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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

Decay of singular values and noise pattern for maize trait-environment combination gray leaf spot-3: (A) decay of all singular values; (B) noise in the ordinary least squares (OLS) estimates for all singular values.
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fig4: Decay of singular values and noise pattern for maize trait-environment combination gray leaf spot-3: (A) decay of all singular values; (B) noise in the ordinary least squares (OLS) estimates for all singular values.

Mentions: Comparing Figure 1C (OLS estimates for MFL-WW) and Figure 4B (OLS estimates for GLS-3), instability of OLS estimates is observed for the last 50 OLS values in MFL-WW but this occurred in only a few of the last OLS estimates for GLS-3. This indicates that trait MFL-WW required more shrinkage than that needed for trait GLS-3. This may explain why, for MFL-WW, model BIR2, which showed more shrinkage than BIRR, had a better predictive correlation than BIRR. For the same reasons, model BIRR was a better predictor than BIR2 for trait GLS-3. BIR1 had a good predictive correlation for most traits because the decay of the variance was smoothed out by (see Equation 17), which allowed “intermediate” shrinkage.


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)

Decay of singular values and noise pattern for maize trait-environment combination gray leaf spot-3: (A) decay of all singular values; (B) noise in the ordinary least squares (OLS) estimates for all singular values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Decay of singular values and noise pattern for maize trait-environment combination gray leaf spot-3: (A) decay of all singular values; (B) noise in the ordinary least squares (OLS) estimates for all singular values.
Mentions: Comparing Figure 1C (OLS estimates for MFL-WW) and Figure 4B (OLS estimates for GLS-3), instability of OLS estimates is observed for the last 50 OLS values in MFL-WW but this occurred in only a few of the last OLS estimates for GLS-3. This indicates that trait MFL-WW required more shrinkage than that needed for trait GLS-3. This may explain why, for MFL-WW, model BIR2, which showed more shrinkage than BIRR, had a better predictive correlation than BIRR. For the same reasons, model BIRR was a better predictor than BIR2 for trait GLS-3. BIR1 had a good predictive correlation for most traits because the decay of the variance was smoothed out by (see Equation 17), which allowed “intermediate” shrinkage.

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