Genomic Selection
Genomic Selection (GBLUP, BayesB)
Predict the genetic merit of unphenotyped plants from their genotype, with cross-validated prediction accuracy.
How it works
Genomic Selection (GS) trains a whole-genome prediction model on individuals with both genotype and phenotype, then predicts genomic estimated breeding values (GEBVs) for unphenotyped candidates. We support GBLUP (genomic best linear unbiased prediction) using the realized G-matrix, and BayesB for sparse-effect traits. We report cross-validated predictive accuracy (Pearson r between predicted and observed phenotypes) so you know exactly how trustworthy the predictions are before deploying them in your breeding program.
Formula
GBLUP: y = μ + g + e, where g ~ N(0, Gσ²g). G is the genomic relationship matrix from centered, scaled marker scores.
What you get
- ▸GEBVs for every individual in the dataset
- ▸Variance components (σ²g, σ²e, narrow-sense h²)
- ▸k-fold cross-validated prediction accuracy (r)
- ▸Top markers by absolute effect (for BayesB)
When to use it
- ▸You want to rank candidates without phenotyping every plant
- ▸You're running a breeding program and want to accelerate generations
- ▸You need to evaluate prediction accuracy before trusting the model
References
Run Genomic Selection on your data
Open the module and upload a CSV.