability of genomic evaluation of Holstein cattle in Canada. Interbull Bull. 39:51–57.

21- Su, G., P. Madsen, U.S. Nielsen, E.A. Mantysaari, G.P. Aamand, O.F Christensen andM.S. Lund. 2012. Genomic prediction for Nordic Red Cattle using one-step and selection index blending. J. Dairy Sci. 95:909–917.

22- VanRaden, P. M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414–4423.

23- VanRaden, P. M., and P. G. Sullivan. 2010. International genomic evaluation methods for dairy cattle. Genet. Sel. Evol. 42:7-21

24- VanRaden, P. M., C. P. Van Tassell, G. R. Wiggans, T. S. Sonstegard, R. D. Schnabel, J. F. Taylor, and F. S. Schenkel. 2009. Invited review: Reliability of genomic predictions for North American Hol-stein bulls. J. Dairy Sci. 92:16–24.

25- Wiggans, G. R., P. M. VanRaden, and T. A. Cooper. 2011. The genomic evaluation system in the United States: Past, present, future. J. Dairy Sci. 94:3202–3211.

26- Zhou, L., X.Ding, Q. Zhang, Y. Wang, M. S. Lund and G. Su. 2013. Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population. Genet Sel Evol. 45:7-14.