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Using Bayesian Multilevel Whole Genome Regression Models for Partial Pooling of Training Sets in Genomic Prediction.

Technow F, Totir LR - G3 (Bethesda) (2015)

Bottom Line: No pooling was superior; however, when populations were large.A simulation showed that no pooling is superior when differences in genetic effects among populations are large and partial pooling when they are intermediate.We conclude that partial pooling with multilevel models can maximize the potential of pooling by making optimal use of information in pooled training sets.

View Article: PubMed Central - PubMed

Affiliation: DuPont Pioneer, Johnston, Iowa 50131 Frank.Technow@pioneer.com.

No MeSH data available.


Related in: MedlinePlus

Average prediction accuracies in interconnected biparental maize populations for populations represented in the training set (). Np denotes the average number of individuals per population in the training set, the number of populations was 5. The red bars over each column indicate the standard errors of the averages. The traits are: ear length (EL), deoxynivalenol content (DON), Giberella ear rot severity (GER), kernel rows (KR), and kernels per row (KpR).
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fig5: Average prediction accuracies in interconnected biparental maize populations for populations represented in the training set (). Np denotes the average number of individuals per population in the training set, the number of populations was 5. The red bars over each column indicate the standard errors of the averages. The traits are: ear length (EL), deoxynivalenol content (DON), Giberella ear rot severity (GER), kernel rows (KR), and kernels per row (KpR).

Mentions: The prediction accuracy increased with increasing Np, for all traits and pooling approaches (Figure 5 and Table S5). Averaged over traits, the increase was largest for no pooling, where the accuracy increased from an average of 0.35 at Np = 31 to 0.48 at Np = 95. The accuracies for the partial and complete pooling approaches increased from 0.39 and 0.38, respectively, at Np = 31 to 0.48 at Np = 95.


Using Bayesian Multilevel Whole Genome Regression Models for Partial Pooling of Training Sets in Genomic Prediction.

Technow F, Totir LR - G3 (Bethesda) (2015)

Average prediction accuracies in interconnected biparental maize populations for populations represented in the training set (). Np denotes the average number of individuals per population in the training set, the number of populations was 5. The red bars over each column indicate the standard errors of the averages. The traits are: ear length (EL), deoxynivalenol content (DON), Giberella ear rot severity (GER), kernel rows (KR), and kernels per row (KpR).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528317&req=5

fig5: Average prediction accuracies in interconnected biparental maize populations for populations represented in the training set (). Np denotes the average number of individuals per population in the training set, the number of populations was 5. The red bars over each column indicate the standard errors of the averages. The traits are: ear length (EL), deoxynivalenol content (DON), Giberella ear rot severity (GER), kernel rows (KR), and kernels per row (KpR).
Mentions: The prediction accuracy increased with increasing Np, for all traits and pooling approaches (Figure 5 and Table S5). Averaged over traits, the increase was largest for no pooling, where the accuracy increased from an average of 0.35 at Np = 31 to 0.48 at Np = 95. The accuracies for the partial and complete pooling approaches increased from 0.39 and 0.38, respectively, at Np = 31 to 0.48 at Np = 95.

Bottom Line: No pooling was superior; however, when populations were large.A simulation showed that no pooling is superior when differences in genetic effects among populations are large and partial pooling when they are intermediate.We conclude that partial pooling with multilevel models can maximize the potential of pooling by making optimal use of information in pooled training sets.

View Article: PubMed Central - PubMed

Affiliation: DuPont Pioneer, Johnston, Iowa 50131 Frank.Technow@pioneer.com.

No MeSH data available.


Related in: MedlinePlus