Divining God's mutation rate

Transcription

1 Commentary Cellscience Reviews Vol 3 No 4 ISSN Divining God's mutation rate Charles F. Baer Department of Zoology, 223 Bartram Hall, P. O. Box , University of Florida, Gainesville, FL, , USA. Received 2nd April Cellscience 2007 The total genomic mutation rate (µ) and the deleterious mutation rate ( U ), are of fundamental importance to a number of areas of biology, including the classic evolutionary conundrum: why sex? Unfortunately, both µ and U have historically proven extremely difficult to measure accurately. A recent study by Haag-Liautard et al. (1) combines traditional experimental mutation accumulation methods with whole-genome mutation screening to estimate µ and U in D. melanogaster. The high average value of U ( 1.2) suggests that deleterious mutations may play an important role in the maintenance of sexual reproduction. Interestingly, the overall mutation rate µ varied significantly among starting genotypes, reinforcing the idea that the mutation rate itself may potentially be fine-tuned by natural selection. An analogous study in the nematode C. elegans reported a similar value of U (2), consistent with the suggestion that the deleterious mutation rate has evolved close to a global optimum value (42). The imminent availability of extremely highthroughput DNA sequencing promises to allow biologists to understand global and local factors underlying the mutation rate. Evolution is the outcome of a vastly complex process involving mutation, population size, and natural selection (Box 1). Understanding evolution requires disentangling the respective contributions of those inherently tangled factors. Population size and selection are known to vary over time and space. For example, if we observe that two populations or species with similar census sizes have very different levels of genetic variation, we might conclude that one population may have recently undergone a demographic bottleneck. Similarly, if two genes or traits exhibit very different levels of genetic variation, we could infer that the strength and/or form of selection differ between genes or traits. In both cases - different demography and different selection - the suggested

2 conclusions assume consistency of the mutational process. In contrast, we expect mutation to be somewhat consistent over time and space. By providing a solid reference point (or at least a firm one), a detailed understanding of the mutational process will facilitate the understanding of genetic variation, and thus of evolution. To the extent that the mutational process itself is variable, generalizations about genetic variation and evolution become even more difficult. Box 1. For neutral genes in a diploid organism at mutation-drift equilibrium the genetic variation θ=4neµ, where Ne is the genetic effective population size and µ is the mutation rate (5). For loci under selection, θ will also depend on the strength and form of selection (46). Similarly, the rate of substitution of neutral alleles k is equal to the mutation rate µ, i.e., k = µ (5). The number of molecular substitutions between two lineages that diverged t generations ago is thus 2 µt. If the divergence time between taxa is known and the assumption of neutrality is valid, the mutation rate per-year can be back-calculated from the number of genetic substitutions separating the taxa. Mutations are rare events and are difficult to measure directly. Historically, genomic mutation rates (µ) have been estimated either by extrapolation from a handful of loci whose phenotypic effects can be easily scored (Method 1; 3,4) or from divergence between taxa at putatively neutral loci (Method 2; 5, see Box 1). Both methods are subject to potential biases. Extrapolation from reporter loci (Method 1) assumes that the mutational properties of the loci under observation faithfully reflect the properties of the entire genome, and that false negative rates can be quantified. Inference of mutation rate from divergence between taxa (Method 2) depends most critically on the assumption that the loci in question are in fact evolving neutrally, and on having a reliable estimate of the time of divergence. If the mutation rate of interest is per-generation, reliable estimation further depends on accurate knowledge of generation time. The rate of deleterious mutations, U, is of special interest because of the importance of deleterious mutations to a wide variety of evolutionary questions, including the evolution of sexual reproduction and recombination (6-8), mating systems (9), adaptation (10,11), speciation (12), and the evolution of the mutation rate itself (13). Deleterious mutations also figure prominently in considerations of conservation biology (14) and human health (15). Evolutionary biologists have exerted considerable effort in estimating U (reviewed in 16-18). Unfortunately, U is even more difficult to estimate accurately than µ. Natural selection removes deleterious mutations from the population, so deleterious mutations must be identified as such by their under-representation in the population. Traditionally, U has been determined by the method of mutation accumulation ('MA'), in which mutations are allowed to accumulate in the relative absence of selection. Most

3 commonly, selection is minimized by allowing genetically identical replicate populations to evolve at very small population size (Figure 1). Bateman (19) and Mukai (20) showed that with certain assumptions, U could be inferred from phenotypic evolution in an MA experiment (Box 2). More recently, sophisticated likelihood methods that make more efficient use of MA data than the Bateman-Mukai method have been employed to jointly estimate U and the distribution of mutational effects (21,22). Figure 1. The method of mutation accumulation ('MA'). +/+ represents homozygous wild-type; x represents a new mutation. An initially homozygous stock population is replicated and allowed to evolve at small population size for many generations, during which time mutations occur and reach fixation independently in the different lines. Over time, mean fitness decreases and the genetic variance increases due to the accumulation of new mutations. Phenotypic MA methods have a serious inherent limitation however, because mutations bearing very small effects will not be detectable, and because some mutations may be deleterious only within the natural ecological milieu (23). Thus, U will be underestimated, perhaps substantially. Kondrashov and Crow (24) introduced a method by which molecular divergence between species can be used to infer U. If there is a class of loci that can safely be assumed to be neutral, it follows from the neutral theory that the substitution rate is the mutation rate, i.e., k = µ. Loci evolving more slowly than the neutral class are assumed to be constrained by selection, and the genome-wide deleterious mutation rate U is the per-locus mutation rate inferred from the neutral class multiplied by the number of constrained loci in the genome. The reliability of the

4 Kondrashov-Crow (KC) method depends on two critical assumptions, (1) that the number of constrained loci in the genome in the genome can be accurately determined, and (2) the ability to identify a class of reference loci that is truly neutral. Most published applications of the KC method restrict the analysis to the coding fraction of the genome; to the extent that non-coding DNA is under selection, U will be underestimated. Box 2. For neutral phenotypic traits, the genetic variance introduced by new mutation per generation, where µ is the mutation rate at locus i, and E(a i ) is the average effect of a new mutation at locus i. At mutation-drift equilibrium VG = 2NeVM (45). For phenotypic traits under selection, VG will additionally depend on the strength and form of selection (47). In a MA experiment, genetic variance accumulates at a rate proportional to VM. If, on average, mutations have deleterious effects on the phenotype, the per-generation change in mean fitness M is equal to the product of the mutation rate U and the average effect of a new mutation E(a) (Figure 1). Bateman (19) and Mukai (20) showed that with certain assumptions, U and E(a) could be estimated from the observable quantities M and VM. The literature is littered with values of µ derived from the substitution rate between taxa, most often using four-fold degenerate sites but also other classes of putative neutral loci including processed pseudogenes (25), introns (26), and intergenic regions (27). However, various types of weak selection (e.g., codon usage bias) are known to influence genome composition. To the extent that the genome is subject to hidden selective constraints, estimates of µ will be downwardly biased, to an unknown extent. For example, estimates of µ in E. coli reveal an approximate 10-fold difference between direct and indirect (divergence-based) estimates of mutation rate (28). Until recently, estimates of U were restricted to a small handful of tractable model organisms. By far the most data are from Drosophila melanogaster, and the results have been controversial (17). Estimates of U in D. melanogaster from phenotypic MA data span almost an order of magnitude, from about 0.01 to 1 mutation per diploid genome per generation; averaged over all studies, U 0.6 (18). Given that MA estimates of U are assumed to be underestimates, the general sentiment has been that U in Drosophila is on the order of 1 per generation. Interestingly, estimates of U from between-species comparisons of coding loci using the method of Kondrashov and Crow are on the low end, around 0.07 (29). The other metazoan for which multiple independent estimates of U exist is the nematode

5 Caenorhabditis elegans (30-33). In sharp contrast to the Drosophila oeuvre, estimates of U from C. elegans MA experiments are consistently small, on the order of per generation. Also in contrast to Drosophila, U estimated from molecular divergence at coding loci is very consistent with the MA estimates, about 0.02 (32), although that value depends on probably unrealistic assumptions about generation time and times of divergence (A. Cutter, personal communication). Recently, two groups of researchers have combined classical MA techniques with wholegenome sequence analysis in an attempt to estimate U without the restrictive assumptions of the classical methods. The technique involves allowing mutations to accumulate under relaxed selection (MA), after which random genomic sequence is screened for the presence of new mutations. Since the number of generations of divergence between lines is known precisely, the genome-wide mutation rate µ can be calculated. The second step is to infer the fraction of constrained sites in the genome, C, using the Kondrashov-Crow method. Extrapolating the observed genomic mutation rate to the fraction of conserved sites C, µc = U. The only assumption is that the fastestevolving class of loci is evolving neutrally. Denver et al. (2) directly sequenced random nuclear loci from the C. elegans MA lines of (31) and determined per-nucleotide mutation rate µ 2 x 10-8 per generation. Using similar assumptions regarding the fraction of constrained loci as (32), they estimated a diploid U 0.96, over an order of magnitude greater than indirect estimates. Interestingly, the ratio of insertions to deletions ( indels ) of the MA lines (insertion bias) differed significantly from the indel spectrum inferred from processed pseudogenes (deletion bias, 25). The authors argued the difference was indicative of natural selection favoring a compact genome in C. elegans. The difference between direct and indirect estimates of the mutational spectrum strongly cautions against accepting any set of loci as 'neutral' on an a priori basis. Haag-Liautard et al. (1) screened 20 Mb of random genomic DNA in three sets of Drosophila melanogaster MA lines for mutations using a combination of DHPLC genotyping and direct sequencing. Their estimate of the per-nucleotide mutation rate µ 8.4 x 10-9 was about 5X greater than that estimated from divergence among species. The indel spectrum (2 insertions/6 deletions) was similar to that observed in Drosophila pseudogenes (34). Using updated information on the fraction of constrained sites, averaged over all three sets of MA lines U 1.2, remarkably consistent with the highend of the fitness-based MA estimates. If the distribution of mutational effects is leptokurtic, as expected (35), the rough agreement between molecular and fitness-based estimates of U is unexpected, because there should be a large fraction of very slightly deleterious mutations whose effects on fitness are undetectable in an MA experiment. Interestingly, there was significant

6 variation among the three sets of MA lines in the mutation rate, and in fact the stock with the lowest molecular mutation rate (Madrid) consistently shows a low U (~0.02/gen) based on fitness assays (36,37). Thus, the apparent discrepancy between molecular and fitness-based essays may be a result of actual genetic variation between strains. For example, if the Madrid line was considered outside the context of the Drosophila MA oeuvre, the results would be very consistent with the C. elegans results, differences in indel spectra notwithstanding. Several of the high-u Drosophila experiments have employed stocks known or thought to harbor active transposable elements (38,39), which could lead to transient mutator phenotypes. It remains to be determined whether withinspecies variation in mutation rate is best explained by mutation-selection balance governing a phenotypic trait (i.e., U) under global stabilizing selection or some other process of evolution. Perhaps the most important result of these studies will turn out to be the relative similarity of U in C. elegans and D. melanogaster. In both species, U estimated from molecular data from short-term MA is on the order of 1 per diploid genome per generation, substantially greater than estimates from silent-site divergence between species. A recent study from murid rodents that used molecular divergence between mice and rats and the same method for inferring constrained sites as used by Haag-Liautard et al. (1) led to an estimate of diploid U 0.91, remarkably consistent with the direct estimates from flies and worms (40). Effective population sizes of mammals are likely much smaller than those of worms and flies, so it is conceivable that in rodents the reference class of fast-evolving sites approaches selective neutrality. Mutation rate is an evolvable property, and the target of direct selection is the genomewide deleterious mutation rate per-generation, U (13,41). Jan Drake has influentially argued that mutation rates may have evolved to a global optimum (4,42). Drake based his argument largely on evidence from microbes, but the results from the studies discussed here are roughly consistent with that argument. However, there is no theoretical reason to expect the global optimum to be close to one mutation per diploid genome per generation. Interestingly, primates, including ourselves, appear to have evolved a substantially larger U ( 3) than the other taxa considered here (43,44). A prominent theory of adaptive evolution holds that the rate of adaptation should be proportional to the genomic mutation rate (10). It is tempting to speculate that the high rate of deleterious mutation that we incur is the price to be paid for adaptation. In the very near future, whole-genome sequencing approaches to mutation detection will be applicable to human pedigree data, laying bare the mutational secrets of our own genomes.

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