Literature DB >> 10368963

Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models.

J Zhang1.   

Abstract

In recent years, likelihood ratio tests (LRTs) based on DNA and protein sequence data have been proposed for testing various evolutionary hypotheses. Because conducting an LRT requires an evolutionary model of nucleotide or amino acid substitution, which is almost always unknown, it becomes important to investigate the robustness of LRTs to violations of assumptions of these evolutionary models. Computer simulation was used to examine performance of LRTs of the molecular clock, transition/transversion bias, and among-site rate variation under different substitution models. The results showed that when correct models are used, LRTs perform quite well even when the DNA sequences are as short as 300 nt. However, LRTs were found to be biased under incorrect models. The extent of bias varies considerably, depending on the hypotheses tested, the substitution models assumed, and the lengths of the sequences used, among other things. A preliminary simulation study also suggests that LRTs based on parametric bootstrapping may be more sensitive to substitution models than are standard LRTs. When an assumed substitution model is grossly wrong and a more realistic model is available, LRTs can often reject the wrong model; thus, the performance of LRTs may be improved by using a more appropriate model. On the other hand, many factors of molecular evolution have not been considered in any substitution models so far built, and the possibility of an influence of this negligence on LRTs is often overlooked. The dependence of LRTs on substitution models calls for caution in interpreting test results and highlights the importance of clarifying the substitution patterns of genes and proteins and building more realistic models.

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Year:  1999        PMID: 10368963     DOI: 10.1093/oxfordjournals.molbev.a026171

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  12 in total

1.  Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution.

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Journal:  Proc Natl Acad Sci U S A       Date:  2004-08-03       Impact factor: 11.205

2.  New methods for detecting positive selection at single amino acid sites.

Authors:  Yoshiyuki Suzuki
Journal:  J Mol Evol       Date:  2004-07       Impact factor: 2.395

3.  Accuracy and power of the likelihood ratio test for comparing evolutionary rates among genes.

Authors:  Jan Erik Aagaard; Patrick Phillips
Journal:  J Mol Evol       Date:  2005-04       Impact factor: 2.395

Review 4.  New methods for inferring population dynamics from microbial sequences.

Authors:  Marcos Pérez-Losada; Megan L Porter; Loubna Tazi; Keith A Crandall
Journal:  Infect Genet Evol       Date:  2006-04-19       Impact factor: 3.342

5.  Evidence for vertical inheritance and loss of the leukotoxin operon in genus Mannheimia.

Authors:  Jesper Larsen; Anders G Pedersen; Henrik Christensen; Magne Bisgaard; Øystein Angen; Peter Ahrens; John E Olsen
Journal:  J Mol Evol       Date:  2007-04-13       Impact factor: 2.395

6.  Phylostratigraphic bias creates spurious patterns of genome evolution.

Authors:  Bryan A Moyers; Jianzhi Zhang
Journal:  Mol Biol Evol       Date:  2014-10-13       Impact factor: 16.240

7.  Consequences of Substitution Model Selection on Protein Ancestral Sequence Reconstruction.

Authors:  Roberto Del Amparo; Miguel Arenas
Journal:  Mol Biol Evol       Date:  2022-07-02       Impact factor: 8.800

8.  High correlation between the turnover of nucleotides under mutational pressure and the DNA composition.

Authors:  M Kowalczuk; P Mackiewicz; D Mackiewicz; A Nowicka; M Dudkiewicz; M R Dudek; S Cebrat
Journal:  BMC Evol Biol       Date:  2001-12-17       Impact factor: 3.260

9.  Spatial and temporal simulation of human evolution. Methods, frameworks and applications.

Authors:  Macarena Benguigui; Miguel Arenas
Journal:  Curr Genomics       Date:  2014-08       Impact factor: 2.236

10.  Multinucleotide mutations cause false inferences of lineage-specific positive selection.

Authors:  Aarti Venkat; Matthew W Hahn; Joseph W Thornton
Journal:  Nat Ecol Evol       Date:  2018-07-02       Impact factor: 15.460

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