Person: Good, Benjamin
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Good, Benjamin
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Publication The Dynamics of Molecular Evolution Over 60,000 Generations(2017) Good, Benjamin; McDonald, Michael J.; Barrick, Jeffrey E.; Lenski, Richard E.; Desai, MichaelThe outcomes of evolution are determined by a stochastic dynamical process that governs how mutations arise and spread through a population. Here, we analyze the dynamics of molecular evolution in twelve experimental populations of Escherichia coli, using whole-genome metagenomic sequencing at 500-generation intervals through 60,000 generations. Despite a declining rate of fitness gain, molecular evolution continues to be characterized by signatures of rapid adaptation, with multiple beneficial variants simultaneously competing for dominance in each population. Interactions between ecological and evolutionary processes play an important role, as long-term quasi-stable coexistence arises spontaneously in most populations, and evolution continues within each clade. We also present new evidence that the targets of natural selection change over time, as epistasis and historical contingency alter the strength of selection on different genes. Together, these results show that long-term adaptation to a constant environment can be a more complex and dynamic process than is often assumed.Publication Genetic Diversity in the Interference Selection Limit(Public Library of Science, 2014) Good, Benjamin; Walczak, Aleksandra M.; Neher, Richard A.; Desai, MichaelPervasive natural selection can strongly influence observed patterns of genetic variation, but these effects remain poorly understood when multiple selected variants segregate in nearby regions of the genome. Classical population genetics fails to account for interference between linked mutations, which grows increasingly severe as the density of selected polymorphisms increases. Here, we describe a simple limit that emerges when interference is common, in which the fitness effects of individual mutations play a relatively minor role. Instead, similar to models of quantitative genetics, molecular evolution is determined by the variance in fitness within the population, defined over an effectively asexual segment of the genome (a “linkage block”). We exploit this insensitivity in a new “coarse-grained” coalescent framework, which approximates the effects of many weakly selected mutations with a smaller number of strongly selected mutations that create the same variance in fitness. This approximation generates accurate and efficient predictions for silent site variability when interference is common. However, these results suggest that there is reduced power to resolve individual selection pressures when interference is sufficiently widespread, since a broad range of parameters possess nearly identical patterns of silent site variability.Publication The Fates of Mutant Lineages and the Distribution of Fitness Effects of Beneficial Mutations in Laboratory Budding Yeast Populations(Genetics Society of America, 2014) Frenkel, Evgeni; Good, Benjamin; Desai, MichaelThe outcomes of evolution are determined by which mutations occur and fix. In rapidly adapting microbial populations, this process is particularly hard to predict because lineages with different beneficial mutations often spread simultaneously and interfere with one another’s fixation. Hence to predict the fate of any individual variant, we must know the rate at which new mutations create competing lineages of higher fitness. Here, we directly measured the effect of this interference on the fates of specific adaptive variants in laboratory Saccharomyces cerevisiae populations and used these measurements to infer the distribution of fitness effects of new beneficial mutations. To do so, we seeded marked lineages with different fitness advantages into replicate populations and tracked their subsequent frequencies for hundreds of generations. Our results illustrate the transition between strongly advantageous lineages that decisively sweep to fixation and more moderately advantageous lineages that are often outcompeted by new mutations arising during the course of the experiment. We developed an approximate likelihood framework to compare our data to simulations and found that the effects of these competing beneficial mutations were best approximated by an exponential distribution, rather than one with a single effect size. We then used this inferred distribution of fitness effects to predict the rate of adaptation in a set of independent control populations. Finally, we discuss how our experimental design can serve as a screen for rare, large-effect beneficial mutations.Publication Fate of a mutation in a fluctuating environment(Proceedings of the National Academy of Sciences, 2015) Cvijovic, Ivana; Good, Benjamin; Jerison, Elizabeth; Desai, MichaelNatural environments are never truly constant, but the evolutionary implications of temporally varying selection pressures remain poorly understood. Here we investigate how the fate of a new mutation in a fluctuating environment depends on the dynamics of environmental variation and on the selective pressures in each condition. We find that even when a mutation experiences many environmental epochs before fixing or going extinct, its fate is not necessarily determined by its time-averaged selective effect. Instead, environmental variability reduces the efficiency of selection across a broad parameter regime, rendering selection unable to distinguish between mutations that are substantially beneficial and substantially deleterious on average. Temporal fluctuations can also dramatically increase fixation probabilities, often making the details of these fluctuations more important than the average selection pressures acting on each new mutation. For example, mutations that result in a trade-off between conditions but are strongly deleterious on average can nevertheless be more likely to fix than mutations that are always neutral or beneficial. These effects can have important implications for patterns of molecular evolution in variable environments, and they suggest that it may often be difficult for populations to maintain specialist traits, even when their loss leads to a decline in time-averaged fitness.Publication Molecular Evolution in Rapidly Evolving Populations(2016-05-17) Good, Benjamin; Desai, Michael; Nelson, David; Sunyaev, Shamil; Wakeley, JohnAdvances in DNA sequencing are creating new opportunities for studying the process of evolution. These measurements can be particularly useful for rapidly evolving microbial organisms, whose small size and fast generation times make them ideal for controlled laboratory experiments and for tracking replicate populations in vivo. However, the interpretation of this new source of data is complicated by the unique ways in which these large microbial populations evolve. The basic problem is that natural selection is forced to do too many things at once. Unlike the classical picture, where new mutations arise one-by-one, rapidly evolving populations often harbor many selected variants at the same time. When recombination is limited, selection cannot act on these mutations individually, but only on combinations of mutations that happen to arise on the same genetic background. These effects, known as clonal interference, create correlations along the genome that are difficult to disentangle. Existing population genetic models often neglect these effects, which leaves us at loss when interpreting data from these populations. In Chapters 2-5, we analyze the effects of clonal interference in a simple ``null model'' of microbial evolution. We focus on the simplest model that is consistent with two empirical observations: (1) many fitness-influencing mutations are created every generation and (2) mutations have a broad range of fitness effects. After analyzing the basic dynamics of this model, we obtain predictions for the substitution rates of individual mutations and the patterns of linked neutral diversity, and we show how these quantities depend on the population size, mutation and recombination rates, and the fitness effects of new mutations. In Chapters 6 and 7, we apply this null model to data from laboratory experiments in S. cerevisiae and E. coli. We develop a statistical framework to infer the underlying parameters (the fitness effects of new mutations), which allows us to quantify deviations from the model over longer evolutionary timescales. Finally, in Chapters 8 and 9, we investigate the behavior of the model when some of the parameters are allowed to evolve or change in time.