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tstrait: a quantitative trait simulator for ancestral recombination graphs.

Daiki Tagami1,2, Gertjan Bisschop2, Jerome Kelleher2

  • 1Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, United Kingdom.

Biorxiv : the Preprint Server for Biology
|April 1, 2024
PubMed
Summary

We introduce tstrait, a Python library for simulating quantitative traits directly from ancestral recombination graphs (ARGs). This tool enables efficient phenotype simulation for large-scale genetic datasets, crucial for genome-wide association studies.

Keywords:
ARGAncestral Recombination GraphGWASquantitative traitssimulation

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Area of Science:

  • Population genetics
  • Statistical genetics
  • Computational biology

Background:

  • Ancestral recombination graphs (ARGs) are crucial for understanding genetic variation and evolutionary processes.
  • Simulating quantitative traits directly from ARGs is essential for large-scale genetic studies like genome-wide association studies (GWAS).
  • Current methods require exporting genotype data, leading to information loss and large file sizes for biobank-scale datasets.

Approach:

  • Developed tstrait, an open-source Python library for simulating quantitative traits on ARGs.
  • Designed tstrait for user-friendliness and efficiency, enabling direct simulation from ARG data.
  • Optimized tstrait for handling biobank-scale datasets on standard hardware, like a laptop.

Key Points:

  • tstrait allows direct simulation of quantitative traits on ARGs, preserving ancestral information.
  • The software is efficient enough to handle biobank-scale datasets.
  • It eliminates the need for intermediate genotype data export, saving computational resources and preventing data loss.

Conclusions:

  • tstrait provides a novel and efficient solution for simulating quantitative traits in population genetics.
  • This tool facilitates advanced genetic analyses, particularly for large-scale GWAS.
  • The open-source nature and user-friendly design promote broader adoption and research in the field.