The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Detecting Individual Sites Subject to Episodic Diversifying Selection
Open accessAbstract
The imprint of natural selection on protein coding genes is often difficult to identify because selection is frequently transient or episodic, i.e. it affects only a subset of lineages. Existing computational techniques, which are designed to identify sites subject to pervasive selection, may fail to recognize sites where selection is episodic: a large proportion of positively selected sites. We present a mixed effects model of evolution (MEME) that is capable of identifying instances of both episodic and pervasive positive selection at the level of an individual site. Using empirical and simulated data, we demonstrate the superior performance of MEME over older models under a broad range of scenarios. We find that episodic selection is widespread and conclude that the number of sites experiencing positive selection may have been vastly underestimated.
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![Figure 1 The standard random effects approach and samples. A) The standard random effects approach, in which the rates vary randomly over sites but are constant over branches. Different values of are showed in different colors. B) Samples from our new random effects approach [20], used by MEME, in which the rate on each branch is drawn independently of the rate on any other branch. All possible assignments of rates to sites are considered.](/paper-figures/7c0c11683f47469f9d5d29a91ca0dd06/detecting-individual-sites-subject-to-episodic-diversifying-selection-figure-1.jpg)
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“Positively selected sites were identified using two different methods: the Bayes Empirical Bayes (BEB) analysis from MA (with a cutoff of 0.90) and the Mixed Effects Model of Evolution (MEME) (with the default cutoff of 0.1)34.”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“However, this test can be misleading because estimates of ! can be increased by processes such as intensification of positive selection when selective pressures vary both along branches and sites (Murrell et al. 2012).”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Murrell et al. 2012
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In the citing paper
“Standard methods for estimating the strength and extent of purifying or positive selection from molecular sequence data are not suitable for detecting relaxed selection, because they lack power and can mistake an increase in the intensity of positive selection for relaxation of both purifying and positive selection.”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Murrell et al. 2012
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“To be conservative, we considered as positively selected only sites also detected by the Mixed Effects Model of Evolution (MEME) method34.”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [34]
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“This development vastly increased the detectable instances of positive selection (Murrell et al. 2012).”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Murrell et al. 2012
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“Our random effects (Kosakovsky Pond et al. 2011) and mixed effects (Murrell et al. 2012) branch-site models allow detection of individual branches on which only a subset of sites are under positive selection or, conversely, individual sites which are under positive selection on only a subset of branches.”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Murrell et al. 2012
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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
Evolutionary trees from DNA sequences: A maximum likelihood approach.
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Evolutionary Rate at the Molecular Level
KIMURA, M. (1968) · Nature · doi:10.1038/217624a0 · Cited by 3,747
Adaptive protein evolution at the Adh locus in Drosophila
McDonald, J. H., & Kreitman, M. (1991) · Nature · doi:10.1038/351652a0 · Cited by 3,313
Self, S., & Liang, K.-Y. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478472 · Cited by 2,861
Not So Different After All: A Comparison of Methods for Detecting Amino Acid Sites Under Selection
Pond, S. L. K., & Frost, S. D. W. (2005) · Molecular Biology and Evolution · doi:10.1093/molbev/msi105 · Cited by 2,320
Codon-substitution models for heterogeneous selection pressure at amino acid sites
Yang, Z., Nielsen, R., Goldman, N., & Pedersen, A.-M. K. (2000) · Genetics · doi:10.1093/genetics/155.1.431 · Cited by 2,286
Hughes, A. L., & Nei, M. (1988) · Nature · doi:10.1038/335167a0 · Cited by 1,976
A codon-based model of nucleotide substitution for protein-coding DNA sequences.
(1994) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040153 · Cited by 1,960
Nielsen, R., & Yang, Z. (1998) · Genetics · doi:10.1093/genetics/148.3.929 · Cited by 1,622
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FUBAR: A Fast, Unconstrained Bayesian AppRoximation for Inferring Selection
Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D. J., Pond, S. L. K., & Scheffler, K. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst030 · Cited by 1,404
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“…in multiple epidemiologically unlinked lineages that have statistical support for non-neutral evolution (Mixed Effects Model of Evolution, MEME) 25 , (ii) sites where these polymorphisms have support for a greater than expected ratio of non-synonymous:synonymous nucleotide substitution rates…”
Self-citation
Metadata
- Authors
- Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., & Kosakovsky Pond, S. L.
- Affiliations
- Stellenbosch University (ZA)
- University of California San Diego (US)
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-07-12
- Volume
- 8
- Issue
- 7
- Pages
- e1002764
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3395634
- OpenAlex
- W2118982822
- Cited by
- 1,811
- References
- 63
- Citations by year
- 2026: 147 · 2025: 135 · 2024: 169 · 2023: 162 · 2022: 161 · 2021: 183 · 2020: 172 · 2019: 123 · 2018: 115 · 2017: 99 · 2016: 87 · 2015: 100 · 2014: 83 · 2013: 66 · 2012: 9
- FWCI
- 49.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- TUScholarShare (Temple University) (submittedVersion, cc-by)
- SUNScholar (Stellenbosch University) (submittedVersion)
- Funders
- National Science Foundation (0714991)
- European Commission
- National Research Foundation
- National Institutes of Health (AI47745, AI74621, AI-36214, AI57167, GM093939)
- Center for AIDS Research, University of Washington
- University of California, San Diego
- National Institute of General Medical Sciences (GM093939, R01GM093939)
- National Institute of Allergy and Infectious Diseases (R01AI057167, AI-74621, AI-47745, AI57167, R21AI047745, AI 43638, R24AI106039, R56AI047745, AI-36214, R01AI047745, U01AI043638, P01AI074621, P30AI036214, T32AI007384)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- episodic selection, positive selection, memes, protein-coding genes, natural selection
- MeSH terms
- Amino Acids, Animals, Computer Simulation, Models, Theoretical, Phylogeny, Rhodopsin, Selection, Genetic, Vertebrates, Open Reading Frames, Evolution, Molecular, Computational Biology
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