Dynamic sampling bias and overdispersion induced by skewed offspring distributions

TL;DR

This paper investigates how skewed offspring distributions in populations cause dynamic sampling biases and overdispersion in allele frequency trajectories, challenging standard population genetics models and providing new scaling relations.

Contribution

It introduces a scaling framework for understanding allele dynamics under broad offspring distributions, incorporating a time-dependent sampling bias.

Findings

Derived scaling relations for allele fluctuations and fixation probabilities.

Validated the model in traveling wave scenarios with gene surfing phenomena.

Highlighted the impact of skewed offspring distributions on population genetic inference.

Abstract

Natural populations often show enhanced genetic drift consistent with a strong skew in their offspring number distribution. The skew arises because the variability of family sizes is either inherently strong or amplified by population expansions, leading to so-called `jackpot' events. The resulting allele frequency fluctuations are large and, therefore, challenge standard models of population genetics, which assume sufficiently narrow offspring distributions. While the neutral dynamics backward in time can be readily analyzed using coalescent approaches, we still know little about the effect of broad offspring distributions on the dynamics forward in time, especially with selection. Here, we employ an exact asymptotic analysis combined with a scaling hypothesis to demonstrate that over-dispersed frequency trajectories emerge from the competition of conventional forces, such as selection or mutations, with an emerging time-dependent sampling bias against the minor allele. The sampling bias arises from the characteristic time-dependence of the largest sampled family size within each allelic type. Using this insight, we establish simple scaling relations for allele frequency fluctuations, fixation probabilities, extinction times, and the site frequency spectra that arise when offspring numbers are distributed according to a power law $~n^{-(1+\alpha)}$. To demonstrate that this coarse-grained model captures a wide variety of non-equilibrium dynamics, we validate our results in traveling waves, where the phenomenon of 'gene surfing' can produce any exponent $1<\alpha <2$. We argue that the concept of a dynamic sampling bias is useful generally to develop both intuition and statistical tests for the unusual dynamics of populations with skewed offspring distributions, which can confound commonly used tests for selection or demographic history.