Selection Analysis
We evaluated selection pressures using maximum-likelihood
models (Goldman and Yang 1994; Yang 1998)
implemented in CODEML of the PAML (Yang 2007). We
first employed the one-ratio model that assumes a single x
for the entire phylogenetic tree. This model tends to be
very conservative and can only detect positive selection if
the x ratio averaged over all the sites along the lineage is
significantly [1. Because such lineage-specific models
assume a single x for the entire tree, they often fail to
identify regions in proteins that might be affected by episodic
selection pressures and ultimately, underestimate the
strength of selection. Hence, we employed site-specific
models which estimate positive selection statistically as a
non-synonymous-to-synonymous nucleotide-substitution
rate ratio (x) significantly [1. We compared likelihood
values for three pairs of models with different assumed x
distributions as no a priori expectation exists for the same:
M0 (constant x rates across all sites) versus M3 (allows the
x to vary across sites within ‘n’ discrete categories, n C 3);
M1a (a model of neutral evolution) where all sites are
assumed to be either under negative (x1) or neutral
selection (x = 1) versus M2a (a model of positive selection)
which in addition to the site classes mentioned for
M1a, assumes a third category of sites; sites with x[1
(positive selection) and M7 (Beta) versus M8 (Beta and x),
and models that mirror the evolutionary constraints of M1
and M2 but assume that x values are drawn from a beta
distribution (Nielsen and Yang 1998). Only if the alternative
models (M3, M2a and M8: allow sites with x[1)
show a better fit in likelihood ratio test (LRT) relative to
their null models (M0, M1a and M8: do not show allow
sites x[1), are their results considered significant. LRT is
estimated as twice the difference in maximum-likelihood
values between nested models and compared with the c2
distribution with the appropriate degree of freedom—the
difference in the number of parameters between the two
models. The Bayes empirical Bayes (BEB) approach (Yang
et al. 2005) was used to identify amino acids under positive
selection by calculating the posterior probabilities that a
Selection Analysis
We evaluated selection pressures using maximum-likelihood
models (Goldman and Yang 1994; Yang 1998)
implemented in CODEML of the PAML (Yang 2007). We
first employed the one-ratio model that assumes a single x
for the entire phylogenetic tree. This model tends to be
very conservative and can only detect positive selection if
the x ratio averaged over all the sites along the lineage is
significantly [1. Because such lineage-specific models
assume a single x for the entire tree, they often fail to
identify regions in proteins that might be affected by episodic
selection pressures and ultimately, underestimate the
strength of selection. Hence, we employed site-specific
models which estimate positive selection statistically as a
non-synonymous-to-synonymous nucleotide-substitution
rate ratio (x) significantly [1. We compared likelihood
values for three pairs of models with different assumed x
distributions as no a priori expectation exists for the same:
M0 (constant x rates across all sites) versus M3 (allows the
x to vary across sites within ‘n’ discrete categories, n C 3);
M1a (a model of neutral evolution) where all sites are
assumed to be either under negative (x1) or neutral
selection (x = 1) versus M2a (a model of positive selection)
which in addition to the site classes mentioned for
M1a, assumes a third category of sites; sites with x[1
(positive selection) and M7 (Beta) versus M8 (Beta and x),
and models that mirror the evolutionary constraints of M1
and M2 but assume that x values are drawn from a beta
distribution (Nielsen and Yang 1998). Only if the alternative
models (M3, M2a and M8: allow sites with x[1)
show a better fit in likelihood ratio test (LRT) relative to
their null models (M0, M1a and M8: do not show allow
sites x[1), are their results considered significant. LRT is
estimated as twice the difference in maximum-likelihood
values between nested models and compared with the c2
distribution with the appropriate degree of freedom—the
difference in the number of parameters between the two
models. The Bayes empirical Bayes (BEB) approach (Yang
et al. 2005) was used to identify amino acids under positive
selection by calculating the posterior probabilities that a
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