UC Berkeley

Deciphering the processes that govern microbial evolution allows us to make predictions of systems ranging from pathogen evolution to climate-driven ecosystem shifts. One fundamental evolutionary process is genetic drift, which is the stochastic change in population composition due to the randomness of birth and death processes. Genetic drift can lead to the loss of genetic diversity and weaken the efficacy of natural selection; thus, inferring its strength and understanding how it arises is important for understanding evolutionary dynamics. Despite decades of research on genetic drift, we still have limited empirical tools to infer the strength of genetic drift from data. Additionally, the strength of genetic drift is often considered to be a static property of a population on short timescales rather than an evolvable trait. In this thesis, we develop new methods for empirically measuring the strength of genetic drift and test hypotheses about the mechanisms that set the strength of genetic drift.

In Chapter 2, we test the hypothesis that mutations can change the strength of genetic drift, the first requirement needed for a trait to be evolvable. We focus on microbial colonies, which are a model system for range expansions. To test this hypothesis, we develop a new experimental method to measure the strength of genetic drift in high throughput using fluorescence microscopy. We find that mutations significantly affect the strength of genetic drift by causing changes in the self-organized spatial structure of the colony. These changes to genetic drift substantially affect the probability that new beneficial mutations escape stochastic extinction, providing evidence that genetic drift may be an evolvable property of a population.

In Chapter 3, we investigate the strength of genetic drift in SARS-CoV-2 evolution at the host population level. We develop a statistical inference framework for inferring the strength of genetic drift simultaneously with measurement noise from lineage frequency time series data. Applying this method to genomic data from England, we find that the strength of genetic drift is consistently, throughout time, higher than expected given the number of people infected with COVID-19 at the community level, even after correcting for measurement noise and epidemiological dynamics. We also find evidence for spatial structure in SARS-CoV-2 transmission at the regional level. The levels of genetic drift that we observe are higher than the estimated levels of superspreading found by modeling studies that incorporate data of contact statistics in England. We discuss how even in the absence of superspreading, high levels of genetic drift can be generated via jackpot events in a deme model.

The new experimental and computational methods developed in this thesis allow us to infer the strength of genetic drift, and thus gain a better understanding of evolutionary dynamics, in a larger range of laboratory and natural settings. We find that random single mutations can change the strength of genetic drift and affect downstream evolution, suggesting that genetic drift can be an evolvable trait of a population. Finally, we find that multiple mechanisms can alter the strength of genetic drift at the population level.