The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Estimating Allele Age
Abstract
The age of an allele can be estimated both from genetic variation among different copies (intra-allelic variation) and from its frequency. Estimates based on intra-allelic variation follow from the exponential decay of linkage disequilibrium because of recombination and mutation. The confidence interval depends both on the uncertainty in recombination and mutation rates and on randomness of the genealogy of chromosomes that carry the allele (the intra-allelic genealogy). Several approximate methods to account for variation in the intra-allelic genealogy have been derived. Allele frequency alone also provides an estimate of age. Estimates based on frequency and on intra-allelic variability can be combined to provide a more accurate estimate or can be contrasted to show that an allele has been subject to natural selection. These methods have been applied to numerous cases, including alleles associated with cystic fibrosis, idiopathic torsion dystonia, and resistance to infection by HIV. We emphasize that estimates of allele age depend on assumptions about demographic history and natural selection.
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.
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
Identification of the Cystic Fibrosis Gene: Genetic Analysis
Kerem, B.-S., Rommens, J. M., Buchanan, J. A., Markiewicz, D., Cox, T. K., Chakravarti, A., Buchwald, M., & Tsui, L.-C. (1989) · Science · doi:10.1126/science.2570460 · Cited by 4,142
Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland
Hästbacka, J., de la Chapelle, A., Kaitila, I., Sistonen, P., Weaver, A., & Lander, E. (1992) · Nature Genetics · doi:10.1038/ng1192-204 · Cited by 558
Dating the Origin of the CCR5-Δ32 AIDS-Resistance Allele by the Coalescence of Haplotypes
Stephens, J. C., Reich, D. E., Goldstein, D. B., Shin, H. D., Smith, M. W., Carrington, M., Winkler, C., Huttley, G. A., Allikmets, R., Schriml, L., et al. (1998) · The American Journal of Human Genetics · doi:10.1086/301867 · Cited by 520
THE AGE OF A NEUTRAL MUTANT PERSISTING IN A FINITE POPULATION
Kimura, M., & Ohta, T. (1973) · Genetics · doi:10.1093/genetics/75.1.199 · Cited by 241
Estimating the Age of Mutant Disease Alleles Based on Linkage Disequilibrium
Guo, S.-W., & Xiong, M. (1997) · Human Heredity · doi:10.1159/000154431 · Cited by 30
Estimating the age of mutations using variation at linked markers
Reich, D. E., & Goldstein, D. B. (1999) · Microsatellites: Evolution and Applications · doi:10.1093/oso/9780198504085.003.0010 · Cited by 29
Age of the ΔF508 cystic fibrosis mutation
Kaplan, N. L., Lewis, P. O., & Weir, B. S. (1994) · Nature Genetics · doi:10.1038/ng1194-216a · Cited by 27
Cited by
Tishkoff, S. A., Varkonyi, R., Cahinhinan, N., Abbes, S., Argyropoulos, G., Destro-Bisol, G., Drousiotou, A., Dangerfield, B., Lefranc, G., Loiselet, J., et al. (2001) · Science · doi:10.1126/science.1061573 · Cited by 630
Recent acceleration of human adaptive evolution
Hawks, J., Wang, E. T., Cochran, G. M., Harpending, H. C., & Moyzis, R. K. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707650104 · Cited by 471
SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct it
Lachance, J., & Tishkoff, S. A. (2013) · BioEssays · doi:10.1002/bies.201300014 · Cited by 398
Genetic disease risks can be misestimated across global populations
Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J., & Lachance, J. (2018) · Genome Biology · doi:10.1186/s13059-018-1561-7 · Cited by 210
Molecular mechanisms of autosomal recessive hypercholesterolemia
Cohen, J. C., Kimmel, M., Polanski, A., & Hobbs, H. H. (2003) · Current Opinion in Lipidology · doi:10.1097/00041433-200304000-00002 · Cited by 52
Metadata
- Authors
- Slatkin, M., & Rannala, B.
- Venue
- Annual Review of Genomics and Human Genetics
- Publisher
- Annual Reviews
- Published
- 2000-09
- Volume
- 1
- Issue
- 1
- Pages
- 225-249
- ISSN
- 1527-8204 (ISSN-L)
- 1545-293X
- OpenAlex
- W2172289055
- Cited by
- 229
- References
- 7
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating Allele Age. https://app.cochat.ai/papers/10.1146/annurev.genom.1.1.225
- The paper
- Slatkin, M., & Rannala, B. (2000). Estimating Allele Age. Annual Review of Genomics and Human Genetics. https://doi.org/10.1146/annurev.genom.1.1.225
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.