The effects of rate variation on ancestral inference in the coalescent.
We describe a Markov chain Monte Carlo approach for assessing the role of site-to-site rate variation in the analysis of within-population samples of DNA sequences using the coalescent. Our framework is a Bayesian one. We discuss methods for assessing the goodness-of-fit of these models, as well as problems concerning the separate estimation of effective population size and mutation rate. Using a mitochondrial data set for illustration, we show that ancestral inference concerning coalescence times can be dramatically affected if rate variation is ignored.
Markovtsova
L
L
Department of Mathematics, University of Southern California, Los Angeles, California 90089-1113, USA.
Marjoram
P
P
Tavaré
S
S
D000465:Algorithms; D001499:Bayes Theorem; D005075:Biological Evolution; D004247:DNA / Q000235:genetics; D004272:DNA, Mitochondrial / Q000235:genetics; D014644:Genetic Variation; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation; D010802:Phylogeny; D011336:Probability; D015203:Reproducibility of Results; D013995:Time
D004272:DNA, Mitochondrial; D004247:DNA