Re-fit variance components from your data, run the BayesC variable-selection sampler to identify which SNPs are actually driving each trait, and validate predictive accuracy on held-out records. The defaults work without ticking any box — toggle features on for the deeper analysis.
Variance components drive every BV — the defaults are industry averages. Calibrating against your own data tightens accuracy.
BayesC PIP tells you which SNPs drive each trait — GBLUP cannot.
Cross-validation reports how well the BVs predict held-out records. The number you actually trust.
Misztal 2014. Inverts only n_core × n_core block. Scales to 1M+ genotyped animals where direct Cholesky breaks past ~3000.
Average-Information REML — what BLUPF90+'s AIREMLF90 uses. 10–100× faster than Gibbs for variance components. Returns posterior σ² with asymptotic standard errors.
Lamb survival, fertility, dystocia are binary — Gaussian BLUP gives wrong BVs. Probit Gibbs sampler with truncated-normal liability is the proper estimator.
Per-animal polynomial coefficients describing growth/milking trajectories. Replaces separate BWT/WWT/PWT/YWT models with one continuous model of weight-at-age.
Erbe 2012. Each SNP belongs to one of (zero, small, medium, large) effect classes. Better than BayesC for traits with major-effect loci on a polygenic background. The method behind Australian dairy MGBSI.
Holds out 20% of phenotypes, predicts them from the remaining 80%, reports correlation. The accuracy metric used in Daetwyler 2010.