GenomicPrediction
@genomic_pred
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Publication and pre-print alert for genomic prediction and genomic selection, by @wpgilks Also see @gwas_lit and @Mendelian_lit
Sussex, England
Joined September 2015
Trait genetic architecture and population structure determine model selection for genomic prediction in natural Arabidopsis Thaliana populations.
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Genomic prediction of heterosis, inbreeding control, and mate allocation in outbred diploid and tetraploid populations
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Multiomic insights into sucrose accumulation in sugarcane
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Impact of genome assemblies, genotyping methods, variant types, ploidy levels and population structures on genomic prediction in switchgrass
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Genome-wide association analysis of flowering date in a collection of cultivated olive tree
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Genotyping-By-Sequencing and DNA array for genomic prediction in soybean oil composition
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High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions
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Genomic prediction of individual inbreeding levels for the management of genetic diversity in populations with small effective size
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SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection
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Complex genetic architecture underlying the plasticity of tobacco leaf width provides insight into across-environment genomic prediction
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Genetic Dissection of Heat Stress Tolerance in Soybean through Genome-Wide Association Studies and the Use of Genomic Prediction to Enhance Breeding Applications
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Improving genomic selection in hexaploid wheat with sub-genome additive and epistatic models
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Genome-Wide association study uncovers pea candidate genes and pathways involved in rust resistance
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Cross Potential Selection: A Proposal for Optimizing Crossing Combinations in Recurrent Selection Based on the Ability of Future Inbred Lines
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Predicting nonlinear genetic relationships between traits in multi-trait evaluations by using a GBLUP-assisted Deep Learning model
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Deep learning based genomic breeding of pest-resistant grapevine
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Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations
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MegaLMM improves genomic predictions in new environments using environmental covariates
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Introducing CHiDO a No Code Genomic Prediction Software implementation for the Characterization & Integration of Driven Omics
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Multi-trait multi-environment genomic prediction of preliminary yield trials in pulse crops
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