The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Understanding admixture fractions: theory and estimation of gene-flow
Abstract
Estimation of admixture fractions has become one of the most commonly used computational tools in population genomics. How ever, there is remarkably little population genetic theory on their statistical properties. We develop theoretical results that can accurately predict means and variances of admixture proportions within a population using models with recombination and genetic drift. Based on established theory on measures of multilocus disequilibrium, we show that there is a set of recurrence relations that can be used to derive expectations for higher moments of the admixture fraction distribution. We obtain closed form solutions for some special cases. Using these results, we develop a method for estimating admixture parameters from estimated admixture proportion obtained from programs such as Structure or Admixture. We apply this method to HapMap data and find that the population history of African Americans, as expected, is not best explained by a single admixture event between people of European and African ancestry. A model of constant gene flow for the past 11 generations until 2 generations ago gives a better fit.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“indicating that it originates from very recent historical admixture without time to reach equilibrium across the population (15).”
From Message in a bottleneck: Nested founder effects from French Polynesia to Rapa Nui and Hawaiʻi (2026) · cites this paper as [15]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
Fast model-based estimation of ancestry in unrelated individuals
Alexander, D. H., Novembre, J., & Lange, K. (2009) · Genome Research · doi:10.1101/gr.094052.109 · Cited by 9,898
Falush, D., Stephens, M., & Pritchard, J. K. (2003) · Genetics · doi:10.1093/genetics/164.4.1567 · Cited by 7,959
Integrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
Genetic Structure of Human Populations
Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., & Feldman, M. W. (2002) · Science · doi:10.1126/science.1078311 · Cited by 2,222
RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference
Maples, B. K., Gravel, S., Kenny, E. E., & Bustamante, C. D. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.06.020 · Cited by 900
Estimation of individual admixture: Analytical and study design considerations
Tang, H., Peng, J., Wang, P., & Risch, N. J. (2005) · Genetic Epidemiology · doi:10.1002/gepi.20064 · Cited by 671
Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes
Kelleher, J., Etheridge, A. M., & McVean, G. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004842 · Cited by 654
A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots
Lawson, D. J., van Dorp, L., & Falush, D. (2018) · Nature Communications · doi:10.1038/s41467-018-05257-7 · Cited by 560
Inferring admixture histories of human populations using linkage disequilibrium
Loh, P.-R., Lipson, M., Patterson, N., Moorjani, P., Pickrell, J. K., Reich, D., & Berger, B. (2013) · Genetics · doi:10.1534/genetics.112.147330 · Cited by 466
Research works printed but not linked: 1
- Haldane JBS (1919) The combination of linkage values and the calculation of distances between the loci of linked factors. J Genet 8(29):299–309
Cited by
Message in a bottleneck: Nested founder effects from French Polynesia to Rapa Nui and Hawaiʻi
Shanks, C., Huang, E. C., Thomassin, C., Emde, A.-K., Rodière, V., Moraga, M., Sandoval, K., Merriman, T., Pascart, T., Moreno-Estrada, A., et al. (2026) · Science · doi:10.1126/science.aec7662
Metadata
- Authors
- Liang, M., Shishkin, M., Shchur, V., & Nielsen, R.
- ORCID
- Shchur, Vladimir
- Venue
- Journal of Mathematical Biology
- Published
- 2024-10-04
- Volume
- 89
- Issue
- 5
- ISSN
- 0303-6812
- 1432-1416
- PMC
- PMC12042791
- Cited by
- 5
- References
- 32
- Language
- en
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Funders
- Russian Science Foundation (22-71-10056)
- NIH (R01GM138634)
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
