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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Inferring population structure in biobank-scale genomic data.

Alec M Chiu¹, Erin K Molloy², Zilong Tan³

¹Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.

American Journal of Human Genetics

|March 17, 2022

Summary

We developed SCOPE, a fast and accurate method for inferring human population structure from genetic data. It efficiently analyzes large biobank-scale datasets, improving insights for population and medical genomics.

Keywords:

admixture ancestry biobank genetic structure population structure scalability

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Area of Science:

Genomics
Population Genetics
Bioinformatics

Background:

Inferring human population structure from genetic variation is crucial for population and medical genomics.
Existing methods struggle with the scale of modern biobank datasets (millions of individuals and variants).

Purpose of the Study:

To introduce SCOPE, a novel, computationally efficient method for population structure inference.
To enable accurate analysis of large-scale genomic datasets.

Main Methods:

SCOPE utilizes a novel approach for population structure inference.
The method was tested on datasets with up to one million individuals and variants, including the UK Biobank.

Main Results:

SCOPE is orders of magnitude faster than existing methods.
It achieves comparable accuracy to current state-of-the-art techniques.
Analysis of large datasets, including UK Biobank, was completed in approximately one day.

Conclusions:

SCOPE offers a scalable and accurate solution for population structure inference.
It can leverage existing allele frequency data to enhance interpretability.
This method facilitates large-scale genomic studies.