Estimating the distribution of selection coefficients from phylogenetic data using sitewise mutation-selection models.
Estimation of the distribution of selection coefficients of mutations is a long-standing issue in molecular evolution. In addition to population-based methods, the distribution can be estimated from DNA sequence data by phylogenetic-based models. Previous models have generally found unimodal distributions where the probability mass is concentrated between mildly deleterious and nearly neutral mutations. Here we use a sitewise mutation-selection phylogenetic model to estimate the distribution of selection coefficients among novel and fixed mutations (substitutions) in a data set of 244 mammalian mitochondrial genomes and a set of 401 PB2 proteins from influenza. We find a bimodal distribution of selection coefficients for novel mutations in both the mitochondrial data set and for the influenza protein evolving in its natural reservoir, birds. Most of the mutations are strongly deleterious with the rest of the probability mass concentrated around mildly deleterious to neutral mutations. The distribution of the coefficients among substitutions is unimodal and symmetrical around nearly neutral substitutions for both data sets at adaptive equilibrium. About 0.5% of the nonsynonymous mutations and 14% of the nonsynonymous substitutions in the mitochondrial proteins are advantageous, with 0.5% and 24% observed for the influenza protein. Following a host shift of influenza from birds to humans, however, we find among novel mutations in PB2 a trimodal distribution with a small mode of advantageous mutations.
Medical Research Council National Institute for Medical Research, London, NW7 1AA, United Kingdom.
Asif U
AU
Tamuri
Mario
M
dos Reis
Richard A
RA
Goldstein
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D019143:Evolution, Molecular; D040961:Genetic Drift; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D010802:Phylogeny; D015203:Reproducibility of Results; D012641:Selection, Genetic