{"doi":"10.1101/2024.03.26.586646","title":"ntRoot: Computational Inference of Human Ancestry at Scale from Genomic Data","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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":488209,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9502,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":459239,"name":"Lauren Coombe","orcid":"0000-0002-7518-2326","position":1,"is_corresponding":false},{"id":408980,"name":"Johnathan Wong","orcid":"0000-0002-1687-8972","position":2,"is_corresponding":false},{"id":897421,"name":"Parham Kazemi","orcid":"0000-0002-2126-5644","position":3,"is_corresponding":false},{"id":18731,"name":"Inanc Birol","orcid":"0000-0003-0950-7839","position":4,"is_corresponding":false},{"id":18717,"name":"René L. Warren","orcid":"0000-0002-9890-2293","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T02:08:15.008850Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}