The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics
Open accessAbstract
We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set.
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
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Molecular Evolution and Phylogenetics
Nei, M., & Kumar, S. (2000) · doi:10.1093/oso/9780195135848.001.0001 · Cited by 8,012
Wu, F. Y. (1982) · Reviews of Modern Physics · doi:10.1103/revmodphys.54.235 · Cited by 3,570
Evidence for Gradients of Human Genetic Diversity Within and Among Continents
Serre, D., & Pääbo, S. (2004) · Genome Research · doi:10.1101/gr.2529604 · Cited by 436
Genetic structure is influenced by landscape features: empirical evidence from a roe deer population
COULON, A., GUILLOT, G., COSSON, J. ‐., ANGIBAULT, J. M. A., AULAGNIER, S., CARGNELUTTI, B., GALAN, M., & HEWISON, A. J. M. (2006) · Molecular Ecology · doi:10.1111/j.1365-294x.2006.02861.x · Cited by 310 · cited 1×
“A field study and a subsequent analysis by Coulon et al. (2006) also support these observations.”
ResultSelf-citation
Cited by
Inferring weak population structure with the assistance of sample group information
HUBISZ, M. J., FALUSH, D., STEPHENS, M., & PRITCHARD, J. K. (2009) · Molecular Ecology Resources · doi:10.1111/j.1755-0998.2009.02591.x · Cited by 3,475
The Genetic Structure and History of Africans and African Americans
Tishkoff, S. A., Reed, F. A., Friedlaender, F. R., Ehret, C., Ranciaro, A., Froment, A., Hirbo, J. B., Awomoyi, A. A., Bodo, J.-M., Doumbo, O., et al. (2009) · Science · doi:10.1126/science.1172257 · Cited by 1,573
Global patterns and predictors of tropical reef fish species richness
Parravicini, V., Kulbicki, M., Bellwood, D. R., Friedlander, A. M., Arias‐Gonzalez, J. E., Chabanet, P., Floeter, S. R., Myers, R., Vigliola, L., D’Agata, S., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2013.00291.x · Cited by 186
Population persistence of California Current fishes and benthic crustaceans: a marine drift paradox
Shanks, A. L., & Eckert, G. L. (2005) · Ecological Monographs · doi:10.1890/05-0309 · Cited by 172
Scale dependent inference in landscape genetics
Cushman, S. A., & Landguth, E. L. (2010) · Landscape Ecology · doi:10.1007/s10980-010-9467-0 · Cited by 162
Metadata
- Authors
- François, O., Ancelet, S., & Guillot, G.
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-10-01
- Volume
- 174
- Issue
- 2
- Pages
- 805-816
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- OpenAlex
- W2113662295
- Cited by
- 348
- References
- 6
- FWCI
- 14.67
- 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.
