ntRoot: Computational Inference of Human Ancestry at Scale from Genomic Data
Bibliographic record
Abstract
Abstract Ancestry information is essential to large cohort studies, yet it is often unavailable or inconsistently measured. For studies with a genome sequencing component, current ancestry prediction approaches are hindered by high computational demands and complex input requirements. We present ntRoot, a computationally-lightweight method for inferring human super-population-level ancestry from whole genome assemblies or raw short or long sequencing data. Utilizing an alignment-free variant detection framework, ntRoot employs a succinct Bloom filter data structure to efficiently query diverse genomic data inputs. Demonstrated on over 600 human genome sequencing datasets—including complete genomes, draft assemblies, and over 280 independently-generated datasets—ntRoot accurately predicts geographic labels, a descriptor of human populations, and shows high concordance with traditional methods such as ADMIXTURE ( R 2 = 0.9567) when predicting ancestry fractions. It achieves these predictions within 30 minutes for complete and draft genomes and within 1 hour and 15 minutes for 30X sequencing data, using a maximum of 13GB and 68GB of RAM, respectively. ntRoot offers both global and local ancestry inference, delivering high-resolution predictions across genomic loci. This paradigm fills a critical gap in cohort studies by enabling rapid, resource-efficient, and accurate ancestry inference at scale, advancing the characterization of continental-level ancestry in the genomic era. Author Summary Study concept: RLW. Software implementation: RLW, LC, JW, PK. Data analysis: RLW, LC. Manuscript development: RLW, LC. Manuscript editing: RLW, LC, JW, PK, IB. Funding acquisition: IB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".