Impact of reducing the markers density on the genomic evaluation of parametric methods

Document Type : Research Paper

Authors

1 PhD Candidate, department of Animal Science, Ramin University of Agriculture and Natural Resources, Khuzestan, Iran

2 Associate Professor, Department of Animal Science, Khuzestan Ramin Agricultural & Natural Resources University

3 Professor of Genetics and Animal Sciences, Animal Sciences Department , Ramin Agricultural and Natural Resources University, Mollasani, IRAN

4 Assistant Professor, Department of Animal and Poultry Science, College of Aburaihan, University of Tehran

Abstract

The higher number of markers (p) to the number of observations (n) is the first challenge (i.e., course of dimensionality; p>>n) in genomic selection. Therefore with aim to reduce the course of dimensionality, the effect of reduce markers density on the accuracy of genomic prediction breeding values of parametric methods investigated. In this order a genome consisted of 10000 bi-allelic single nucleotide polymorphism (SNP) over 10 chromosomes, with 100 cM length each, was simulated. In this research, the different gene distributions (i.e., uniform, Gaussian and gamma), different levels of heritability (0.05, 0.25, 0.45 or 0.65) and number of quantitative trait loci (QTL; 100 or 500) were considered as assumption of simulation. Then in a selection scenario only 10% of the markers and in the other scenario 20% of the markers were selected (randomly), so that for each of the population three different marker matrices with different dimension (all, 10% and 20% of markers) were defined. According to the finding of this research, in order of preference the accuracy of genomic breeding values is influenced by the trait inheritance, the distance with the reference population, the marker density, the distribution and the number of QTLs and statistical models (p <0.001). So that in all three genetic effects distribution and with each form of the marker structure (selection or without selection of markers) it was observed that the accuracy of predictions declined significantly (p <0.05) as a result of reduction of heritability or with increasing distance from the reference population. The effect of reduce markers density on the genomic prediction accuracy of traits with different genetic architecture showed that except for traits with low heritability (0.05) and high number of QTL (500) and allele effects drown from a gamma distribution, in general reduction of marker density resulted to decrease accuracy of genomic predictions (p <0.05).

Keywords

References

عبداللهی آرپناهی، ر.، پاکدل، ع.، نجاتی جوارمی، الف. و مرادی شهربابک، م. (1392). مقایسه روش‌‌های گوناگون ارزیابی ژنومیک در صفاتی با معماری ژنتیکی متفاوت. تولیدات دامی. شماره 1، ص ص. 65-77.
Brito, F.V., Neto, J.B., Sargolzaei, M., Cobuci, J.A. and Schenkel, F.S. (2011). Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle. BMC genetics. 12:80.
Daetwyler, H.D., Pong-Wong, R., Villanueva, B. and Woolliams, J.A. (2010). The impact of genetic architecture on genome-wide evaluation methods. Genetics. 185:1021-1031.
Daetwyler, H.D., Villanueva, B., Bijma, P. and Woolliams, J.A. (2007). Inbreeding in genome‐wide selection. Journal of Animal Breeding and Genetics. 124:369-376.
de Los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E. et al. (2009). Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics. 182:375-385.
de los Campos, G. and Perez Rodriguez, P. (2016). BGLR: Bayesian Generalized Linear Regression. R package version 1.0.5. https://CRAN.R-project.org/package=BGLR.
Goddard, M.E. and Hayes, B. (2007). Genomic selection. Journal of Animal Breeding and Genetics. 124:323-330.
Goddard, M.E. and Hayes, B.J. (2009). Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature reviews. Genetics. 10:381.
 
Habier, D., Fernando, R.L. and Dekkers, J.C.M. (2007). The impact of genetic relationship information on genome-assisted breeding values. Genetics. 177:2389-2397.
Habier, D., Fernando, R.L., Kizilkaya, K. and Garrick, D.J. (2011). Extension of the Bayesian alphabet for genomic selection. BMC bioinformatics. 12:186.
Hayes, B.J., Bowman, P.J., Chamberlain, A.J. and Goddard, M.E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of dairy science. 92:433-443.
Hayes, B., Carrick, M., Bowman, P. and Goddard, M. (2003). Genotype× environment interaction for milk production of daughters of Australian dairy sires from test-day records. Journal of dairy science. 86:3736-3744.
Hayes, B.J. and Goddard, M. (2008). Technical note: Prediction of breeding values using marker-derived relationship matrices. Journal of animal science. 86:2089-2092.
Hayes, B. and Goddard, M. (2010). Genome-wide association and genomic selection in animal breeding. Genome. 53:876-883.
Henderson, C.R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics. 423-447.
Jannink, J.L., Lorenz, A.J. and Iwata, H. (2010). Genomic selection in plant breeding: from theory to practice. Briefings in functional genomics. 9:166-177.
Luan, T., Woolliams, J.A., Lien, S., Kent, M., Svendsen, M. and Meuwissen, T.H. (2009). The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics. 183:1119-1126.
Meuwissen, T., Hayes, B. and Goddard, M. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157:1819-1829.
Muir, W. (2007). Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. Journal of Animal Breeding and Genetics. 124:342-355.
Park, T. and Casella, G. (2008). The Bayesian lasso. Journal of the American Statistical Association. 103:681-686.
Sax, K. (1923). The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics. 8:552.
Schaeffer, L. (2006). Strategy for applying genome‐wide selection in dairy cattle. Journal of Animal Breeding and Genetics. 123:218-223.
Technow, F. (2013). R Package hypred: Simulation of Genomic Data in Applied Genetics. University of Hohenheim.
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. 92:16-24.
Visscher, P.M., Medland, S.E., Ferreira, M.A., Morley, K.I., Zhu, G. and Cornes, B.K. et al. (2006). Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS genetics. 2:e41.
Wang, W.Y., Barratt, B.J., Clayton, D.G. and Todd, J.A. (2005). Genome-wide association studies: theoretical and practical concerns. Nature reviews. Genetics. 6:109.
Yang, J., Benyamin, B., McEvoy, B.P., Gordon, S., Henders, A.K., Nyholt, D.R. et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature genetics. 42:565-569.
Zhang, Z., Liu, J., Ding, X., Bijma, P., de Koning, D.J., Zhang, Q. (2010). Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix. PLoS ONE. 5:e12648.
Animal Sciences Journal
Volume 32, Issue 122
June 2019
Pages 3-16

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