Generalization of the Ewens sampling formula to arbitrary fitness landscapes

TL;DR

This paper extends the Ewens sampling formula to complex fitness landscapes, enabling better inference of selection and evolutionary parameters from genomic data in populations with high allelic diversity.

Contribution

It introduces a generalized sampling formula for arbitrary fitness landscapes, including epistatic interactions, facilitating analysis of allelic diversity under selection.

Findings

Sampling probabilities are computationally tractable for landscapes with multiple fitness states.

The theory accurately infers selection coefficients from high-throughput sequencing data.

Allelic diversity can reveal signatures of selection even in complex fitness landscapes.

Abstract

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.