Accuracy of genomic evaluation considering the interaction effect between estimation method of marker effects, population structure, and genetic architecture of the trait

Document Type : Research Paper

Authors

1 Former Ph.D. Student in Animal Breeding and Genetics, Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

2 Professor, Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

Abstract

This study aimed to investigate the interaction effects between marker effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations. A reference population with two different effective population sizes (100 and 500) was simulated using the QMSim software. 500 markers and two different numbers of quantitative trait loci or QTLs (50 and 200) were distributed randomly through the genome including a 100 cM chromosome. In this study, three traits with different heritabilities (0.1, 0.3, and 0.5) were simulated. The genomic breeding values were predicted using Bayesian ridge regression, Bayes A, Bayes B, Bayes C, Bayesian LASSO, Reproducing kernel Hilbert space, and neural networks methods. Through the three heritabilities, as the effective population size increased, the accuracy of genomic evaluation decreased with different trends. As the number of QTLs increased, the accuracy of low heritability trait increased, but the accuracy of medium and high heritability traits decreased. Similarly, as the number of QTLs increased, the accuracy of the trait with normal distributed QTLs increased, but the accuracy of traits with gamma and univariate distributed QTLs decreased. For all types of QTL distributions, the increment of effective population size decreased the accuracy of genomic evaluations. The results of this study clearly showed the interaction effects between markers effect estimation methods, population structure, and genetic architecture of the trait on the accuracy of genomic evaluations.

Keywords

Main Subjects


محمودی ن.، آیت الهی مهرجردی ا.، هنرور م.، و اسماعیلی زاده کشکوئیه، ع. 1394. مطالعه توزیع آماری آثار QTL  بر صحت ارزش­های اصلاحی ژنومی برآورد شده با روش.Bayesian  پژوهشهای علوم دامی ایران، 7(3): 356-363.
Alanoshahr F., Rafat S. A., Imany-Nabiyyi R., Alijani S. and Robert Granie C. 2017. Effects of marker density, number of quantitative trait loci and heritability of trait on genomic selection accuracy. Iranian Journal of Applied Animal Science, 7(4): 595-601.
Alanoshahr F., Rafat S. A., Imany-Nabiyyi R., Alijani S. and Robert Granie C. 2018. The impact of different genetic architectures on accuracy of genomic selection using three Bayesian methods. Iranian Journal of Applied Animal Science, 8(1): 53-59.
Atefi A., Shadparvar A. A. and Ghavi Hossein-Zadeh N. 2018. Accuracy of genomic prediction under different genetic architectures and estimation methods.  Iranian Journal of Applied Animal Science, 8(1): 43-52.
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.
Daetwyler H., Villanueva B. and Woolliams J. A. 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PloS One, 3(10): e3395.
Gianola D., Fernando R. L. and Stella A. 2006. Genomic assisted prediction of genetic value with semi-parametric procedures. Genetics, 173(3): 1761-1776.
Gonzalez-Recio O., Gianola D., Long N., Weigel K. A., Rosa G. J. M. and Avendano S. 2008. Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics, 178: 2305-2313.
Gorgani Firozjah N., Atashi H., Dadpasand M. and Zamiri M. 2014. Effect of marker density and trait heritability on the accuracy of genomic prediction over three generations. Journal of Livestock Science and Technologies, 2(2): 53:58.
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. 2007. QTL mapping, mas, and genomic selection. A short-course. Department of Animal Science, Iowa State University. https://www.ans.iastate.edu/files/page/files/notes.pdf.
Hoerl A. E. and Kennard R. W. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1): 55-67.
Honarvar M. and Rostami M. 2013. Accuracy of genomic prediction using rr-blup and bayesian lasso. European Journal of Experimental Biology, 3(3): 42-47.
Howard R., Carriquiry A. L. and Beavis W. D. 2014. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures. G3: Genes, Genomes, Genetics,  4(6): 1027-1046.
Liu T., Qu H., Luo C., Shu D., Wang J., Lund M. S. and Su G. 2014. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC genetics, 15(1): 110.
Meuwissen M. H. E., Hayes B. J. and Goddard M. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics,157(4): 1819-1829.
Muir W. M. 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(482): 681-686.
Sargolzaei M. and Schenkel F. S. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25(5): 680-681.
Uemoto Y., Sasaki S., Kojima T., Sugimoto Y. and Watanabe T. 2015. Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle. BMC Genetics, 16: 134.
Valente M. S. F., Viana J. M. S., Resende M. D. V., Silva F. F. and Lopes M. T. G. 2016. Genomic selection for plant breeding with different population structures. Pesquisa Agropecuária Brasileira, 51(11): 1857-1867.
Weber K., Thallman R., Keele J., Snelling W., Bennett G., Smith T., McDaneld T., Allan M., Van Eenennaam A. and Kuehn L. 2012. Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes. Journal of Animal Science, 90(12): 4177-4190.