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Pastrami: a fast and efficient algorithm for fine-scale genetic ancestry inference.

Andrew B Conley1,2, Lavanya Rishishwar1,2, Shivam Sharma3

  • 1National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD 20892, United States.

NAR Genomics and Bioinformatics
|December 25, 2025
PubMed
Summary

A new algorithm, Pastrami, offers fast and efficient genetic ancestry inference for large biobanks. It achieves similar accuracy to existing methods but runs approximately 45 times faster, significantly reducing computational time for genomics research.

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Large-scale genomics research relies on biobanks with numerous participants.
  • Current genetic ancestry inference methods are too slow for these large datasets.

Purpose of the Study:

  • To develop a computationally efficient algorithm for fine-scale genetic ancestry inference in biobank-sized cohorts.
  • To address the limitations of existing methods in terms of speed and resource requirements.

Main Methods:

  • Developed the Pastrami algorithm for supervised genetic ancestry inference.
  • Pastrami compares haplotypes, creates copying vectors, and uses non-negative least squares regression.
  • Evaluated Pastrami on genomic datasets from Africa, the Americas, and the UK, comparing it to ChromoPainter and RFMix.

Main Results:

  • Pastrami demonstrated highly similar ancestry estimates compared to ChromoPainter and RFMix.
  • The algorithm exhibits a linear increase in CPU time with sample size.
  • Pastrami achieved approximately 45x faster runtime than ChromoPainter, processing large datasets significantly quicker.

Conclusions:

  • Pastrami provides a fast and efficient solution for genetic ancestry inference in large biobanks.
  • The algorithm's performance makes large-scale genomic studies more computationally feasible.
  • Pastrami is freely available on GitHub, promoting wider adoption in the research community.