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Comparison of genomic evaluation methods in complex traits with different genetic architecture

Document Type : Research Paper

Authors

1 Ph.D. Student, Department of Animal Sciences, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

2 Associate Professor, Department of Animal Sciences, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

3 Professor, Department of Animal Sciences, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

Abstract

The objective of this study was to compare six statistical methods for prediction of genomic breeding
values for traits with different genetic architecture in term of gene effects distributions and number of
Quantitative Traits Loci (QTLs). A genome consisted of 500 bi-allelic single nucleotide polymorphism
(SNP) markers distributed over a chromosomes with 100 cm length was simulated. Three different gene
effects distributions (uniform, normal and gamma) were considered. Number of QTLs varied from 50 to
200. Finally, nine quantitative traits with different genetic architecture were generated. The performance
of six statistical methods of genomic prediction that differ with respect to assumptions regarding
distribution of marker effects, including i) Genomic Best Linear Unbiased Prediction (GBLUP), ii) Ridge
Regression Best Linear Unbiased Prediction (RRBLUP), iii) Bayes A, iv) Bayes B, v) Bayes C, and vi)
Bayesian least absolute shrinkage and selection operator (Bayes L) are presented. The accuracy of
prediction declined significantly over generations (P< 0.05) but Bayesian methods outperformed GBLUP
and RRBLUP in persistence of accuracy of genomic estimated breeding values over generations.
Bayesian methods were superior to GBLUP and RRBLUP when the gene effects distribution generated
from gamma distribution. The highest accuracy of genomic breeding values was observed when the gene
effects come from normal distribution. In all statistical evaluation methods with increasing the number of
QTLs towards 200, the accuracy of predicted genomic values has been decreased. In general, Bayesian
and GBLUP methods performed better in prediction than RRBLUP method. These results gave some
evidences that when the genetic architecture of quantitative traits deviated from infinitesimal model
assumptions, Bayesian methods usually perform better than GBLUP and RR-BLUP.

Keywords

References
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Animal Production
Volume 15, Issue 1 - Serial Number 1
July 2014
Pages 65-77
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