Center for Computational Molecular Biology Seminar Series Lecture
"Predicting Evolutionary Trajectories in Sexual Populations"
Daniel Weinreich, Ph.D., Ecology and Evolutionary Biology, Center for Computational Molecular Biology, Brown University
Wednesday, March 19, 2008 at 4:00 P.M.
Room 241 Swig Boardroom (2nd Floor CIT)
Epistasis means that the functional consequence of mutations varies with genetic background, and the evolutionary consequences of epistasis are profound in asexual populations because a novel mutation's fate is then determined by its fitness effect on only one of many alternative genetic backgrounds. This possibility motivates interest in Sewall Wright's adaptive landscape, the projection of genotypic fitness values over a discrete, multidimensional nucleotide sequence space. In this framework, populations follow a temporal succession of individual points through this space determined by the interplay between mutational pressure, the local selective gradient defined by the landscape, and stochastic loss of novel genotypes. Several recent empirical characterizations of small regions of this landscape have demonstrated that in asexual populations functional epistasis i) sharply limits the number of mutational trajectories to high-fitness genotypes that are selectively accessible and ii) gives rise to a very sharp non-uniform probability distribution among selectively accessible trajectories. To date however, the absence of an analogous formal framework in which to characterize selectively accessible recombinational trajectories has limited understanding of this problem in sexual populations. I will describe a novel definition of the adaptive landscape appropriate for this problem: the vector field reflecting the joint pressures of mutation, selection and recombination over a continuous multidimensional space that represents both allele frequencies and linkage disequilibrium among alleles. Populations are again regarded as occupying a temporal succession of points in the underlying space, and I will illustrate the potential of this approach by describing how recombination influences a population's evolutionary trajectory single- and multi-peaked fitness models.
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