Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods

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2012-01-01
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Zeng, Jian
Pszczola, Marcin
Strabel, Tomasz
Fernando, Rohan
Garrick, Dorian
Dekkers, Jack
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Wolc, Anna
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Animal Science

The Department of Animal Science originally concerned itself with teaching the selection, breeding, feeding and care of livestock. Today it continues this study of the symbiotic relationship between animals and humans, with practical focuses on agribusiness, science, and animal management.

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The Department of Animal Husbandry was established in 1898. The name of the department was changed to the Department of Animal Science in 1962. The Department of Poultry Science was merged into the department in 1971.

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Background: The goal of this study was to apply Bayesian and GBLUP methods to predict genomic breeding values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15th QTL-MAS workshop.

Methods: Three methods with models considering dominance and epistasis inheritances were used to fit the data: (i) BayesB with a proportion π = 0.995 of SNPs assumed to have no effect, (ii) BayesCπ, where π is considered as unknown, and (iii) GBLUP, which directly fits animal genetic effects using a genomic relationship matrix.

Results: BayesB, BayesCπ and GBLUP with various fitted models detected 6, 5, and 4 out of 8 simulated QTL, respectively. All five additive QTL were detected by Bayesian methods. When two QTL were in either coupling or repulsion phase, GBLUP only detected one of them and missed the other. In addition, GBLUP yielded more false positives. One imprinted QTL was detected by BayesB and GBLUP despite that only additive gene action was assumed. This QTL was missed by BayesCπ. None of the methods found two simulated additive-by-additive epistatic QTL. Variance components estimation correctly detected no evidence for dominance gene-action. Bayesian methods predicted additive genetic merit more accurately than GBLUP, and similar accuracies were observed between BayesB and BayesCπ.

Conclusions: Bayesian methods and GBLUP mapped QTL to similar chromosome regions but Bayesian methods gave fewer false positives. Bayesian methods can be superior to GBLUP in GEBV prediction when genomic architecture is unknown.

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This proceeding is published as Zeng, J., Pszczola, M., Wolc, A. et al. Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods. BMC Proc 6, S7 (2012). doi: 10.1186/1753-6561-6-S2-S7.

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Sun Jan 01 00:00:00 UTC 2012