Polymorphism data can be used to identify loci at which a beneficial allele has recently gone to fixation, given that an accurate description of the signature of selection is available. In the classical model that is used, a favored allele derives from a single mutational origin. This ignores the fact that beneficial alleles can enter a population recurrently by mutation during the selective phase. In this study, we present a combination of analytical and simulation results to demonstrate the effect of adaptation from recurrent mutation on summary statistics for polymorphism data from a linked neutral locus. We also analyze the power of standard neutrality tests based on the frequency spectrum or on linkage disequilibrium (LD) under this scenario. For recurrent beneficial mutation at biologically realistic rates, we find substantial deviations from the classical pattern of a selective sweep from a single new mutation. Deviations from neutrality in the level of polymorphism and in the frequency spectrum are much less pronounced than in the classical sweep pattern. In contrast, for levels of LD, the signature is even stronger if recurrent beneficial mutation plays a role. We suggest a variant of existing LD tests that increases their power to detect this signature. Populations adapt to their environment through fixation of beneficial alleles. Such fixation events leave a signature in neutral DNA variation of the population. An accurate description of this signature, also called a selective sweep, can be used to identify genes that have been involved in recent adaptations. The classical model of a selective sweep assumes that the beneficial allele was created only once by mutation, whereas the authors have shown, in a previous paper, that this assumption does not always hold. If a substitution involves multiple copies of an allele that have originated by independent mutation, it leads to a different signature, which the authors call a soft selective sweep. In this study, Pennings and Hermisson use analytical tools and coalescent simulations to describe this soft-sweep pattern. They show that this pattern is characterized by strong linkage disequilibrium. They also analyze the power of standard tests of neutrality to detect this pattern and suggest a variant of existing linkage-disequilibrium–based tests that increase the power to detect positive selection in the form of a soft selective sweep.

The evolution of drug resistance in HIV occurs by the fixation of specific, well-known, drug-resistance mutations, but the underlying population genetic processes are not well understood. By analyzing within-patient longitudinal sequence data, we make four observations that shed a light on the underlying processes and allow us to infer the short-term effective population size of the viral population in a patient. Our first observation is that the evolution of drug resistance usually occurs by the fixation of one drug-resistance mutation at a time, as opposed to several changes simultaneously. Second, we find that these fixation events are accompanied by a reduction in genetic diversity in the region surrounding the fixed drug-resistance mutation, due to the hitchhiking effect. Third, we observe that the fixation of drug-resistance mutations involves both hard and soft selective sweeps. In a hard sweep, a resistance mutation arises in a single viral particle and drives all linked mutations with it when it spreads in the viral population, which dramatically reduces genetic diversity. On the other hand, in a soft sweep, a resistance mutation occurs multiple times on different genetic backgrounds, and the reduction of diversity is weak. Using the frequency of occurrence of hard and soft sweeps we estimate the effective population size of HIV to be ( confidence interval ). This number is much lower than the actual number of infected cells, but much larger than previous population size estimates based on synonymous diversity. We propose several explanations for the observed discrepancies. Finally, our fourth observation is that genetic diversity at non-synonymous sites recovers to its pre-fixation value within 18 months, whereas diversity at synonymous sites remains depressed after this time period. These results improve our understanding of HIV evolution and have potential implications for treatment strategies. It is well known that HIV can evolve to become drug resistant if it acquires specific drug-resistance mutations, but the underlying population genetic processes are not well understood. We found that the evolution of drug resistance in HIV populations within infected patients occurs by one mutation at a time (as opposed to multiple mutations simultaneously) and involves both hard and soft sweeps. In a hard sweep, a mutation originates in a single viral particle and then spreads to the entire viral population within the patient. As this mutation increases in frequency, other mutations linked to it hitchhike to high frequencies, which greatly reduces genetic diversity in the population. In a soft sweep, on the other hand, the same resistance mutation originates multiple times on different genetic backgrounds, and hitchhiking may have very little or no effect on diversity. The fact that drug resistance evolves by means of both hard and soft sweeps implies that the HIV populations are limited by the supply of resistance mutations. Using the frequency of hard and soft sweeps we obtain a point estimate of 150,000 for the effective population size of the virus, a number that is much higher than estimates based on diversity at neutral (synonymous) sites, but much lower than the actual number of HIV infected cells in a human patient.

Evolutionary theory has produced two conflicting paradigms for the adaptation of a polygenic trait. While population genetics views adaptation as a sequence of selective sweeps at single loci underlying the trait, quantitative genetics posits a collective response, where phenotypic adaptation results from subtle allele frequency shifts at many loci. Yet, a synthesis of these views is largely missing and the population genetic factors that favor each scenario are not well understood. Here, we study the architecture of adaptation of a binary polygenic trait (such as resistance) with negative epistasis among the loci of its basis. The genetic structure of this trait allows for a full range of potential architectures of adaptation, ranging from sweeps to small frequency shifts. By combining computer simulations and a newly devised analytical framework based on Yule branching processes, we gain a detailed understanding of the adaptation dynamics for this trait. Our key analytical result is an expression for the joint distribution of mutant alleles at the end of the adaptive phase. This distribution characterizes the polygenic pattern of adaptation at the underlying genotype when phenotypic adaptation has been accomplished. We find that a single compound parameter, the population-scaled background mutation rate Θ_bg, explains the main differences among these patterns. For a focal locus, Θ_bg measures the mutation rate at all redundant loci in its genetic background that offer alternative ways for adaptation. For adaptation starting from mutation-selection-drift balance, we observe different patterns in three parameter regions. Adaptation proceeds by sweeps for small Θ_bg ≲ 0.1, while small polygenic allele frequency shifts require large Θ_bg ≳ 100. In the large intermediate regime, we observe a heterogeneous pattern of partial sweeps at several interacting loci. It is still an open question how complex traits adapt to new selection pressures. While population genetics champions the search for selective sweeps, quantitative genetics proclaims adaptation via small concerted frequency shifts. To date the empirical evidence of clear sweep signals is more scarce than expected, while subtle shifts remain notoriously hard to detect. In the current study we develop a theoretical framework to predict the expected adaptive architecture of a simple polygenic trait, depending on parameters such as mutation rate, effective population size, size of the trait basis, and the available genetic variability at the onset of selection. For a population in mutation-selection-drift balance we find that adaptation proceeds via complete or partial sweeps for a large set of parameter values. We predict adaptation by small frequency shifts for two main cases. First, for traits with a large mutational target size and high levels of genetic redundancy among loci, and second if the starting frequencies of mutant alleles are more homogeneous than expected in mutation-selection-drift equilibrium, e.g. due to population structure or balancing selection.

Significance: The evolution of drug resistance is a major health threat. In chronic infections with rapidly mutating pathogens—including HIV, tuberculosis, and hepatitis B and C viruses—multidrug resistance can cause even aggressive combination drug treatment to fail. Oftentimes, individual drugs within a combination do not penetrate equally to all infected regions of the body. Here we present a mathematical model suggesting that this imperfect penetration can dramatically increase the chance of treatment failure by creating regions where only one drug from a combination reaches a therapeutic concentration. The resulting single-drug compartments allow the pathogen to evolve resistance to each drug sequentially, rapidly causing multidrug resistance. More broadly, our model provides a quantitative framework for reasoning about trade-offs between aggressive and moderate drug therapies.

Access to combination antiretroviral treatment (ART) has improved greatly over recent years. At the end of 2011, more than eight million HIV-infected people were receiving ART in low-income and middle-income countries. ART generally works well in keeping the virus suppressed and the patient healthy. However, treatment only works as long as the virus is not resistant against the drugs used. In the last decades, HIV treatments have become better and better at slowing down the evolution of drug resistance, so that some patients are treated for many years without having any resistance problems. However, for some patients, especially in low-income countries, drug resistance is still a serious threat to their health. This essay will review what is known about transmitted and acquired drug resistance, multi-class drug resistance, resistance to newer drugs, resistance due to treatment for the prevention of mother-to-child transmission, the role of minority variants (low-frequency drug-resistance mutations), and resistance due to pre-exposure prophylaxis.

Drug resistance remains a major problem for the treatment of HIV. Resistance can occur due to mutations that were present before treatment starts or due to mutations that occur during treatment. The relative importance of these two sources is unknown. Resistance can also be transmitted between patients, but this process is not considered in the current study. We study three different situations in which HIV drug resistance may evolve: starting triple-drug therapy, treatment with a single dose of nevirapine and interruption of treatment. For each of these three cases good data are available from literature, which allows us to estimate the probability that resistance evolves from standing genetic variation. Depending on the treatment we find probabilities of the evolution of drug resistance due to standing genetic variation between and . For patients who start triple-drug combination therapy, we find that drug resistance evolves from standing genetic variation in approximately 6% of the patients. We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation. We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-parameters which determine the probability that resistance evolves from standing genetic variation. Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used. For HIV patients who are treated with antiretroviral drugs, treatment usually works well. However, the virus can, and sometimes does, become resistant against one or more drugs. HIV drug resistance results from the acquisition of specific and well known mutations. It is currently unknown whether drug resistance mutations usually stem from standing genetic variation, i.e., they were already present at low frequency before treatment started, or whether they tend to occur during treatment. In the current manuscript, I make use of several large datasets and evolutionary modeling to estimate the probability that drug resistance mutations are present before treatment starts and lead to viral failure. I find that for the most common type of treatment with a combination of three drugs, drug resistance evolves from pre-existing mutations in 6% of the patients. With other types of treatment, this probability varies from 0 to 39%. I conclude that there is room for improvement in preventing the evolution of drug resistance from pre-existing mutations.