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Activity Number:
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198
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2004 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302062 |
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Title:
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Hierarchical Models for Detecting Positive Selection in DNA Sequences
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Author(s):
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Raquel Prado*+ and Daniel Merl
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Companies:
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University of California, Santa Cruz and University of California, Santa Cruz
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Address:
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AMS, Baskin Engineering, Santa Cruz, CA, 95064,
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Keywords:
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molecular adaptation ; hierarchical GLMs ; Bayesian modeling
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Abstract:
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We present a model-based approach for detecting molecular adaptation in a large number of DNA protein coding sequences that are closely related in evolutionary time and for which very little or no phylogenetic information is available. Our statistical modeling is based on a class of hierarchical generalized linear models. A Bayesian approach that requires the implementation of efficient computational methods for parameter estimation is followed. The proposed class of models is motivated by the study of several DNA sequences encoding malaria antigens taken in various geographical locations in Asia, Africa, and South America. The models allow the incorporation of information that might be relevant to infer the pattern of substitutions in the sequences, such as geographical location of the sequences or pairwise evolutionary distances, if available. We assess the predictive performance of the new models via simulation studies for different kinds of data. We compare the results obtained with the new methodology to those obtained using traditional methods for detecting positive selection.
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