MAC5: Bayesian inference of phylogenetic trees from DNA sequences incorporating gaps.

SUMMARY: MAC5 implements MCMC sampling of the posterior distribution of tree topologies from DNA sequences containing gaps by using a five state model of evolution (the four nucleotides and the gap character).

Models of sequence evolution for DNA sequences containing gaps.

Most evolutionary tree estimation methods for DNA sequences ignore or inefficiently use the phylogenetic information contained within shared patterns of gaps. This is largely due to the computational difficulties in implementing models for insertions and deletions. A simple way to incorporate this information is to treat a gap as a fifth character (with the four nucleotides being the other four) and to incorporate it within a Markov model of nucleotide substitution. This idea has been dismissed in the past, since it treats a multiple-site insertion or deletion as a sequence of independent events rather than a single event. While this is true, we have found that under many circumstances it is better to incorporate gap information inadequately than to ignore it, at least for topology estimation. We propose an extension to a class of nucleotide substitution models to incorporate the gap character and show that, for data sets (both real and simulated) with short and medium gaps, these models do lead to effective use of the information contained within insertions and deletions. We also implement an ad hoc method in which the likelihood at columns containing multiple-site gaps is downweighted in order to avoid giving them undue influence. The precision of the estimated tree, assessed using Markov chain Monte Carlo techniques to find the posterior distribution over tree space, improves under these five-state models compared with standard methods which effectively ignore gaps.

The problems of using the transmission/disequilibrium test to infer tight linkage.

Family-based association methods such as the transmission/disequilibrium test (TDT) have become very popular during the past few years, often being preferred to case-control studies because family-based approaches avoid the difficulties of ascertainment of appropriate populations of cases and controls for case-control studies. Significant TDT results indicate both linkage and allelic association. However, significant TDT results are often interpreted as implying tight linkage of marker and disease locus, and we shall argue here that, in general, this interpretation is not justified.

Marker-assisted selection using ridge regression.

In cross between inbred lines, linear regression can be used to estimate the correlation of markers with a trait of interest; these marker effects then allow marker assisted selection (MAS) for quantitative traits. Usually a subset of markers to include in the model must be selected: no completely satisfactory method of doing this exists. We show that replacing this selection of markers by ridge regression can improve the mean response to selection and reduce the variability of selection response.