about
Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data.Hybrid Performance of an Immortalized F2 Rapeseed Population Is Driven by Additive, Dominance, and Epistatic Effects.Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space.Bayesian reversible-jump for epistasis analysis in genomic studies.Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE).Multikernel linear mixed models for complex phenotype prediction.Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes.Enhancing genetic gain in the era of molecular breeding.Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.Omics-based hybrid prediction in maize.Orthogonal Estimates of Variances for Additive, Dominance, and Epistatic Effects in Populations.Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.Validating the prediction accuracies of marker-assisted and genomic selection of Fusarium head blight resistance in wheat using an independent sample.Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize.A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.Cross-Validation Without Doing Cross-Validation in Genome-Enabled Prediction.Epistasis and covariance: how gene interaction translates into genomic relationship.Genomic selection in a commercial winter wheat population.Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast Central European elite winter wheat population.Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat.Genomic Prediction of Sunflower Hybrids Oil Content.An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding.Misspecification in Mixed-Model-Based Association Analysis.Polygenicity and Epistasis Underlie Fitness-Proximal Traits in the Caenorhabditis elegans Multiparental Experimental Evolution (CeMEE) Panel.A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat.Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses.Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.A Rapid Epistatic Mixed-model Association Analysis by Linear Retransformations of Genomic Estimated Values.Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.Genetic Variance Partitioning and Genome-Wide Prediction with Allele Dosage Information in Autotetraploid Potato.Haplotype-Based Genome-Wide Prediction Models Exploit Local Epistatic Interactions Among Markers.Non-additive Effects in Genomic Selection.Bayesian Approximate Kernel Regression With Variable SelectionQuantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions
P2860
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P2860
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Modeling Epistasis in Genomic Selection.
@en
type
label
Modeling Epistasis in Genomic Selection.
@en
prefLabel
Modeling Epistasis in Genomic Selection.
@en
P2860
P1433
P1476
Modeling Epistasis in Genomic Selection
@en
P2093
Yong Jiang
P2860
P304
P356
10.1534/GENETICS.115.177907
P407
P577
2015-07-27T00:00:00Z