Behavior is a complex trait that results from interactions among multiple genes and the environment. Both additive and nonadditive effects are expected to contribute to broad-sense heritability of complex phenotypes, although the relative contribution of each of these mechanisms is unknown. Here, we mapped genetic variation in the correlated phenotypes of thermal preference and isothermal dispersion in the nematode Caenorhabditis elegans. Genetic variation underlying these traits is characterized by a set of linked quantitative trait loci (QTL) that interact in a complex epistatic network. In particular, two loci located on the X chromosome interact with one another to generate extreme thermophilic behavior and are responsible for ∼50% of the total variation observed in a cross between two parental lines, even though these loci individually explain very little of the among-line variation. Our results demonstrate that simultaneously considering the influence of a quantitative trait locus (QTL) on multiple scales of behavior can inform the physiological mechanism of the QTL and show that epistasis can explain significant proportions of otherwise unattributed variance within populations.
THE precise nature of the genetic variants underlying complex traits within natural populations has been the subject of a century-old (and often contentious) debate (Provine 1971). It is still unclear whether the genetic component of phenotypic variation tends to be generated predominantly via the contributions of many loci, each with small additive effects (Fisher 1918); a smaller number loci, some with alleles of large effects segregating in a largely Mendelian fashion (Bateson 1913); and/or loci whose allelic effects are strongly dependent on the effects of alleles at other loci [epistasis (Wright 1932)]. Ultimately, the genetic basis of quantitative variation will almost certainly depend on the functional and evolutionary processes that have served to shape the variation over time (Hanson 2006). In the case of gene interactions, complexities in the underlying genetic architecture generated by epistasis can obscure the genotype–phenotype relationship, especially from a statistical point of view (Phillips 1998, 2008; Cordell 2002; Zuk et al. 2012). When a specific combination of alleles at multiple loci is required to produce a particular phenotype, then any one allele will frequently fail to significantly associate with variation in that phenotype, even though such loci can play an important role in the overall pattern of genetic variation within the population (Whitlock et al. 1995). In general, epistasis will reveal itself as a nonadditive interaction between the effects of two independent genes and is expected to emerge at some level from any complex regulatory system underlying a particular phenotype (Dixon et al. 2009). Although there is a general impression tracing back to R. A. Fisher that epistasis is of little importance for understanding genetic variation (Hill et al. 2008; Crow 2010), the reality is that we still actually know very little about the incidence or significance of gene interactions for structuring the genetics and evolution of complex phenotypes within natural populations (Phillips 2008).
There is perhaps no more complex a phenotype than behavior, as it integrates across environmental stimuli, neural networks, physiology, and cellular function. For example, a behavior such as the response to temperature flux requires that an organism sense the temperature, assess the fitness and performance impacts of that temperature, and then migrate accordingly. Thermal preference is a particularly informative trait because of the direct tie between behavior and fitness, since migration to a thermal optimum influences growth rate and physiological stress (Huey 1974; Huey and Berrigan 2001). Studying thermal preference in a quantitative genetic framework thus presents an opportunity to not only understand the complex genetic architecture of behavior, but also place its variation in an appropriate evolutionary context.
Thermal preference, as assayed experimentally by measuring migration on a thermal gradient, varies among wild isolates of the nematode Caenorhabditis elegans (Anderson et al. 2011). The neural and molecular bases of this behavior are well characterized in the canonical laboratory strain of this model organism, with thermosensation and thermotaxis under the control of only a few neurons and a well-defined class of genes defined via mutagenesis (Garrity et al. 2010). Fitness as measured by both intrinsic rate of increase and total reproductive output is correlated with thermal preference (Anderson et al. 2011), but the genetic architecture of this variation is unknown. In this study, we used a set of recombinant inbred advanced intercross lines (RIAILs) in C. elegans (Rockman and Kruglyak 2009) to identify both additive and nonadditive genetic components to the phenotypes associated with thermal preference, finding that the majority of natural genetic variation for this trait is determined by highly complex interactions among multiple loci.
Materials and Methods
Worm maintenance methods
Strains were stored at −80° and were thawed and maintained in standard conditions (Brenner 1974) for 1 month prior to phenotyping. Lines were maintained on standard 10-cm NGM-lite petri dishes seeded with Escherichia coli OP-50 (Caenorhabditis Genetics Center, University of Minnesota, Minneapolis) at 20° and chunked twice weekly. The parental strains used in this work were N2, originally isolated from Bristol, England, but long maintained in the laboratory, and CB4856 (abbreviated HW), originally isolated from Hawaii (Wicks et al. 2001).
RIAILs were generated as described in Rockman and Kruglyak (2008, 2009). Briefly, N2 and HW were reciprocally crossed in P0, F1, and F2 generations. F3 progeny were random pair mated for 10 generations and then inbred by picking individual hermaphrodites for 10 generations. Lines were genotyped at 1455 single-nucleotide polymorphisms.
Nearly Isogenic Line construction
Nearly isogenic lines (NILs) were generated in both N2 and HW genetic backgrounds, using marker-assisted selection. Restriction-fragment length polymorphic markers (RFLPs) were selected as described originally in Wicks et al. (2001). Candidate RIAILs were selected based on their genotypes at the QTL peaks. These RIAILs were then crossed and backcrossed into the background contragenic to the genotype across the QTL for at least 10 generations. At each generation of backcrossing, individuals were genotyped across 10 evenly spaced markers on the X chromosome to identify recombination events. Double-NILs were constructed by crossing appropriate NILs to each other and genotyping F3 progeny at loci relevant for identifying meiotic recombination. This was used to generate the following strains: PX513 [thrm-3(fxIR1), an HW introgression at thrm-3 in an otherwise N2 background], PX514 [thrm-5(fxIR2), an HW introgression at thrm-5 in an N2 background], PX515 [thrm-3(fxIR1);thrm-5(fxIR2)], PX516 [thrm-3(fxIR3), an N2 introgression at thrm-3 in an otherwise HW background], PX517 [thrm-5(fxIR4), an N2 introgression at thrm-5 in an HW background], and PX518 [thrm-3(fxIR3);thrm-5(fxIR4)].
Thermal gradient construction and calibration
Three replicate linear thermal gradients each with a slope of ∼1.0°/cm were used in all assays and constructed as described previously (Anderson et al. 2007). For each gradient an aluminum slab was placed over two separate piping systems, one carrying heated (55°) and one carrying cooled (4°) liquid. A 10 × 17-cm slab of NGM-lite agar was placed on the aluminum slab and enclosed in a plexiglass frame. The slope of each of the three gradients was determined by assessing their temperature at 1-cm intervals and fitting these readings by linear regression. In all cases, r2 > 0.98, indicating that the gradients were linear. The slopes of the gradient remained stable over repeated measurements but were slightly different among the three replicates. This slight difference in slope (ranging from 0.8°/cm to 1.1°/cm) did not affect the measured thermal preference within a line.
Thermal preference and isothermal dispersion assay
Lines were age synchronized by sodium hypochlorite treatment (Stiernagel 1999). Approximately 600 isogenic L1’s were plated onto three 6-cm NGM-agar plates seeded with E. coli OP-50 for a total of 1800 individuals. These individuals were then raised at 20° for 48 hr until reaching the L4 larval stage. Worms were washed from each plate with 1 mL S Basal into 1.5-mL microcentrifuge tubes. Supernatant S Basal was pipetted off once the worms settled. Worms, suspended in ∼100 μL of S Basal, were transferred via filter paper onto the gradient-equilibrated NGM-agar gel and placed equidistant between either edge of the gel at 24°. Approximately half of the worms were lost in this transfer such that ∼250 individuals per gel were assayed. To ensure that thermal preference, and not any behavioral response to starvation, was being assayed, agar gels were seeded with E. coli (OP-50) 24 hr prior to use. Worms were permitted to travel freely across the gel for 1 hr. As worms on food move between 0.025 mm/sec and 0.25 mm/sec (Arous et al. 2009), there is a potential total movement of between 9 and 108 cm over the duration of the experiment. After 1 hr, individual positions were scored by overlaying transparency film and marking worm locations with a semipermanent marker. In all, the responses of an average of 700 individuals per RIAIL line and 91,831 individuals overall were measured using this approach.
Statistical analysis of phenotypes
Once scored, transparencies were scanned and analyzed in ImagePro Plus 5.1 (Media Cybernetics, Bethesda, MD). The Cartesian coordinates of each individual worm’s marked position were recorded and related back to the calculated slope of the gradient such that the temperature of each individual (derived from the “y” Cartesian coordinate) as well as the distance it moved laterally (derived from the “x” Cartesian coordinate) from where it was initially placed was identified. These two values represent the thermal preference and isothermal dispersion of any individual. Overall RIAIL and NIL phenotypes were found by performing a nested random-effects ANOVA, with a main effect of line and a nested effect of replicate using JMP 9.0 (SAS Institute, Cary, NC). The average RIAIL response was determined using the least-squares mean estimated from this analysis, although using individual line means yielded essentially identical results. Thermal preference and isothermal dispersion served as the two principal phenotypes to be mapped. A third phenotype, which found thermal preference after taking movement into account, was found by using the residuals of regressing individual thermal preference onto isothermal dispersion.
QTL mapping of phenotypes
Interval mapping and two-dimensional genome scans were performed as implemented in R/QTL (Broman et al. 2003) using genotypes and phenotypes provided in Supporting Information, File S1. Due to the tight linkage of identified QTL, there was not sufficient power to perform multiple-interval mapping. For interval mapping, genotype probabilities were calculated using Haldane’s map function with a 1-cM step size and an error probability of 1−10−4. Whole-genome scans with a single-QTL model for isothermal dispersion and thermal preference were performed using the EM algorithm (Lander and Botstein 1989), although results were qualitatively the same when Haley–Knott regression was used instead (Haley and Knott 1992). When appropriate, covariate markers were used to account for variation due to previously identified large-effect QTL (see Results) in the single-QTL model. The analysis was permuted 1000 times to obtain a genome-wide or a chromosome-wide LOD significance threshold (Churchill and Doerge 1994). For single-QTL models, a LOD score of ∼3.19 signified a genome-wide P < 0.05 threshold.
A two-dimensional genome scan was used to test for the likelihood of interacting QTL for all pairwise combinations of intervals by including a statistical interaction effect between intervals (Dupuis et al. 1995), which is a signature of epistasis. This scan was performed using the EM algorithm (Lander and Botstein 1989) with no specified covariates and a 1-cM step size. Genome-wide significance levels for the reduced (epistasis only) and full models were determined using a permutation test. LOD scores for the full model (Lf) at locus pair j, considering the additive and interactive effects of any two intervals (q1, q2), are calculated by the equationwhere L0 is the null model. Epistasis LOD scores are calculated aswhere La is the likelihood under the additive model. The difference between these two likelihoods demonstrates the likelihood of the interaction term by itself (Broman et al. 2003).
Thermal preference and isothermal dispersion vary continuously among RIAILs
Consistent with previous results (Anderson et al. 2007; Jurado et al. 2009), we find that when started at a relatively warm position, the N2 parental strain remained in the warm portion of the gradient (Figure 1A) ( = 21.87°, SEM = 0.32°) while the HW strain migrated to a much colder region of the gradient ( = 17.62°, SEM = 0.28°). In contrast, thermal preference among the RIAILs displayed substantial transgressive segregation, ranging from 17.34° to 27.01°, with approximately half of all lines assayed having a thermal preference that was warmer than that of the more thermophilic N2 parent. The warmer thermal preferences are well above those observed for other natural isolates (Anderson et al. 2011) and are also above the upper thermal limit for continuous population growth (Anderson et al. 2011). Almost half (49.4%) of the total variance in thermal preference was attributable to between-line or genetic differences (F118, 247 = 22.01, P < 0.001).
Isothermal dispersion for the parents ranged from 0.68 cm/hr (N2) to 2.13 cm/hr (HW), while that for the RIAILs ranged from 0.44 cm/hr to 2.86 cm/hr (Figure 1B). Approximately 16% of total variance in isothermal dispersion was explained by among-line differences (F118, 247 = 15.23, P < 0.001). Unlike thermal preference, then, isothermal dispersion was well bounded by the parental values. There was a slight but significant correlation between isothermal dispersion and thermal preference among lines (r2 = 0.20, P < 0.01; Figure S1).
For both thermal preference and isothermal dispersion there were some differences among replicates (isothermal dispersion, F246,85078 = 9.64, P < 0.001; thermal preference, F246,85079 = 36.44, P < 0.001), although these differences did not display any systematic bias and accounted for only 3.1% and 6.8% of total phenotypic variance, respectively.
Several loci contribute nonadditively across phenotypes to explain variation in thermal preference
To identify the loci responsible for variation in isothermal dispersion and thermal preference, we used interval mapping with and without covariates. For isothermal dispersion, we found evidence for a single locus of large effect on the X chromosome (Figure 2A, LOD = 20, P < 0.0001). This peak is centered over npr-1, allelic variation at which has been identified as responsible for variation in a variety of behavioral responses, including the tendency to “clump” on a petri dish (de Bono and Bargmann 1998), the response to thermal stress (Glauser et al. 2011), avoidance of high oxygen concentrations (McGrath et al. 2009; Persson et al. 2009), and tendency to leave food patches (Bendesky et al. 2011). Because the N2 npr-1 allele was previously identified as laboratory derived and was shown to interact epistatically with other loci (McGrath et al. 2009), we accounted for variation at this locus in subsequent isothermal migration analyses by using the marker most closely associated with this peak as an interactive covariate. In doing so, we found that the original QTL on the X chromosome is likely to be composed of at least two QTL, with a significant second QTL emerging ∼800 kb distal to npr-1 (Figure 2B, LOD = 4.95, P < 0.001). We named this second locus disp-1 (DISPersion). To identify the nature of the relationship between the npr-1 and disp-1 loci, we tested for interaction effects between the two loci (Figure 3). We found that lines possessing N2 alleles at both loci tend to disperse only slightly (1.02 ± 0.043 cm/hr), whereas lines possessing one HW allele at either disp-1 or npr-1 moved significantly farther (1.86 ± 0.139 cm/hr, F1,110 = 95.95, P < 0.0001) (Figure 3). Dispersal of a subset of the RIAILs was assayed on an isothermal (20°) agar slab in the absence of a gradient and yielded qualitatively similar mapping results (Figure S2).
For thermal preference, we identified two significant and one strongly suggestive main-effect QTL, all of which were located on the X chromosome (Figure 2, A and C). We named these THeRMal preference (thrm) loci in proximal–distal order on the chromosome: thrm-1, thrm-2, and thrm-4. thrm-2 colocalized with the disp-1 locus described above, so further analysis uses disp-1/thrm-2 to indicate this locus. The same QTL were identified by analysis of residuals of thermal preference regressed on isothermal dispersion, implying that the genetics of thermal preference are largely independent of isothermal dispersion phenotypes. No QTL colocalized with npr-1, and incorporation of a marker at npr-1 as an interactive covariate did not substantially change the LOD profile for thermal preference, implying that the QTL mapped for this phenotype are also largely independent of npr-1. No genes known to cause thermotaxis defects when mutated colocalized within the 1.5-LOD confidence intervals of these novel QTL, suggesting novel variants are contributing to observed thermal preference differences (Table S1).
Accounting for variation at disp-1 significantly changed the LOD profile of the QTL peaks in thermal preference (in contrast to using npr-1 as a covariate). To explore this further, we explicitly tested for interactions between genotypes at disp-1/thrm-2 and genotypes at thrm-1 and thrm-4 (Figure 4). Here, an interesting pattern emerged. We found that for lines with an N2 genotype at disp-1/thrm-2, variation in thrm-1, but not in thrm-4 explained variation in thermal preference. In this case, lines with an HW genotype at thrm-1 had a cooler thermal preference (22.51° ± 0.44°) and lines with an N2 genotype at thrm-1 had a warmer thermal preference (23.95° ± 0.26°, F1, 108 = 8.06, P < 0.01). Conversely, we found that for lines with an HW genotype at disp-1/thrm-2, variation in thrm-4, but not in thrm-1 explained variation in thermal preference. In this case, lines with an HW genotype at thrm-4 had a cooler thermal preference (21.53° ± 0.33°) and lines with an N2 genotype at thrm-4 had a warmer thermal preference (23.31° ± 0.52°, F1, 108 = 8.96, P < 0.01, summarized in Table S2). In summary, we found that thermal preference variation among RIAILs was best explained by accounting for genetic variation in the isothermal dispersion QTL of disp-1/thrm-2, in that the genotype at thrm-4 explained variation in thermal preference for lines with an HW genotype at disp-1/thrm-2, but the genotype at thrm-1 explained variation in thermal preference for lines with an N2 genotype at disp-1/thrm-2 (Figure 8).
Given the preponderance of nonadditive interactions among significant QTL, we performed an unbiased test of all pairwise combinations of 1-cM intervals across the genetic map to detect epistasis for both isothermal dispersion and thermal preference (Figure S3, X chromosome, and Figure 5, A and B) to determine whether suggestive but not significant peaks were accounting for nonadditive genetic variation. We found no such loci for isothermal dispersion. However, for thermal preference we identified strong evidence for epistasis between two loci, also on the X chromosome, termed thrm-3 and thrm-5 (Figure 2, A and C, and Figure 5B, LOD = 4.62, P < 0.001). On their own, thrm-3 and thrm-5 did not display significant main effects (t118 = −1.15, 1.19, respectively, for thrm-3 and thrm-5, P > 0.1; see also Figure 2C). However, when considered together, the interaction effect between thrm-3 and thrm-5 explained half the overall genetic variation in thermal preference, accounting for virtually all of the transgressive segregation observed in the RIAILs. Averaging across effects at all other loci, we found that RIAILs with a “matching” genotype between thrm-3 and thrm-5 (i.e., either N2 genotype at both loci or HW at both loci) exhibited thermal preferences consistent with the phenotypes of the parents. However, “mismatching” genotypes across loci (i.e., N2 genotype at one locus and HW at the other) corresponded with the excessively thermophilic phenotype (Figure 6).
Genetic mismatch between two loci causes extraordinarily thermophilic behavior
To confirm the genetic basis for the identified statistical epistasis pattern between thrm-3 and thrm-5, we generated NILs, using marker-assisted selection. NILs were made in both genetic backgrounds, and these lines were then run in triplicate on linear gradients. We found that the thermophilic interaction effect inferred in the RIAILs was verified in the NILs. Regardless of overall genetic background, our evidence for epistatic interactions remained because single-introgression NILs preferred warm temperatures (F1, 10 = 194.75, P < 0.0001; Figure 7). Additionally, the double-introgression NIL in the N2 genetic background has a thermal preference that is not significantly different from that of the N2 parent (22.05° ± 0.38°, 21.07° ± 0.27°, respectively), and the double-introgression NIL in the HW genetic background has a thermal preference that is significantly colder than that of the single-introgression NILs, although still slightly more thermophilic than that of the HW parent (19.49° ± 0.39°, 17.67° ± 0.22° respectively; Figure 7). Put simply, a genetic mismatch between these two loci in either genetic background causes excessively thermophilic behavior, and a genetic match between these loci permits the main-effect QTL (thrm-1, disp-1/thrm-2, and thrm-4) to determine thermal preference (Figure 8).
Complex epistasis underlying thermal preference behavior
Nearly all ecologically and evolutionary relevant traits are complex, as are most common human diseases such as heart disease, autism, and diabetes (Manolio et al. 2009; Gibson 2010). Yet despite tremendous effort, success in identifying the genetic basis of complex traits has been achieved only in a handful of cases, usually when the underlying variation behaves in a largely Mendelian fashion (Rockman 2012). One possible explanation for this lack of success is that most studies still lack sufficient statistical power, especially in terms of detecting rare alleles with moderate effects. However, another possible explanation is that nonadditive genetic interactions (epistasis) are responsible for much of the segregating variation and do so in a fashion that obscures the effects of individual loci (Phillips 2008; Moore and Williams 2009). This is because in natural populations, such interactions depend on the generation of particular combinations of alleles that, because of their infrequent co-occurrence, are ephemeral and may be difficult to identify using association-mapping approaches. Such genetic interactions are instead best identified using experimental mapping crosses and a substitution-based approach. Here, we used these approaches to characterize the genetic architecture underlying thermal preference in C. elegans, finding it to be highly complex, involving multiple layers of epistasis. In particular, ∼50% of broad-sense heritable variation is caused by a compensatory relationship between two loci.
Because of limited power to investigate all pairwise interactions, a typical experimental approach in mapping studies is to first identify main-effect QTL and then test for interactions among them (Culverhouse et al. 2002). This approach, although utilized in part of this study, suffers from ascertainment bias, particularly because loci with the largest interaction effects are precisely those that are likely to have smaller relative individual effects (Phillips 2008). For thermal preference, a comprehensive pairwise genome scan identified statistical epistasis (Fisher 1918) in the form of two loci that interact with each other to generate extreme thermophilic behavior, a phenotype observed in our mapping population but whose variance was not explained through main-effect QTL. The asymmetric transgressive segregation we observed among lines is a signature of nonadditive variation (Rieseberg et al. 1999). We used single and double NILs in both genetic backgrounds at both interactive loci to demonstrate unequivocally that nonadditive interactions between these two loci cause the excessively thermophilic behavior observed in the RIAILs, well beyond the range of thermal preference observed in other natural isolates (Anderson et al. 2011). Especially telling in this case was the fact that double-introgression NILs were able to recover nearly all of the original parental phenotype that had been strongly disrupted by single substitution of either one of the other loci. This strategy of using introgression lines to functionally test these interacting QTL allowed us to connect Fisher’s original concept of statistical epistasis estimated from variation segregating in a population with the more traditional, Batesian concept of compositional epistasis estimated via the segregational effects of specific loci (Phillips 2008). Although we cannot say at this point precisely how (or whether) the alleles underlying thrm-3 and thrm-5 are functionally interacting [the final common usage of “epistasis” (Phillips 2008)], we can say that in two separate genetic backgrounds (HW and N2), an allelic mismatch causes thermophilic behavior. By taking advantage of known mutations in these regions, in addition to the well-studied developmental and physiological characteristics of the thermotaxis neural circuitry, we should be able to identify not just the novel genes involved in this thermophilic behavior but also the precise nature of their epistatic interactions.
Phenotypic epistasis is generated by functional hierarchies
Breaking the linkage between the thrm-3 and thrm-5 loci, as seen in our single-introgression NILs, masks the ability of main-effect QTL to influence thermal preference. By “rescuing” this linkage through double NILs, which are matching at thrm-3 and thrm-5 but in the contragenic background, we were able to show that the thrm-3/thrm-5 complex is itself upstream of, or epistatic to, the main-effect QTL modules we identified using interval mapping (Figure 8) and that in the presence of matching genotypes at thrm-3 and thrm-5, the main-effect QTL we identified predicted thermal preference. The nature of these interactions suggests a hierarchical epistatic network underlying natural variation in thermal preference (Figure 8).
The downstream module of this network is itself generated by interactions between loci related to hierarchically structured phenotypes of movement and thermal preference, which created a pattern of epistasis similar to that expected for directly interacting gene products. Under this “phenotypic epistasis” (Segrè et al. 2004), the expression of one trait is influenced by the expression of other traits. Specifically, we found that worms can be divided into high- and low-movement groups, where worms with an N2 genotype at disp-1 tend to move little, conditional on the N2 npr-1 genotype, and worms with an HW genotype at disp-1 tend to move substantially more. This is not due to a deficiency in the assay in which, say, expression of specific thermal preference phenotypes becomes contingent upon movement per se. Instead, it is clear that even low-moving lines are “motivated” to go to colder temperatures: some lines with an average isothermal dispersion of ∼1 cm/hr still migrate >4° cooler than the start temperature (Figure S1), a distance of ∼4 cm. Thus, despite a tendency to not disperse in isothermal conditions, these lines are still capable of navigating a thermal gradient and do so to find colder temperatures. Additionally, lines predicted to be thermophilic because of a mismatching genotype between thrm-3 and thrm-5 migrate to warm temperatures, despite also possessing an N2 “low-movement” phenotype. This is true for both RIAIL and NIL genotypes.
Thus movement per se, as measured by total distance traveled, cannot be the sole explanation for the phenotypic epistasis we observed. Extensive behavioral and neurophysiological analysis of the N2 strain navigating on a chemical gradient (Pierce-Shimomura et al. 1999; Iino and Yoshida 2009; McCormick et al. 2011) has shown that individuals use different search strategies as they experience different concentration changes (i.e., klinotaxis vs. klinokinesis), with additional evidence that N2 uses the same strategies in navigating a thermal gradient (Ryu and Samuel 2002) or thermal step (Zariwala et al. 2003; McCormick et al. 2011). In this context, rate of movement affects the effective concentration change that each individual is experiencing. This presents a connection between the movement-conditional QTL presented in this study and the different navigation strategies: perhaps the thrm-1 and thrm-4 QTL have different effects in high- and low-moving individuals because fundamentally different navigation strategies are being used.
The unusual genomic history of C. elegans
In contrast to the complex genetic interactions seen in thermal preference, isothermal dispersion displayed a more classic pattern of epistasis in which the low-movement phenotype is generated only when an individual possesses N2 alleles at both causal loci. Interestingly, these loci are located in what was initially identified as a single large-effect QTL, with one of the interacting loci directly centered over the previously identified laboratory-derived allele of npr-1. Considering the hundreds of generations that the canonical N2 strain has lived under laboratory conditions, it is not surprising that fixation due to drift, as well as selection on mutations beneficial for a laboratory environment, has occurred (McGrath et al. 2009, 2011a; Weber et al. 2010). The latter is clearly the case for npr-1, in which the laboratory-derived mutation simultaneously inhibits burrowing in agar and clumping on a plate, two characteristics that would be selected against under laboratory rearing conditions and facilitated by increasing tolerance of atmospheric levels of oxygen in a nematode that appears to spend most of its natural life in rotting substrates (Kiontke et al. 2011). The laboratory-derived nature of this allele is apparent both through association mapping across wild isolates (McGrath et al. 2009) and through whole-genome resequencing of N2 against its close relatives [LSJ1 and LSJ2 (Weber et al. 2010; McGrath et al. 2011b)] and has been implicated as a causal variant in many behaviors that differ between N2 and HW (Gaertner and Phillips 2010). In this study, npr-1 was clearly implicated in variation in isothermal dispersion. However, the effect of this allele is masked in the presence of an HW genotype at disp-1 (see also McGrath et al. 2009). Additionally, for thermal preference, genetic variation at npr-1 did not change the proportion of variance explained at any other locus, strongly suggesting that variation in the behavior of thermal preference is not due to npr-1–derived laboratory adaptation.
The finding of pervasive epistasis, especially among a large number of tightly linked loci, may be more common in C. elegans than in other animals because of their rare androdieocious mating system consisting of self-fertile hermaphrodites and the occasional outcrossing male. Pervasive self-fertilization tends to decrease the effective recombination rate and greatly increases the probability that interacting alleles will be found (and evolve) in the same genetic background (Fenster et al. 1997; Nordborg 2000). In part due to this mating system, most variation in genetic diversity and gene expression in C. elegans can be explained by genetic hitchhiking (Graustein et al. 2002; Cutter and Payseur 2003; Rockman et al. 2010; Andersen et al. 2012), with reduced genetic variation locked in long haplotype blocks and a strong negative correlation between polymorphism frequency and gene density across the chromosome. In this context, interacting loci with effects that balance one another may evolve in regions of low recombination, and breaking these regions apart through generations of mapping crosses can act to decanalize the assayed phenotype (Gibson 2009).
There is a noted mismatch between the estimated outcrossing frequency in nature based on genomic estimates of global effective population size (Rockman and Kruglyak 2009) and empirical observation of heterozygosity and male frequency (Barrière and Félix 2007) in locally isolated populations of C. elegans (Anderson et al. 2010). This incongruence supports a model of selection against incompatible genotype combinations such as the ones described here [and elsewhere (Seidel et al. 2008; 2011)], where local haplotype and phenotypic diversity might be generated through decanalizing epistasis but masked in a global survey (Barrière and Félix 2007; Anderson et al. 2010). Interestingly, this pattern of reduced genetic diversity does not appear to extend to the X chromosome, which displays slightly more nucleotide diversity and lower and more even linkage disequilibrium (Andersen et al. 2012) and was where all of our identified thermal preference and isothermal dispersion QTL are located. Thus even in regions with a more uniform distribution of polymorphism, recombination, and haplotype diversity, epistasis can persist, suggesting a more central role of these nonadditive interactions in generating diversity within natural populations.
In summary, our study demonstrates the central role of epistasis in generating natural variation in behavior. Epistasis was identified statistically through mapping and connected to compositional epistasis through the generation of introgression lines in two genetic backgrounds. By generating these lines, we were able to demonstrate that interactions also exist between complexes of epistatic loci and hierarchically across phenotypes. Thus, understanding how to construct the genotype–phenotype map through a cellular, physiological, and organismal context requires not only statistical, but also experimental analysis of these interactions.
We thank W. Cresko, J. Fierst, R. Reynolds, and two anonymous reviewers for critical reading of the manuscript and S. Lockery, M. Weir, T. Herman, and the Phillips Laboratory for helpful discussions. This work was supported by grants IOS-0909816 (to B.E.G. and P.C.P.) and DEB-1120417 (to P.C.P.) from the U.S. National Science Foundation and grants R01-HG004321 (to L.K.) and R01-GM089972 (to M.V.R.) from the National Institutes of Health.
Communicating editor: D. W. Threadgill
- Received June 12, 2012.
- Accepted October 9, 2012.
- Copyright © 2012 by the Genetics Society of America