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. 2013 Feb;193(2):539–544. doi: 10.1534/genetics.112.146571

Genomic Background and Generation Time Influence Deleterious Mutation Rates in Daphnia

Leigh C Latta IV *,1, Kendall K Morgan †, Casse S Weaver ‡, Desiree Allen §, Sarah Schaack *, Michael Lynch **
PMCID: PMC3567742  PMID: 23183667

Abstract

Understanding how genetic variation is generated and how selection shapes mutation rates over evolutionary time requires knowledge of the factors influencing mutation and its effects on quantitative traits. We explore the impact of two factors, genomic background and generation time, on deleterious mutation in Daphnia pulicaria, a cyclically parthenogenic aquatic microcrustacean, using parallel mutation-accumulation experiments. The deleterious mutational properties of life-history characters for individuals from two different populations, and for individuals maintained at two different generation times, were quantified and compared. Mutational properties varied between populations, especially for clutch size, suggesting that genomic background influences mutational properties for some characters. Generation time was found to have a greater effect on mutational properties, with higher per-generation deleterious mutation rates in lines with longer generation times. These results suggest that differences in genetic architecture among populations and species may be explained in part by demographic features that significantly influence generation time and therefore the rate of mutation.

Keywords: deleterious mutation, fitness, generation time, genomic background, Daphnia pulicaria

AS the ultimate source of all genetic variation, mutation is an important evolutionary force affecting the ability of natural populations to respond to selective pressures. Most spontaneous mutations are deleterious (Lynch et al. 1999; Eyre-Walker and Keightley 2007), which is thought to explain many evolutionary phenomena, including inbreeding depression, mating system evolution, senescence, and risk of extinction to small populations (Charlesworth and Charlesworth 1998; Lynch et al. 1999). Despite the importance of knowing mutation rates in both theoretical and applied biology, few empirical estimates exist other than those for classic genetic model organisms (Baer et al. 2007), and little is known about the factors influencing the rate of mutation among individuals, populations, and species (Lynch 2010).

In addition to direct estimates based on sequencing, estimates of the parameters for mutations affecting fitness [i.e., the genome-wide deleterious mutation rate (U) and the average effect (š)] have now been reported for several species (reviewed in Baer et al. 2007). However, little empirical attention has been given to variability in the phenotypic effects of deleterious mutation [i.e., per-generation rates of change in the mean phenotype (ΔM) and mutational variance (ΔV)] or to the associated deleterious mutation parameters that can be inferred from these quantities (U and š) among populations within a species. Recent theoretical treatments of mutation-rate evolution, however, predict individual variation in mutation rates (Lynch 2008; Desai and Fisher 2011) and fitness dependence of mutation rates (Agrawal 2002; Shaw and Baer 2011), highlighting the importance of this variability.

The deepest understanding comes from recent mutation-accumulation studies in Drosophila melanogaster that provide evidence for variability in mutation rates among genotypes, using both direct methods of rate estimation based on sequence data (Haag-Liautard et al. 2007) and indirect methods using fitness data (Avila et al. 2006). Interestingly, intraspecific variability in mutation rates in Drosophila appears to be correlated with the quality of the genomic background in which mutations accumulate: genotypes of poor condition (harboring a high number of deleterious mutations initially) display higher mutation rates than genotypes with fewer starting mutations (Avila et al. 2006; Agrawal and Wang 2008; Sharp and Agrawal 2012). In contrast to the situation in Drosophila, where both direct and indirect estimates suggest intraspecific mutation-rate variability, in rhabditid nematodes indirect estimates of mutation rate (Baer et al. 2005) and direct estimates of insertion–deletion mutation processes (Phillips et al. 2009) suggest variability among genotypes, but direct estimates of the base-substitution mutation rate in the same genotypes imply rate homogeneity (Denver et al. 2012).

If mutation rate variability is fitness dependent, as appears to be the case in Drosophila, it suggests that life-history traits facilitating the production of higher deleterious mutation loads could drive evolution of the mutation rate. Generation time, in particular, is a life-history trait that influences mutation load, given that the number of mutations that an individual inherits depends on the number of parental germline cell divisions and germline divisions increase with generation time (reviewed in Bronham 2009). Thus, populations with short generation times are expected to undergo fewer germline cell divisions than populations with long generation times, thereby passing on fewer mutations to their offspring. The numerous examples of male mutation bias, whereby males undergo more germline cell divisions and generate more mutations each generation than females, provide compelling evidence for such generation-time effects (Sayres and Makova 2011; Kong et al. 2012).

In this study, we compare the patterns of deleterious mutation accumulation in individuals from two natural populations of Daphnia pulicaria from Oregon. These populations were chosen based on their divergent ecological origins. One population, Klamath Lake, is from a lake in the southern Cascade Mountains, while the other population, Lake Marie, is located near the Pacific coast. Specifically, these populations represent the high and low extremes of the phenotypic distribution for life-history traits and body size of natural populations in Oregon (Morgan et al. 2001; Baer and Lynch 2003). Not only do phylogenetic analyses identify these populations as among the most genetically divergent in western Oregon, but also quantitative assessments of the expressed levels of genetic variation show that Lake Marie D. pulicaria are low relative to Klamath Lake D. pulicaria, despite similar levels of genetic diversity for both allozyme and microsatellite loci in these populations (Morgan et al. 2001). In addition to the population comparison, for one population (Klamath Lake) we experimentally imposed different generation times to assess the impact on the mutation parameters. By exploring the properties of spontaneous deleterious mutation in multiple genotypes from two divergent populations, as well as by manipulating generation time in one population, we characterize the variability in deleterious mutation properties that arises due to genomic background and assess whether differences in generation time provide an explanation for such variability.

Methods

Mutation accumulation experiment

Ten D. pulicaria individuals (genotypes) from each of two Oregon lakes, Klamath Lake and Lake Marie (populations), were isolated into individual beakers. Each founding individual was allowed to reproduce clonally until 10 genetically identical offspring were produced (clones). These 10 replicate clones of each genotype from each population were then placed into their own beakers to establish 200 mutation-accumulation (MA) lines for the subsequent experiments (two populations × 10 genotypes × 10 clones = 200 MA lines). All MA lines were maintained under constant environmental conditions [at 18°, 12 light (L):12 dark (D) light cycle] in a controlled environmental chamber.

Mutation-accumulation lines were propagated by single-offspring descent by haphazardly choosing a juvenile every generation (see below for conditions for short and long generation times). Parental lines were kept as backups until the next transfer was performed to allow for the replacement of lost lines. In this manner, the 10 clonal lines started from each genotype were allowed to diverge based on incoming mutations. In contrast to the MA lines, control lines of each genotype were maintained under conditions designed to greatly slow the rate of deleterious mutation accumulation (at 7°, 12L:12D light cycle, in large populations, without single-offspring descent).

For both populations, MA lines were propagated by random offspring from the second clutch [short generation time (SGT) lines]. For one population (Klamath Lake), a parallel set of long generation time (LGT) lines was constructed, where the propagating juvenile was obtained from the last possible clutch. In this way, all three sets of lines (Lake MarieSGT, KlamathSGT, and KlamathLGT) were maintained under identical conditions (at 18°, 12L:12D light cycle), except that the KlamathLGT lines were manipulated to have a longer generation time. At the close of the experiment, the Lake MarieSGT and KlamathSGT lines had undergone 65 generations of divergence on average, while the KlamathLGT lines had undergone only 25 generations of divergence over the same absolute period of time. Thus, the generation time was extended by a factor of 2.5 in the KlamathLGT lines.

Phenotypic assays

Representatives of each surviving MA line, along with controls, were phenotypically assayed using a standard life table design (Lynch 1985). Assays were conducted after Lake MarieSGT and KlamathSGT lines had diverged for ∼15, 30, and 65 generations. Measurements from phenotypic assays were used to calculate the mean and variance for life-history characters among diverging lines within and among genotypes from each population. Prior to each assay, each line was replicated into three sublines, each of which was then taken through two generations of clonal reproduction under controlled conditions (18°, 12L:12D light cycle). Such an experimental design ensures that maternal and grandmaternal effects contribute to the environmental (within-line) rather than the genetic (among-line) component of variance in the final analysis (Lynch 1985). During the life-history assays, the following measurements were made: time to maturity, size at maturity (to the nearest 0.01 mm), and clutch size in each of the first four clutches.

Data analysis

For each of the surviving sets of lines in each of the assays, the within- and among-line components of variance were extracted by one-way analysis of variance. The within-line variance represents the environmental variance (VE) and the among-line variance represents the genetic variance (VG). Estimates of the rate of change in the genetic variance were obtained by weighted least-squares regression of the VG estimates on generation number, with the slope of the regression representing an estimate of the rate of change in the variance due to mutational input, ΔV (Lynch and Walsh 1998). Data points in the regression were weighted by the inverse of the sampling variance of VG. The final estimates of ΔV for Lake MarieSGT, KlamathSGT, and KlamathLGT lines were obtained by averaging the line-specific estimates. The standard error of this estimate was obtained by treating the line-specific estimates as independent as described in Lynch et al. (1998). Estimates of the rate of change in the mean due to mutation accumulation, ΔM, were obtained by weighted least-squares regression of the assay-specific phenotypic means on generation number. Due to line extinction over the course of the mutation-accumulation phase in the Lake MarieSGT lines, estimates of ΔV and ΔM were calculated using data obtained in all three phenotypic assays in some cases (n = 2) and from two phenotypic assays when necessary (n = 5).

For each assay, 10 control representatives of a subset of the founding clones (three Klamath clones and two Marie clones) were isolated from the larger, low-temperature (7°) control populations (each of which was then replicated into three sublines, as in the case of the MA lines). Measurements from control lines were used to calculate a mean and variance (among the 10 isolated individuals) for each founding clone at each assay. Regression analyses of mean control phenotype and among-line variance were then conducted such that estimates of ΔM and ΔV for MA lines could be corrected for change due to environmental variation among assays. Lower-bound estimates of the genomic mutation rate, Umin = ΔM2/ΔV, and upper-bound estimates of the heterozygous mutational effect as a fraction of the initial mean phenotype (ž0), a′max = ΔV/ΔM/ž0, were obtained according to the method of Bateman (1959) and Mukai et al. (1972) and modified for clonal lines (Lynch and Walsh 1998). Bateman–Mukai estimates were corrected for bias caused by sampling error in estimating ΔM and ΔV (Lynch 1994). Standard errors for Umin and a′max were obtained using the formula for the standard error of a ratio, taking the sampling variances of the numerator and denominator into account (Lynch and Walsh 1998).

It should be noted that application of the Bateman–Mukai technique, which assumes equal, unidirectional mutational effects, is unlikely to generate realistic estimates of deleterious mutation parameters (Halligan and Keightley 2009). Lake-dwelling Daphnia populations, including those used here, are exposed to strong directional selection via size-selective vertebrate predation (e.g., Gliwicz and Boavida 1996), which may, however, justify the assumption of unidirectional mutational effects. Furthermore, estimates obtained are specific to the assay environment employed, given the environmental dependence of spontaneous mutations (Kondrashov and Houle 1994; Fry and Heinsohn 2002). Despite these limitations, the method is informative in that it: (1) provides lower- and upper-bound estimates of deleterious mutation parameters and (2) can be used to assess the impact of genomic background and generation time within a given experiment.

Results

Mutation accumulation in divergent populations

The phenotypic means and variances of the control lines did not vary significantly over the course of the experiment (Table 1; Supporting Information, Table S1). Therefore, the estimates of ΔM and ΔV from the mutation-accumulation lines were not corrected for environmental variation among assays, and we interpret changes in the MA lines to be a consequence of genetic, rather than environmental, change.

Table 1 . Per-generation rates of change in control mean phenotype, and among-line variance, over the three life-history assays.

Trait Mean Variance
Size at maturity (mm) 0.0001 (0.0066) 0.0000 (0.0000)
Clutch size −0.0536 (0.1000) −0.0179 (0.1100)
Age at maturity (days) 0.0099 (0.0059) −0.0089 (0.0100)
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Reported values are coefficients for regressions on generation number; standard errors are given in parentheses. Estimates for clutch size represent an average value of the separate analyses of clutches 1–4. None of the estimates deviates significantly from zero. The variance for size at maturity was <10−4.

The estimates of ΔM are identical between the KlamathSGT and Lake MarieSGT lines for size at maturity, with an increase in body size in response to new mutations (Table 2; Table S2). However, the two populations differ by an order of magnitude in the rate of decline in mean clutch size, with the Lake MarieSGT lines experiencing an elevated rate relative to the KlamathSGT lines. KlamathSGT lines also showed a significant rate increase in age at maturity relative to the Lake MarieSGT lines. Estimates of ΔV for the KlamathSGT lines are quite consistent with those for Lake MarieSGT lines, with none of the values differing significantly (Table 2; Table S3).

Table 2 . Estimates of the initial mean phenotype (ž0), per-generation rate of change in the mean phenotype (ΔM), per-generation rate of input of mutational variance (ΔV), genomic mutation rate (Umin), and average mutational effect as a fraction of the initial mean phenotype (a′max) for Lake Marie, KlamathSGT, and KlamathLGT lines.

Trait Population ž0 ΔM ΔV Umin a′max
SM Marie 1.84 (0.02) 0.003 (0.001) 0.0001 (0.0000) 0.027 (0.001) 0.013 (0.001)
KlamathSGT 2.03 (0.03) 0.003 (0.001)a 0.0001 (0.0000)a 0.028 (0.001)a 0.012 (0.001)a
KlamathLGT 2.03 (0.03) 0.012 (0.002) 0.0002 (0.0001) 0.397 (0.069) 0.006 (0.000)
CS Marie 7.63 (0.45) −0.194 (0.055) 0.1299 (0.0930) 0.157 (0.014) −0.082 (0.031)
KlamathSGT 8.11 (1.01) −0.020 (0.018)a,b 0.1114 (0.0380)a 0.002 (0.000)a,b −0.141 (0.055)a
KlamathLGT 8.11 (1.01) −0.061 (0.059) 0.2216 (0.1048) 0.015 (0.046) −0.022 (0.000)
AM Marie 6.61 (0.28) −0.002 (0.005) 0.0088 (0.0030) NA NA
KlamathSGT 6.38 (0.30) 0.007 (0.002)a,b 0.0038 (0.0020)a 0.006 (0.000)a 0.074 (0.012)
KlamathLGT 6.38 (0.30) 0.019 (0.012) 0.0102 (0.0020) 0.010 (0.000) 0.060 (0.010)
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Estimates for clutch size represent an average value of the separate analyses of clutches 1–4. Standard errors are given in parentheses. SM, size at maturity (mm); CS, clutch size; and AM, age at maturity (days). Estimates of Umin and a′max were not calculated for Marie AM because the standard error of ΔM exceeds the mean (indicated as NA).

a

Indicates a significant difference between KlamathSGT and KlamathLGT lines.

b

Indicates a significant difference between Marie and Klamath D. pulicaria populations.

Application of the observed temporal changes in the mean and the genetic variance to the Bateman–Mukai estimators yields lower-bound estimates of the mutation rate (Umin) for body size that are nearly identical for the KlamathSGT and Lake MarieSGT lines (Table 2; Table S4). In contrast, the mutation rate for clutch size in Lake MarieSGT lines is significantly higher than the rate estimated for the KlamathSGT lines. Upper-bound estimates of mutational effects as a fraction of the initial mean phenotype (a′max) are all <15% of the initial mean phenotypic value and do not vary significantly between populations.

Generation-time effect on mutation accumulation

Estimates of ΔM for the KlamathLGT lines are on average 3.3 times greater than those for the KlamathSGT lines (Table 2; Table S2). Similarly, estimates of ΔV for the KlamathLGT lines are on average 2.2 times greater than those for the KlamathSGT lines, which is very similar to the 2.5-fold difference in generation time between the two sets of lines (Table 2; Table S3). For each of the traits measured, the per-character estimate of Umin for the KlamathLGT lines significantly exceeds that for the KlamathSGT lines (Table 2). The estimates of a′max are significantly elevated in the KlamathSGT lines relative to the KlamathLGT lines for both body size and clutch size, but not for age at maturity (Table 2).

Discussion

Our data demonstrate trait-specific variation in mutation parameters among natural isolates from lake-dwelling populations of D. pulicaria that have diverged phenotypically and genetically through natural processes, a result consistent with other recent studies showing high levels of intraspecific variation in mutation rates among strains in classic model organisms (Baer et al. 2005; Haag-Liautard et al. 2007). Furthermore, we show evidence for a generation time effect on mutation rate estimates, providing insight into a possible mechanism explaining mutation rate variability among populations and species where ecological conditions or demographics may influence this feature of the life history.

Among the differences in mutation parameters observed between populations, the largest change was the rate of decline in clutch size, which may be explained by several factors. First, low levels of expressed genetic variation observed in the Marie population, in conjunction with the high levels of molecular variation (Morgan et al. 2001), may indicate that this population maintains substantial levels of hidden quantitative genetic variation. Hidden genetic variation could result from prolonged periods of asexual reproduction leading to the accumulation of mutations (Barton and Charlesworth 1998). If the Marie population harbors an elevated base mutation load, this may lead to an increased rate of change in the phenotype because new mutations are predicted to accumulate faster in more loaded populations (Agrawal 2002; Shaw and Baer 2011). Given that the Marie lines experienced a greater rate of extinction over the course of the mutation accumulation experiment, an indication of higher mutation load (Lynch 1994), and exhibited large reductions in competitive ability (a complex fitness trait) as a result of mutation accumulation relative to Klamath Lake (Schaack et al. 2012), the possibility of high mutation load in this population is plausible.

Alternatively, the elevated rate of change in clutch size in Marie may reflect a difference in genotype × environment interactions between the populations. In the current study, all MA lines were assayed in the same benign laboratory environment, but this environment may represent different levels of stress for animals orginating from the Marie vs. Klamath Lake populations (Baer and Lynch 2003). Decreased reproductive success in Marie may be due to magnification of mutational effects and/or increases in the number of mutations that produce a measurable effect due to stress, if the laboratory environment differs from the field environment to different degrees (Kondrashov and Houle 1994; Fry and Heinsohn 2002).

The magnitude of our trait-specific estimates of the rates of change in mean phenotype and the variance in D. pulicaria are generally lower than those obtained from MA experiments conducted under similar environmental conditions involving the pond-dwelling sister species, Daphnia pulex (Lynch et al. 1998). These differences in rates of change in the mean and variance translate into lower estimates of the mutation rate and higher estimates of the average mutational effect in D. pulicaria relative to D. pulex. Also, while the direction of phenotypic change in response to new mutations is consistent across the two D. pulicaria populations, with body size increasing and clutch size decreasing, this directional change is opposite the results for D. pulex [decreased body size and increased clutch size (Lynch et al. 1998)]. This observation suggests that there is a characteristic direction of mutational effects among divergent populations within a species, but that the direction of effects among species may differ, perhaps resulting from differences in selection pressures in lakes vs. ponds (Dudycha and Tessier 1999).

The positive relationship between generation time and the estimate of per-generation mutation rate is consistent with previous data based on broad, indirect comparisons among organisms with a wide range of generation times (Lynch et al. 1999; Keightley and Eyre-Walker 2000). The close match between the differences that we observed and the experimentally manipulated 2.5-fold difference in generation time between treatments within a species, however, provides more direct evidence supporting the hypothesis that per-generation mutation rates scale with the absolute generation time by circumventing the confounding effects of an interspecific comparison.

An effect of generation time may be largely due to the increase in germline cell divisions occurring in older parents, which result in additional opportunities for DNA replication error (Li et al. 1996; Drake et al. 1998). In Daphnia, longevity varies among closely related populations and species according to habitat permanence [e.g., permanent lakes vs. temporary ponds (Dudycha and Tessier 1999)]. Thus, environmental factors may shift the average maternal age in this genus, leading to the significant differences in per-generation mutation rates, levels of standing genetic variation, and mutation loads empirically predicted by ecological and evolutionary models (e.g., Hansen and Price 1999).

Over long time periods, because generation time is inversely related to population size (Ohta and Kimura 1971; Ohta 1987), populations with long generation times may exhibit not only increased per-generation mutation rates, but also a reduced ability to purge incoming deleterious mutations due to small effective population size (Lynch and Conery 2003). The resulting high deleterious mutation load may lead to a further increase in mutation rate (Avila et al. 2006; Sharp and Agrawal 2012), meaning that small populations with long generation times will exhibit accelerated declines in fitness and lower viability for multiple reasons. Thus, the intraspecific variation and generation-time effects reported here may be important considerations for future work on biological phenomena, in both the theoretical and applied realm, that depend on mutation rate estimates and their variabilty among populations and species.

Supplementary Material

Supporting Information
supp_193_2_539__index.html (2.1KB, html)

Acknowledgments

We thank Emília Martins and John Postlethwait for comments on the manuscript. This work was funded by fellowships from National Institutes of Health Training grant 5 T32 GM07413 (to K.K.M.), National Science Foundation Training Grant DBI-9413223 (to M.L.), National Science Foundation grant DEB-9903920 (to M.L.), M. J. Murdock Charitable Trust grant 2011287, and National Science Foundation grant MCB-1150213 (to S.S.).

Footnotes

Communicating editor: D. Charlesworth

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Supplementary Materials

Supporting Information
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