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Evolution: The Molecular Landscape

Cold Spring Harbor’s 74th Symposium
The Molecular Landscape
Edited by Bruce Stillman,
David Stewart, and
Jan Witkowski,
Cold Spring Harbor Laboratory


Chapter 19 Notes

Measuring Selection


Direct Measurement of Selection

Measuring Selection Directly Is Difficult

Genetic Manipulation Is Needed to Find the Effect of a Specific Genetic Difference

Selection Can Be Measured by Correlating Fitness Differences with Genetic Variation

Enzyme Variation Is Often Selected

Selection Can Be Measured by Correlating Quantitative Traits with Fitness

Selection on a Correlated Trait Can Cause Apparent Selection on the Trait Being Measured

Selection on Multiple Traits

Quantitative Traits Are Often Subject to Natural Selection

Indirect Measurement

Selection Can Be Measured Indirectly through Its Interaction with Other Forces

Cline Widths Reflect the Strength of Selection

Deviations from the Molecular Clock Indicate Selection

A High Rate of Amino Acid Evolution Relative to Synonymous Change Indicates Positive Selection: Ka/Ks

The Pattern of Variation within Populations Can Reveal Deviations from the Neutral Theory

Under the Neutral Theory, Polymorphism within Species Should Be Proportional to Divergence between Species

Selection on Linked Loci

Selection Can Be Detected through Its Effects on Linked Neutral Variation

Selective Sweeps Cause a Sudden Burst of Coalescence

Measuring Distortions in the Pattern of Neutral Variation

Deleterious Mutations Reduce Variation at Linked Sites: Background Selection

Balancing Selection Can Increase Neutral Variation

Polymorphism in the Alcohol dehydrogenase Gene

Selection on Noncoding DNA

Codon Usage Bias is Caused by Weak Selection for Translational Efficiency and Accuracy

Selection for Pairing in RNA Molecules Can Be Detected

Functional Sequences Can Be Detected by Looking for Conserved Sequences

The Extent of Selection

It Is Difficult to Measure the Extent to Which Fitness Itself Is Inherited

The Overall Extent of Selection Is Limited by Genetic Load

There Are Several Kinds of Load, Corresponding to Different Kinds of Selection

Deleterious Mutations and Balancing Selection Also Cause a Genetic Load

Interactions between Genes Can Greatly Alleviate the Load


Direct Measurement of Selection

Measuring Selection Directly Is Difficult

Figure 19.1A is from Thatcher et al. (1998). Figure 19.1B is from Fowler et al. (1997).

Rozen et al. (2003) survey the fitness effects of beneficial mutations that arose in a laboratory population of Escherichia coli. They find a distribution of effects that has a mode at s ~ 10% per generation.

Genetic Manipulation Is Needed to Find the Effect of a Specific Genetic Difference

Figure 19.2 is from Giaever et al. (2002).

Selection Can Be Measured by Correlating Fitness Differences with Genetic Variation

Figure 19.3A is from Van Delden et al. (1978). Figure 19.3B is from Berry and Kreitman (1993). Evidence for selection on Adh is reviewed by Van Delden (1982), Laurie-Ahlberg (1985), and Chambers (1988).

Polymorphism at PGI in Colias butterflies is reviewed by Watt and Dean (2000). LDH polymorphism in Fundulus is reviewed by Powers et al. (1993).

Enzyme Variation Is Often Selected

Figure 19.5 is from Rosenzweig et al. (1994).

The survey of the first published polymorphisms in Drosophila melanogaster is made by Gillespie (1991, Table 1.3).

The study of enzyme loci in chemostat populations of E. coli is discussed by Hartl (1989) and Dykhuizen (1990); see also Dean (1989, 1995).

Selection Can Be Measured by Correlating Quantitative Traits with Fitness

The first measurement of selection on a quantitative trait was by Bumpus (1899). These data were discussed by Johnston et al. (1972) and reanalyzed by O’Donald (1973).

Endler (1986) extensively reviews “natural selection in the wild.”

Figure 19.6B is from Bell (1985). Vivid floral displays attract pollinators, thus ensuring that the ovule is pollinated and also exporting pollen to fertilize other plants. Bell (1985) showed that flowers function primarily to export pollen (and, hence, increase the male component of fitness); much smaller flowers would suffice to ensure that the ovule is fertilized. Sexual selection in plants is discussed further on pages 573–584.

Selection on a Correlated Trait Can Cause Apparent Selection on the Trait Being Measured

The example in which stabilizing selection on bristle number acts via larval viability is from Barnes and Kearsey (1970) and Kearsey and Barnes (1970).

Barton (1990) and Kondrashov and Turelli (1992) discuss how apparent stabilizing selection on a trait can be caused by pleiotropic effects of the underlying genes.

The red deer example of Figure 19.8, showing stasis despite apparently strong selection, is from Kruuk et al. (2002). For a broader review of this issue, see Merila et al. (2001).

Manipulations of swallow tail streamers (Fig. 19.9B) are from Rowe et al. (2001).

Lande and Arnold (1983) introduced the statistical methodology for identifying the target of selection among a set of correlated traits. This approach is close to the early research program of Pearson and Weldon (p. 21), who also measured selection on sets of correlated traits (e.g., see Fig. 1.23). See, for example, Pearson (1903).

Quantitative Traits Are Often Subject to Natural Selection

Endler (1986) compiled a review of measurements of “natural selection in the wild,” which suggested surprisingly strong selection. Kingsolver et al. (2001) survey many more studies, which suggest a skewed distribution of selection strength (most weak, some strong) and also an upward publication bias (Fig. 19.10). Morjan and Rieseberg (2004) and Rieseberg et al. (2003) compile a still larger survey and bring estimates of selection gradients together with estimates of quantitative trait locus (QTL) effect sizes to find the strength of selection on an individual QTL. Hereford et al. (2004) review estimates of the selection gradient, standardized by the trait mean, and discuss why estimates are remarkably high.

If disruptive selection were as widespread as is suggested by Figure 19.10B, populations would be unstable. They would either evolve rapidly to one or another extreme, maintain very high levels of genetic variation, or split into distinct species (see pp. 650–653). However, much of the scatter in Figure 19.10B may be random sampling error; extremely large studies are needed to get accurate estimates of γ. In addition, γ is estimated by fitting a quadratic curve to the data. If the actual relationship between fitness and trait has a more complicated shape, then disruptive selection may be estimated even if selection is purely directional. (For example, the pattern in Fig. 19.7B yields a positive estimate of γ [see Schluter 1988].) Finally, all of these estimates are for components of fitness, rather than fitness itself; they usually measure survival or mating success over a brief period. Selection is likely to fluctuate over time and over different stages of the life cycle, and so we do not know whether in the long run it acts to increase or decrease the variance of a trait. (Recall the large fluctuations in selection on beak shape in the Galapágos finches [Fig. 17.25].)

Orr (1998) devised an ingenious test for selection on quantitative traits, based on the sign of effects of the underlying QTL. If divergence has been neutral, then one expects that it will be due to QTL with a mixture of positive and negative effects. In contrast, if directional selection has acted, increase in the trait will be due to QTL with predominantly positive effects. Rieseberg et al. (2003) review applications of this test, which gives evidence for widespread selection.

The evidence for selection discussed in this chapter is all from present-day population. Bell et al. (2006) use an exceptionally detailed series of stickleback fossils to show that directional selection has acted on their defensive armor. See Hendry (2008) for a commentary.

Indirect Measurement

Selection Can Be Measured Indirectly through Its Interaction with Other Forces

The estimates of weak selection against nulls in D. melanogaster was by Langley et al. (1981). Note that only one of the 20 loci surveyed gave nulls that were lethal when made homozygous. One possible problem with this survey is that alleles that do not function under the conditions of the electrophoretic assay may still function in vivo.

The strength of selection can be estimated from clines in the grasshopper Chorthippus parallelus (Butlin et al. 1991).

Barton and Hewitt (1985) and Barton and Gale (1993) discuss how selection can be estimated from cline width.

Selection can also be detected by looking for markers that show excessive variation from place to place, as measured by FST. See Chapter 16 Web Notes.

Deviations from the Molecular Clock Indicate Selection

Figure 19.11 is from Kimura (1983, Chapter 4.2). Gillespie (1991, Chapter 3.5) has argued most strongly that variation in the rate of the molecular clock implies that selection causes bursts of substitutions.

Smith and Eyre-Walker (2003) partition variation in rate into effects of gene, lineage, and gene-by-lineage interaction. It is the latter that gives evidence for deviations from neutrality (i.e., variation in rates between genes, after lineage-specific effects have been taken out). Smith and Eyre-Walker find significant gene-by-lineage interactions for amino acid substitutions, but not for synonymous variation. This supports Gillespie’s arguments against the neutral theory.

Gillespie (1991, pp. 30–32) gives a detailed account of the lysozyme example of Figure 19.12.

A High Rate of Amino Acid Evolution Relative to Synonymous Change Indicates Positive Selection: Ka/Ks

Figure 19.13 is from Yang et al. (2000). Methods for estimating excess rates of amino acid substitution are reviewed by Yang and Bielawski (2000).

Swanson (2003) reviews recent examples of high rates of amino acid evolution.

The Pattern of Variation within Populations Can Reveal Deviations from the Neutral Theory

Figure 19.14 is from Ward and Beardmore (1977), discussed by Kimura (1983, Chapter 8.2). Observed frequencies are compared with the distribution predicted by the neutral theory, on the assumptions that every mutation produces a new allele (i.e., the infinite-alleles model), and assuming the same neutral mutation rate at all genes (blue columns in Fig. 19.14). The latter assumption is highly implausible, since these genes vary in degree of selective constraint and in size. However, allowing for variation between them makes rather little difference to the distribution (black columns in Fig. 19.14).

In the Xanthine dehydrogenase example, Ewens’ (1972) sampling formula was applied. See Coyne (1982).

Ohta has been primarily responsible for developing the extension to the neutral theory, in which slightly deleterious alleles segregate at low frequency within populations, and occasionally are fixed by chance (Box 18.1). For reviews, see Ohta and Gillespie (1996) and Ohta (1996, 2002).

Charlesworth and Eyre-Walker (2007) give evidence for the slightly advantageous back-mutations that are required to compensate for fixation of slightly deleterious mutations.

Under the Neutral Theory, Polymorphism within Species Should Be Proportional to Divergence between Species

The survey of Figure 19.16 is from Skibinski et al. (1993).

Smith and Eyre-Walker (2002) and Fay et al. (2001) applied the McDonald–Kreitman test across large numbers of genes.

The figure of 13,600 for gene number in D. melanogaster is that used by Smith and Eyre-Walker (2002) and is taken from the first publication of the genome sequence of D. melanogaster (Adams et al. 2000). Because it is difficult to identify genes simply by examining the sequence, this figure is still uncertain. Hild et al. (2003) estimate 17,000 genes; Yandell et al. (2005) reevaluate these genes and come to a more widely accepted estimate of about 14,000, which is not much higher than the earlier estimate (C. Bergman, pers. comm.).

In the McDonald–Kreitman test, divergence between species is compared with polymorphism within them. We can also compare divergence between populations within species with polymorphism within them, using the statistics FST and QST. For examples where this approach is used to detect selection, see Chapter 16 Web Notes.

For more on the test described in Box 19.1, see McDonald and Kreitman (1991).

Selection on Linked Loci

Selection Can Be Detected through Its Effects on Linked Neutral Variation

The Duffy example is from Hamblin and Di Rienzo (2000) and Hamblin et al. (2002).

Selective Sweeps Cause a Sudden Burst of Coalescence

Kim and Stephan (2002) use simulations to show that chance reductions in diversity are likely to span a small region of genome (Fig. 19.17).

Hey (1997) provides evidence that human mtDNA may have experienced recent selection.

The example of a local reduction in diversity in European D. melanogaster (Fig. 19.18) is from Harr et al. (2002). This method identifies regions of exceptionally low diversity; it is not at all straightforward to show that these extremes are more extreme than would be expected by chance. Even if there were no selection, some regions of genome would have exceptionally low diversity. Further evidence is needed to determine whether these really have experienced selective sweeps.

Storz et al. (2004) make a genome-wide scan of variability in human populations, which identifies candidates for selective sweeps in non-African populations, an example similar to that in Figure 19.18.

In bacterial populations, which reproduce asexually, the phenomenon of hitchhiking causes a phenomenon known as periodic selection, which in principle allows advantageous substitutions to be detected. An initially homogeneous population will accumulate variation by mutation. However, when a favorable mutation sweeps through the population, this variation is eliminated. For further discussion, see Dykhuizen (1990) and Notley-McRobb and Ferenci (2000).

Variation around the tb1 locus suggest that it was selected during the domestication of maize (p. 317). Sequencing the tb1 gene from many populations of maize and teosinte revealed equivalent levels of variations within the coding region for both maize and teosinte alleles. In contrast, sequencing the DNA immediately 5′ of the start site for tb1 transcription indicated that the maize DNA exhibits very low levels of nucleotide variation, whereas the teosinte DNA 5′ to the transcription start site of tb1 had greater amounts of variation. The DNA immediately 5′ of the tb1 transcription start site is a likely location for regulatory domains controlling the transcription of tb1. These results can be interpreted as a molecular signature of selection for regulatory changes in tb1 during the domestication of this plant (Wang et al. 1999).

Williamson et al. (2007) identify 101 regions of the human genome that have patterns of variation that suggest a recent selective sweep. Teshima et al. (2006) show that these kinds of scans will often miss selective sweeps. Thus, it is hard to get a reliable estimate of the rate of sweeps.

Several statistics have been proposed that test distributions of allele frequencies for deviations from neutrality. We explain these here.

Shapiro et al. (2007) use data from multiple Drosophila genome sequences to estimate the rate of selective sweeps; they argue that nearly one-third of the amino acid substitutions between D. melanogaster and its close relatives were fixed by positive selection. Hahn (2008) gives a wide-ranging commentary on this work, arguing that the neutral theory should no longer be taken as our null model.

Deleterious Mutations Reduce Variation at Linked Sites: Background Selection

The concept of background selection was introduced by Charlesworth et al. (1993).

Under the simplest model, where each mutation reduces fitness by a factor (1 – s), the distribution of numbers of mutations is Poisson (Chapter 28) with mean U/s. Therefore, the fraction of the population with no deleterious mutations is eU/s. This can be small if the average number of mutations per chromosome, U/s, is large. (For example, if U/s = 20, this fraction is e–20 ~ 2 × 10–9.)

Hudson and Kaplan (1995b) derive the elegant result for the strength of background selection with recombination, eU/R.

Figure 19.20 is from Baudry et al. (2001). The two genes on the right are in regions with high recombination compared with the three genes on the left, but this has no significant effect on diversity. (See Chapter 23 Web Notes.)

Figure 19.21A is from Andolfatto and Przeworski (2001). The comparison between observed and predicted diversity along the third chromosome of D. melanogaster (Fig. 19.21B) is from Hudson and Kaplan (1995a).

Balancing Selection Can Increase Neutral Variation

The example of polymorphism shared between species (Fig. 19.22) is from Richman (2000).

The Brassica sequence comparisons in Figure 19.23 are from Charlesworth (2002). One of the pairs of alleles that are found across different species has been shown to have retained the same incompatibility pattern, which requires that a particular combination of SP11 and SRK alleles has stayed together throughout the divergence of the species.

Hughes (1999) reviews MHC polymorphism.

The first attempt to detect balancing selection through its effect on neutral differences was the observation by Kreitman (1983) that variation is increased around the F/S polymorphism in the Alcohol dehydrogenase gene of D. melanogaster. This study was highly influential, because it was among the first to demonstrate the possibility of detecting selection using sequence variation. However, the example has turned out to be not quite so straightforward. The F allele is relatively recent and has not in fact had time to accumulate many differences from the older S allele. The increase in diversity is instead found within the S allele of Adh and is also seen in an African sample, which did not contain the F allele (Begun et al. 1999). At present, the cause of this peak of increased variability is unknown.

The effects of inversions on sequence diversity in Drosophila are reviewed by Andolfatto et al. (2001).

Figure 19.24B is from Stolz et al. (2003). For further evidence on the origins of the color alleles, see Velez and Feder (2006).

The HLA region of the human genome shows exceptionally high diversity, and indeed, the depth of this genealogy traces back to before the human–chimpanzee divergence. This reflects long-term balancing selection at this locus. However, there are few such regions in the human genome, and so long-term balancing selection is exceptional (Bubb et al. 2006). Nevertheless, balancing selection could still maintain substantial variation, because it would not increase neutral variation substantially even if it kept the selected alleles in a stable polymorphism for up to about Ne generations. Indeed, transient balancing selection may be associated with reduced variation t-linked neutral loci, as with Drosophila chromosome inversions, or human malaria-resistance polymorphisms (p. 542).

Selection on Noncoding DNA

Codon Usage Bias is Caused by Weak Selection for Translational Efficiency and Accuracy

Figure 19.25 is from Akashi (2001). For a general review of codon usage bias, see Duret (2002).

One possible complication is that transcription of genes may alter the pattern of mutation. In principle, such mutational biases could explain correlations between codon usage and gene expression. However, in D. melanogaster and Caenorhabditis elegans, codon bias tends to increase GC content at the third position; yet, no such increase is seen in introns. This contrast between introns and third positions argues against mutation bias.

The example involving alternative splicing (p. 543) is from Iida and Akashi (2000).

Evidence for selection on translational accuracy, rather than rate, comes from Akashi (1994).

Figure 19.26 is from Smith and Eyre-Walker (2001), who discuss why suboptimal codons are used in E. coli.

Evidence that, in Drosophila, codon bias is weaker in regions of low recombination was given by Kliman and Hey (1994). Marais et al. (2001) find no such relationship in nematodes and argue that the Drosophila pattern is due to a mutational bias toward GC in regions of high recombination. Hey and Kliman (2002) give a convincing reply (see note 2 above).

Gillespie (2000, 2001) makes the argument that most random drift is due to selective sweeps, thus explaining why codon bias is not absolute even in very abundant species such as E. coli (Fig. 19.26).

Selection for Pairing in RNA Molecules Can Be Detected

The bicoid example in Figure 19.28 is from Parsch et al. (2000).

If selection were strong relative to drift (i.e., Nes large), then changes from one complementary pair to another could occur only if both occurred in the same sequence and increased without being broken up by recombination. This would give a rate of substitution proportional to the square of the mutation rate (~µ2) compared with a rate equal to the mutation rate (~µ) for neutral sites, which is an extremely slow rate. Further evidence that change is by random drift, in opposition to selection, is that long stems evolve faster than short stems. This is presumably because a single change in a long paired region is less disruptive and so is opposed by weaker selection.

Just as with codon bias, the complementary pairing is not perfect: In some positions and in some species, the bases do not complement. This suggests that selection may not be strong enough to maintain the optimal sequence in the face of mutation and random drift. The strength of selection can be estimated from the rate at which complementary pairs evolve relative to the changes in other regions not involved in pairing. If selection is weak relative to drift (Nes ~ 1), a single mutation can get fixed by chance, despite slightly reducing the fidelity of pairing. The rate of such random fixations is ~µ eNes (Fig. 18.6). Once such a slightly deleterious mutation is fixed, changes that restore pairing will quickly be established, and so the site may shift to a new pair. The regions of bicoid involved in base pairing diverge between Drosophila species about half as fast as nonpairing regions, which implies that eNes ~ 1/2, and Nes ~ 0.7 (Parsch et al. 2000).

The example of segregating haplotypes in Drosophila Adh (Fig. 19.29) is from Kirby et al. (1995).

Functional Sequences Can Be Detected by Looking for Conserved Sequences

The mouse–human example of Figure 19.30 is from Shabalina et al. (2001).

Keightley and Gaffney (2003) use the mouse–rat comparison to estimate that the amount of functional noncoding sequence is similar to that of coding sequence; both contribute a mutational load of 0.22 per diploid per generation.

Waterston et al. (2002) (cited by Bentley 2003) estimate from comparison between mouse and human genomes that 5% of the human genome is conserved, 1.3% of that is coding sequence and the rest is noncoding.

Figure 19.31 is from Ludwig et al. (1998).

Several recent studies have identified short sequences that are absolutely conserved across a wide taxonomic range and that are presumably functional (e.g., Bejerano et al. 2004). However, a direct test of this hypothesis gave surprising results: Mice lacking sequences that were absolutely conserved between mouse and rat were apparently normal (Gross 2007; Ahituv et al. 2007).

A class of conserved “microRNA” molecules has recently been found to be involved in gene regulation. See Chen and Rajewsky (2006) and Michalak (2006).

See Chapter 26 Web Notes for evidence that noncoding regions in hominids are less constrained than those in rodents, possibly because the latter have larger effective population size.

Asthana et al. (2007) compare human, chimpanzee, mouse, dog, and rat genomes to show that there are widespread constraints on noncoding sequence.

The Extent of Selection

It Is Difficult to Measure the Extent to Which Fitness Itself Is Inherited

Burt (1995) reviews evidence on the genetic variance in fitness.

The study of Australian twins is from Kirk et al. (2001).

The study of heritability of fitness in red deer is from Kruuk et al. (2000). Kruuk (2004) gives a more recent review of this approach.

A special issue of the Proceedings of the Royal Society B (vol. 275, no. 1635) is devoted to studies of selection in natural populations of animals.

Evidence for the general importance of frequency-dependent selection comes from the observation that mixtures of varieties of crops yield more than homogeneous stands (see Bell 1997). This is partly due to better exploitation of diverse soil resources by diverse genotypes but may also be due to the slower spread of disease when the population contains a mixture of resistant and sensitive individuals. However, we do not know how many polymorphisms might be maintained by this general advantage to diversity. This kind of selection can give an advantage to sex and recombination (pp. 670–672).

Shabalina et al. (1997) measured the rate of fitness degradation in D. melanogaster. Rates of fitness decline in the absence of selection are reviewed by Burt (1995) and by Lynch et al. (1999). Burt (1995) explores the argument that the additive variance in fitness must equal the rate of fitness decline when selection is removed. Burt (1995) also discusses the fitness decline due to migration and estimates that gene flow between natural populations of plants reduces fitness by approximately 0.1–0.7% per generation.

The Overall Extent of Selection Is Limited by Genetic Load

Crow (1993) and Barton and Partridge (2000) give general reviews of genetic load.

There Are Several Kinds of Load, Corresponding to Different Kinds of Selection

Haldane (1957) introduced the idea of a “cost of natural selection.” Maynard Smith (1968, 1976) argued that it is better termed a “lag load” (i.e., the loss of mean fitness arising because the population lags behind the current environment). Kimura (1961) uses an intriguing argument to relate the load to the increase in information introduced by natural selection; see also Kimura (1995). That argument was developed independently by Worden (1995).

For further details on Biston, see the Majerus (1998, 2005).

The exact formula for the substitution load depends on the degree of dominance. As we saw in Box 17.2 (p. 476), a completely dominant allele increases faster at first, and therefore leads to a smaller net load of loge(1/p). This is half as great as with additive action.

Deleterious Mutations and Balancing Selection Also Cause a Genetic Load

Haldane (1937) first showed that the loss of fitness due to mutation is independent of selection coefficient. Muller (1950) coined the term mutation load; he had shown that radiation generates mutations, and he emphasized the damaging consequences of atomic weapons tests. See Crow (1993, 1997, 2000) and Crow and Abrahamson (1997).

Keightley and Eyre-Walker (2000) review estimates of genomic mutation rate, U, and conclude that U is likely to be much less than 1 in organisms with a short generation time (but see the reply by Kondrashov 2000). However, U may be substantial in longer-lived organisms such as humans; a recent direct measurement of mutation rate suggests that U ~ 1.2 for Drosophila (Haag-Liautard et al. 2007).

Balancing selection entails a segregation load. If heterozygotes are fitter than either homozygote, then a polymorphism will be maintained (Box 17.1). However, homozygotes will continually be generated, and so the population cannot contain more than 50% of the fittest genotype, the heterozygote. For example, imagine that heterozygotes are s = 1% fitter than homozygotes at each of 3000 genes. (This is approximately comparable with the level of amino acid polymorphism in Drosophila, although for simplicity, we have assumed that there are two alleles at equal frequency.) An average individual would be homozygous at 1500 of these genes and so would have its fitness reduced by a factor ~(1 – s)1500 ~ e–15 ~ 1/3,300,000, relative to the optimal genotype, which is heterozygous at all 3000 polymorphic loci. It is absurd to suppose that the ideal individual could produce three million times as many offspring as the average. This argument suggests that if heterozygote advantage is responsible for widespread polymorphism, and if the effects of different genes multiply together, as we have assumed, then selection must be quite weak (s < 0.1%, say).

Interactions between Genes Can Greatly Alleviate the Load

Papers that discuss the difficulties in using load arguments to set limits on the power of selection include Maynard Smith (1968), Sved et al. (1967), Sved (1968), Turner and Williamson (1968), and Wills (1978). Kimura (1983, Chapter 6.5) gives a review that is more sympathetic to load arguments.

Weatherall (1991, Chapter 2) discussed the incidence of inherited disease in human populations. See also Vogel and Motulsky (1997, Chapter 4.1.8).

Kimura and Maruyama (1966) show that the mutation load can be reduced by epistasis in a sexually reproducing population but not with asexual reproduction. Kondrashov (1988) takes this argument further to show that sexual reproduction can be selected because it reduces the mutation load (see p. 680).

Problem 23.10 shows how interactions between deleterious mutations can greatly reduce the genetic load when reproduction is sexual.


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