MVP v0.6.4 — early decision engine

Selection Strategy Simulator

Compare selection strategies as an early decision engine: Monte Carlo replicates, neutral/random baselines, genomic selection mockups, OCS-like constraints, cross planning, Pareto trade-offs, risk probabilities, and minimal CRISPR-aware edit introgression.

Small populations are intentionally allowed. Try N=8 or N=12 to see rapid drift and fixation. Runs are manual: if parameters are unchanged, recalculation is skipped. The Run simulation button shows worker-pool progress. Solid lines are the current run; dotted lines show the previous run.

Simulation inputs

Random seed ? reproducible

Population size ? 2–5000

Markers ? 10–5000

QTL count ? causal loci

Generations ? 1–200

Selection intensity ? % parents

Heritability ? 0–1

Mutation rate ? per allele

Strategy set ? core / advanced

Replicates ? 1–100

Worker count ? 0 = auto

Inbreeding limit ? risk threshold

Diversity loss limit ? risk threshold

Enable minimal CRISPR integration ?

CRISPR candidate edits ? decision-layer

Edit intro percent ? founders seeded

Decision engine output

Run notes

Genetic gain

trait mean vs baseline

Diversity

mean heterozygosity

Inbreeding risk

approx. diversity loss

Allele-frequency drift

mean abs. shift from start

Rare useful loci lost

warning metric

Fixed loci

rapid fixation becomes obvious in tiny populations

Pareto decision chart

genetic gain vs combined risk probability

Upper-left is usually better: more gain with less combined risk. White outline marks non-dominated Pareto candidates.

CRISPR edit candidates

This is intentionally not guide design. It is a minimal decision-layer demonstration: which beneficial low-frequency loci might be worth seeding into a breeding strategy simulation.

Rank	Locus	Effect	Allele freq.	Gain score	Risk	Decision

Strategy recommendations

Strategy	Rank	Score	Final gain ±σ	Diversity ±σ	Inbreeding ±σ	Risk	P(inb/div/loss)	Fixed loci	Parents	Pareto	Δ vs previous	Recommendation