Cited by 16Genomics and Phylogenetic StudiesEvolution and Genetic DynamicsGenetic diversity and population structure

On the validity of evolutionary models with site-specific parameters

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Scheffler, Konrad, Murrell, Ben, Kosakovsky Pond, Sergei L.·2014·PLoS ONE

Abstract

Evolutionary models that make use of site-specific parameters have recently been criticized on the grounds that parameter estimates obtained under such models can be unreliable and lack theoretical guarantees of convergence. We present a simulation study providing empirical evidence that a simple version of the models in question does exhibit sensible convergence behavior and that additional taxa, despite not being independent of each other, lead to improved parameter estimates. Although it would be desirable to have theoretical guarantees of this, we argue that such guarantees would not be sufficient to justify the use of these models in practice. Instead, we emphasize the importance of taking the variance of parameter estimates into account rather than blindly trusting point estimates - this is standardly done by using the models to construct statistical hypothesis tests, which are then validated empirically via simulation studies.

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Crossref · 10.1371/journal.pone.0094534

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  • Branch-specific k_b maximum-likelihood estimates have high sampling variances because they are derived from a small volume of information (a single branch).

    “However, we suggest exercising due caution when comparing k b maximum-likelihood estimates (or any of the other parameter estimates describing the !distribution) directly, as they are derived from a small volume of information (a single branch) and therefore have high sampling variances (Scheffler et al. 2014).”

    From RELAX: Detecting Relaxed Selection in a Phylogenetic Framework (2015) · cites this paper as Scheffler et al. 2014

    The Results report that branch-specific maximum-likelihood estimates have sampling variances that shrink as the number of sites increases, supporting caution when data are limited. The paper does not specifically discuss k_b or its distribution, and it does not say that estimating a parameter for a single branch alone makes its variance high. The Introduction’s statement about limited information at a single site concerns site-specific, not branch-specific, parameters. (LLM, conf=0.83)

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Research works printed but not linked: 14

  1. [1]Nielsen R, Yang Z (1998) Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics 148: 929– 936. 9539414 PMC1460041
  2. [2]Yang Z, Nielsen R, Goldman N, Pedersen AM (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155: 431– 449. 10790415 PMC1461088
  3. [3]Kosakovsky Pond S, Muse SV (2005) Site-to-site variation of synonymous substitution rates. Mol Biol Evol 22: 2375– 2385. 16107593
  4. [4]Kosakovsky Pond SL, Murrell B, Fourment M, Frost SD, Delport W, et al (2011) A random effects branch-site model for detecting episodic diversifying selection. Mol Biol Evol 28: 3033– 3043. 21670087 PMC3247808
  5. [5]Murrell B, Moola S, Mabona A, Weighill T, Sheward D, et al (2013) FUBAR: A Fast, Unconstrained Bayesian AppRoximation for Inferring Selection. Mol Biol Evol 30: 1196– 1205. 23420840 PMC3670733
  6. [6]Kosakovsky Pond SL, Frost SD (2005) Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol Biol Evol 22: 1208– 1222. 15703242
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  8. [8]Murrell B, de Oliveira T, Seebregts C, Kosakovsky Pond SL, Scheffler K (2012) Modeling HIV-1 drug resistance as episodic directional selection. PLoS Comput Biol 8: e1002507. 22589711 PMC3349733
  9. [12]Steel M (2011) Can we avoid “SIN” in the house of “no common mechanism”? Systematic biology 60: 96– 109. 21084501
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Authors
Scheffler, K., Murrell, B., & Kosakovsky Pond, S. L.
Affiliations
  • Stellenbosch University (ZA)
  • University of California San Diego (US)
Corresponding author
Scheffler, Konrad
Venue
PLoS ONE
Published
2014-04-10
Volume
9
Issue
4
Pages
e94534
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2036475287
Cited by
16
References
1
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2021: 1 · 2020: 2 · 2018: 4 · 2016: 2 · 2015: 2 · 2014: 2
FWCI
0.76
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (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)
  • eScholarship (California Digital Library) (submittedVersion)
  • TUScholarShare (Temple University) (submittedVersion, cc-by)
  • SUNScholar (Stellenbosch University) (submittedVersion)
Funders
  • National Institutes of Health (AI47745, AI74621, AI 036214)
  • National Institute of Mental Health (R01MH083552, MH101012, R01MH101012, MH083552)
  • National Institute on Drug Abuse (DA-034978, DP1DA034978)
  • National Institute of General Medical Sciences (GM093939, R01GM093939)
  • National Institute of Allergy and Infectious Diseases (R01AI057167, AI-74621, AI-47745, AI57167, R21AI047745, AI 036214, R56AI047745, R01AI047745, P01AI074621, 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 › Genetics › Genetic diversity and population structure
Keywords
site-specific parameters, evolutionary models, parameter estimation, convergence, phylogenetic inference, hypothesis testing, simulation study
MeSH terms
Algorithms, Biological Evolution, Phylogeny, Models, Statistical

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