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Bayesian genomic-enabled prediction as an inverse problem.

Cuevas J, Pérez-Elizalde S, Soberanis V, Pérez-Rodríguez P, Gianola D, Crossa J - G3 (Bethesda) (2014)

Bottom Line: Genomic-enabled prediction in plant and animal breeding has become an active area of research.Many prediction models address the collinearity that arises when the number (p) of molecular markers (e.g. single-nucleotide polymorphisms) is larger than the sample size (n).Because shrinkage of estimates is affected by the prior variance of transformed effects, we propose four structures of the prior variance as a way of potentially increasing the prediction accuracy of the models fitted.

View Article: PubMed Central - PubMed

Affiliation: Colegio de Posgraduados, 56230, Montecillo, Texcoco, Edo. de México.

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Related in: MedlinePlus

Maize trait-environment combination male flowering in well-watered environments: the prior variances for the i-th singular values for four inverse Bayesian regression models: (A) Bayesian inverse ridge regression (line) and Bayes A inverse (dots); (B) Bayesian inverse regression model 1 (solid line) and Bayesian inverse regression model 2 (dashed line).
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fig2: Maize trait-environment combination male flowering in well-watered environments: the prior variances for the i-th singular values for four inverse Bayesian regression models: (A) Bayesian inverse ridge regression (line) and Bayes A inverse (dots); (B) Bayesian inverse regression model 1 (solid line) and Bayesian inverse regression model 2 (dashed line).

Mentions: Figure 2, A and B depicts the prior variance for models BIRR, BAI, BIR1, and BIR2 for trait-environment combination MFL-WW. In Figure 2A, the prior variance of for BIRR is represented by a solid line, whereas used in BAI are scattered dots, each representing an individual. It is interesting to note that, for MFL-WW, most of the values for BAI, BIR1, and BIR2 were smaller than those for BIRR, represented by a solid line. This indicates that BAI, BIR1, and BIR2 cause more shrinkage. Figure 2B depicts the decay of singular values for BIR2 (dashed line) and BIR1 (solid line), both mimicking the current decay of singular values shown in Figure 1, A and B. The decay of BIR1 reflects the polynomial function , but smoothed by the parameter h, as indicated in (18), with less shrinkage for the first singular values and increasing shrinkage toward the later singular values.


Bayesian genomic-enabled prediction as an inverse problem.

Cuevas J, Pérez-Elizalde S, Soberanis V, Pérez-Rodríguez P, Gianola D, Crossa J - G3 (Bethesda) (2014)

Maize trait-environment combination male flowering in well-watered environments: the prior variances for the i-th singular values for four inverse Bayesian regression models: (A) Bayesian inverse ridge regression (line) and Bayes A inverse (dots); (B) Bayesian inverse regression model 1 (solid line) and Bayesian inverse regression model 2 (dashed line).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4199705&req=5

fig2: Maize trait-environment combination male flowering in well-watered environments: the prior variances for the i-th singular values for four inverse Bayesian regression models: (A) Bayesian inverse ridge regression (line) and Bayes A inverse (dots); (B) Bayesian inverse regression model 1 (solid line) and Bayesian inverse regression model 2 (dashed line).
Mentions: Figure 2, A and B depicts the prior variance for models BIRR, BAI, BIR1, and BIR2 for trait-environment combination MFL-WW. In Figure 2A, the prior variance of for BIRR is represented by a solid line, whereas used in BAI are scattered dots, each representing an individual. It is interesting to note that, for MFL-WW, most of the values for BAI, BIR1, and BIR2 were smaller than those for BIRR, represented by a solid line. This indicates that BAI, BIR1, and BIR2 cause more shrinkage. Figure 2B depicts the decay of singular values for BIR2 (dashed line) and BIR1 (solid line), both mimicking the current decay of singular values shown in Figure 1, A and B. The decay of BIR1 reflects the polynomial function , but smoothed by the parameter h, as indicated in (18), with less shrinkage for the first singular values and increasing shrinkage toward the later singular values.

Bottom Line: Genomic-enabled prediction in plant and animal breeding has become an active area of research.Many prediction models address the collinearity that arises when the number (p) of molecular markers (e.g. single-nucleotide polymorphisms) is larger than the sample size (n).Because shrinkage of estimates is affected by the prior variance of transformed effects, we propose four structures of the prior variance as a way of potentially increasing the prediction accuracy of the models fitted.

View Article: PubMed Central - PubMed

Affiliation: Colegio de Posgraduados, 56230, Montecillo, Texcoco, Edo. de México.

Show MeSH
Related in: MedlinePlus