Unpredictable repeatability in molecular evolution

TL;DR

This paper reveals that heavy-tailed beneficial fitness effect distributions lead to unpredictable and highly variable parallel evolution outcomes, challenging previous models that assumed light-tailed distributions.

Contribution

It demonstrates that heavy-tailed distributions cause anomalously slow decay of parallel evolution probability and high variability, making evolutionary outcomes less predictable.

Findings

Parallel evolution probability decays slowly or is independent of mutation number.

Evolutionary outcomes are dominated by few high-impact mutations.

Empirical data on antibiotic resistance supports the theoretical predictions.

Abstract

The extent of parallel evolution at the genotypic level is quantitatively linked to the distribution of beneficial fitness effects (DBFE) of mutations. The standard view, based on light-tailed distributions (i.e. distributions with finite moments), is that the probability of parallel evolution in duplicate populations is inversely proportional to the number of available mutations, and moreover that the DBFE is sufficient to determine the probability when the number of available mutations is large. Here we show that when the DBFE is heavy-tailed, as found in several recent experiments, these expectations are defied. The probability of parallel evolution decays anomalously slowly in the number of mutations or even becomes independent of it, implying higher repeatability of evolution. At the same time, the probability of parallel evolution is non-self-averaging, that is, it does not converge to its mean value even when a large number of mutations are involved. This behavior arises because the evolutionary process is dominated by only a few mutations of high weight. Consequently, the probability varies widely across systems with the same DBFE. Contrary to the standard view, the DBFE is no longer sufficient to determine the extent of parallel evolution, making it much less predictable. We illustrate these ideas theoretically and through analysis of empirical data on antibiotic resistance evolution.