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RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference

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Maples, Brian K., Gravel, Simon, Kenny, Eimear E., Bustamante, Carlos D.·2013·The American Journal of Human Genetics

Abstract

Local-ancestry inference is an important step in the genetic analysis of fully sequenced human genomes. Current methods can only detect continental-level ancestry (i.e., European versus African versus Asian) accurately even when using millions of markers. Here, we present RFMix, a powerful discriminative modeling approach that is faster (~30×) and more accurate than existing methods. We accomplish this by using a conditional random field parameterized by random forests trained on reference panels. RFMix is capable of learning from the admixed samples themselves to boost performance and autocorrect phasing errors. RFMix shows high sensitivity and specificity in simulated Hispanics/Latinos and African Americans and admixed Europeans, Africans, and Asians. Finally, we demonstrate that African Americans in HapMap contain modest (but nonzero) levels of Native American ancestry (~0.4%).

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  • More recent studies using high-density genotype data provide reliable individual ancestry estimates, illustrate large variability in African and European ancestry proportions at an individual level, and detect low proportions of Native American ancestry.

    “More recent studies that utilized high-density genotype data provide reliable individual ancestry estimates, illustrate the large variability in African and European ancestry proportions at an individual level, and are able to detect low proportions of Native American ancestry.3–11”

    From The genetic ancestry of African Americans, Latinos, and European Americans across the United States (2015) · cites this paper as [11]

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  1. [14]14. Gravel, S., Henn, B.M., Gutenkunst, R.N., Indap, A.R., Marth, G.T., Clark, A.G., Yu, F., Gibbs, R.A., and Bustamante, C.D.; 1000 Genomes Project. (2011). Demographic history and rare allele sharing among human populations. Proc. Natl. Acad. Sci. USA 108 , 11983-11988.
  2. [24]24. Ng, A.Y., and Jordan, M.I. (2001). On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes. In Advances in Neural Information Processing Systems 14, T.G. Dietterich, S. Becker, and Z. Ghahramani, eds. (Cambridge: MIT Press), pp. 841-848.
  3. [25]25. Lafferty, J., McCallum, A., and Pereira, F.C.N. (2001). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the 18 th International Conference on Machine Learning, 282-289.

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Authors
Maples, B. K., Gravel, S., Kenny, E. E., & Bustamante, C. D.
Affiliations
  • Stanford University (US)
  • McGill University (CA)
Corresponding author
Bustamante, Carlos D.
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2013-08
Volume
93
Issue
2
Pages
278-288
ISSN
0002-9297 (ISSN-L)
OpenAlex
W2128371599
Cited by
900
References
5
Citations by year
2026: 90 · 2025: 111 · 2024: 94 · 2023: 106 · 2022: 88 · 2021: 108 · 2020: 88 · 2019: 66 · 2018: 45 · 2017: 35 · 2016: 21 · 2015: 19 · 2014: 26 · 2013: 3
FWCI
15.26
Type
journal article
Language
en
Open access
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Human Genome Research Institute (R01HG003229)
  • U.S. National Library of Medicine (LM007033, T15LM007033)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
Keywords
local ancestry inference, conditional random fields, random forest, reference panel, admixture, African American ancestry, Native American ancestry, HapMap
MeSH terms
Black or African American, Computer Simulation, Genetic Testing, Haplotypes, Humans, Indians, North American, Models, Genetic, Genome, Human, Polymorphism, Single Nucleotide, Black People, White People, Asian People

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