Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalization of the Ewens sampling formula to arbitrary fitness
  landscapes   In considering evolution of transcribed regions, regulatory modules, and
other genomic loci of interest, we are often faced with a situation in which
the number of allelic states greatly exceeds the population size. In this
limit, the population eventually adopts a steady state characterized by
mutation-selection-drift balance. Although new alleles continue to be explored
through mutation, the statistics of the population, and in particular the
probabilities of seeing specific allelic configurations in samples taken from a
population, do not change with time. In the absence of selection, probabilities
of allelic configurations are given by the Ewens sampling formula, widely used
in population genetics to detect deviations from neutrality. Here we develop an
extension of this formula to arbitrary, possibly epistatic, fitness landscapes.
Although our approach is general, we focus on the class of landscapes in which
alleles are grouped into two, three, or several fitness states. This class of
landscapes yields sampling probabilities that are computationally more
tractable, and can form a basis for the inference of selection signatures from
sequence data. We demonstrate that, for a sizeable range of mutation rates and
selection coefficients, the steady-state allelic diversity is not neutral.
Therefore, it may be used to infer selection coefficients, as well as other key
evolutionary parameters, using high-throughput sequencing of evolving
populations to collect data on locus polymorphisms. We also find that our
theory remains sufficiently accurate even if the assumptions such as the
infinite allele limit and the "full connectivity" assumption in which each
allele can mutate into any other allele are relaxed. Thus, our framework
establishes a theoretical foundation for inferring selection signatures from
samples of sequences produced by evolution on epistatic fitness landscapes.

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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: stat
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph