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Updated: Aug 6, 2025

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Rye: genetic ancestry inference at biobank scale.

Andrew B Conley1,2,3, Lavanya Rishishwar1,2,3,4, Maria Ahmad1

  • 1National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Nucleic Acids Research
|March 17, 2023
PubMed
Summary

Rye is a new algorithm for genetic ancestry inference that scales to biobank-sized genomic datasets. It provides accurate ancestry estimates and is 50x faster than existing methods, enabling large-scale genomic research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Biobank projects generate massive genomic datasets requiring scalable computational tools.
  • Current genetic ancestry (GA) inference methods do not efficiently handle biobank-scale data.

Purpose of the Study:

  • Introduce Rye, a novel algorithm for GA inference at biobank scale.
  • Evaluate Rye's accuracy and performance against existing GA inference tools.

Main Methods:

  • Rye utilizes principal component analysis with Metropolis-Hastings optimization for accuracy and non-negative least squares regression for speed.
  • Compared Rye against RFMix, ADMIXTURE, and iAdmix using UK Biobank data (488,221 individuals).

Main Results:

  • Rye achieves high accuracy for three-way admixed populations (African, European, Native American) with R² values from 0.998 to 1.00.
  • Rye demonstrates a 50x runtime improvement over ADMIXTURE on the UK Biobank dataset.
  • Rye successfully infers GA at both continental and subcontinental levels.

Conclusions:

  • Rye is an accurate and highly efficient algorithm for genetic ancestry inference at biobank scale.
  • The algorithm facilitates large-scale genomic studies by overcoming computational limitations of existing methods.
  • Rye and its documentation are publicly available on GitHub.