# Evolution of Recombination Due to Random Drift

^{*}School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom^{†}Department of Zoology, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4

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*Corresponding author:*Department of Zoology, University of British Columbia, 6270 University Blvd., Vancouver, BC, Canada V6T 1Z4. E-mail: otto{at}zoology.ubc.ca

## Abstract

In finite populations subject to selection, genetic drift generates negative linkage disequilibrium, on average, even if selection acts independently (*i.e*., multiplicatively) upon all loci. Negative disequilibrium reduces the variance in fitness and hence, by Fisher's (1930) fundamental theorem, slows the rate of increase in mean fitness. Modifiers that increase recombination eliminate the negative disequilibria that impede selection and consequently increase in frequency by “hitchhiking.” Thus, stochastic fluctuations in linkage disequilibrium in finite populations favor the evolution of increased rates of recombination, even in the absence of epistatic interactions among loci and even when disequilibrium is initially absent. The method developed within this article allows us to quantify the strength of selection acting on a modifier allele that increases recombination in a finite population. The analysis indicates that stochastically generated linkage disequilibria do select for increased recombination, a result that is confirmed by Monte Carlo simulations. Selection for a modifier that increases recombination is highest when linkage among loci is tight, when beneficial alleles rise from low to high frequency, and when the population size is small.

RECOMBINATION breaks down the linkage disequilibrium generated between genes. In the absence of mutation and in a constant environment, the linkage disequilibrium present in a population will largely be a product of selection favoring specific gene combinations. In this case, one would expect that recombination would be detrimental and that genes that modify recombination rates would evolve to reduce recombination. This intuition is confirmed by the “reduction principle”: in models at equilibrium under viability selection, modifier alleles that reduce recombination are favored in the absence of mutation (Feldman 1972; Feldman *et al.* 1980; Altenberg and Feldman 1987). How then is recombination maintained at appreciable levels in most higher organisms? In this article, we explore one possibility: that recombination is favored because it reduces the linkage disequilibrium generated stochastically in a population by random genetic drift.

For recombination to have an influence on the process of evolution, linkage disequilibria must exist within the genome. Although disequilibria can be generated by stochastic as well as deterministic forces (Felsenstein 1988; Kondrashov 1993), most work on the evolution of recombination has focused on deterministic models that assume an infinite population size. In a single unstructured population of infinite size, the spread of modifier alleles that increase recombination has been observed under three selective regimes. When epistatic interactions fluctuate over time, recombination can be favored, but only if fluctuations occur over a timescale of a few generations, such that linkage disequilibria that were initially advantageous soon become disadvantageous (Charlesworth 1976, 1990; Maynard Smith 1978; Barton 1995a). When there is directional selection, increased recombination rates can evolve under a wider range of conditions: essentially, there must be weak negative epistasis among favorable alleles (Maynard Smith 1980, 1988; Charlesworth 1993; Barton 1995a). Weak negative epistasis implies that the fitness of a genotype with several advantageous alleles is slightly less than would be expected from the product of the fitnesses of each allele measured separately. In this case, selection generates negative linkage disequilibrium between favorable alleles, which reduces the genetic variance present in the population. Recombination destroys this disequilibrium, increasing the variance in the population and improving the response of the population to selection. Finally, purifying selection against recurrent deleterious mutations also favors the evolution of recombination (Feldman *et al.* 1980; Kondrashov 1982, 1984, 1988; Barton 1995a; Otto and Feldman 1997). Again, increased recombination is favored if there are weak negative epistatic interactions among mutations, implying that the mutations interact synergistically to cause a further than expected reduction in fitness.

While negative epistasis can provide the raw material (negative linkage disequilibrium) for recombination to be favored at a modifier locus, it is not clear why epistasis should generally be negative—that is, why fitness interactions should impede selection. Experimental evidence for negative epistasis among random mutations is limited (Rice 2002). Although negative epistasis has been observed in some cases (*e.g.*, Mukai 1969 in *Drosophila melanogaster*; de Visser *et al.* 1996 in *Chlamydomonas moewusii*; but see West *et al.* 1998), positive epistasis has been observed in other cases (*e.g.*, Seager and Ayala 1982, Seager *et al*. 1982 in *D. melanogaster*; but see Charlesworth 1990). Further studies in *Escherichia coli* (Elena and Lenski 1997) and *Aspergillus niger* (de Visser *et al.* 1997) have found significant evidence for both positive and negative epistasis but without a preponderance of either form. On theoretical grounds, negative epistasis has been invoked to account for the survival of populations in the face of a high rate of deleterious mutations. In the absence of epistasis, mean fitness would be exp(−*U*), where *U* is the genome-wide rate of deleterious mutations. Thus, mean fitness would be quite low in organisms with a high genomic mutation rate. The mean fitness of a population can be increased, however, in sexual populations if deleterious mutations interact synergistically (*i.e.*, exhibit negative epistasis; Kimura and Maruyama 1966; Kondrashov 1982, 1988). Yet synergistic interactions need not be invoked to explain the persistence of populations if the rate of deleterious mutation is low (Keightley 1997; Keightley and Caballero 1997; Keightley and Eyre-Walker 2000), if compensatory mutations are common, or if selection is “soft” such that the mean fitness of a population has little impact on the number of surviving individuals (Turner and Williamson 1968). Another approach has been to try to predict the form of epistasis, *a priori*, from models of the underlying biological processes affecting fitness. For example, using metabolic control theory, Szathmary (1993) found that negative epistasis among mutations predominates when flux through a metabolic pathway is under stabilizing selection but that positive epistasis predominates when maximal flux is favored. At this stage, both the experimental evidence and theoretical arguments concerning the nature of interactions among mutations remain equivocal and form a weak basis for explaining the predominance of sex and recombination.

If negative epistasis fails to explain the evolution of sex and recombination, what other process might be responsible? For insight into this question, it is worth returning to some of the earliest arguments for the evolution of sex. Morgan (1913), Fisher (1930), and Muller (1932) argued that an important advantage of sex is that beneficial mutations that arise in separate individuals can be brought together within the same individual. In the absence of sex, only one lineage will leave descendants in the long term, and beneficial mutations in all other asexual lineages will be lost. Thus, the probability of fixation of a new beneficial mutation is severely limited in the absence of sex by the fate of the rest of the genome in which it arises.

The source of disequilibrium implicit in the words of Morgan, Fisher, and Wright is not epistasis but random sampling. When a beneficial allele first arises, it occurs on (“samples”) one particular genetic background and, unless recombination occurs, the fate of the beneficial allele is tied to the evolutionary fate of that background. If the population were infinitely large, this sampling problem would disappear because all possible combinations of alleles would be generated instantaneously by mutation. Simulations of finite populations by Hill and Robertson (1966) demonstrated that the probability of fixation of beneficial alleles is limited by linkage among selected loci, a phenomenon now known as the Hill-Robertson effect (see also Felsenstein 1974). Furthermore, both Hill and Robertson (1966) and Felsenstein (1974) demonstrated that, even when epistasis is absent, the disequilibria that develop in a finite population tend to slow the spread of beneficial alleles, especially when recombination is rare. That is, even when fitness is multiplicative across loci, drift generates negative disequilibria among selected loci such that beneficial alleles at one locus become associated with deleterious alleles at other loci. In subsequent simulations, Felsenstein and Yokoyama (1976) found that modifiers that increase the rate of recombination spread in response to the negative disequilibria created by drift in the presence of selection.

The Hill-Robertson effect provides an alternative source for the negative linkage disequilibria that cause indirect selection for modifiers that increase recombination. The way in which random drift generates negative associations is most easily seen by considering two beneficial mutations that arise at different loci in the same generation. Let *D* measure the gametic linkage disequilibrium between the two loci in a diploid population of size *N*. Initially, the expected linkage disequilibrium is zero, because the very high chance that the alleles arise on different chromosomes [*D* = −1/(2*N*)^{2} with probability 1 − 1/(2*N*)] is counterbalanced by the extremely small chance of a strong positive association if the mutations arise in coupling [*D* = (1 − 1/(2*N*))1/(2*N*) with probability 1/(2*N*)]. Consider what would happen to the disequilibrium in the absence of recombination assuming that the mutations rise deterministically in frequency after their appearance. At first, both the negative and the positive disequilibria grow exponentially as the beneficial alleles increase within the population. Eventually, however, the growth of the positive disequilibrium slows down as the coupled beneficial alleles reach fixation. That is, beneficial alleles that arise in coupling rise rapidly and fix within the population, leading to the disappearance of the positive disequilibrium. The negative disequilibrium persists for a much longer period of time, until one or the other allele becomes fixed. Therefore, with multiplicative selection, the variance in disequilibrium present in the first generation ultimately leads to negative disequilibrium, on average.

This heuristic argument applies not only when disequilibrium is generated by the random appearance of mutations on particular genetic backgrounds, but also more generally when disequilibrium is generated by random genetic drift (Qureshi 1963; Latter 1965; Hill and Robertson 1966; Felsenstein 1974). As shown by Hill and Robertson, negative disequilibrium accumulates, on average, whenever genetic drift occurs in the presence of multiplicative selection. Although drift can generate both positive and negative disequilibria, which cancel out on average, selection does not act symmetrically upon the disequilibria thus created. Negative associations persist for longer simply because such associations (“good alleles on bad genetic backgrounds”) impede selection. Conversely, positive associations are relatively transient, as selection fixes the best combinations (good with good) and eliminates the worst ones (bad with bad). When averaged across replicates or loci or time, the overall level of disequilibrium expected between selected loci becomes more negative than expected in an infinite population. As we shall see, this process can be significant even in fairly large populations as long as the allele frequencies are initially low and subject to drift.

Analyzing the Hill-Robertson effect is made difficult by the fact that it requires a stochastic model tracking the frequencies of several chromosomes, each of which changes over time in a nonlinear fashion. While difficult, incorporating stochasticity into models of the evolution of sex and recombination is essential, because the fate of a modifier allele depends critically on whether or not drift is included (Felsenstein and Yokoyama 1976; Otto and Barton 1997, 2001). Previous analytical results have used branching processes with multiplicative selection to examine the probability that beneficial alleles are lost or fixed from very large populations. Barton (1995b) calculated the amount by which recombination increases the probability of fixation of beneficial alleles. We extended this analysis to determine the extent to which increased recombination would be favored at a modifier locus (Otto and Barton 1997). Because beneficial mutations are more likely to fix when associated with modifier alleles that increase recombination, these modifier alleles get dragged along as the beneficial alleles spread through the population. This process of genetic hitchhiking causes the modifier alleles, and hence the recombination rate, to increase within the population.

These analyses focused only on the linkage disequilibrium generated by the random occurrence of mutations on specific genetic backgrounds. Even when beneficial alleles are sufficiently common that their fixation is assured, however, random fluctuations in genotype frequencies generate linkage disequilibria among selected loci. Indeed, in our computer simulations, linkage disequilibria generated among beneficial mutations after they first arose affected the dynamics of a modifier of recombination (Otto and Barton 1997). Thus, we must consider the dynamics of disequilibrium generated by random genetic drift over the entire time course as beneficial alleles spread through a population. Because drift tends to generate negative linkage disequilibria, on average (with good alleles residing on bad genetic backgrounds), modifier alleles that increase the rate of recombination bring together good alleles from different individuals, generating indirect selection for recombination. We showed through simulation that this indirect selection is often stronger than the indirect selection for recombination generated by epistasis in populations of small (2*N* = 100) to intermediate (2*N* = 10,000) size (Otto and Barton 2001). More importantly, modifiers that increased the frequency of sex and recombination spread, on average, across a broad range of epistasis, from weak negative epistasis to positive epistasis, when drift was incorporated.

While previous analyses have shown that fluctuations in linkage disequilibrium can substantially increase the effects of a favorable allele on linked neutral loci (Stephan *et al.* 1992; Barton 1998), we lack an analytical framework to predict the dynamics of disequilibrium between selected loci in the presence of drift and selection. This article develops such a framework, allowing us to track fluctuations in linkage disequilibrium during the spread of beneficial alleles and to measure the impact on a modifier allele of recombination.

Our analysis makes two crucial simplifications: that the population size (*N*) is large enough that only leading terms (*O*(1/*N*)) need be included and that no allele is very rare. Furthermore, because we wish to concentrate on stochastic effects, we assume multiplicative fitnesses throughout. Including epistasis would induce linkage disequilibria and selection on recombination even in an infinite population. These simplifications allow us to approximate the full stochastic dynamics with recursion equations giving the mean and variance for the allele frequencies and disequilibria over time. Approximate solutions to these recursions are then obtained under the assumption of weak selection. This analysis shows that genetic drift creates variance in linkage disequilibrium, which in turn generates negative linkage disequilibrium, on average. The amount of linkage disequilibrium is larger when the beneficial alleles start at low frequency, when selection favoring the alleles is strong, and when linkage is tight. Under these circumstances, a small amount of linkage disequilibrium generated by random genetic drift while the alleles are rare is amplified by selection and only slowly destroyed by recombination. Finally, we determine the amount of indirect selection for recombination generated by drift in the presence of selection by examining evolutionary changes at a third locus that modifies recombination rates. By destroying the negative disequilibrium built up by drift in the presence of directional selection, a modifier allele that increases recombination rates improves the response of its carriers to selection and rises in frequency as a consequence.

## TWO-LOCUS MODEL

We begin by examining the linkage disequilibrium generated in a model with two loci, *j* and *k*, separated by a recombination rate of *r _{jk}*. We count genotypic frequencies immediately after meiosis and assume that multiplicative viability selection acts within the population. At locus

*j*, alleles

*Q*,

_{j}*P*segregate at frequencies

_{j}*q*and

_{j}*p*and are assumed to have relative fitnesses (1 −

_{j}*s*/2):(1 +

_{j}*s*/2), respectively. Similarly, allele

_{j}*P*has a selective advantage of

_{k}*s*over allele

_{k}*Q*at locus

_{k}*k*. This assumes that either the population is haploid or fitnesses in a diploid population are multiplicative within as well as between loci [

*i.e.*,

*PQ*:

*PP*have fitnesses (1 −

*s*/2)

^{2}:(1 −

*s*

^{2}/4):(1 +

*s*/2)

^{2}]. This notation is consistent with that of Barton and Turelli (1991) and Barton (1995a) and simplifies the algebra.

In an infinite population, if gametic linkage disequilibrium (*D _{jk}* = freq(

*P*)freq(

_{j}P_{k}*Q*) − freq(

_{j}Q_{k}*P*)freq(

_{j}Q_{k}*Q*)) is initially absent, it remains zero under multiplicative selection (Maynard Smith 1968). In this case, the allele frequencies at each locus increase logistically. Specifically, the ratio

_{j}P_{k}*p*/

*q*increases by a factor (1 +

*s*/2)/(1 −

*s*/2) every generation or by approximately exp(

*st*) after

*t*generations. The technique that we develop in this article assumes that the trajectory of allele frequencies closely follows the deterministic equations for this model but that small perturbations from this trajectory are caused by random drift. We develop recursion equations for the first and second moments of these perturbations to determine the combined effects of drift and selection over time.

### Fluctuations around the deterministic trajectory:

We assume a discrete-generation model in which a population is chosen by random sampling from the propagules produced by the previous generation. The frequencies of the chromosomes among the propagules are given by the deterministic recursions for either a haploid population (with selection on haploids, followed by random mating and meiosis, to produce haploid propagules) or a diploid population (with selection on diploids, followed by meiosis and random mating to produce diploid propagules). From these propagules, either 2*N* haploid or *N* diploid juveniles are sampled with replacement, implying that the chromosome frequencies follow a multinomial distribution. We census the population immediately after sampling occurs.

Random sampling in any particular generation causes genotype frequencies to differ from that expected on the basis of the propagule frequencies. We measure the perturbations generated by a single round of sampling by the random variables ζ* _{j}*, ζ

*(for perturbations in the allele frequencies at loci*

_{k}*j*,

*k*) and ζ

*(for perturbations in the linkage disequilibrium). The exact equations for change in the allele frequencies and linkage disequilibrium due to selection, recombination, and random genetic drift are given in appendix A.*

_{jk}The moments of the multinomial distribution can be used to determine the expected values of the perturbations generated by a single round of random sampling, as well as their variances and covariances. For instance, because random genetic drift does not cause any directional change in allele frequency, *E*[ζ* _{j}*] = 0, but the allele frequency will vary around its deterministic trajectory by an amount that is inversely proportional to the population size with Var[ζ

*] =*

_{j}*p*/(2

_{j}q_{j}*N*). The first and second moments for the perturbations are given in appendix A. Higher-order moments are of the order 1/

*N*

^{2}or smaller and are ignored in our analysis.

To describe the dynamics over multiple generations, we must keep track of the cumulative deviations from the deterministic trajectory, which we do using the vector **z** = (δ*p _{j}*, δ

*p*, δ

_{k}*D*). The random variable δ

_{jk}*p*measures the difference between the actual allele frequency at any point in time and the allele frequency predicted in the absence of random genetic drift. Similarly, δ

_{j}*D*measures the difference between the actual linkage disequilibrium and the disequilibrium predicted deterministically. Throughout, we assume that the perturbations in

_{jk}**z**remain small and keep only the leading-order terms.

In the text, we assume that there is no epistasis and that the initial level of disequilibrium, if present, is of the order *N*^{−1}. Under these assumptions, the disequilibrium predicted deterministically can be set to zero, so that δ*D _{jk}* measures the actual amount of disequilibrium in the presence of drift. The method can be extended, however, to account for epistasis or for strong initial disequilibrium by keeping track of perturbations from the deterministic trajectory for

*D*(see appendix A).

_{jk}Recursions for cumulative effects of drift in the presence of selection can be written as **z*** = *f*(**z**) and can be determined from (A2). Assuming that the perturbations are small, **z*** can be expanded in a Taylor series, 1where the subscripts (*a*, *b*, *c*) are set to *j* or *k* when referring to deviations in the allele frequencies and to *jk* when referring to the disequilibrium. The partial derivatives in (1) measure the sensitivity of each recursion to the perturbations caused by random sampling. They were derived from the recursions (A2) using the computer algebra package *Mathematica* (Wolfram 1991) and are given in supplementary information (S1; http://www.genetics.org/supplemental/). Note that all partial derivatives are evaluated along the deterministic trajectory (**z** = **0**) and are therefore independent of the perturbations. Taking the expectation of (1) gives 2Similarly, second moments are given by the expectation of the product of (1) with itself: 3Contributions to the first- and second-order moments arise by drift and are of *O*(*N*^{−1}) as long as the allele frequencies are not too small (appendix A). In contrast, starting from a population with a specific genotypic composition, higher moments are initially absent, are generated by drift only to *O*(*N*^{−2}), and are henceforth ignored. Note that the recursion equation (3) for *E* also describes the change in the covariance, , because *E**E* is *O*(*N*^{−2}) and can be ignored. Similarly, *E* describes the change in the variance, Var, of the perturbations around the deterministic trajectory. Finally, the *E*[*z _{c}*ζ

*] in (3) are also*

_{a}*O*(

*N*

^{−2}) and are dropped.

Having dropped terms of *O*(*N*^{−2}) in (2) and (3) and using the fact that many of the partial derivatives in supplementary information (S1) equal zero, fluctuations in the allele frequency *p _{j}* around the deterministic trajectory satisfy the following recursions: 4a4bFluctuations in the allele frequency

*p*are described by analogous equations, with subscripts

_{k}*j*and

*k*interchanged. Of greater relevance to the evolution of recombination are fluctuations that occur in the disequilibrium, δ

*D*, around the deterministic equilibrium of

_{jk}*D*= 0: 5a5b5c

_{jk}*E*is given by (5c), with subscripts

*j*and

*k*interchanged. These equations apply even if selection coefficients vary through time, provided that there is no frequency dependence. Frequency-dependent selection could be modeled in a similar fashion, taking into account that the partial derivatives will contain additional components depending on the form of selection. With either constant or variable selection coefficients, recursions (4) and (5) can be evaluated numerically (

*Mathematica*package available upon request) to predict the means, variances, and covariances of the perturbations over time.

The recursions for the perturbations can be further analyzed under the assumption that selection is weak (appendix B). Solution (B3) for the perturbations involving the disequilibrium is derived assuming that linkage is sufficiently loose relative to selection that the terms involving disequilibria reach a steady-state level over a faster timescale than the changes in allele frequencies; this steady-state level is known as the quasi-linkage equilibrium or QLE (Kimura 1965; Nagylaki 1993; Barton 1995a). Solution (B5) is derived assuming that both selection coefficients and recombination rates are small enough that the recursions can be well approximated by differential equations, which are then solved.

### Development of disequilibria under directional selection:

We now consider how the above equations can be used to infer how disequilibrium develops in finite populations. Variance in linkage disequilibrium is generated directly by random drift, contributing the *p _{j}q_{j}p_{k}q_{k}*/2

*N*term to (5b). As long as the direction of selection remains the same, all of the terms in (5c) are positive , indicating that a positive covariance between the frequency of each beneficial allele and linkage disequilibrium will develop as a result of this variance in linkage disequilibrium. This covariance arises because allele frequencies increase more rapidly in populations that happen to have positive linkage disequilibrium. The presence of both variance in disequilibrium and covariance between allele frequencies and linkage disequilibrium then causes the expected linkage disequilibrium (5a) to depart from zero. Because ∂

^{2}δ

*D*

^{*}

_{jk}/∂δ

*D*

^{2}

_{jk}is negative (appendix A), variance in disequilibrium across replicate populations leads, on average, to negative disequilibrium. Mathematically, this occurs because the mean fitness,

*W̅*, given by (A1) is higher with positive disequilibrium than with negative disequilibrium for any given set of allele frequencies. Consequently, the amount of positive disequilibrium gets divided by a larger quantity (

*W̅*

^{2}) in (A2b) and becomes relatively smaller in the next generation. Another, perhaps more intuitive, explanation, is that the expected disequilibrium across replicates becomes negative because selection is more efficient when good alleles are found on good genetic backgrounds (

*i.e.*, when disequilibrium is positive by chance), causing positive disequilibrium to decay more rapidly than negative disequilibrium whenever it occurs. The positive covariance generated between each allele frequency and the linkage disequilibrium leads the expected disequilibrium to become even more negative (∂

^{2}δ

*D*

^{*}

_{jk}/∂δ

*p*

_{j}∂δ

*D*

_{jk}, ∂

^{2}

*D*

^{*}

_{jk}/∂δ

*p*

_{k}∂δ

*D*

_{jk}< 0). This occurs because those populations that happen to have positive disequilibrium and, consequently, faster rising allele frequencies (generating the positive covariance) also have higher mean fitness values in future generations. The linkage disequilibrium in future generations will then continue to be divided by a larger quantity (

*W̅*

^{2}) in (A2b), compounding the reduction in positive disequilibrium relative to negative disequilibrium. As a consequence of all of these effects, the expected value of linkage disequilibrium becomes negative even though it was initially zero and even though selection acts independently on the two loci.

Provided that allele frequencies are intermediate [*i.e.*, *p*, *q* are *O*(1)], the average amount of negative disequilibrium that develops is always proportional to 1/*N*: if the population size is doubled, the amount of disequilibrium generated by drift and selection is halved. When linkage is loose, disequilibrium does not accumulate appreciably over time. With tight linkage, however, substantial amounts of disequilibrium can accumulate within a population, especially when allele frequencies are initially low. This is a consequence of the fact that a small amount of disequilibrium generated by drift between rare alleles becomes a large amount of disequilibrium as the alleles approach a frequency of ^{1}/_{2}. If the initial allele frequency is too small [*O*(1/*N*)], as is the case with a single favorable mutation, then higher-order terms in the Taylor series approximation (1) become appreciable and can no longer be ignored.

Figure 1 shows the distribution of allele frequencies over time in a population of size 2*N* = 10,000 when alleles *P _{j}* and

*P*are initially at frequency

_{k}*p*

_{0}= 0.01 (

*s*=

_{j}*s*=

_{k}*r*= 0.1). The thick curve shows the mean trajectory (which is nearly equal to the deterministic trajectory), while the thin curves indicate the range within which allele frequencies lie 95% of the time as calculated from (4). These predictions were compared to simulation results based on a Monte Carlo simulation that sampled 2

_{jk}*N*chromosomes each generation using a multinomial distribution, with parameters equal to the frequency of each chromosome given by the deterministic recursions. Simulations (dots) indicate that (4) accurately captures the effects of drift on allele frequencies. Note that drift causes substantial variation in the trajectory of allele frequencies even in a population of size 10,000. This variation arises primarily during the early stages when only a small number of beneficial alleles are present (2

*Np*

_{0}= 100) and represents random accelerations or decelerations in the spread of favorable alleles. On average, beneficial alleles spread slightly less rapidly than expected in an infinite population, indicating that the Hill-Robertson effect is present, if small, in populations of size 10,000. The effects of drift become even more important when there are initially fewer alleles within the population (

*e.g.*, 2

*Np*

_{0}≤ 10). In this case, however, iterating (4) and (5) overestimates the Hill-Robertson effect (results not shown), because higher-order moments in the perturbations are ignored and yet play an important role whenever allele frequencies are near zero.

Figure 2 shows how drift affects (a) the variance in disequilibrium scaled relative to the allele frequencies, *E*/, (b) the correlation between δ*p _{j}* and disequilibrium,

*E*/, and (c) the average disequilibrium. The average magnitude of the linkage disequilibrium is greater when the initial frequency of the beneficial alleles is lower, because random associations generated in the early stages persist and are amplified as the beneficial alleles rise in frequency (compare dotted, dashed, and solid curves, which correspond to initial frequencies 0.001, 0.01, and 0.1). In this example, where

*s*=

_{j}*s*=

_{k}*r*= 0.1, the QLE approximation (B3) fails to capture the influence of the initial allele frequency, which is pronounced when selection is strong relative to recombination [

_{jk}*e.g.*, the QLE prediction (B3a) for the scaled variance is constant at 0.00053]. On the other hand, the weak selection approximation (B5) overestimates the impact of initial fluctuations (compare thin to thick curves), although calculations with weaker selection (0.01) show that the approximation then agrees closely.

Figure 3 shows that the expected disequilibrium scales with 1/(2*N*) and matches the disequilibrium observed in simulations. The only exception is when the initial number of copies is very small (in this example, for 2*Np*_{0} = 10). With few initial copies of the beneficial alleles, three issues arise that are not accounted for by our analysis. First, the fluctuations around the deterministic trajectory become so large that the Taylor approximation to Equation 1 is no longer adequate. Second, with few initial copies, beneficial alleles do not necessarily fix within the population. Third, those beneficial alleles that do fix are more likely to be positively associated with other beneficial alleles. Therefore, during the first few generations, rare beneficial alleles are more likely to be lost when negative disequilibrium develops (thereby destroying the disequilibrium) and more likely to persist when positive disequilibrium develops. Therefore, the average disequilibrium among beneficial alleles that have survived loss while rare might very well become positive, at least during the initial spread of the beneficial alleles. There is some evidence for this effect within the simulations: in the fifth generation, significantly positive disequilibrium was found when 2*N* = 10,000 and initial frequencies were 0.001 but in no other case. These positive associations will counteract the accumulation of negative disequilibrium to some extent, explaining, in part, the smaller amount of disequilibrium observed for this case (Figure 3, *p*_{0} = 0.001).

Figure 4 shows the most extreme value of the expected linkage disequilibrium observed during the spread of alleles *P _{j}* and

*P*as a function of

_{k}*r*. Results from Monte Carlo simulations are well approximated by iterating Equations 4 and 5 (compare dots to thick dashed curve for

_{jk}*p*

_{0}= 0.01 and thick solid curve for

*p*

_{0}= 0.1). The QLE approximation (B3c; thin curve) is accurate only for loose linkage (

*r*>

_{jk}*s*,

_{j}*s*= 0.1) and fails to capture the dependence of disequilibrium on initial allele frequencies. With tight linkage, random fluctuations produced when small numbers of the favorable alleles are present persist to produce large fluctuations when the alleles become common. Consequently, the most extreme disequilibrium observed is very sensitive to starting allele frequencies when linkage is tight. When

_{k}*p*

_{0}= 0.01 and

*r*< 0.01, drift and selection are substantial enough to drive the expected linkage disequilibrium to near its minimum value (−0.25), even though disequilibrium is initially absent and even though selection acts independently on all loci (

_{jk}*s*=

_{j}*s*= 0.1).

_{k}### Including a modifier of recombination:

The negative disequilibrium generated by drift in the presence of directional selection reduces the genotypic variability within a population and slows the response of that population to selection. A modifier allele that increases recombination among the selected genes will regenerate the genetic variation in fitness hidden in linkage disequilibrium. In particular, individuals carrying such a modifier allele are more likely to produce offspring of the fittest genotype. This effect can select for increased recombination at modifier loci, an effect examined in this section. Suppose that a modifier of recombination segregates at locus *i*. We assume that the modifier has no direct effect on fitness and alters recombination rates by a small amount δ*r*. If terms of *O*(δ*r*^{2}) are neglected, then the only significant terms involve the change in recombination between the selected loci (denoted δ*r _{j}*

_{,}

_{k}_{|}

*). The modifier may also alter its own rates of recombination with other loci (δ*

_{i}*r*

_{i}_{,}

_{j}_{|}

*, etc.) and may show dominance (δ*

_{i}*r*

_{j}_{,}

_{k}_{|}

_{i}_{,}

*). However, these effects are negligible for weak modifiers (Barton 1995a).*

_{i}The equations for the effects of drift on the modifier frequency and its associations *D _{ij}*,

*D*,

_{ik}*D*are given by (A2c–e). Disequilibria are measured as central moments of effects (Barton 1995a). For two loci, this definition coincides with the standard two-locus measure of gametic disequilibrium. For three loci,

_{ijk}*D*is equal to ∑

_{ijk}*g*[

**X**](

*X*−

_{i}*p*)(

_{i}*X*−

_{j}*p*)(

_{j}*X*−

_{k}*p*), where the sum is taken over all chromosomes, each at frequency

_{k}*g*[

**X**], where

**X**is a vector indicating whether or not an allele is present at each locus (

*i.e.*,

*X*is 0 if allele

_{i}*P*is absent and 1 if it is present). A modifier allele that increases recombination between loci

_{i}*j*and

*k*becomes associated with the fittest genotype,

*P*, if that genotype is underrepresented within the population (

_{j}P_{k}*i.e.*, if

*D*is negative). This association is represented by the first term in (A2e), which generates

_{jk}*D*in proportion to −δ

_{ijk}*r*

_{j}_{,}

_{k}_{|}

*. This three-way association then generates positive two-way associations,*

_{i}D_{jk}*D*and

_{ij}*D*, between a modifier allele that increases recombination and the beneficial alleles at loci

_{ik}*j*and

*k*(A2d). All three associations,

*D*,

_{ij}*D*,

_{ik}*D*, cause changes in the frequency of the modifier allele (A2c). Note that because the modifier is assumed to have a small effect, these equations are linear in

_{ijk}*D*,

_{ij}*D*,

_{ik}*D*, and all will change in proportion to δ

_{ijk}*r*

_{j}_{,}

_{k}_{|}

*.*

_{i}Equations 2 and 3 can be used to find the mean and variance of the three-locus system, as before. The first step is to find the first and second differentials of (A2c–e), such as ∂δ*p*^{*}_{i}/∂δ*D*_{jk}. These differentials are calculated as in supplementary information (S1) and are available upon request. The second step is to calculate the expected values and the covariances for the perturbations involving the modifier by incorporating these differentials into Equations 2 and 3. Following this method and dropping terms of *O*(*N*^{−2}), the recursion describing the cumulative effects of drift and selection at loci *j* and *k* on the frequency of a modifier allele at locus *i* is 6where φ* _{j}* = 1 +

*s*(

_{j}*p*−

_{j}*q*)/2 and φ

_{j}*= 1 +*

_{k}*s*(

_{k}*p*−

_{k}*q*)/2. The expected change in the modifier is driven both by expectation terms

_{k}*E*[δ

*D*] and by covariances such as

_{ij}*E*[δ

*p*δ

_{j}*D*], each of which is proportional to δ

_{ij}*r*

_{j}_{,}

_{k}_{|}

*/(2*

_{i}*N*). The recursions for these expectation terms are complicated but can be calculated symbolically in a straightforward way from (2) and (3). These recursions may be iterated numerically or may be approximated by assuming that selection is weak as described in supplementary information (S2; http://www.genetics.org/supplemental/).

Assuming weak selection and loose linkage, the system approaches quasi-linkage equilibrium, and the per generation change in the frequency of a modifier at QLE can be found (supplementary information, S2). This gives a complicated function of the recombination rates (S2.4), which is proportional to δ*r _{j}*

_{,}

_{k}_{|}

*/2*

_{i}p_{i}q_{i}V_{j}V_{k}*N*, where and are the additive genetic variances in diploid fitness at the two selected loci. Modifier alleles that increase recombination (δ

*r*

_{j}_{,}

_{k}_{|}

*> 0) thus increase in frequency even in the absence of epistasis in finite populations.*

_{i}Expression (S2.4) takes a simple form if there is no genetic interference and if recombination rates are small in absolute terms but large relative to the selection coefficients, as required for QLE (*i.e*., *s _{j}*,

*s*≪

_{k}*r*,

_{jk}*r*,

_{ij}*r*,

_{ik}*r*≪ 1). Assuming, without loss of generality, that locus

_{ijk}*k*is the right-most locus, we can describe the relative position of the modifier by defining α =

*r*/

_{ik}*r*; α varies between zero and one when the gene order is

_{jk}*jik*and is greater than one when the gene order is

*ijk*. Then, keeping only the leading-order term in (S2.4), the per generation change in the modifier becomes 7awhere 7b7cFigure 5 illustrates

*g*(α) and shows how evolution of a modifier of recombination depends on the relative positions of the loci, α. The function

*g*(α) rises rapidly near α = 0 and α = 1, where it is approximately

*g*(α) ≈ 2/α

^{2}and 2/(1 − α)

^{2}, respectively, although the QLE approximation breaks down if recombination rates become too small relative to selection. For gene order

*jik*,

*g*(α) reaches a minimum value of 37

^{1}/

_{3}, when the modifier is halfway between the selected loci. For gene order

*ijk*,

*g*(α) lies between 2/(1 − α)

^{2}and 4/(1 − α)

^{2}and decreases rapidly for relatively distant modifier loci.

Equation 7a demonstrates that the effect that drift and selection have on a modifier of recombination falls precipitously as recombination becomes more frequent within the genome (∼*r*^{−5}_{jk}). If selected loci are scattered randomly over a genome, then the effect of a modifier on tightly linked loci will dominate its dynamics. As recombination rates become comparable with the selection coefficients, however, the QLE assumptions break down, making it impossible to determine the average force on the modifier when selected loci are scattered randomly across a genome. Note that as selection and recombination decrease together toward zero in (7a), selection on the modifier approaches a positive limit, inversely proportional to population size (*i.e.*, if *s*, *r*, and δ*r* are all small and of the same order, Δ*E*[δ*p _{i}*] ∼ 1/

*N*). This limit appears puzzling, because the average frequency of the modifier should not change in the absence of selection. There is no contradiction here, because we have assumed throughout that

*s*≫ 1/

*N*when dropping terms of

*O*(

*N*

^{−2}). Thus, our approximations leading to (7) require that

*N*increases toward infinity as

*s*decreases toward zero, causing Δ

*E*[δ

*p*] to decrease implicitly as selection weakens.

_{i}In Figures 6–9, we describe changes in the frequency of the modifier using the standardized function, β* _{i}* = Δ

*p*/(δ

_{i}*r*

_{j}_{,}

_{k}_{|}

*), which we call the*

_{i}p_{i}q_{i}*selection gradient*acting on the modifier allele. When multiplied by the genetic variance in recombination within the population, , the selection gradient describes the expected increase in recombination (= 2Δ

*p*δ

_{i}*r*

_{j}_{,}

_{k}_{|}

*). The selection gradient can also be related to the effective selection coefficient,*

_{i}*s*

_{e}, defined as the function that causes Δ

*p*to equal

_{i}*s*

_{e}

*p*. For a modifier of effect δ

_{i}q_{i}*r*

_{j}_{,}

_{k}_{|}

*, the effective selection coefficient is*

_{i}*s*

_{e}= β

*δ*

_{i}*r*

_{j}_{,}

_{k}_{|}

*. It is important to recall that the modifier does not directly experience selection. Rather, the selection gradient measures the indirect selection arising from genetic associations between the modifier locus and the directly selected loci*

_{i}*j*and

*k*. To describe the total effect of selection at linked loci on recombination, we use the

*cumulative selection gradient*, ∑β

*, which represents the sum of β*

_{i}*from the initial generation up until a given generation, or the*

_{i}*net selection gradient*, β

_{i}_{,net}, which represents the sum of β

*over the course of selection at loci*

_{i}*j*and

*k*until fixation of the beneficial alleles.

As a consequence of the fact that disequilibrium is more likely to develop by drift when allele frequencies are initially low at selected loci (Figure 3), the selection gradient acting on a modifier is larger under these conditions, as shown in Figure 6. Simulation results for the change in frequency of the modifier are in general agreement with iteration of (2) and (3), even though the assumptions of weak selection and a weak modifier are violated in the simulations (where *s _{j}* =

*s*= 0.1 and δ

_{k}*r*

_{j}_{,}

_{k}_{|}

*=*

_{i}*r*/2). The largest discrepancies are observed when the initial number of alleles is low (

_{jk}*e.g.*, when 2

*N*= 10,000 and

*p*

_{0}= 0.001), such that the perturbations due to drift become larger than allowed by our analysis. It is worth emphasizing that the simulation results presented in Figure 6 represent the mean ± standard errors based on 10

^{6}replicates. Consequently, the standard deviations are enormous (multiply the length of the bars by 1000). Thus, while our analysis accurately predicts the mean change in frequency of a modifier, the effect of one bout of selection on a modifier is highly variable with only two selected loci and one modifier locus. Only when many loci affect fitness and recombination does the frequency of a modifier of recombination increase consistently (Otto and Barton 2001; Iles

*et al*. 2003).

The net selection gradient, β_{i}_{,net}, acting on a modifier over the entire time course of substitution at two linked loci is shown in Figure 7. The net increase of the modifier can be large with tight linkage: for example, with *r _{ij}* =

*r*= 0.01, Δ

_{jk}*p*= 38

_{i}*p*δ

_{i}q_{i}*r*

_{j}_{,}

_{k}_{|}

*. For very tight linkage, the change in the modifier is underestimated by iteration of (2) and (3) because the disequilibria caused by drift become substantial and higher-order effects begin to contribute [*

_{i}*e.g.*, for

*r*= 0.001 and

_{jk}*r*= 0.01, Δ

_{ij}*p*= 334

_{i}*p*δ

_{i}q_{i}*r*

_{j}_{,}

_{k}_{|}

*from simulations but only 75*

_{i}*p*δ

_{i}q_{i}*r*

_{j}_{,}

_{k}_{|}

*from (2) and (3)]. The QLE approximation (S2.4) is fairly accurate for loose linkage (*

_{i}*r*,

_{ij}*r*>

_{jk}*s*,

_{j}*s*= 0.1) but substantially overestimates the change in modifier frequency for tight linkage, as shown by the thin lines in Figure 7.

_{k}### Fluctuating polymorphisms:

The analysis described in this article can be used whether selection coefficients are constant or vary over time, as long as the population size is reasonably large and the numbers of each allele are never very small. With directional selection, indirect selection on a modifier ceases with the fixation of beneficial alleles. With fluctuating selection, on the other hand, polymorphism at the selected loci can be maintained over longer periods of time, prolonging selection for increased recombination. Even in the absence of epistatic interactions among loci and even when a population is initially in linkage equilibrium, drift in a finite population subject to fluctuating selection will lead to the accumulation of disequilibrium that reduces genetic variability in fitness. We have used Equations 2 and 3 to track the change in modifier frequency in a population subject to sinusoidal fluctuations in selection at two loci with *s _{j}*[

*t*] = α

*Cos(2π ·*

_{j}*t*/τ) and

*s*[

_{k}*t*] = α

*Cos(2π ·*

_{k}*t*/τ + Φ), where τ is the period (assumed to be the same for both loci) and Φ measures the shift in phase of selection at the two loci (Figure 8). Note that when the direction of selection changes, the selection gradient on the modifier weakens (Figure 8a) or becomes negative (Figure 8b). Nevertheless, recombination rates rise over successive cycles.

The QLE result (S2.4) continues to apply when selection fluctuates over time, as long as recombination is frequent relative to the strength of selection and to the changes over time in selection (*s _{j}*[

*t*],

*s*[

_{k}*t*], 1/τ ≪

*r*,

_{ij}*r*,

_{jk}*r*,

_{ik}*r*). Under sinusoidal selection, the product of the additive genetic variances in fitness at the two loci (

_{ijk}*V*) is ∼ if the allele frequencies do not change substantially over a cycle. This result indicates that, when linkage is loose, indirect selection on a modifier will be stronger when the allele frequencies remain near

_{j}V_{k}^{1}/

_{2}, such that the genetic variance in fitness remains high.

For weak selection and very tight linkage (*r*τ ≪ 1), we can obtain an approximate solution to the recursion equations (S2.2) and (S2.3). Assuming that several cycles of fluctuating selection have passed such that the dynamical system has reached a steady state, the solution to these recursions may be approximated as described in supplementary information (S3; http://www.genetics.org/supplemental/). The change in the modifier per generation averaged over one cycle is, to leading order in the recombination rates, 8where *H _{jk}* is the harmonic mean value of

*p*, Var and Var measure the variance in the rate of allele frequency change over the cycle, and Cov measures the covariance in these rates at the two loci: 9a9bFigure 9 compares Equation 8 to the iterated solution of (2) and (3) as well as to the QLE solution. Equation 8 is accurate only when selection is approximately the same strength as the product of the recombination rates and the period (α ≈

_{j}q_{j}p_{k}q_{k}*r*τ as in Figure 9b), which reflects the order assumptions made in supplementary information (S3). In other cases, the iterated solution of (2) and (3) should be used.

As with the QLE solution (S2.4), Equation 8 indicates that the change in allele frequency at a locus that modifies recombination is proportional to the effect of the modifier on recombination (δ*r _{j}*

_{,}

_{k}_{|}

*), the inverse of the population size (2*

_{i}p_{i}q_{i}*N*), and the square of the selection coefficients at each selected locus (through Var Var). Unlike the QLE solution for loose linkage, Equation 8 is not maximized when the allele frequencies remain near

^{1}/

_{2}, because the harmonic mean of the allele frequencies in the denominator is much smaller when the allele frequencies approach zero or one. Consequently, with tighter linkage, selection on a modifier is maximized when there are high-amplitude fluctuations in allele frequencies, such that beneficial alleles reach low frequencies (where drift is stronger) as well as intermediate frequencies (where selection on a modifier is more effective). Selection on the modifier is also strongest when changes in allele frequency at the two loci occur at similar times, maximizing Cov

^{2}.

Our analysis indicates that there are two qualitatively different regimes with respect to selection on a modifier of recombination, depending on whether recombination rates are high relative to selection [the QLE regime, (S2.4)] or not [the tight linkage regime, (8)]. In the former case, the disequilibrium generated by drift dissipates rapidly, so that disequilibrium can be approximated as the consequence of recent selection (within a time frame proportional to 1/*r*), which is well captured by the current additive genetic variance in fitness. When recombination rates are weak relative to selection, however, disequilibrium generated while alleles are at low frequency persists as the alleles rise to higher frequency, and the current additive genetic variance in fitness no longer determines the change in frequency of the modifier. Instead, the force on the modifier is strongest whenever the harmonic mean allele frequency at the two loci, *H _{jk}*, is small yet the selected alleles spend time at intermediate frequency (with high Var, Var, and Cov). We also see a qualitatively different relationship between the recombination rates and the change in the modifier frequency in these two regimes. Equation 8 shows that a modifier of recombination changes in frequency by an amount proportional to

*r*

^{−3}for tight linkage, while the QLE analysis (see Equations 7) predicts a change that is proportional to

*r*

^{−5}. Thus, while both analyses predict that selection for recombination is strongest under tight linkage, the selection gradient is overestimated by the QLE analysis (Figure 9).

## DISCUSSION

By breaking down linkage disequilibria that limit the response of a population to selection, sex and recombination can hasten the rate of adaptive evolution of a population. Consequently, genotypes that are best adapted to a novel environment are more likely to be recombinant and more likely to carry modifiers that increase the recombination rate. As these adaptive genotypes sweep through a population, modifier alleles that increase recombination also rise in frequency. Disequilibria do not, however, always limit the response of a population to selection. In fact, the genetic variance for a trait and consequently the response to selection are increased by positive disequilibria among alleles contributing to that trait. If positive disequilibria predominate, recombination is selected against, because the genetic variance and the rate of adaptation are lower among recombinant individuals. In short, a necessary condition for the evolution of increased recombination in a single unstructured population is that negative disequilibria predominate among favored alleles.

Within an unstructured population, negative disequilibrium is expected to arise under two different scenarios. First, negative disequilibrium develops when there is negative epistasis among selected loci, implying that a genotype carrying multiple beneficial alleles has a lower fitness than the product of the fitnesses of the genotypes carrying only one of the beneficial alleles. Second, negative disequilibrium arises by the combined action of random genetic drift and selection, even in the absence of epistasis. Sampling error during reproduction of a finite population causes some genotypes to become more or less common than expected, generating variance in disequilibria. Selection eliminates positive disequilibria more efficiently than it eliminates negative disequilibria, because negative disequilibrium reduces the genetic variance upon which selection acts. Consequently, the average disequilibrium over loci or over replicate populations becomes negative over time.

In this article, we have demonstrated that the random genetic drift caused by sampling in finite populations selects for increased recombination even in populations that are initially in linkage equilibrium and that are subject to selection acting independently upon all loci. Our method keeps track of the perturbations in genetic associations caused by genetic drift and acted upon by selection, using recursion equations for the first and second moments of these perturbations. The main assumptions of the method are that (a) recombination is altered by only a small amount by a modifier gene, (b) the population is large, and (c) each allele is initially present in several copies. Although we have simplified the analysis by ignoring epistasis, the method can be extended to include epistasis by explicitly tracking the deterministic trajectory for the disequilibrium *D _{jk}*. Using recursions for the moments describing the distribution of a population around its expected trajectory, the effects of drift and selection can be easily and efficiently studied. Simulations of selection in finite populations require large amounts of computer time, particularly since millions of replicates are needed to obtain accurate measurements of the frequency of a modifier. The simulations that we performed (see figures) indicate that Equations 2 and 3 provide accurate estimates of the average change that occurs at a modifier locus, as long as alleles are always present in multiple copies. It should be kept in mind that we used strong selection (

*s*= 0.1) and a strong modifier (δ

*r*

_{j}_{,}

_{k}_{|}

*=*

_{i}*r*/2) in the simulations to magnify the amount of indirect selection acting on the modifier locus. The approximations should be more accurate with weaker selection and weaker modifiers, although it then becomes exceedingly difficult to detect an effect on the modifier with only two selected loci and one modifier locus. Nevertheless, the method provides a promising route to predicting the evolution of recombination when multiple loci of weak effect underlie the selected trait, using the approximation that each pair of selected loci independently asserts an influence on the modifier(s) (see Barton 1995a).

_{jk}We have calculated the expected change in frequency of the modifier caused by random linkage disequilibria with selected loci, presented in the figures using the selection gradient, β* _{i}* = Δ

*p*/(δ

_{i}*r*

_{j}_{,}

_{k}_{|}

*). For a modifier of given effect, the effective selection acting on a modifier of recombination is*

_{i}p_{i}q_{i}*s*

_{e}= β

*δ*

_{i}*r*

_{j}_{,}

_{k}_{|}

*. Although absolute values of*

_{i}*s*

_{e}are small, they can still be much stronger than the effect of random drift, since 2

*Ns*

_{e}can be large. For example, for

*s*= 0.1,

*r*=

_{ij}*r*= 0.1, 2

_{jk}*N*= 10,000,

*p*

_{0}= 0.01, the effective selection is at most

*s*

_{e}= 0.008 δ

*r*

_{j}_{,}

_{k}_{|}

*(on the basis of the simulations reported in Figure 6a), but 2*

_{i}*Ns*

_{e}is 80 δ

*r*

_{j}_{,}

_{k}_{|}

*. Even stronger selection for recombination is expected when beneficial alleles start at lower frequency and/or when recombination rates are lower than the selection coefficients (see Figures 7 and 9 and Equation 8). This argument is complicated by the extra stochasticity introduced by the hitchhiking process itself, which reduces the effective population size witnessed by the modifier locus; this effect can be substantial when the rate of selective sweeps is comparable to the recombination rate (Barton 2000, Equation 4).*

_{i}The QLE approximation for the change in the modifier (7, or more accurately S2.4) shows that when selection is weaker than recombination, the advantage of recombination is proportional to the product of the additive variance in fitness at the directly selected loci and decreases steeply with recombination. This implies that when we average over selective sweeps that are scattered across the genome, the main contribution will come from selection on loci that are most closely linked to the modifier. We cannot use the QLE results to calculate this average, however, because the QLE approximation breaks down for tight linkage. However, it is possible to calculate this average assuming weak selection using integral solutions similar to (A2.4) and (A2.5); this leads to an expression proportional to the square of the total variance in fitness (N. H. Barton, unpublished results).

By comparing Figure 6a and 6b, it can be seen that a modifier allele that increases recombination rates is most strongly selected in smaller populations. Although our approach breaks down when the population size becomes so small that some alleles are present in only a few copies (specifically, approximating Equation 1 to order 1/*N* becomes inadequate), we have shown through simulation that randomly generated disequilibria generate strong selection for increased recombination in populations of small to intermediate size (Otto and Barton 2001). In fact, we found that modifiers of recombination were more influenced by disequilibria generated by drift than by disequilibria generated by epistasis unless the population size was fairly large. Directional selection experiments, such as the one performed by Korol and Iliadi (1994) in *D. melanogaster* (see Otto and Barton 2001 for further review), have demonstrated that recombination rates can increase substantially in small populations subject to strong selection. Our work has shown that the evolution of recombination in such cases can be explained by stochastically generated disequilibria that, over time, accumulate to oppose the action of selection. Furthermore, the stochastic advantage of recombination can even favor the evolution of sex in the face of a twofold cost of sex as long as modifiers increase the amount of sex by small amounts (Otto and Lenormand 2002).

For large populations, our results indicate that selection on recombination should be inversely proportional to the population size. Thus, while drift in the presence of selection can account for increased recombination rates in populations of small size, it would appear to provide a weak basis for explaining the maintenance of sex and recombination in natural populations whose effective sizes are tens of thousands or more. There are three reasons, however, to believe that drift might drive the evolution of sex and recombination even in large populations. First, if population sizes tend to be small when conditions are harsh and selection is strong, the advantage of breaking down randomly generated disequilibria could drive the evolution of sex and recombination during those rare periods in time that selection is intense. Second, the cumulative selective force on a modifier of recombination might be substantial if there are many loci responding to selection within the genome. Indeed, simulation results reported by Iles *et al*. (2003) demonstrate that selection acting on a modifier of recombination increases as a function of the number of selected loci, even when the initial additive genetic variance in fitness is held constant. Third, and most importantly, the forces described in this article can be substantial even in very large populations as long as those populations are spatially structured (see Martin *et al*. 2005). That is, the disequilibrium generated by drift in the presence of selection depends strongly on the local (deme) size and not just the total size of a population. Because every population is finite, spatial structure is common and selection acts at multiple loci throughout a genome, and because drift in the presence of selection causes an accumulation of good alleles on bad genetic backgrounds, the ubiquity of sex and recombination might well be explained by the fact that genetic mixing allows beneficial alleles in different individuals to be brought together, as initially proposed by Morgan (1913), Fisher (1930), and Muller (1932).

## APPENDIX A:

### EXACT EQUATIONS FOR TWO AND THREE LOCI

We use the methods of Barton and Turelli (1991) to derive exact recursions for two selected loci (labeled *j*, *k*), and a modifier of small effect at locus *i*. We census immediately after juveniles have been randomly sampled from the propagules produced by the previous generation. After the census, haploid populations undergo selection, random mating, and meiosis to produce the next generation of haploid propagules, where we assume that the number of potential propagules is so large that these processes can be treated deterministically. The model also applies to a diploid population, as long as fitness is the product of the fitness contribution of each haplotype. For diploids, the life cycle involves a census of diploid juveniles, selection, meiosis, syngamy of gametes to produce diploid propagules, and random sampling of *N* juveniles. Equations A1.2 of Barton (1995a) differ slightly, because diploid fitness was assumed instead to be the sum of the fitness contribution of each haplotype. We assume that the modifier allele *P _{i}* increases recombination between loci

*j*and

*k*by a small amount δ

*r*

_{j}_{,}

_{k}_{|}

*. For ease of analysis, we follow Barton (1995a) and set the recombination rate between loci*

_{i}*j*and

*k*to

*r*+ 2

_{jk}*q*δ

_{i}*r*

_{j}_{,}

_{k}_{|}

*in*

_{i}*P*individuals,

_{i}P_{i}*r*+ (

_{jk}*q*−

_{i}*p*)δ

_{i}*r*

_{j}_{,}

_{k}_{|}

*in*

_{i}*P*individuals, and

_{i}Q_{i}*r*− 2

_{jk}*p*δ

_{i}*r*

_{j}_{,}

_{k}_{|}

*in*

_{i}*Q*individuals. (Note that the

_{i}Q_{i}*effect*of the modifier, measured by the difference between genotypes, is not frequency dependent.) Let

*r*and

_{ij}*r*be the recombination rates between the modifier locus and the selected loci,

_{ik}*j*and

*k*, respectively, and let

*r*be the probability of recombination between any of the loci

_{ijk}*i*,

*j*,

*k*; these terms are used only to determine the offspring of heterozygous

*P*individuals and so their values in other modifier genotypes are immaterial. Disequilibrium terms involving the modifier locus (

_{i}Q_{i}*D*,

_{ij}*D*,

_{ik}*D*) will be dominated by the indirect effects of changing recombination between the selected loci and will be proportional to δ

_{ijk}*r*

_{j}_{,}

_{k}_{|}

*. In the following, we assume that the modifier is weak and keep only linear-order terms in δ*

_{i}*r*

_{j}_{,}

_{k}_{|}

*.*

_{i}Let φ* _{j}* = 1 +

*s*(

_{j}*p*−

_{j}*q*)/2 and φ

_{j}*= 1 +*

_{k}*s*(

_{k}*p*−

_{k}*q*)/2 measure the mean fitness of each locus considered separately. Accounting also for the disequilibrium within a population, the mean fitness is equal to A1The three-locus recursions are A2aA2bA2cA2dA2ewhere

_{k}*r*= (

_{ijk}*r*+

_{ij}*r*+

_{ik}*r*)/2 for any gene order with and without interference.

_{jk}*p*

^{*}

_{k}and

*D*

^{*}

_{ik}can be obtained from (A2a) and (A2d), respectively, by interchanging subscripts

*j*and

*k*. These equations assume only that δ

*r*

_{j}_{,}

_{k}_{|}

*is small and are valid for strong selection (*

_{i}*s*,

_{j}*s*) and strong linkage disequilibrium between the selected loci (

_{k}*D*). (A2) can be derived from the three-locus recursion equations presented in Feldman (1972). A supplementary Mathematica package is available that derives (A2) as well as the other main results given in appendixes a and b.

_{jk}We assume that 2*N* haploid (or *N* diploid) juveniles are sampled from the propagules produced by the parental generation according to a multinomial distribution as in the standard Wright-Fisher model (Ewens 1979). The moments of the multinomial distribution can therefore be used to determine the expected values of the perturbations in (A2) and the covariances between them. Although there is no expected change in allele frequencies due to drift, *E*[ζ* _{i}*] =

*E*[ζ

*] =*

_{j}*E*[ζ

*] = 0, the expected change in linkage disequilibrium is*

_{k}*E*[ζ

*] = −*

_{jk}*D*/(2

_{jk}*N*). Because disequilibrium terms involving the modifier locus are proportional to δ

*r*

_{j}_{,}

_{k}_{|}

*, the expected amount of drift in these terms will be of the order δ*

_{i}*r*

_{j}_{,}

_{k}_{|}

*/*

_{i}*N*, which is assumed to be very small and is ignored. Thus, we need to quantify the effects of drift only on the following variances and covariances, which are written to order (1/

*N*): Because

*E*[ζ

*] is either 0 or order 1/*

_{a}*N*, the

*E*[ζ

*ζ*

_{a}*] required in Equation 3 are, to order 1/*

_{b}*N*, equal to the cov(ζ

*, ζ*

_{a}*) given here. Note that the allele frequencies and disequilibrium in these terms are evaluated after the parental generation produces propagules. When selection is weak, however, the allele frequencies and disequilibrium from the previous census can be used to obtain leading-order approximations.*

_{b}These derivations apply for any amount of disequilibrium, allowing the method to be extended to models where there is a deterministic source of disequilibrium (*e.g*., epistasis or an initially high level of disequilibrium). In the text and appendix B, we assume that the disequilibria remain *O*(1/*N*) throughout the process. In this case, *E*[ζ* _{jk}*] and the covariance terms become negligible. In the numerical iterations of (2) and (3), however, no assumptions are made about the order of the disequilibrium, so that the Mathematica package (available upon request) can be used regardless of the initial level of disequilibrium.

## APPENDIX B:

### WEAK SELECTION APPROXIMATIONS FOR TWO SELECTED LOCI

When selection is weak (*s _{j}* and

*s*of order

_{k}*s*, where

*s*≪ 1), the recursions for the perturbations given by (1) can be further approximated by B1aB1bThroughout this appendix, values for allele

*P*can be obtained from values given for allele

_{k}*P*by interchanging subscripts

_{j}*j*and

*k*. In (B1b), we have included the leading-order terms for each of the perturbations with respect to

*s*even when these are

*O*(

*s*

^{2}). These lower-order terms are critical to the initial development of disequilibria when δ

*D*is zero.

_{jk}Taking expectations as in (4) and (5), assuming that the perturbations including the disequilibria are *O*(*N*^{−1}), ignoring terms that are *O*(*N*^{−2}), and using the results of appendix A for the contributions due to drift gives B2aB2bB2cB2dB2eSupplementary information (S2) describes similar weak selection recursions for the three-locus case.

Equations B2 can be solved explicitly. First, consider loose linkage (*r _{jk}* ≫

*s*,

_{j}*s*). In this case, the disequilibrium rapidly approaches a QLE distribution, with

_{k}*E*and

*E*[δ

*D*] changing slowly relative to the action of recombination. One can therefore set

_{jk}*E*to

*E*[δ

*D*], etc., in (B2) and solve for the QLE values. For the disequilibrium: B3aB3bB3c, B3cshows that negative disequilibrium is generated on average in a finite population subject to multiplicative selection. The magnitude of this disequilibrium is quite small with loose linkage but increases rapidly as

_{jk}*r*becomes small. It also increases with the strength of selection acting on loci

_{jk}*j*and

*k*and with the amount of genetic variability at these loci. Note that the covariance between allele frequencies and linkage disequilibrium (B3b) always contributes more than the variance in linkage disequilibrium to the accumulation of negative linkage disequilibrium by a factor 2(1 −

*r*)/

_{jk}*r*.

_{jk}With tight linkage (1/*N* ≪ *s* ≈ *r _{jk}* ≪ 1), disequilibrium decays slowly over time, and we can no longer assume quasi-linkage equilibrium. We can, however, move from the discrete recursion equations to continuous-time approximations, which can be more readily solved. We introduce the method in general and then apply the solution to (B2).

In the ODE approach, the discrete recursion equations (B2) all have the form B4awhere *C* is a constant involving recombination rates, *F*[*t*] is a function of time involving terms such as −*s*(*p* − *q*), and *G*[*t*] is a function of time involving the expectations, *E*[ ]. When selection and recombination are rare, the change in *x*[*t*] over a short time period can be approximated by *x*[*t* + Δ*t*] − *x*[*t*], where *x*[*t* + Δ*t*] is obtained from (B4a) under the assumption that the amount of selection and recombination in a shorter interval of time, Δ*t*, scales with Δ*t* as *s*Δ*t* and *r*Δ*t*. Taking the limit of (*x*[*t* + Δ*t*] − *x*[*t*])/Δ*t* as Δ*t* goes to zero yields an ordinary differential equation: B4bwhere *c*, *f*, and *g* are used to denote *C*, *F*, and *G* to linear order in *s* and *r*, using a Taylor series that ignores any cross products or higher-order terms. The solution to differential equations of the form (B4b) is B4c

The integrals involving *f*[*t*] in (B4c) can be solved by noting that, when selection is weak and frequency independent, selection can be defined according to the change in allele frequency that it causes: *p*′ = *dp*/*dt* = *spq*. When *f*[*t*] = −*s*(*p* − *q*), for example, applying this definition and integrating yields Letting *v*[*t*] be the product of the appropriate allele frequencies (depending on which terms are included in *f*[*t*]), (B4c) becomes B4dThe solution (B4d) applies regardless of how selection varies over time. Barton (2000) used a similar method to find the net hitchhiking effect of fluctuating selection on linked neutral loci and gave an approximation valid for tight linkage (*r* ≪ *s*; his Equation 4).

Following this procedure and assuming that terms of order *r _{jk}s_{j}* are negligible relative to

*r*and

_{jk}*s*, we obtain the following weak selection approximations: B5aB5bB5cWe have focused on the effects of drift on the disequilibrium; analogous equations can be obtained for the effects on allele frequencies. Note that if allele frequencies increase from some low value faster than linkage disequilibrium is dissipated by recombination (

_{j}*s*,

_{j}*s*>

_{k}*r*), the initial distribution (

_{jk}*E*

_{0}[ ]) will make a substantial contribution. Even when disequilibrium is initially absent, substantial disequilibrium can build up if the beneficial alleles start at low frequency, because periods with low values of

*p*contribute disproportionately to the integrals in (B5).

_{j}q_{j}p_{k}q_{k}## Acknowledgments

We are grateful to Aneil Agrawal, Mark Kirkpatrick, Thomas Lenormand, Guillaume Martin, Marcy Uyenoyama, and three anonymous reviewers for comments on the manuscript. Financial support for this research was provided by the Biotechnology and Biological Science Research Council, the Natural Environment Research Council, and the Natural Sciences and Engineering Research Council of Canada.

## Footnotes

Communicating editor: M. K. Uyenoyama

- Received June 24, 2004.
- Accepted January 10, 2005.

- Genetics Society of America