CORGData uses computational models to convert biological data into testable system outputs. These examples show how outcome surfaces, cohort structure, modeled pressure, and diversity constraints can be simulated before real-world decisions are made.

Model Output 01: Polygenic Trait Dynamics

Simulation of a polygenic trait in a synthetic population of 10,000 over 50 generations under defined reproductive-weighting parameters. Mating is random.

Trajectory shows:

• early signal emergence

• accelerated distributional shift under modeled pressure

• saturation toward high population frequency

Demonstrates how defined simulation parameters can shift trait distributions over time.

Model Output 02: Selection vs Neutral Dynamics

Same population and baseline parameters. Under modeled reproductive-weighting pressure, the trait propagates rapidly and approaches high population frequency.

Isolates how defined model parameters alter population-level trait dynamics.

Model Output 03: ART Success Probability Across Age Cohorts

Public national ART data show a steep decline in cumulative live birth rate with increasing maternal age when using a patient’s own eggs.

Cohort curve provides a real-world outcome surface for predictive modeling, cohort simulation, and scenario design.

Source: SART national summary, patients’ own eggs, all embryo transfers.

Model Output 04: Synthetic Population Structure (PCA)

Synthetic population projected into principal component space. Distinct clusters reflect underlying population structure and variation.

Provides a foundation for modeling cohort stratification, population structure, and trait distribution across subpopulations.

Model Output 05: Selection vs Diversity Tradeoff

Increasing modeled pressure can accelerate distributional change while reducing population diversity.

Defines a core constraint: strong directional pressure can reduce genetic variation, introducing long-term risk.