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PLoS Comput Biol. Jan 2010; 6(1): e1000660.
Published online Jan 29, 2010. doi:  10.1371/journal.pcbi.1000660
PMCID: PMC2813257

Recombination Rate and Selection Strength in HIV Intra-patient Evolution

Christophe Fraser, Editor

Abstract

The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be ρ = 1.4±0.6×10−5 recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.

Author Summary

Evolution, in viruses and other organisms, is the result of random genetic diversification by mutation or recombination and selection for survival. In most organisms, evolution is too slow to be observed directly and the evolutionary past has to be reconstructed from static snapshots of the population. This reconstruction requires simple models of evolution that typically neglect selection or recombination. In vigorously evolving organisms like HIV, such assumptions are questionable. However, HIV evolves rapidly enough that substantial evolution is observable during a chronic HIV infection within single patients. Using such time series data of evolution, we estimate the effective recombination rate of HIV (the rate of viral sex) to be similar to the mutation rate, rather than much larger as previously reported. We also study the strength of selection exerted on the virus by the immune system. We find that about 15% of the observed virus variants with mutations in the surface protein are favored and selected at a rate of 0.8% to 2% per virus replication cycle. Knowledge of the recombination rate and the strength of selection is essential for quantitative modeling and understanding of HIV evolution.

Introduction

The human immunodeficiency virus (HIV-1) ranks among the most rapidly evolving entities known [1], enabling the virus to continually escape the immune system. After infection with HIV, patients typically enter an asymptomatic period lasting several years during which the virus is present at low to medium levels, typically at a viral load of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e001.jpg to An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e002.jpg copies per ml plasma. Nevertheless, the number of virions produced and removed is estimated to be around An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e003.jpg per day with a generation time slightly less than two days [2]. Due to this rapid turnover and the high mutation rate of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e004.jpg per site and generation, the sequence diversity of HIV within a single patient can rise to An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e005.jpg% ( env gene) within a few years and the divergence from the founder strain increases by An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e006.jpg% per year [3], although this rate is not constant [4]. The genotypic diversity is subject to positive selection for novel variants that are not recognized by the host immune system or that reduce the sensitivity to anti-retroviral drugs [5][7], as well as to purifying selection by functional constraints [8]. In addition to high substitution rates and strong selection, genomes of different HIV particles within the same host frequently exchange genetic information. This form of viral recombination works as follows: Whenever a cell is coinfected by two or more viruses, the daughter virions can contain two RNA strands from different viruses[9],[10]. In the next round of infection, recombinant genomes are generated by template switching of the reverse transcriptase while producing cDNA. It has been shown that recombination in HIV contributes significantly to the genetic diversity within a patient [11][13]. In cases of super-infection with several HIV-1 subtypes, recombination can give rise to novel forms that become part of the global epidemic [14].

The observation of recombinant viruses after a change in anti-retroviral drug therapy [15] suggests that recombination might play an important role in the evolution of drug resistance, as predicted by theoretical models [16]. The amount by which recombination speeds up the evolution of drug resistance depends on the parameters governing the population dynamics [17], many of which are not known to sufficient accuracy. In vitro estimates of the recombination rate have shown that the reverse transcriptase switches templates about An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e007.jpg times while transcribing the entire genome, resulting in a recombination rate of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e008.jpg per site and generation [18],[19]. However, the bare template switching rate is only of secondary importance, since recombination can generate diversity only if the virion contains two RNA strands that originate from different viruses, which requires coinfection of host cells[20]. The effective in vivo recombination rate is therefore a compound quantity, to which the template switching rate and the probability of coinfection of a single host cell contribute. This effective recombination rate has been estimated with coalescent based methods developed in population genetics [13],[21]. These methods use a single sample of sequences obtained from the diverse population and estimate the recombination rate from topological incongruencies in the phylogenetic tree of the sequence sample. Together with an estimate of the mutation rate, this allows to estimate the recombination rate in real time units. Shriner et al. [13] report an estimate of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e009.jpg per site and generation, implying almost ubiquitous coinfection of host cells.

Here, we present a different method to estimate recombination rates from longitudinal sequence data, which has been obtained from 11 patients at approximately 6 month intervals [3],[22]. By comparing sequence samples from successive time points, we can estimate recombination rates from the distance and time dependence of the probability of cross-over between pairs of polymorphic sites. We find that the effective rate of recombination is An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e010.jpg per site and generation. Furthermore, we estimate the strength of selection on nonsynonymous polymorphisms by measuring the rate at which allele frequencies change. We find that a fraction of about 15% of the observed nonsynonymous polymorphisms are selected stronger than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e011.jpg% per generation.

Results

Time series sequence data of the C2-V5 region of the env gene was available from eleven patients over a time-span of 6-13 years with sequence samples taken typically every 6–10 month [3],[23]. At each time point, about 5–20 viruses had been sequenced successfully. These sequences were aligned as described in Methods. We will use the temporal variation in genetic diversity to estimate the recombination rate and selection coefficients that govern the dynamics of the viral population.

Recombination rate

Recombination produces new combinations of alleles from existing genetic variation and randomizes the distribution of genotypes. To illustrate this process and the challenges of estimating recombination rates, consider the pair of polymorphic sites in Figure 1. Generically such a pair will have arisen by the following sequence of events: (i) Site 1 becomes polymorphic by mutation, e.g. AAn external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e012.jpgC. (ii) A mutation occurs at site 2 on a genome that carries one of the variants of site 1, e.g. giving rise to the haplotypes A…T, A…G and C…T. (iii) The missing haplotype, in this example C…G, can be generated by further mutation (AAn external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e013.jpgC at site 1 or TAn external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e014.jpgG at site 2) or by crossing over two of the existing haplotypes, as illustrated in Figure 1. In population genetics, the occurrence of the fourth haplotype is often taken as sufficient condition for recombination (the four gamete test[24]). While this is true for bacteria and eukaryotes because of their low mutation rates, the HIV population within a patient is large and mutates rapidly [25]. Hence, the biggest challenge in estimating recombination rates is to separate recombination from recurrent mutations or homoplasy. A second confounding effect stems from the small number of sequences available per time point, such that the sequences containing the fourth haplotype could have been missed due to undersampling. To disentangle these two effects from recombination, we make use of the fact that only the recombination rate depends strongly on the distance between the two sites, while recurrent mutations and sampling noise should not.

Figure 1
Recombination produces new haplotypes.

For each pair of biallic sites at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e018.jpg that was found in three of the four possible haplotypes, we asked whether the missing haplotype is observed the at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e019.jpg (comp. Figure 1) and calculated the frequency of this event as a function of the separation of the two sites, as shown in Figure 2A. This frequency increases with the separation of the two sites from about 0.1 to about 0.35 at 500 bp separation, in line with the expectation that recombination is more rapid between distant sites. To corroborate that this distance dependence is indeed due to recombination, we performed the following similar analysis: The curve labelled “other haplotypes” in Figure 2A shows the frequency of observing a haplotype at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e020.jpg, which contains alleles not observed at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e021.jpg, again averaged over all available data. Any such haplotype could have arisen by mutation in the time interval between An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e022.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e023.jpg, or could have been present at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e024.jpg but not sampled. It cannot, however, be assembled by recombination from the alleles found at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e025.jpg. The important observation is that the frequency of observing such a haplotype does not increase with distance. This is consistent with our expectation that an additional mutation or undersampling should be independent of an additional polymorphism nearby. The clear separation between the two classes of haplotypes suggests that the contribution from homoplasy and sampling can be accounted for by a distance independent constant.

Figure 2
Estimating recombination rates from time resolved data.

The probability of recombination between two sites increases with the product of the time elapsed and the distance between the sites, rather than with distance alone as plotted in Figure 2A. Panel B of Figure 2 shows the probability of appearance of a putative recombinant haplotype as a function of the product of distance and number of generations (generation time 2 days). To estimate the recombination rate, we have to know how the observed saturation behavior is related to recombination. Let An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e033.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e034.jpg be the alleles at site 1 and 2 and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e035.jpg the frequency of the missing haplotype An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e036.jpg. The probability not to detect haplotype An external file that holds a picture, illustration, etc.
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(1)

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Object name is pcbi.1000660.e043.jpg through recombination[26]. The frequency of detecting a haplotype at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e044.jpg, given it was not detected in the previous sample at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e045.jpg, is therefore An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e046.jpg. This quantity, averaged over all pairs of polymorphic sites at distance An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e047.jpg such that An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e048.jpg falls into a specified bin, is shown in Figure 2B (the average extends over all patients and time points). To understand how An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e049.jpg depends on the recombination rate, it is useful to consider the two limiting cases of small and large An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e050.jpg

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(2)

where we assumed that An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e052.jpg is small compared to one (inspection of Figure 2B shows this is true, we discuss this in more detail in Methods). Hence, the recombination rate is proportional to the slope An external file that holds a picture, illustration, etc.
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Object name is pcbi.1000660.e055.jpg. The intercept An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e056.jpg is simply the probability that we detect An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e057.jpg at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e058.jpg in absence of recombination, given we missed it at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e059.jpg. We determine the slope and the intercept by fitting the function An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e060.jpg to the data. The recombination rate is then given by

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(3)

where An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e062.jpg is measured directly. The best fit yields An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e063.jpg recombinations per site and generation (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e064.jpg one standard deviation). The uncertainty of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e065.jpg was estimated by resampling the patients with replacement 500 times. This bootstrap distribution of the recombination rate estimate is shown in Figure 3. We have assumed that the allele frequencies An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e066.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e067.jpg remain constant in the interval An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e068.jpg. We will see below, however, that some allele frequencies change rapidly. We expect that repeated sweeps will cause our method to overestimate the recombination rate: When the frequencies of the minor alleles increase, the missing haplotype is produced more rapidly then expected.

Figure 3
Bootstrap distribution of the recombination rate estimate.

Selection

Positive selection on the variable regions in env and purifying selection on the conserved regions have been repeatedly reported in the literature [5], [23], [27][29]. Most of the these studies compared the rates of synonymous and non-synonymous substitutions (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e069.jpg ratio). An observation of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e070.jpg indicates selection for novel variants, while An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e071.jpg indicates that the amino acid sequence changes more slowly than the nucleotide sequence, indicating functional constraint at the protein level. The overall rate of substitutions, however, yields only very limited information about the strength of selection.

Selection for a specific variant implies that this variant confers an elevated rate of reproduction compared to the population mean. Ignoring random drift, the strength of selection An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e072.jpg is related to the rate at which the frequency An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e073.jpg of the variant changes [26]:

equation image
(4)

This equation has a straightforward solution An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e075.jpg and from two measurements of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e076.jpg at time An external file that holds a picture, illustration, etc.
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Object name is pcbi.1000660.e079.jpg can be determined to be An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e080.jpg (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e081.jpg)[11] (for a review on selection and drift, see [30]). However, when using this formula, emphasis is put on rare alleles, whose frequencies can't be measured accurately in small samples. It proves more robust to estimate the rate of allele frequency change directly as An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e082.jpg, where An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e083.jpg is the difference in allele frequency between consecutive samples and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e084.jpg is the time interval. The discrete derivative An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e085.jpg can serve as a proxy for selection which is less sensitive to rare alleles. The observed An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e086.jpg will be a sum of contributions from selection, noise from random sampling, and genetic drift. The latter can be estimated by measuring An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e087.jpg for synonymous polymorphisms which are putatively neutral and their observed frequency changes are assumed to be dominated by sampling noise. However, they can be affected by selection on nearby non-synonymous polymorphisms (hitch-hiking) or be themselves under selection, e.g. for translation efficiency or RNA secondary structure.

Despite the limited resolution of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e088.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e089.jpg due to small sample sizes and large time intervals between samples (6–10 month), we can make a meaningful statement about the strength of selection when averaging over all sites, patients and time points. Figure 4 shows the cumulative distributions of the rates of change of allele frequencies observed during the interval between two consecutive time points for non-synonymous and synonymous polymorphisms. The histograms are shown as insets in the Figure. There are consistently more fast changing non-synonymous polymorphisms than there are synonymous ones, suggesting that a fraction of the non-synonymous polymorphisms is indeed responding to selection. To check whether the fast changing synonymous polymorphisms can be attributed to hitchhiking, we excluded synonymous polymorphisms that are closer than 100bp to a non-synonymous polymorphism that changes faster than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e090.jpg per generation. The resulting distribution is much narrower with no allele frequency changes beyond An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e091.jpg per generation, indicating that the fast changing synonymous polymorphisms are indeed “hitch-hiking”. The cumulative histograms can be compared by the Kolmogorov-Smirnov test, which uses the maximal vertical distance between curves as a test statistics. The test reveals that the non-synonymous distribution is significantly different from both the unconditional synonymous distribution (p-value An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e092.jpg) and the synonymous distribution without hitch-hiking (p-value An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e093.jpg). Note that not all observations are independent since nearby sites are linked and move coherently. Hence, realistic p-values will be larger.

Figure 4
Selection on non-synonymous mutations.

The fastest allele frequency changes detected are about An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e099.jpg per generation, which is our resolution limit (6 month An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e100.jpg100 generations). This low time resolution results in a tendency to underestimate the rates of change, while the finite sample size will generate spurious frequency changes due to sampling noise. However, the narrow distribution of allele frequency changes of synonymous polymorphisms excluding hitchhiking suggests that the contribution from sampling noise is small (on the order of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e101.jpg per generation). Hence, the tail of the histogram of the frequency changes of nonsynonymous polymorphisms contains a rough measure of the distribution of selection coefficients[31]: about 15% of the observed non-synonymous polymorphisms change faster than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e102.jpg per generation, compared to almost none of the synonymous polymorphisms. Assuming that this difference is due to selection, we conclude that about 15% of the observed non-synonymous polymorphisms are positively selected with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e103.jpg per generation. In the last step, we rearranged Eq. (4) to obtain An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e104.jpg with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e105.jpg. Most of the strongly deleterious polymorphism are of course never observed in the samples.

Discussion

The dynamics of HIV within a single patient is characterized by large diversity due to high mutation rates, intense selection, frequent recombination and stochasticity resulting from bursts of viruses originating from a single cell. The simultaneous importance of these four evolutionary forces makes HIV evolution difficult to analyze with traditional population genetic methods, which typically assume that one of the evolutionary forces is predominant. Coalescent based methods, for example, assume that evolution is neutral, i.e. stochasticity dominates over selection. While it is possible to include recombination or selection into a coalescent description [32],[33], the analysis becomes difficult and computationally demanding. Estimators based on site frequency spectra, like Tajima's An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e106.jpg, work best when recombination is strong compared to selection. Phylogenetic analysis, on the other hand, assumes absence of recombination. These methods have been designed to infer parameters of the population dynamics from snapshots of the population, which is a formidable challenge. The lack of time series data requires the assumption of a model of the population dynamics, which can be extrapolated back in time to the most recent common ancestor. In neutral models, the time to the most recent common ancestor is on the order of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e107.jpg generations, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e108.jpg being the population size. During this long time interval, there is ample opportunity for selection or demography to invalidate the assumption of the model.

If time series data is available, the task of estimating parameters is greatly simplified since one can trace the dynamics of alleles and genotypes directly. Such longitudinally sampled data has for example been used to gauge the molecular clock of bacterial evolution [34]. We have used time resolved data from 11 patients to estimate parameters of the population dynamics directly. In accordance with existing studies, we find that recombination in HIV is frequent and contributes significantly to sequence diversity [11][13],[21]. The template switching rate of HIV is known to be about An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e109.jpg per site[18],[19]. For template switching to result in a novel genetic variant, the two RNA strands in the infecting virus have to be different, which implies co-infection of the cell the virus originated from. Any estimate of recombination rates from sequence diversity will therefore measure an effective recombination rate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e110.jpg being approximately the product of the coinfection probability, the probability of forming a heterozygote and the template switching rate. Our estimate of this effective recombination rate An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e111.jpg per site and generation is about a factor of 20 lower than the template switching rate, but still implies a probability of coinfection of about 10%. Our estimate is lower than the estimate by Shriner et al. [13], who estimated An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e112.jpg per site and generation. However, two other estimators also reported in that paper yielded lower recombination rates comparable to our estimate.

In our analysis we have assumed that the rate of recombination is constant across the env gene. However, the breakpoint distribution in circulating recombinant forms show strong variation along the genome, with particulary little recombination in env [35]. The variation of the breakpoint distribution can largely be explained by low sequence homology between different subtypes and dysfunctional recombinants [36]. In the present study, however, all patients were infected with a single subtype and gradually built up diversity which remained much lower than the distance between subtypes. The effects causing recombination rate variation should therefore be of minor importance.

By comparing the distribution of the rate of allele frequency changes of synonymous (putatively neutral) and nonsynonymous (possibly selected) polymorphisms, we estimated the distribution of selection coefficients on single sites. We find that 15% of the observed nonsynonymous polymorphisms are selected with coefficients greater than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e113.jpg% per generation. In using the distribution of allele frequency changes to infer selection coefficients, we have assumed that each locus is selected for its own effect on fitness, rather than changing its frequency due to selection on a linked neighboring locus [37] or some epistatic combination of loci [38],[39]. A sweeping polymorphism “drags” along neutral variation in a region An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e114.jpg [40], which using our estimates of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e115.jpg, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e116.jpg and an effective population size of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e117.jpg evaluates to An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e118.jpgbp. This is consistent with our finding that most of the rapid allele frequency changes of synonymous polymorphisms occur in the vicinity (An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e119.jpg) of rapidly changing non-synonymous polymorphisms (Figure 4).

The sequence diversity in our samples is on the order of 3% [3], such that two polymorphisms are expected to be on average 30bp apart. Roughly half of the observed polymorphisms are non-synonymous, of which 15% are under strong positive selection. Hence the distance between simultaneously sweeping loci is on the order of 400bp, which is of the same order as An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e120.jpg. If the rate of sweeps was much higher, sweeps would cease to be independent and interfere. While these estimates have large uncertainty, it is conceivable that the rate of sweeps is limited by recombination [41].

Selection coefficients in HIV have been estimated before by Liu et al. [11] in a patient infected with two HIV-1 subtype B viruses. In this patient, a small number of recombinant forms competed against the ancestral strains and selection differentials were estimated to lie between An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e121.jpg% and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e122.jpg%. These selection coefficients are higher than our estimates, which is plausible since they are associated with entire recombinant genotypes that differ at many sites rather than the single site estimate presented here [39]. The strength of selection for novel epitopes in several HIV genes (rate of escape from cytotoxic T-lymphocyte mediated killing) during the asymptomatic phase of HIV infection has been shown to be of similar magnitude as our estimates [42].

The present study is limited by low time resolution and small sequence samples and more accurate and detailed answers could be obtained from larger samples. Larger sample sizes will require generalizations of the method used to estimate recombination rates which depends on pairs of sites where one of the four possible haplotypes is absent. In large samples, pairs of high frequency polymorphisms will most likely be present in all four possible haplotypes. In this case, one can measure linkage disequilibrium An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e123.jpg directly and observe how it decays from one timepoint to the next, e.g. by measuring its autocorrelation function An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e124.jpg. The method presented here is an approximation of this more general method suiteable for noisy data.

Methods

Longitudinal sequence data and alignment

Sequences of the C2-V5 region of env from 11 patients, which were part of the MACS study [3],[22], were obtained from the Los Alamos National Lab HIV data base (Special interest alignments, accession numbers: AF137629-AF138703, AF204402-AF204670, AY449806 - AY450055 & AY450056 - AY450257). The sequences were translated into amino acid sequences and aligned for each patient separately using MUSCLE v3.6 with default settings [43]. Aligned nucleotide sequences were then constructed by inserting a 3bp gap for each gap in the amino acid alignment. Scripting and plotting was done in Python using the NumPy and Matplotlib environment [44],[45].

Estimating the recombination rate

To estimate recombination rates, we calculated the frequency of generating the missing haplotype as a function of the distance between polymorphic sites. Specifically, the algorithm proceeds as follows: For each pair of biallelic and gapless sites, we constructed a list of haplotypes, i.e. a list of alleles that co-occur in the same sequence. The list typically contains 3 or 4 haplotypes, but can also contain only 2 of the 4 possible pairs due to undersampling or selection against some of the allele combinations. Only those pairs with 3 haplotypes were included in the estimation. Furthermore, we used only those sites where both alleles were observed at least three times, since rare alleles are very sensitive to sampling noise. The analysis was repeated with this cutoff at two or four alleles, yielding similar results.

For each pair of biallelic sites for which 3 haplotypes were detected in the sample at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e125.jpg, we determined the 4th haplotype that can be formed from the alleles observed at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e126.jpg, i.e. C…G in the example given in Figure 1. We then checked whether this missing haplotype is detected at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e127.jpg. By averaging over all time points (but the last one), all patients, and all pairs within a given distance interval, we determined the frequency of finding the missing haplotype as a function of the distance between the sites and as a function of the product of distance and time difference. The error bars indicated in Figure 2 are calculated as the product of the estimated value and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e128.jpg. This would be approximately An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e129.jpg one standard deviation if all counts were independent, which they are not since the observations are pairs of sites and each site contributes to multiple pairs. However, these error bars indicate the relative uncertainties of the different points, which is all that is needed for an unbiased fit. According to Eq. 2, we can estimate the recombination rate from the axis intercept and the slope at An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e130.jpg, which are extracted from the data by fitting

equation image
(5)

to the data. The fit is done by minimizing the squared deviation, weighted by the relative uncertainty of the data points indicated by the error bars in Figure 2. An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e132.jpg was averaged over all pairs of sites contributing. To estimate a confidence interval for the estimate of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e133.jpg, the estimation was repeated 500 times with a set of 11 patients sampled with replacement from the original 11 patients.

The method relies on pairs of biallelic sites in a sample of size An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e134.jpg, where 3 out of the 4 possible haplotypes are observed. In large samples from a recombining population, one would naively expect to observe all possible haplotypes in most cases. However, due to the very skewed allele frequency distributions An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e135.jpg, the haplotype formed by the rare alleles is often sufficiently rare that it is missed even in large samples. We denote the alleles at site 1 by An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e136.jpg and at site 2 by An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e137.jpg, with the An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e138.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e139.jpg being the minor alleles. First, observe that the mean frequency of haplotype An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e140.jpg, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e141.jpg, at linkage equilibrium is An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e142.jpg. Hence An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e143.jpg for An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e144.jpg, in accord with An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e145.jpg observed in the data. We further assumed that the frequency of the unobserved haplotype, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e146.jpg, is significantly smaller than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e147.jpg. There are several reasons why An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e148.jpg is typically significantly smaller than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e149.jpg and hence smaller than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e150.jpg: (i) the condition that haplotype An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e151.jpg is not observed pushes An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e152.jpg down, (ii) minor alleles tend to be in negative linkage equilibrium if they are involved in selective sweeps, (iii) allele frequency spectra are even more skewed than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e153.jpg due to purifying selection. The degree to which these 3 reasons reduce An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e154.jpg can be best observed in Figure 2B at small An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e155.jpg. The Figure shows the probability to observe haplotype An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e156.jpg at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e157.jpg, given it was not observed at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e158.jpg. At small An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e159.jpg, linkage disequilibrium is not yet broken down by recombination and the probability to observe haplotype An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e160.jpg at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e161.jpg gives us a measure of An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e162.jpg at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e163.jpg, which is indeed much smaller than 1. Hence we can expand An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e164.jpg for small An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e165.jpg in Eq. 2.

Estimating the distribution of selection coefficients

The distribution of the rate of change of allele frequencies was estimated from pairs of successive time slices with the following characteristics: (i) site biallelic without indels at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e166.jpg with no constraint on timeslice An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e167.jpg, and (ii) monomorphic sites at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e168.jpg that are biallelic at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e169.jpg (without indels). In both cases there are two alleles An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e170.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e171.jpg whose frequencies can be meaningfully compared. The difference in allele frequencies were calculated as An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e172.jpg, where An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e173.jpg is the count of allele An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e174.jpg at time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e175.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e176.jpg is the sample size. Unless a third allele arose between An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e177.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e178.jpg (case (i)), An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e179.jpg and the common estimate of the rate of change, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e180.jpg, was added to the cumulative distribution. In rare cases where a third allele did arise, both An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e181.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e182.jpg have been added. Sometimes, a nucleotide changed from one state to another between time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e183.jpg or An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e184.jpg with no polymorphisms detected. In such a case, An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e185.jpg was used.

To detect the action of selection, we produced cumulative histograms of the rate of change in allele frequency for nonsynonymous and synonymous (putatively neutral) polymorphisms by averaging over all patients and all pairs of consecutive time points An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e186.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e187.jpg with both An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e188.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e189.jpg greater than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e190.jpg. We then tested for excess of fast changes among the nonsynonymous polymorphisms using the Kolmogorov-Smirnov test. The test statistic is the maximal vertical difference between the cumulative distributions An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e191.jpg divided by An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e192.jpg, where An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e193.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e194.jpg are the number of observations of synonymous and non-synonymous polymorphims. The Kolmogorov-Smirnov test was performed using the statistics module of SciPy [46].

To assess the role of hitch-hiking of synonymous polymorphisms with nearby selected non-synonymous polymorphisms, we produced a list of positions of non-synonymous polymorphisms whose allele freqency changes faster than An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e195.jpg per generation between time An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e196.jpg and An external file that holds a picture, illustration, etc.
Object name is pcbi.1000660.e197.jpg. Synonymous polymorphism closer than 100bp to any of these positions where excluded from the conditioned histogram.

Acknowledgments

We are grateful to Boris Shraiman and Erwin Neher for valuable suggestions and discussions and would like to thank two anonymous referees for constructive criticism. We also profited from discussion with participants of the Kavli Institute for Theoretical Physics on population genetics and genomics.

Footnotes

The authors have declared that no competing interests exist.

This work was supported by the National Science Foundation through Grant PHY05-51164, a LANL LDRD-DR grant (X9R8), and a Harvey L. Karp Discovery Award to RAN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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