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AncestryGeni : a novel genetic ancestry classification pipeline for small and noisy sequence data

Elhaik, Eran LU orcid ; Behnamian, Sara LU ; Howe, Michael ; Tang, Hongwei ; Yan, Huihuang ; Tian, Shulan ; Shivaram, Suganti ; Zepeda Mendoza, Cinthya ; Maclachlan, Kylee and Usmani, Saad , et al. (2025) In Bioinformatics 41(7).
Abstract

Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we... (More)

Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioinformatics
volume
41
issue
7
article number
btaf391
publisher
Oxford University Press
external identifiers
  • pmid:40627371
  • scopus:105011509390
ISSN
1367-4803
DOI
10.1093/bioinformatics/btaf391
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 The Author(s).
id
6a397608-a45e-4a1b-b963-f68019bb80f5
date added to LUP
2025-12-16 14:15:39
date last changed
2025-12-17 03:00:14
@article{6a397608-a45e-4a1b-b963-f68019bb80f5,
  abstract     = {{<p>Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group.</p>}},
  author       = {{Elhaik, Eran and Behnamian, Sara and Howe, Michael and Tang, Hongwei and Yan, Huihuang and Tian, Shulan and Shivaram, Suganti and Zepeda Mendoza, Cinthya and Maclachlan, Kylee and Usmani, Saad and Pirooznia, Mehdi and Morgan, Gareth and Blaney, Patrick and Maura, Francesco and Baughn, Linda B.}},
  issn         = {{1367-4803}},
  language     = {{eng}},
  month        = {{07}},
  number       = {{7}},
  publisher    = {{Oxford University Press}},
  series       = {{Bioinformatics}},
  title        = {{AncestryGeni : a novel genetic ancestry classification pipeline for small and noisy sequence data}},
  url          = {{http://dx.doi.org/10.1093/bioinformatics/btaf391}},
  doi          = {{10.1093/bioinformatics/btaf391}},
  volume       = {{41}},
  year         = {{2025}},
}