Evidence map›Paper›PMID 42649165›Full record

ArticleNature communications2026

Scalable high resolution ancestry deconvolution for genomic data.

Helgi Hilmarsson, Arvind S Kumar, Míriam Barrabés, Richa Rastogi, Carlos D Bustamante, Daniel Mas Montserrat, Alexander G Ioannidis

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Pan-African model explainsbioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
  7. Observational
  8. Genomic landscape of autism spectrum disorder in Brazil.Genetics and molecular biology · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Helgi Hilmarsson *Stanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, USA.ORCID 0000-0002-2464-9547
Arvind S Kumar *Stanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, USA.ORCID 0000-0002-1804-2343
Míriam Barrabés *Stanford University, Department of Biomedical Data Science, Stanford, CA, USA.
Richa RastogiCornell University, Department of Computer Science, New York, USA.ORCID 0000-0002-5073-1667
Carlos D BustamanteStanford University, Department of Biomedical Data Science, Stanford, CA, USA.
Daniel Mas MontserratStanford University, Department of Biomedical Data Science, Stanford, CA, USA.
Alexander G IoannidisStanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, USA. ioannidis@stanford.edu.ORCID 0000-0002-4735-7803

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Development and application of machine learning-based approaches for estimating disease risk in diverse and admixed populations.R43HG013628 · NHGRI · GALATEA BIO INC · PI BUSTAMANTE, CARLOS DANIEL, IOANNIDIS, ALEXANDER · 2024 to 2024
$399k
NHGRI NIH HHS HG009080NHGRI NIH HHS R43 HG013628NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

As genome-wide association studies and genetic risk prediction models extend to globally diverse and admixed biobanks, accurate, scalable ancestry deconvolution, also called local ancestry inference (LAI), has become crucial. LAI assigns ancestry to each genomic segment within an individual, enabling studies of population history and ancestry-associated haplotypic effects. Existing LAI methods scale poorly to biobank-scale data, to the distant past, and to large numbers of ancestries. Here, we introduce several independent LAI methods implemented in the Gnomix software suite, achieving higher accuracy and faster computational performance than all existing approaches and with portable models that can be shared without exposing individual-level training data. Gnomix is paired with Gnofix, a swift, scalable phase correction counterpart. We demonstrate performance on worldwide whole-genome data from humans and canids, leveraging high-resolution accuracy to localise ancient New World haplotypes in the Xoloitzcuintli, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix .

Indexed as

Genome, HumanGenome-Wide Association StudyGenomicsAlgorithmsHaplotypesHumansModels, GeneticPolymorphism, Single NucleotideSoftware

Identifiers

PMID42649165
PMCPMC13518915

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.