The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges
Abstract
Recent improvements in the speed, cost and accuracy of next generation sequencing are revolutionizing the discovery of single nucleotide polymorphisms (SNPs). SNPs are increasingly being used as an addition to the molecular ecology toolkit in nonmodel organisms, but their efficient use remains challenging. Here, we discuss common issues when employing SNP markers, including the high numbers of markers typically employed, the effects of ascertainment bias and the inclusion of nonneutral loci in a marker panel. We provide a critique of considerations specifically associated with the application and population genetic analysis of SNPs in nonmodel taxa, focusing specifically on some of the most commonly applied methods.
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
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
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
Detecting the number of clusters of individuals using the software structure: a simulation study
EVANNO, G., REGNAUT, S., & GOUDET, J. (2005) · Molecular Ecology · doi:10.1111/j.1365-294x.2005.02553.x · Cited by 21,895
genalex 6: genetic analysis in Excel. Population genetic software for teaching and research
PEAKALL, R., & SMOUSE, P. E. (2006) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2005.01155.x · Cited by 16,589
EXCOFFIER, L., & LISCHER, H. E. L. (2010) · Molecular Ecology Resources · doi:10.1111/j.1755-0998.2010.02847.x · Cited by 16,326
EVOLUTION IN MENDELIAN POPULATIONS
Wright, S. (1931) · Genetics · doi:10.1093/genetics/16.2.97 · Cited by 8,916
genepop’007: a complete re‐implementation of the genepop software for Windows and Linux
ROUSSET, F. (2008) · Molecular Ecology Resources · doi:10.1111/j.1471-8286.2007.01931.x · Cited by 8,497
adegenet: a R package for the multivariate analysis of genetic markers
Jombart, T. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn129 · Cited by 8,107
Falush, D., Stephens, M., & Pritchard, J. K. (2003) · Genetics · doi:10.1093/genetics/164.4.1567 · Cited by 7,959
FSTAT (Version 1.2): A Computer Program to Calculate F-Statistics
Goudet, J. (1995) · Journal of Heredity · doi:10.1093/oxfordjournals.jhered.a111627 · Cited by 7,635
Cited by
QST–FST comparisons: evolutionary and ecological insights from genomic heterogeneity
Leinonen, T., McCairns, R. J. S., O'Hara, R. B., & Merilä, J. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3395 · Cited by 422
Genome-Wide Scan for Selection Signatures in Six Cattle Breeds in South Africa
Makina, S. O., Muchadeyi, F. C., van Marle-Köster, E., Taylor, J. F., Makgahlela, M. L., & Maiwashe, A. (2015) · Genetics Selection Evolution · doi:10.1186/s12711-015-0173-x · Cited by 125
Pecoraro, C., Babbucci, M., Franch, R., Rico, C., Papetti, C., Chassot, E., Bodin, N., Cariani, A., Bargelloni, L., & Tinti, F. (2018) · Scientific Reports · doi:10.1038/s41598-018-32331-3 · Cited by 80
Self-citationMullins, R. B., McKeown, N. J., Sauer, W. H. H., & Shaw, P. W. (2018) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsy102 · Cited by 46
Metadata
- Authors
- HELYAR, S. J., HEMMER‐HANSEN, J., BEKKEVOLD, D., TAYLOR, M. I., OGDEN, R., LIMBORG, M. T., CARIANI, A., MAES, G. E., DIOPERE, E., CARVALHO, G. R., & NIELSEN, E. E.
- Venue
- Molecular Ecology Resources
- Publisher
- Wiley
- Published
- 2011-01-10
- Volume
- 11
- Issue
- s1
- Pages
- 123-136
- ISSN
- 1755-098X (ISSN-L)
- 1755-0998
- OpenAlex
- W2155309921
- Cited by
- 478
- References
- 136
- FWCI
- 23.75
- 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: Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. https://app.cochat.ai/papers/10.1111/j.1755-0998.2010.02943.x
- The paper
- HELYAR, S. J., HEMMER‐HANSEN, J., BEKKEVOLD, D., TAYLOR, M. I., OGDEN, R., LIMBORG, M. T., CARIANI, A., MAES, G. E., DIOPERE, E., CARVALHO, G. R., & NIELSEN, E. E. (2011). Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Molecular Ecology Resources. https://doi.org/10.1111/j.1755-0998.2010.02943.x
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.