A random effects branch-site model for detecting episodic diversifying selection
Open accessAbstract
Adaptive evolution frequently occurs in episodic bursts, localized to a few sites in a gene, and to a small number of lineages in a phylogenetic tree. A popular class of "branch-site" evolutionary models provides a statistical framework to search for evidence of such episodic selection. For computational tractability, current branch-site models unrealistically assume that all branches in the tree can be partitioned a priori into two rigid classes--"foreground" branches that are allowed to undergo diversifying selective bursts and "background" branches that are negatively selected or neutral. We demonstrate that this assumption leads to unacceptably high rates of false positives or false negatives when the evolutionary process along background branches strongly deviates from modeling assumptions. To address this problem, we extend Felsenstein's pruning algorithm to allow efficient likelihood computations for models in which variation over branches (and not just sites) is described in the random effects likelihood framework. This enables us to model the process at every branch-site combination as a mixture of three Markov substitution models--our model treats the selective class of every branch at a particular site as an unobserved state that is chosen independently of that at any other branch. When benchmarked on a previously published set of simulated sequences, our method consistently matched or outperformed existing branch-site tests in terms of power and error rates. Using three empirical data sets, previously analyzed for episodic selection, we discuss how modeling assumptions can influence inference in practical situations.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Random effects and mixed effects 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.
“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 Kosakovsky Pond et al. 2011
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredIn a BS-REL framework, the ! ratio along each branch at a given site is drawn from a discrete distribution of ! values independently of other branches in the tree.
“In a BS-REL framework, the !ratio along each branch at a given site is drawn from a discrete distribution of !values independently of other branches in the tree, affording RELAX a flexible and powerful mechanism for handling heterogeneous selective pressures.”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Kosakovsky Pond et al. 2011
Variation in the ! distribution across the phylogeny can be studied using the BS-REL model.
“Such variation across the phylogeny can be studied using the BS-REL model (Kosakovsky Pond et al. 2011).”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Kosakovsky Pond et al. 2011
RELAX builds upon the random effects branch-site (BS-REL) models, which were developed to overcome statistical and methodological restrictions imposed by the popular branch-site class of evolutionary models.
“It builds upon the random effects branch-site (BS-REL) models (Kosakovsky Pond et al. 2011), which were developed to overcome statistical and methodological restrictions imposed by the popular”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Kosakovsky Pond et al. 2011
The sum over !-configurations can be computed efficiently using Felsenstein's pruning algorithm.
“We have previously shown (Kosakovsky Pond et al. 2011) that this sum can be computed efficiently using Felsenstein's pruning algorithm (Felsenstein 1981) with the transition probability matrix operating on branch b (with branch length t b ) given by a weighted sum of the three transition probability matrices:”
From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Kosakovsky Pond et al. 2011
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Evolutionary trees from DNA sequences: A maximum likelihood approach.
Felsenstein, J. (1981) · Journal of Molecular Evolution · doi:10.1007/bf01734359 · Cited by 14,571 · see citing works
Dating of the human–ape splitting by a molecular clock of mitochondrial DNA.
Hasegawa, M., Kishino, H., & Yano, T.-A. (1985) · Journal of Molecular Evolution · doi:10.1007/bf02101694 · Cited by 8,687 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Detecting Individual Sites Subject to Episodic Diversifying Selection
Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., & Kosakovsky Pond, S. L. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002764 · Cited by 1,811 · see citing works
Self-citationRELAX: Detecting Relaxed Selection in a Phylogenetic Framework
Wertheim, J. O., Murrell, B., Smith, M. D., Kosakovsky Pond, S. L., & Scheffler, K. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msu400 · Cited by 778 · see citing works
Self-citationEvolutionary insights into host–pathogen interactions from mammalian sequence data
Sironi, M., Cagliani, R., Forni, D., & Clerici, M. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3905 · Cited by 302 · see citing works
Convergent evolution of escape from hepaciviral antagonism in primates
Patel, M. R., Loo, Y.-M., Horner, S. M., Gale, M., & Malik, H. S. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001282 · Cited by 95 · see citing works
- Authors
- Kosakovsky Pond, S. L., Murrell, B., Fourment, M., Frost, S. D. W., Delport, W., & Scheffler, K.
- Affiliations
- Stellenbosch University (ZA)
- University of California San Diego (US)
- University of Cambridge (GB)
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-06-13
- Volume
- 28
- Issue
- 11
- Pages
- 3033-3043
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- PMC
- PMC3247808
- OpenAlex
- W2119904585
- Cited by
- 400
- References
- 2
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 11 · 2023: 7 · 2022: 10 · 2021: 20 · 2020: 17 · 2019: 27 · 2018: 35 · 2017: 35 · 2016: 48 · 2015: 47 · 2014: 51 · 2013: 46 · 2012: 18
- FWCI
- 14.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Molecular Biology and Evolution (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- SUNScholar (Stellenbosch University) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (GM093939, R01GM093939)
- National Institute of Allergy and Infectious Diseases (R01AI057167, AI-47745, AI57167, R21AI047745, AI 43638, R24AI106039, R56AI047745, AI-36214, R01AI047745, U01AI043638, P30AI036214)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- episodic diversifying selection, branch-site model, random effects models, likelihood computation, adaptive evolution, false positive rate, phylogenetic inference
- MeSH terms
- Algorithms, Codon, Markov Chains, Models, Genetic, Phylogeny, Selection, Genetic, Likelihood Functions, Evolution, Molecular
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
