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<META NAME="description" CONTENT="Estimation of population parameters using genetic data usi
ng a maximum likelihood approach with Metropolis-Hastings Monte Carlo Markov chain importanc
e sampling">
<META NAME="keywords" CONTENT="MCMC, Markov chain, Monte Carlo, Metropolis-Hastings, populat
ion, parameters, migration rate, population size, recombination rate, maximum likelihood">
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<TITLE>LAMARC Documentation: Future</title>
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<!-- coalescent, coalescence, Markov chain Monte Carlo simulation, migration
rate, effective population size, recombination rate, maximum likelihood -->
<H2>Upcoming Features for LAMARC</H2>
<P>Here are some features we plan to add to LAMARC, both
in the next release, and in the more distant future.</P>
<UL>
<LI> <b>Natural selection</b>. We will estimate selection parameters by
treating the selected and non-selected alleles as defining "populations"
with "migration" due to mutation and recombination allowing haplotypes or
sections of haplotypes to move between the "populations".</LI><br>
<LI> <b>Ascertainment bias</b>. We will deal with ascertainment bias (for example,
the preferential choice of diseased individuals in case/control studies)
by modeling diseased and healthy as "populations".</LI><br>
<LI> <b>Recombination hotspot estimation</b>. We will estimate the presence of
recombination hot or cold regions. (Chul Joo Kang is currently working on
implementing this in Recombine.)
</LI><br>
<LI> <b>Alternative models for growth</b>. Our current model of
continuously exponential growth is inadequate for certain populations, and
we plan to add both linear and stair-step models of growth (and shrinkage).
</LI><br>
<LI> <b>Sequential sampling</b>. Sometimes it is possible to collect or
obtain data from multiple generations of your organism (HIV is a good
candidate for this). If the mutation rate is known, it is theoretically
possible to include this data in a LAMARC-type analysis, and we plan to
allow this.</LI><br>
</UL>
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