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Fast Phenotype Simulation for Genotype Representation Graphs

  • 0Department of Computational Biology, Cornell University, Ithaca, NY.
Biorxiv : the Preprint Server for Biology +

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September 2, 2025

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September 2, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

We introduce GrgPhenoSim, a fast phenotype simulator for Genotype Representation Graphs (GRGs). This tool accelerates statistical genetics research on large biobank-scale datasets by enabling rapid phenotype simulation.

Area Of Science

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background

  • The Genotype Representation Graph (GRG) offers a compact and efficient graph-based representation for whole genome polymorphisms.
  • Existing methods for analyzing large-scale genomic data, particularly for genome-wide association studies, require faster computational approaches.

Purpose Of The Study

  • To develop an extremely fast phenotype simulator specifically designed for GRGs.
  • To facilitate scalable statistical genetics research on biobank-scale datasets.

Main Methods

  • Developed GrgPhenoSim, a phenotype simulator tailored for GRG data structures.
  • Benchmarked GrgPhenoSim against existing simulators like tstrait using varying sample sizes.

Main Results

  • GrgPhenoSim demonstrates significant speed improvements, being dozens to hundreds of times faster than tstrait for large sample sizes.
  • The simulator supports customized simulations and provides standardized outputs, encompassing essential phenotype simulation functionalities.

Conclusions

  • GrgPhenoSim is a highly efficient tool for simulating phenotypes on GRG data, significantly advancing the scalability of statistical genetics.
  • The availability of the GrgPhenoSim library and its documentation promotes its adoption in large-scale genetic studies.

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