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.