Molecular Biology and Evolution, Vol 16, 868-875, Copyright © 1999 by Society for Molecular Biology and Evolution
J Zhang
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.
ORIGINAL ARTICLE
Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models
Institute of Molecular Evolutionary Genetics, Pennsylvania State University, USA. jzhang@atlas.niaid.nih.gov
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