Predictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive

الموضوعات :

م. مومن ¹ (Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)
ا. آیت‌آللهی مهرجردی ² (Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)
ا. شیخی ³ (Department of Animal Science, Faculty of Mathematics and Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran)
ع. اسماعیلی‌زاده ⁴ (Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)
م. اسدی فوزی ⁵ (Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran)

تاريخ الإرسال : 19 الإثنين , جمادى الثانية, 1437 تاريخ التأكيد : 28 الأربعاء , ذو القعدة, 1437 تاريخ الإصدار : 02 الخميس , ربيع الأول, 1438

الکلمات المفتاحية: genomic statistical method, non parametric, parametric, predictive ability,

ملخص المقالة :

A simulation study was conducted to address the issue of how purely additive (simple) genetic architecture might impact on the efficacy of parametric and non-parametric genomic prediction methods. For this purpose, we simulated a trait with narrow sense heritability h2= 0.3, with only additive genetic effects for 300 loci in order to compare the predictive ability of 14 more practically used genomic prediction models based on four criteria (mean squared error (MSE), Bias, γy,GEBV and γGEBV,TBV). Results suggested that parametric genomic prediction models have greater superiority over non parametric genomic models under a simple purely additive genetic architecture. Our result also showed that, all parametric methods, other than ridge-regression BLUP (RR-BLUP), could explain most of phenotypic variation because they showed lower MSE, higher predictive correlation (γy,GEBV), the least amount of bias (by,GEBV) and the higher correlations between true breeding values and the estimated genomic breeding values (γTBV,GEBV). Random forest regression had the worst performance among non parametric methods. The simulation results suggested that there is a large difference between performances of non parametric methods in comparison with parametric methods when underlying architecture is purely additive. But this may not happen when dominance and epistatic genetic effects contributing to both additive and non-additive genetic variances.

المصادر:

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

Daetwyler H.D., Calus M.P., Pong-Wong R., de Los Campos G. and Hickey J.M. (2013). Genomic prediction in animals and plants: simulation of data, validation, reporting and benchmarking. Genetics. 1932, 347-365.

de Los Campos G., Hickey J.M., Pong-Wong R., Daetwyler H.D. and Calus M.P. (2013). Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics. 1932, 327-345.

Desta Z.A. and Ortiz R. (2015). Genomic selection: genome-wide prediction in plant improvement. Trends. Plant. Sci. 199, 592-601.

Do D.N., Janss L.L., Jensen J. and Kadarmideen H.N. (2015). SNP annotation-based whole genomic prediction and selection: an application to feed efficiency and its component traits in pigs. J. Anim. Sci. 93(5), 2056-2063.

Fernández J., Toro M.A., Gómez-Romano F. and Villanueva B. (2016). The use of genomic information can enhance the efficiency of conservation programs. Anim. Fronts. 6(1), 59-64.

Gianola D., Fernando R.L. and Stella A. (2006). Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics. 1733, 1761-1776.

Gianola D. and Rosa G.J.M. (2015). One hundred years of statistical developments in animal breeding. Ann. Rev. Anim. Biosci. 31, 19-56.

Hayes B. J., J. Pryce, A. J. Chamberlain, P. J. Bowman and M. E. Goddard (2010). Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage and type in Holstein cattle as contrasting model traits. PLoS Genet. 6(9), 1001139.

Holland J.B. (2007). Genetic architecture of complex traits in plants. Curr. Opin. Plant. Biol. 10(2), 156-161.

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 Bethesda. 46, 1027-1046.

Lee S.H., van der Werf J.H., Hayes B.J., Goddard M.E. and Visscher P.M. (2008). Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet. 4, 1000231.

Lidan Sun R.W. (2015). Mapping complex traits as a dynamic system. Phys. Life Rev. 13, 194-197.

Meuwissen T., Hayes B. and Goddard M. (2016). Genomic selection: a paradigm shift in animal breeding. Anim. Front. 6, 6-14.

Meuwissen T.H.E. and Goddard M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829.

Ober U., Ayroles J.F., Stone E.A., Richards S., Zhu D., Gibbs R.A., Stricker C., Gianola D., Schlather M., Mackay T.F. and Simianer H. (2012). Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster. PLoS Genet. 8(5), 1002685.

R Core Team. (2015). R: a language and environment for statistical computing. Vienna, Austria: the R Foundation for Statistical Computing. Available at: http://www.R-project.org/.

Resende M.D.V., Resende M.F.R., Sansaloni C.P., Petroli C.D., Missiaggia A.A., Aguiar A.M., Abad J.M., Takahashi E.K., Rosado A.M., Faria D.A., Pappas G.J., Kilian A. and Grattapaglia D. (2012). Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees. New Phytol. 194(1), 116-128.

Stranger B.E., Stahl E.A. and Raj T. (2011). Progress and promise of genome-wide association studies for human complex trait genetics. Genetics. 187(2), 367-383.

Su G., Christensen O.F., Ostersen T., Henryon M. and Lund M.S. (2012). Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS One. 7(9), 45293.

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

Yang J., Zhu J. and Williams R.W. (2007). Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics. 23(12), 1527-1536.