Ancestry Composition: A Novel, Efficient Pipeline for Ancestry Deconvolution
Open accessAbstract
Ancestry deconvolution, the task of identifying the ancestral origin of chromosomal segments in admixed individuals, has important implications, from mapping disease genes to identifying candidate loci under natural selection. To date, however, most existing methods for ancestry deconvolution are typically limited to two or three ancestral populations, and cannot resolve contributions from populations related at a sub-continental scale. We describe Ancestry Composition, a modular three-stage pipeline that efficiently and accurately identifies the ancestral origin of chromosomal segments in admixed individuals. It assumes the genotype data have been phased. In the first stage, a support vector machine classifier assigns tentative ancestry labels to short local phased genomic regions. In the second stage, an autoregressive pair hidden Markov model simultaneously corrects phasing errors and produces reconciled local ancestry estimates and confidence scores based on the tentative ancestry labels. In the third stage, confidence estimates are recalibrated using isotonic regression. We compiled a reference panel of almost 10,000 individuals of homogeneous ancestry, derived from a combination of several publicly available datasets and over 8,000 individuals reporting four grandparents with the same country-of-origin from the member database of the personal genetics company, 23andMe, Inc., and excluding outliers identified through principal components analysis (PCA). In cross-validation experiments, Ancestry Composition achieves high precision and recall for labeling chromosomal segments across over 25 different populations worldwide.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Estimates from Ancestry Composition are extremely well calibrated, with correlations of African, European, and Native American ancestry estimates showing r2 > 0.98 with 1000 Genomes Project African American and Latino consensus estimates.
“Estimates from Ancestry Composition are extremely well calibrated, with correlations of African, European, and Native American ancestry estimates showing r2 > 0.98 with 1000 Genomes Project African American and Latino consensus estimates (Figure 5 from Durand et al.33).”
From The genetic ancestry of African Americans, Latinos, and European Americans across the United States (2015) · cites this paper as [33]
Section 3.3 (page unavailable) compares African, European, and Native American ancestry estimates with 1000 Genomes consensus estimates in African American and Latino populations and reports very high agreement. It states that r² exceeds 0.9, not 0.98, and notes that Ancestry Composition underestimates Native American ancestry in the Colombian, Mexican, and Puerto Rican groups at the default precision threshold. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAncestry Composition estimates are well calibrated and have been shown to accurately estimate African, European, and Native American ancestry in tests of precision and recall.
“Although our Ancestry Composition estimates are well calibrated and have been shown to accurately estimate African, European, and Native American ancestry in tests of precision and recall,33 we were concerned”
From The genetic ancestry of African Americans, Latinos, and European Americans across the United States (2015) · cites this paper as [33]
Ancestry Composition is a three-step pipeline that efficiently and accurately identifies the ancestral origin of chromosomal segments in admixed individuals.
“We apply Ancestry Composition, a three-step pipeline that efficiently and accurately identifies the ancestral origin of chromosomal segments in admixed individuals, which is described in Durand et al.33”
From The genetic ancestry of African Americans, Latinos, and European Americans across the United States (2015) · cites this paper as Durand et al. [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Transforming classifier scores into accurate multiclass probability estimates
Zadrozny, B., & Elkan, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775151 · Cited by 1,280 · see citing works
Reliability of Subjective Probability Forecasts of Precipitation and Temperature
Murphy, A. H., & Winkler, R. L. (1977) · Applied Statistics · doi:10.2307/2346866 · Cited by 386 · see citing works
Discerning the ancestry of European Americans in genetic association studies
Price, A. L., Butler, J., Patterson, N., Capelli, C., Pascali, V. L., Scarnicci, F., Ruiz-Linares, A., Groop, L., Saetta, A. A., Korkolopoulou, P., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.0030236 · Cited by 327 · see citing works
Flagged first, then by citations
Research works printed but not linked: 22
- Andersen, M. et al. (2011). Interior-point methods for large-scale cone programming. Optimization for machine learning, pages 55-83.
- Boser, B. E. et al. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144-152. ACM.
- Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge university press.
- Dempster, A. P. et al. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1-38.
- Falush, D. et al. (2003). Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics, 164(4), 15671587.
- Gourraud, P.-A. et al. (2014). HLA diversity in the 1000 genomes dataset. PLoS ONE, 9(7), e97282.
- Hellenthal, G. et al. (2014). A genetic atlas of human admixture history. Science, 343(6172), 747-751. PMID: 24531965.
- Henn, B. M. et al. (2012). Cryptic distant relatives are common in both isolated and cosmopolitan genetic samples. PLoS ONE, 7(4), e34267.
- Jaakkola, T. et al. (1999). Exploiting generative models in discriminative classifiers. Advances in neural information processing systems, pages 487-493.
- Lafferty, J. et al. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. "Proceedings of the 18th International Conference on Machine Learning (ICML-2001).
- Nelis, M. et al. (2009). Genetic structure of europeans: A view from the NorthEast. PLoS ONE, 4(5), e5472.
- Ng, A. and Jordan, A. (2002). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 14, 841.
- Niculescu-mizil, A. and Caruana, R. (2005). Predicting good probabilities with supervised learning. Proceedings of the 22nd International Conference on Machine Learning, (1999).
- Novembre, J. and Ramachandran, S. (2011). Perspectives on human population structure at the cusp of the sequencing era. Annu Rev Genomics Hum Genet, 12, 245-274. PMID: 21801023.
- Omberg, L. et al. (2012). Inferring genome-wide patterns of admixture in qataris using fifty-five ancestral populations. BMC genetics, 13, 49. PMID: 22734698.
- Pasaniuc, B. et al. (2009). Inference of locus-specific ancestry in closely related populations. Bioinformatics, 25(12), i213221.
- Price, A. L. et al. (2009). Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet, 5(6), e1000519.
- Price, A. L. et al. (2010). New approaches to population stratification in genome-wide association studies. Nat Rev Genet, 11(7), 459-463.
- Seldin, M. F. et al. (2006). European population substructure: Clustering of northern and southern populations. PLoS Genet, 2(9), e143.
- Seldin, M. F. et al. (2011). New approaches to disease mapping in admixed populations. Nat Rev Genet, 12(8), 523-528.
- Tang, H. et al. (2006). Reconstructing genetic ancestry blocks in admixed individuals. Am J Hum Genet, 79(1), 1-12.
- The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature, 491(7422), 56-65.
Cited by
Sorted by citations
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., Tung, J. Y., Hinds, D. A., Perlis, R. H., & Winslow, A. R. (2016) · Nature Genetics · doi:10.1038/ng.3623 · Cited by 784 · see citing works
The genetic ancestry of African Americans, Latinos, and European Americans across the United States
Bryc, K., Durand, E. Y., Macpherson, J. M., Reich, D., & Mountain, J. L. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.11.010 · Cited by 712 · see citing works
Self-citationGazal, S., Finucane, H. K., Furlotte, N. A., Loh, P.-R., Palamara, P. F., Liu, X., Schoech, A., Bulik-Sullivan, B., Neale, B. M., Gusev, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3954 · Cited by 525 · see citing works
Quantifying prion disease penetrance using large population control cohorts
Minikel, E. V., Vallabh, S. M., Lek, M., Estrada, K., Samocha, K. E., Sathirapongsasuti, J. F., McLean, C. Y., Tung, J. Y., Yu, L. P. C., Gambetti, P., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad5169 · Cited by 442 · see citing works
the 23andMe Research Team, Sanchez-Roige, S., Fontanillas, P., Elson, S. L., Pandit, A., Schmidt, E. M., Foerster, J. R., Abecasis, G. R., Gray, J. C., de Wit, H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0032-x · Cited by 128 · see citing works
Sanchez‐Roige, S., Fontanillas, P., Elson, S. L., The 23andMe Research Team, Gray, J. C., de Wit, H., Davis, L. K., MacKillop, J., & Palmer, A. A. (2019) · Addiction Biology · doi:10.1111/adb.12574 · Cited by 101 · see citing works
Multiethnic GWAS Reveals Polygenic Architecture of Earlobe Attachment
Shaffer, J. R., Li, J., Lee, M. K., Roosenboom, J., Orlova, E., Adhikari, K., Gallo, C., Poletti, G., Schuler-Faccini, L., Bortolini, M.-C., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.10.001 · Cited by 47 · see citing works
Self-citation
- Authors
- Durand, E. Y., Do, C. B., Mountain, J. L., & Macpherson, J. M.
- Affiliations
- 23andMe (United States) (US)
- Chapman University (US)
- Corresponding author
- Durand, Eric Y.
- ORCID
- Mountain, Joanna L.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2014-10-18
- OpenAlex
- W2174572513
- Cited by
- 108
- References
- 3
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 19 · 2023: 13 · 2022: 9 · 2021: 14 · 2020: 12 · 2019: 6 · 2018: 5 · 2017: 7 · 2016: 7 · 2015: 1 · 2014: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- local ancestry inference, admixed populations, support vector machine, hidden Markov models, isotonic regression, reference panel, genotype data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
