Bayesian Models For
Detecting Selection
in Malaria Genes
In this project we
consider
the development of new statistical methods to detect positive natural
selection
in genes encoding malaria antigens. The research is motivated by the
analysis
of multiple DNA sequences encoding the Apical Membrane Antigen 1
(AMA-1),
the Circumsporozoite Protein (CSP) and the Merozoite Surface Protein
(MSP-1)
in the P.falciparum and P.vivax human malaria parasites. Several DNA
sequences
collected in different geographical areas in Africa, Asia and South
America are available for each antigen. The fact that a large number of
very similar sequences is available implies that a single phylogenetic
structure cannot
be assumed, as very many phylogenies are equally likely to explain the
evolution
of these sequences. The development of new statistical methods based on
a class of hierarchical models is proposed. These methods do not
require
the specification of a phylogenetic structure and can predict sites
under
positive selection in a relatively fast way. The new models allow for
the
incorporation of information that might be relevant to infer the
pattern
of substitutions, such as geographical location and information on
pairwise
evolutionary distances if available. The specific goals of this
research
are: (1) developing new statistical models to detect molecular
adaptation
in a large number of DNA protein coding sequences that are closely
related
in evolutionary time, and for which little or no phylogenetic structure
is available, (2) assessing the predictive performance of the new
models
via simulation studies for different kinds of datasets and comparing
the
new and current methodologies to address the problem of identifying
sites
under positive selection in DNA sequences, (3) analyzing a large number
of DNA sequences encoding malaria antigens.
This project is supported by NIH/NIGMS grant 1R01GM072003-01.
People currently
involved
in this project: Daniel
Merl Duke University Ananias Escalante
School of Life Sciences, Arizona State University.
Daniel
Merl Duke University