The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Population differentiation as a test for selective sweeps
Open accessAbstract
Selective sweeps can increase genetic differentiation among populations and cause allele frequency spectra to depart from the expectation under neutrality. We present a likelihood method for detecting selective sweeps that involves jointly modeling the multilocus allele frequency differentiation between two populations. We use Brownian motion to model genetic drift under neutrality, and a deterministic model to approximate the effect of a selective sweep on single nucleotide polymorphisms (SNPs) in the vicinity. We test the method with extensive simulated data, and demonstrate that in some scenarios the method provides higher power than previously reported approaches to detect selective sweeps, and can provide surprisingly good localization of the position of a selected allele. A strength of our technique is that it uses allele frequency differentiation between populations, which is much more robust to ascertainment bias in SNP discovery than methods based on the allele frequency spectrum. We apply this method to compare continentally diverse populations, as well as Northern and Southern Europeans. Our analysis identifies a list of loci as candidate targets of selection, including well-known selected loci and new regions that have not been highlighted by previous scans for 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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.
Tajima, F. (1989) · Genetics · doi:10.1093/genetics/123.3.585 · Cited by 13,941
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
Statistical tests of neutrality of mutations.
Fu, Y. X., & Li, W. H. (1993) · Genetics · doi:10.1093/genetics/133.3.693 · Cited by 4,034
The hitch-hiking effect of a favourable gene
Smith, J. M., & Haigh, J. (1974) · Genetical Research · doi:10.1017/s0016672300014634 · Cited by 3,415
A Map of Recent Positive Selection in the Human Genome
Voight, B. F., Kudaravalli, S., Wen, X., & Pritchard, J. K. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040072 · Cited by 2,893
The hitch-hiking effect of a favourable gene
MAYNARD, J., & HAIGH, J. (2007) · Genetics Research · doi:10.1017/s0016672308009579 · Cited by 2,643
Generating samples under a Wright–Fisher neutral model of genetic variation
Hudson, R. R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.337 · Cited by 2,364
Genome-wide detection and characterization of positive selection in human populations
The International HapMap Consortium, Sabeti, P. C., Varilly, P., Fry, B., Lohmueller, J., Hostetter, E., Cotsapas, C., Xie, X., Byrne, E. H., McCarroll, S. A., et al. (2007) · Nature · doi:10.1038/nature06250 · Cited by 2,131
Cited by
Comparative population genomics of maize domestication and improvement
Hufford, M. B., Xu, X., van Heerwaarden, J., Pyhäjärvi, T., Chia, J.-M., Cartwright, R. A., Elshire, R. J., Glaubitz, J. C., Guill, K. E., Kaeppler, S. M., et al. (2012) · Nature Genetics · doi:10.1038/ng.2309 · Cited by 960
Detecting Natural Selection in Genomic Data
Vitti, J. J., Grossman, S. R., & Sabeti, P. C. (2013) · Annual Review of Genetics · doi:10.1146/annurev-genet-111212-133526 · Cited by 694
Fumagalli, M., Sironi, M., Pozzoli, U., Ferrer-Admettla, A., Pattini, L., & Nielsen, R. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002355 · Cited by 600
Progress and promise of genome-wide association studies for human complex trait genetics
Stranger, B. E., Stahl, E. A., & Raj, T. (2011) · Genetics · doi:10.1534/genetics.110.120907 · Cited by 567
Qi, J., Liu, X., Shen, D., Miao, H., Xie, B., Li, X., Zeng, P., Wang, S., Shang, Y., Gu, X., et al. (2013) · Nature Genetics · doi:10.1038/ng.2801 · Cited by 517
Self-citation
Metadata
- Authors
- Chen, H., Patterson, N., & Reich, D.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2010-01-19
- Volume
- 20
- Issue
- 3
- Pages
- 393-402
- ISSN
- 1088-9051 (ISSN-L)
- OpenAlex
- W2050340111
- Cited by
- 888
- References
- 39
- Type
- posted content
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Population differentiation as a test for selective sweeps. https://app.cochat.ai/papers/10.1101/gr.100545.109
- The paper
- Chen, H., Patterson, N., & Reich, D. (2010). Population differentiation as a test for selective sweeps. Genome Research. https://doi.org/10.1101/gr.100545.109
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.