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

Model Output 01: Polygenic Trait Dynamics

Simulation of a polygenic trait in a population of 10,000 over 50 generations under moderate positive selection (~20% reproductive advantage). Mating is random.

Trajectory shows:

• early signal emergence

• accelerated propagation under selection

• saturation toward population dominance

Demonstrates how defined selection parameters shift trait distribution at the population level over time.

Model Output 02: Selection vs Neutral Dynamics

Same population and baseline parameters. Under neutral conditions, the trait remains largely stable. Under positive selection, the trait propagates rapidly and approaches saturation.

Isolates the causal effect of selection on 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 intervention 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, selection targeting, and trait distribution across subpopulations.

Model Output 05: Selection vs Diversity Tradeoff

Increasing selection intensity accelerates trait optimization but reduces population diversity.

Defines a fundamental constraint: maximizing outcomes can degrade genetic variation, introducing long-term risk.