ArticleBMC bioinformatics2024
SNVstory: inferring genetic ancestry from genome sequencing data.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- plinkQC: an integrated tool for ancestry inference, sample selection, and quality control in population genetics.Bioinformatics (Oxford, England) · 2026Article
- AScience advances · 2026Article
- A transparent and generalizable deep-learning framework for genomic ancestry prediction.American journal of human genetics · 2026Article
- Population-scale genomic medicine with the Hong Kong Genome Project.Nature medicine · 2026Article
- A comprehensive tandem repeat catalog of the human genome.Nature communications · 2026Article
- Article
- Molecular profiling of endometrial cancer in Martinique reveals frequent CCNE1 amplification in poor prognosis tumors.Scientific reports · 2025Article
- ntRoot: computational inference of human ancestry at scale from genomic data.Bioinformatics advances · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level.
resultsTo address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy.
conclusionsSNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory .
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