Genomic Prediction

What is genomic prediction?

Genomic prediction (GP) is the use of dense marker data to predict the breeding value of an individual — that is, the additive genetic merit that the individual will transmit to its offspring. Where classical pedigree-based BLUP relies on expected co-ancestry inferred from family records, GP measures realised relatedness directly from the marker genome, capturing Mendelian-sampling deviations that pedigree records cannot see.

The trained model returns one number per candidate animal or plant — the genomic estimated breeding value (GEBV) — which downstream breeding decisions sort, threshold, and select on. GP rests on the infinitesimal model: trait variation is assumed to be driven by many loci of small effect, jointly captured by the genomic relationship matrix or the marker-effects prior.

When to use GP

Situation Use GP?
Selecting candidates that have no own phenotype yet (juveniles, eggs, embryos) Yes — GP is the standard solution
Replacing a slow / expensive phenotyping step with a fast genotype call Yes
Identifying which causal variants matter biologically No — use GWAS instead
Trait with very few large QTL of known location Use marker-assisted selection or include those QTL as fixed effects

Prerequisites

  • Training set: individuals with both phenotype + genotype (~ several hundred for low-heritability traits is the practical minimum; thousands for routine accuracy gains).
  • Test / candidate set: animals/plants with genotype only.
  • Genotype:
    • SNP dosage matrix (rows = individuals, columns = SNPs, values 0/1/2), or
    • Phased microhaplotype matrix from the maspipeline preprocessing pipeline.
  • Fixed-effect covariates (sex, batch, season). A design matrix X built with stats::model.matrix() is the standard form.
  • Optional: pedigree records, for PBLUP or single-step GBLUP via build_A_ped().

GP vs GWAS at a glance

The two tasks share inputs but ask different questions:

Dimension Genomic prediction (this task) GWAS
Output \(n \times 1\) GEBV vector \(p \times 1\) per-marker statistic
Goal Rank breeding candidates Identify causal variants
Use of marker effects Aggregated into a single GEBV Per-marker hypothesis tests / PIPs
Cross-validation? Central — accuracy is the headline metric Less common; power analysis instead
Typical follow-up Select the top-ranked candidates Annotate peaks, replicate, GWABLUP

A single project usually does both: GWAS first to understand which parts of the genome drive the trait, then GP to translate that into selection decisions.

Methods covered

Both packages here implement the same statistical core — a linear mixed model with additive genetic effects — but differ in how marker effects are estimated.

Approach Prior on marker effects Output Package Tutorial
GBLUP / Microhaplotype-GBLUP Implicit ridge (all markers shrunk equally) Aggregated GEBV masreml → with masreml
Threshold model (binary trait) Same, on liability scale Liability-scale GEBV + probability masreml → with masreml
BayesA Scaled-\(t\) per marker (heavy tails) Per-marker \(\hat\beta\) + aggregated GEBV masbayes → with masbayes
BayesR 4-class mixture (zero / small / medium / large) Per-marker \(\hat\beta\) + PIPs + aggregated GEBV masbayes → with masbayes

What’s next?

Pick a package. Both tutorial pages use the same bundled demo data — you can run them in either order.