The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The Allele Frequency Spectrum in Genome-Wide Human Variation Data Reveals Signals of Differential Demographic History in Three Large World Populations
Open accessAbstract
We have studied a genome-wide set of single-nucleotide polymorphism (SNP) allele frequency measures for African-American, East Asian, and European-American samples. For this analysis we derived a simple, closed mathematical formulation for the spectrum of expected allele frequencies when the sampled populations have experienced nonstationary demographic histories. The direct calculation generates the spectrum orders of magnitude faster than coalescent simulations do and allows us to generate spectra for a large number of alternative histories on a multidimensional parameter grid. Model-fitting experiments using this grid reveal significant population-specific differences among the demographic histories that best describe the observed allele frequency spectra. European and Asian spectra show a bottleneck-shaped history: a reduction of effective population size in the past followed by a recent phase of size recovery. In contrast, the African-American spectrum shows a history of moderate but uninterrupted population expansion. These differences are expected to have profound consequences for the design of medical association studies. The analytical methods developed for this study, i.e., a closed mathematical formulation for the allele frequency spectrum, correcting the ascertainment bias introduced by shallow SNP sampling, and dealing with variable sample sizes provide a general framework for the analysis of public variation data.
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
The sequence of the human genome
Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) · Science · doi:10.1126/science.1058040 · Cited by 13,909
The Structure of Haplotype Blocks in the Human Genome
Gabriel, S. B., Schaffner, S. F., Nguyen, H., Moore, J. M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., et al. (2002) · Science · doi:10.1126/science.1069424 · Cited by 5,863
Prospects for whole-genome linkage disequilibrium mapping of common disease genes
Kruglyak, L. (1999) · Nature Genetics · doi:10.1038/9642 · Cited by 1,416
Clark, A. G., Weiss, K. M., Nickerson, D. A., Taylor, S. L., Buchanan, A., Stengård, J., Salomaa, V., Vartiainen, E., Perola, M., Boerwinkle, E., et al. (1998) · The American Journal of Human Genetics · doi:10.1086/301977 · Cited by 435 · cited 3×
“(2)Zhivotovsky et al. 2000), and, more recently, SNPs in nuclear DNA (Harding et al. 1997; Clark et al. 1998; Cargill et al. 1999; Zhao et al. 2000; Reich et al. 2001; It is sometimes useful to consider the “full” allele frequency spectrum, P fulln (i), considering sizes 0 and n,Sachidanandam et…”
BackgroundWiehe, T. (1998) · Theoretical Population Biology · doi:10.1006/tpbi.1997.1346 · Cited by 77
Cited by
Inference of human population history from individual whole-genome sequences
Li, H., & Durbin, R. (2011) · Nature · doi:10.1038/nature10231 · Cited by 2,590
Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H., & Bustamante, C. D. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000695 · Cited by 1,906
“, [4–6,16,17], but also see [10,18]).”
BackgroundMethodInfluentialInferring human population size and separation history from multiple genome sequences
Schiffels, S., & Durbin, R. (2014) · Nature Genetics · doi:10.1038/ng.3015 · Cited by 1,073
A genetic atlas of human admixture history
Hellenthal, G., Busby, G. B. J., Band, G., Wilson, J. F., Capelli, C., Falush, D., & Myers, S. (2014) · Science · doi:10.1126/science.1243518 · Cited by 798
Calibrating a coalescent simulation of human genome sequence variation
Schaffner, S. F., Foo, C., Gabriel, S., Reich, D., Daly, M. J., & Altshuler, D. (2005) · Genome Research · doi:10.1101/gr.3709305 · Cited by 616
Searching for missing heritability: Designing rare variant association studies
Zuk, O., Schaffner, S. F., Samocha, K., Do, R., Hechter, E., Kathiresan, S., Daly, M. J., Neale, B. M., Sunyaev, S. R., & Lander, E. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322563111 · Cited by 635
Kryukov, G. V., Pennacchio, L. A., & Sunyaev, S. R. (2007) · The American Journal of Human Genetics · doi:10.1086/513473 · Cited by 598
Genome-wide association studies in diverse populations
Rosenberg, N. A., Huang, L., Jewett, E. M., Szpiech, Z. A., Jankovic, I., & Boehnke, M. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2760 · Cited by 566
Localizing Recent Adaptive Evolution in the Human Genome.
Williamson, S. H., Hubisz, M. J., Clark, A. G., Payseur, B. A., Bustamante, C. D., & Nielsen, R. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030090 · Cited by 507
Exome sequencing and the genetic basis of complex traits
Kiezun, A., Garimella, K., Do, R., Stitziel, N. O., Neale, B. M., McLaren, P. J., Gupta, N., Sklar, P., Sullivan, P. F., Moran, J. L., et al. (2012) · Nature Genetics · doi:10.1038/ng.2303 · Cited by 372
Length Distributions of Identity by Descent Reveal Fine-Scale Demographic History
Palamara, P. F., Lencz, T., Darvasi, A., & Pe’er, I. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.08.030 · Cited by 247
A comprehensive map of mobile element insertion polymorphisms in humans
Stewart, C., Kural, D., Strömberg, M. P., Walker, J. A., Konkel, M. K., Stütz, A. M., Urban, A. E., Grubert, F., Lam, H. Y. K., Lee, W.-P., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002236 · Cited by 307
Self-citationGenomic regions exhibiting positive selection identified from dense genotype data
Carlson, C. S., Thomas, D. J., Eberle, M. A., Swanson, J. E., Livingston, R. J., Rieder, M. J., & Nickerson, D. A. (2005) · Genome Research · doi:10.1101/gr.4326505 · Cited by 298
Estimating the mutation load in human genomes
Henn, B. M., Botigué, L. R., Bustamante, C. D., Clark, A. G., & Gravel, S. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3931 · Cited by 281
Voight, B. F., Adams, A. M., Frisse, L. A., Qian, Y., Hudson, R. R., & Di Rienzo, A. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507325102 · Cited by 262
Keinan, A., Mullikin, J. C., Patterson, N., & Reich, D. (2007) · Nature Genetics · doi:10.1038/ng2116 · Cited by 272
Rapid, global demographic expansions after the origins of agriculture
Gignoux, C. R., Henn, B. M., & Mountain, J. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914274108 · Cited by 267
Context Dependence, Ancestral Misidentification, and Spurious Signatures of Natural Selection
Hernandez, R. D., Williamson, S. H., & Bustamante, C. D. (2007) · Molecular Biology and Evolution · doi:10.1093/molbev/msm108 · Cited by 169
Laval, G., Patin, E., Barreiro, L. B., & Quintana-Murci, L. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010284 · Cited by 93
Understanding the accuracy of statistical haplotype inference with sequence data of known phase
Andrés, A. M., Clark, A. G., Shimmin, L., Boerwinkle, E., Sing, C. F., & Hixson, J. E. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20185 · Cited by 68
Did a discrete event 200,000–100,000 years ago produce modern humans?
Weaver, T. D. (2012) · Journal of Human Evolution · doi:10.1016/j.jhevol.2012.04.003 · Cited by 43
Slavney, A., Arbiza, L., Clark, A. G., & Keinan, A. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msv225 · Cited by 26
Metadata
- Authors
- Marth, G. T., Czabarka, E., Murvai, J., & Sherry, S. T.
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-01-01
- Volume
- 166
- Issue
- 1
- Pages
- 351-372
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W2150883566
- Cited by
- 327
- References
- 5
- FWCI
- 10.27
- Type
- journal article
- Language
- en
- Open access
- bronze
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.
