Bayesian genomic-enabled prediction as an inverse problem.
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.
Affiliation: Colegio de Posgraduados, 56230, Montecillo, Texcoco, Edo. de México.Show MeSH
Related in: MedlinePlus
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.
Affiliation: Colegio de Posgraduados, 56230, Montecillo, Texcoco, Edo. de México.