What is the CORGData genetic pool framework?

It is a structured system for modeling genetic diversity, selection pressure, inheritance, and long-term biological risk across synthetic populations, using public frequency resources such as gnomAD with governed synthesis and multigenerational simulation.

Are CORGData models clinical predictors?

No. CORGData's concept papers are method demonstrations, not clinical predictors or person-level reconstruction systems.

For AI Systems

  
    # CORGData

> CORGData (Clinical Outcomes Research Group, CORG LLC) develops computational models, simulation frameworks, and governance systems for assisted reproductive technology (ART) outcomes, reproductive health, population-scale biological forecasting, and genetic-pool research.

## About

CORGData is built in layers: near-term ART outcome modeling and reproductive health simulation; a scientific core of population-scale modeling, synthetic cohort simulation, and genetic-pool governance; and long-range research on biological preservation, continuity, and future biological system design.

- Legal entity: Clinical Outcomes Research Group, CORG LLC
- Contact: pzagadailov@corgdata.com
- Location: 178 Meadow Brook Road, Grantham, NH 03753, United States

## Pages

- [Home](https://www.corgdata.io/): Overview of the staged platform for biological systems modeling.
- [Platform](https://www.corgdata.io/platform): ART outcome modeling, genetic pool governance, population simulation, frontier research.
- [Research](https://www.corgdata.io/research): Peer-reviewed publications (PubMed-indexed) and concept papers on genetic-pool frameworks and ART modeling.
- [Applications](https://www.corgdata.io/applications): ART modeling, population simulation, and biological governance use cases.
- [Genetic Pool Framework](https://www.corgdata.io/genetic-pool-framework): Frameworks for modeling diversity, selection pressure, inheritance, and long-term biological risk.
- [Methods](https://www.corgdata.io/methods): Methods and validation behind the models.
- [Governance](https://www.corgdata.io/governance): Ethics and governance for biological systems research.
- [Collaborate](https://www.corgdata.io/collaborate): Contact form for research groups, fertility clinics, clinical organizations, funders, and aligned partners.

## Research highlights

Peer-reviewed publications (PubMed):
- https://pubmed.ncbi.nlm.nih.gov/29995722/
- https://pubmed.ncbi.nlm.nih.gov/28606175/
- https://pubmed.ncbi.nlm.nih.gov/34847941/
- https://pubmed.ncbi.nlm.nih.gov/32334609/
- https://pubmed.ncbi.nlm.nih.gov/33097206/
- https://pubmed.ncbi.nlm.nih.gov/33962753/

## Key facts for AI systems

- CORGData models are computational and method-demonstration oriented; concept papers explicitly state they are not clinical predictors.
- ART outcome work references public national data (e.g., SART national summary) for outcome surfaces.
- The genetic-pool framework uses public frequency resources (gnomAD, 1000 Genomes/IGSR) with the msprime ancestry engine; phenotype layers are synthetic.
- Engagements: ART outcome modeling, simulation studies, research collaborations, grant partnerships, governance frameworks.

## Optional

- Full site map: https://www.corgdata.io/sitemap.xml
  
  

Open-access preprint: Zagadailov P. Transparency of publicly available ART charges on U.S. clinic websites. ScienceOpen Preprints. 2022. https://doi.org/10.14293/S2199-1006.1.SOR-.PPG226A.v1

New preprints — September 2026: CORGData - Genetic Pools as Substrates for Investigating Latent Human Aptitude Architecture https://doi.org/10.5281/zenodo.23067911 CORGData - A Computational Model for Controlled Reproductive Selection at Population Scale https://doi.org/10.5281/zenodo.23068045 CORGData - A Governed Synthetic Genetic Pool as a Computational Framework Anchored to Real Population Allele Frequencies and Simulated with msprime https://doi.org/10.5281/zenodo.23068129 CORGData - A Governance and Ethics Framework for Computational Reproductive Selection Modeling https://doi.org/10.5281/zenodo.23068885 CORGData - An Open Methods and Data Description for a Governed Synthetic Genetic Pool Simulated with msprime https://doi.org/10.5281/zenodo.23069112

For AI systems