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Peer-reviewedCC BYNature Communications

Scalable high resolution ancestry deconvolution for genomic data

  • Helgi Hilmarsson1
  • Arvind Kumar1
  • Míriam Barrabés2
  • Richa Rastogi3
  • Carlos D. Bustamante2,4
  • Daniel Mas Montserrat2
  • Alexander G. Ioannidis5,6,7
7 affiliations
  1. 1Stanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, USA
  2. 2Stanford University, Department of Biomedical Data Science, Stanford, CA, USA
  3. 3Cornell University, Department of Computer Science, New York, USA
  4. 4University of Oklahoma, College of Medicine, Norman, OK, USA
  5. 5Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA. [email protected]
  6. 6Stanford University, Department of Biomedical Data Science, Stanford, CA, USA. [email protected]
  7. 7Stanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, USA. [email protected]
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Abstract

As genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individual’s genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix .

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Cite

BibTeXRIS
BibTeX
@article{hilmarsson2026scalable,
  title = {{Scalable high resolution ancestry deconvolution for genomic data}},
  author = {Helgi Hilmarsson and Arvind Kumar and Míriam Barrabés and Richa Rastogi and Carlos D. Bustamante and Daniel Mas Montserrat and Alexander G. Ioannidis},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-75391-0},
  url = {https://doi.org/10.1038/s41467-026-75391-0},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Scalable high resolution ancestry deconvolution for genomic data
AU  - Helgi Hilmarsson
AU  - Arvind Kumar
AU  - Míriam Barrabés
AU  - Richa Rastogi
AU  - Carlos D. Bustamante
AU  - Daniel Mas Montserrat
AU  - Alexander G. Ioannidis
PY  - 2026
DA  - 2026/07/25
JO  - Nature Communications
DO  - 10.1038/s41467-026-75391-0
UR  - https://doi.org/10.1038/s41467-026-75391-0
AB  - As genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individual’s genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix .
SN  - 2041-1723
ER  -