Effect of enlarging the number of reference population cows and imputed markers on reliability of genomic prediction in Jersey breed

Document Type : Genetics & breeding

Authors

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

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

3 Department of Animal Sciences, Faculty of Agriculture, University of Shiraz, Shiraz,, Iran

4 uantitative Genetics and Genomics Center of Molecular Biology and Genetics Department Aarhus University Denmark

Abstract

The objective of this study was to evaluate the potential gain in accuracy of predictions by imputing genotypes from a low-density marker panel (6640 marker with 10 replicates) to a medium-density marker panel in a simulated population of Jersey cattle using FImpute software and also to evaluate the reliability of genomic estimate of breeding values (GEBV) for milk yield (h2=0.40) and fertility rate (h2=0.04) with different number of cows in the reference population. A population of 900 medium-density panel of genotyped proven sires was simulated to quantify the accuracy of imputation of reference cows. To evaluate the reliability of GEBVs of 3000 randomly selected animals of test population, nine scenarios were employed. Average accuracy of imputation for cows was 98.64 percent. Only one percent difference observed in GEBVs of 54k and GEBVs achieved for imputed markers. According to high rate of imputation, however, just 1 percent difference obtained between comparing GEBV of 54k and GEBV achieved of imputed markers. Therefore, despite of little difference and although the genotyping cost of a medium-density panel is two-fold of low-density, using of 50K genotyped proven sires plus all cows imputed to medium-density is recommended. Following this procedure, not only the higher rate of GEBV for both traits would be achieved but also by decreasing the cost of genotyping, more animals of reference population would be used in prediction model. The more the reference animals, the more the pedigree, phenotypic and genotypic data are available to predict marker effects and, therefore, genomic estimated breeding value.

Keywords


1- سید دخت، ع.، ع. ا. اسلمی نژاد، و م. طهمورث پور. 1391. آنالیز ژنتیکی صفت تولید شیر گاوهای هلشتاین استان تهران با استفاده از مدل روز آزمون. نشریه پژوهش های علوم دامی ایران. 4: 174-168.
2- Buch, L. H., M. K. Sørensen, P. Berg, L. D. Pedersen, and A. C. Sørensen. 2012. Genomic selection strategies in dairy cattle: strong positive interaction between use of genotypic information and intensive use of young bulls. J. Anim. Breed. Gene. 129(2):138-51.
3- Browning, B. L., and S. R. Browning. 2008. 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.
4- Chen, J., Z. Liu, F. Reinhardt, and R. Reents. 2011. Reliability of genomic prediction using imputed genotypes for German Holsteins: Illumina 3K to 54K bovine chip. The 2011 Interbull Open Meeting, Stavanger, Norway. Interbull, Uppsala, Sweden.
5- Daetwyler, H. D., B. Villanueva, and J. A. Woolliams. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3:e3395.
6- Daetwyler, H. D., G. R. Wiggans, B. J. Hayes, J. A. Woolliams, and M. E. Goddard. 2010. Imputation of missing genotypes fromsparse to high density using long-range phasing. Manuscript 539in Proc. World Congress of Genetics Applied to Livestock Production, Leipzig, Germany.www.wcgalp2010.org.
7- Dassonneville, R., R. F. Brøndum, T. Druet, S. Fritz, F. Guillaume, B. Guldbrandtsen, M. S. Lund, V. Ducrocq, and G. Su. 2011. Effectofimputingmarkersfromalow-densitychiponthereliabilityofgenomic breeding values in Holstein populations. J. Dairy Sci.94:3679–3686.
8- Druet, T., and M. Georges. 2010. A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics, 184:789–798.
9- Goddard, M. 2009. Genomic selection: prediction of accuracy and maximization of long term response. Genetica 136, 245–257.
10- Habier, D., R. L. Fernando, and J. C. Dekkers. 2009. Genomic selection using low-density marker panels. Genetics 182:343–353.
11- Harris, B. L., and D. L. Johnson. 2010. Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. J. Dairy Sci. 93:1243–1252.
12- 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.
13- Johnston, J., G. Kistemaker. 2011. Comparison of different imputation methods. Interbull open meeting. Stavanger, Norway.
14- Kolbehdari, D., L. R. Schaeffer, and J. A. B. Robinson. 2007. Estimation of genome wide haplotype effect in half sib designs. J. Anim. Breed. Genet. 124:356-361.
15- Liu, Z. T., F. R. Seefried, F. Reinhardt, S. Rensing, G. Thaller, and R. Reents. 2011. Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction. Genet. Sel. Evol. 43:19.
16- Long, N., D. Gianola, G. J. M.Rosa, K. A. Weigel, and S. Avendano. 2007. Machine learning classification procedure for selecting SNPs in genomic selection: Application to early mortality in broilers. J. Anim. Breed. Genet. 124:377-389.
17- Ma, P., R. F. Brøndum, Q. Zhang, M. S. Lund, and G. Su. 2013. Comparison of different methods for imputing genome-wide marker genotypes in Swedish and Finnish Red Cattle. J. Dairy Sci. 96:4666–4677.
18- Madsen P., and J. Jensen. 2007. DMU: A user’s Guide. A Package for Analyzing Multivariate Mixed Models. Version 6, Release 4.7. http://dmu.agrsci.dk/dmuv6_guideR4-6-7.pdf Accessed Nov. 15.
19- 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.
20- Meuwissen, T., and M. Goddard. 2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics. 185:623–631.
21- Moser, G., M. S. Khatkar, B. J. Hayes, and H. W. Raadsma. 2010. Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genet. Sel. Evol. 42:37.
22- Muir, W. M. 2007. Comparison of genomic and traditional BLUP- estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim. Breed. Genet. 124:342–355.
23- Pedersen, L. D., A. C. Sørensen, M. Henryon, S. Ansari-Mahyari, and P. Berg. 2009. ADAM: A computer program to simulate selective breeding schemes for animals. Livestock sci. 121(2-3): 343-344.
24- Pryce, J. E., M. E. Goddard, H. W. Raadsma, and B. J. Hayes. 2010. Deterministic models of breeding scheme designs that incorporate genomic selection. J. Dairy Sci. 93:5455–5466.
25- Pszczola, M., A. Mulder, and M. P. L. Calus. 2010. Effect of enlarging the reference population with (un) genotyped animals on the accuracy of genomic selection in dairy cattle. J. Dairy Sci. 94:431-441.
26- Sargolzaei, M., J. P. Chesnais, and F. S. Schenkel. 2011. FImpute- An efficient imputation algorithm for dairy cattle populations. J. Dairy Sci. 94(E-Suppl. 1):421. (Abstr).
27- Schaeffer, L. R. 2006. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123:218–223.
28- Scheet, P., and M. Stephens. 2006. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78:629–644.
29- Su, G., R. F. Brøndum, P. Ma, B. Guldbrandtsen, G. P. Aamand, and M. S. Lund. 2012. Comparison of genomic predictions using medium-density (~54,000) and high-density (~777,000) single nucleotide polymorphism marker panelsin Nordic Holstein and Red Dairy Cattle populations. J. Dairy Sci. 95:4657–4665.
30- VanRaden, P. M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414–4423.
31- 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 Holstein bulls. J. Dairy Sci. 92:16–24.
32- Weigel, K. A., C. P. Van Tassell, J. R. O’Connell, P. M. VanRaden, and G. R. Wiggans. 2010. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using panels and population-based imputation algorithms. J. Dairy Sci. 93:2229– 2238.
33- Zhang, Z., and T. Druet. 2010. Marker imputation with low-density marker panels in Dutch Holstein cattle. J. Dairy Sci. 93:5487–5494.
CAPTCHA Image