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Limitations of LAMARC
This documentation has a lot to say about what LAMARC can do.
Here is a concise guide to what it cannot do, with some suggestions
for other approaches.
Some combinations of analyses are not possible.
Due to program or mathematical limitations, some combinations
of analyses are not possible and will be rejected if attempted:
Gamma-distributed variation in mutation rate among regions is
not compatible with estimation of growth, nor can it currently be
used in a Baysian run.
Newick trees cannot be written out if migration or recombination
Migration rate estimation requires at least two populations.
Recombination estimation requires at least two linked markers.
Mapping requires recombination.
Some cases cannot be well modelled by LAMARC.
LAMARC has a wide range of evolutionary models but not all possible
ones by any means. Some significant omissions:
Recent divergence of populations from a common ancestor. Consider the
program of Nielson and Wakeley for this.
Samples from multiple time points in a fast-evolving population.
Consider the BEAST
program of Drummond and Rambaut for this.
RFLP, AFLP, or insertion/deletion data. You may be able to use the
ARLEQUIN program of Excoffier;
we know of no full coalescent likelihood or
Bayesian analysis which can handle these data.
Growth models other than simple exponential growth or decline. Consider
the BEAST program of
Drummond and Rambaut.
Geographic isolation as a function of distance, rather than
via separation into distinct subpopulations. Consider the Dancing
Trees algorithm of Baird,
if an implementation becomes available.
Combining data which have recombination and data which do not
(i.e. nuclear and mitochondrial DNA) in the same recombination-aware
analysis. We know of no alternative. You may want to do two separate
analyses with LAMARC.
Sequences from multi-gene families. The underlying coalescent
model in LAMARC is not correct for such data. Consider the gene-families
ML algorithm of Dubb, if an implementation becomes available.
Recombination rates which vary among regions or across the sequence.
Consider the LDHAT
program of McVean.
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