Partitioning variation in body size

To assess the contribution of genotype and temperature to body size variation, we quantified length of abdomens and thoraxes (Fig 1A, S1A Fig) of adult females from ~196 isogenic lines reared at either 17°C or 28°C (S1 Dataset, S2 Table). We found significant differences between DGRP genotypes and between developmental temperatures, as well as significant GxE interaction effects (thermal plasticity) for the size of both body parts (Fig 1A, S1C Fig). We also found variation between individuals of (presumably) the same genotype and same rearing temperature; the coefficients of variation (CV) varied between 0.6 and 15 for thorax measurements and between 0.3 and 23.8 for abdomen measurements (Table 1). Broad-sense heritabilities for body size (at 17°C and 28°C) and body size plasticity (between-environment variation) varied between 33 and 52% (Table 1).

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Table 1. Measurement summary and broad-sense heritability estimates for size and size plasticity.

https://doi.org/10.1371/journal.pgen.1007686.t001

We computed a correlation matrix to assess relationships between the different components of variation in body size (Fig 2). We found a significant positive correlation between thorax and abdomen size within each rearing temperature (Fig 2A and 2B). We also found significant positive correlations between our measurements of thorax (both temperatures), but not abdomen size, and the size of several body parts measured in other studies [51] for the same genotypes reared at 25°C (S2A Fig). To address the question of association between body size and fitness, we measured correlations between our measurements of body size and a series of fitness related traits quantified in other studies of the DGRPs [46, 49, 50, 56]. We found significant correlations with chill coma recovery (thorax size at both temperatures and abdomen size at 28°C) and with survival upon infection with Metarhizium anisopliae fungi (abdomen size at 17°C), but not with longevity, starvation resistance, and other immune-defense traits (S2B Fig). To address the question of what might explain the extent of inter-individual variation within genotype and temperature, we calculated correlations between our measurements of CV and of size. We found the CV to be: 1) positively correlated between body parts for flies reared at 28°C, but not for those reared at 17°C, and 2) negatively correlated with mean size for thoraxes, but not for abdomens (Fig 2B).

Thermal reaction norms for body size

Using reaction norms, we studied the extent and properties of thermal plasticity for body size in the DGRP lines (Fig 1B). We calculated the slope of the regression lines for size across temperatures for each body part and DGRP genotype, and found genetic variation for both the intercept and slope of the reaction norms (Fig 1B, S2 Table). From each reaction norm, we extracted two properties of the thermal plasticity in body size: i) the absolute value of the slope, describing only the magnitude of the response to temperature, and ii) the raw value of the slope, which describes magnitude and direction of the response (Fig 1C, S1D Fig). Using the reaction norms for 191 DGRP lines, we identified plastic and non-plastic genotypes. Slopes of the thermal reaction norm were significantly different from zero for 55% and 57% of the lines for the size of thoraxes and abdomens, respectively, with most of the plastic genotypes having smaller sizes when reared at higher temperatures (Fig 1B). However, we also found plasticity in the opposite direction (i.e. genotypes with smaller flies at lower temperatures), corresponding to a positive significant slope for the thermal reaction norms: 8% of the DGRP reaction norms for the thorax and 22% for the abdomen (Fig 1B).

Even though the levels of thermal plasticity for thorax and abdomen size were significantly positively correlated (Fig 2B), lines having the highest levels of plasticity for one body part were not necessarily the most plastic for the other body part (S1B Fig). Furthermore, for thorax, but not abdomen, we found that genotypes with larger CVs had steeper negative reaction norms. Finally, we also found no correlation between our size plasticity measurements and various fitness-related traits measured in the DGRPs (S2B Fig), including longevity [49], starvation resistance, chill coma recovery [46], and immune-defense traits [50, 56].

Genetic basis of variation in body size plasticity

We used a GWAS approach to identify DNA sequence polymorphisms associated with variation in thermal plasticity for thorax and abdomen size in the DGRP. Because the loci carrying allelic variation for the direction and extent of environmental responsiveness are not necessarily the same, we used both the raw and absolute values of the slopes of the DGRP reaction norms as quantitative traits (Fig 3A, S3 Fig). The candidate QTLs significantly associated with variation in plasticity were typically only so in relation to a single property of the reaction norm (raw or absolute slope) or body part (thorax or abdomen; Fig 3C, S3 Table). We also found that these allelic variants fell within different genomic regions (e.g. UTR, intronic, coding) within or nearby 192 different putative genes (Table 2, S3 Table). Gene ontology enrichment analysis of the candidate QTLs showed an over-representation of vesicle-mediated processes (e.g. phagocytosis and endocytosis; S6A Fig), while network enrichment analyses (protein-protein interaction network followed by a KEGG pathways enrichment analysis) revealed an over-representation for SNARE interactions and Notch pathways (S6B Fig), both of which have been implicated in diverse biological functions [57–60]. We also found that in the vast majority of cases, alleles associated to increased environmental responsiveness were at lower frequencies in the DGRP (Fig 3D, S5 Fig).

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Table 2. Details of candidate QTLs selected for functional validation.

https://doi.org/10.1371/journal.pgen.1007686.t002

To explore to what extent loci that contribute to variation in size plasticity also contribute to variation in body size within environments, we also performed GWAS analyses using body size at 17°C and 28°C as quantitative traits (Fig 3B, S4 Fig, S4 Table). This analysis revealed QTLs that were mostly environment- and body part-specific (Fig 3C, S4 Table), including no overlap between our candidate QTLs associated with variation in thorax and abdomen size and those reportedly associated with head size at 25°C [51]. Moreover, we found little overlap between candidate QTLs contributing to variation in size plasticity and those contributing to within-environment size variation (Fig 3C), but some overlap in terms of network enrichment (S6 Fig). None of our size traits or plasticity therein was affected by chromosomal inversions (p-value > 0.01), or by the genetic relatedness among DGRP lines (low and non-significant coefficients of phylogenetic signal Blomberg’s K and Pagel’s λ; S2C Fig).

Validation of selected GWAS hits

We selected a number of significant QTLs for validation via different approaches (Fig 4, Table 2, S2 Dataset). To test selected candidate genes, we used available null mutants and inducible gene knock-downs (with RNA interference using the Gal4/UAS system). If a candidate gene affects plasticity, we expected to see a difference in the slope of thermal reaction norms between genotypes with abolished (mutant) or reduced (knock-down) gene function in relation to the corresponding control genotypes (with “wildtype” gene function). To test specific significant SNPs/Indels, we used a SNP-based validation approach, hereafter called Mendelian Randomization (see Materials and methods), that allowed comparisons between genotypes fixed for the candidate SNP (minor versus major alleles) but not for any other significant SNP for the same trait. If a candidate SNP affects plasticity, we expected a difference in slope of the reaction norms between newly-established genotypes carrying the minor versus the major allele at the target SNP.

Using these methods, we confirmed a role in thermal plasticity for six out of seven candidate QTLs (Fig 4, S7 Fig, Table 2). For genes Hsp60 (Fig 4A), btv (Fig 4B), Men (Fig 4E), and Eip75B (Fig 4F), plasticity was different between genotypes with impaired gene function (knock-out or knock-down) versus controls. For SNPs in genes CG43902 (Fig 4C) and ACC (Fig 4D), plasticity was different between new genotypes with minor versus major allele. We did not find a difference in abdomen size plasticity between genotypes for candidate QTL CG43117 (S7G Fig, Table 2, S2 Dataset). For all the confirmed candidates for plasticity, the genotypes with impaired gene function were more plastic than their respective controls, and the DGRP genotypes harboring the minor allele were more plastic than those harboring the major allele (S7 Fig). We also validated three out of four candidate QTLs for within-temperature variation in body size (Fig 4, S7 Fig, Table 2, S2 Dataset). Genes Nmdmc (Fig 4G) and Optix (Fig 4I) affected thorax size at 28°C and at 17°C, respectively, while a SNP in gene CG14688 (Fig 4H) affected thorax size at 28°C. We did not find a difference in abdomen size at 28°C between genotypes for candidate QTL Wapl (S7K Fig, Table 2, S2 Dataset).

To explore the pleiotropic effect of validated plasticity QTLs, we investigated whether the plastic response was also seen in the body part for which the SNP/gene had not been significantly associated to in the GWAS analysis (S7 Fig, Table 2). Out of the six validated plasticity QTLs, we only found cross-body part effects for gene btv; initially implicated in variation in plasticity of thorax size, this gene was also found to influence variation in plasticity of abdomen size (S7B Fig, Table 2).

Components of variance in body size

Studying a panel of populations representing naturally-segregating alleles, the DGRP, we quantified effects of G, E, and GxE interactions on the size of two body parts (thorax and abdomen). As is well documented for various species of insects [32, 67, 68], most D. melanogaster genotypes we analyzed showed larger bodies when flies were reared at our lower temperature. However, we also documented cases of genotypes showing no plasticity (robustness) or showing size plasticity in the opposite direction. We also found a positive correlation in the levels of plasticity for the two body parts. The strong associations between the sizes of different body parts and plasticity therein is likely to reflect the tight regulation of body proportions, which is key for organismal performance [7, 69].

It is unclear to what extent a genotype’s responsiveness to environmental conditions (i.e. its plasticity) is associated with inter-individual differences found within a given environment for that same genotype (quantified with the coefficient of variation). While the latter is presumably un-accountable for by the effects of G, E, or GxE, it could reflect small genetic differences between individuals (e.g. derived from somatic mutation), micro-environmental variation (e.g. differences within a vial), or stochasticity in phenotype expression (e.g. developmental noise). We found that genotypes that were more plastic for thorax size (but not abdomen) also had higher levels of intra-genotype, intra-environment variation (i.e. higher coefficient of variation). Whether this component of phenotypic variance is assignable to micro-environmental variation and whether it has its own genetic basis has started to be investigated [53, 70, 71] and will, undoubtedly, be a topic of targeted future research.

QTL for size and size plasticity of different body parts

By using the raw and absolute values of the slopes of reaction norms as quantitative traits, we identified loci associated with variation in size plasticity. Genetic variation for environmental responsiveness could, in principle, involve different types of molecular players and could affect multiple traits in a similar or different manner. We described QTLs influencing size plasticity corresponding to different functions in terms of putative SNP effects (e.g. missense, regulatory, or synonymous mutations; Table 2, S3 Table), and described molecular function and biological process for corresponding genes. These genes could potentially be mediating environmental effects at different levels, from the perception of the environmental cue (e.g. gene btv, which has been implicated in sensory perception [72]), to the transmission of that information to developing tissues (e.g. gene Eip75B, coding for an ecdysone receptor [73]), or the execution of the information on those tissues (e.g. genes Men, ACC, and Hsp60A, coding for two metabolic enzymes and a chaperone [73], respectively).

Presumably, genes higher up in the process of responding to the environment (e.g. those involved in the perception of external conditions versus those responding in specific tissues) would be more likely to affect multiple plastic traits in a concerted manner. With the exception of btv, none of our validated plasticity QTLs affected plasticity for other than the body part they had been identified as QTL for. Even for the complete set of candidate loci, we found very little overlap between QTLs for plasticity of different body parts (thorax versus abdomen), as well as for different properties of the reaction norms (raw versus absolute value of slopes). Furthermore, we also documented mostly private QTLs influencing variation in size at any given environment (i.e. body part and temperature-specific). Sex-, body part-, and environment-specific QTL effects had been previously documented for various traits in different models [51, 74–76]. In D. melanogaster, for example, different loci have been associated to variation in bristle number in different body parts [76], and to variation in size in different environments [77]. Such private QTLs can potentially facilitate independent evolution of the traits.

Previous works exploring the genetic basis of environmentally sensitive variation have mostly focused on investigating QTLs whose effect vary across environments (QTL-by-environment interactions) for a variety of traits in different species [66, 78–80]. Much less attention has been paid to unraveling the allelic variants contributing to differences in plasticity itself. Exceptions include mapping of the genetic basis of thermal plasticity in life-history traits in Caenorhabditis elegans [81], of photoperiodic plasticity in multiple traits in Arabidopsis thaliana [82] and, more recently, of thermal plasticity in cold tolerance in D. melanogaster [53]. Our results revealed little overlap between the QTLs that contribute to variation in trait within environments and the QTLs that contribute to variation in trait plasticity, assessed from the slope of reaction norms. We documented loci underlying variation in size plasticity (i.e. properties of reaction norms) that are different from those underlying variation in size at any temperature (i.e. at 17°C and at 28°C). These results shed light onto a long-standing discussion about the genetic underpinnings of plasticity, which argue that either the genetic control of phenotypic plasticity happens via specific loci determining plastic responses or via the same loci that control trait values at a given environment [83–86]. Our data show that the genetic basis for trait plasticity, to a large extent, differs from the genetic basis for phenotypic variation in the trait itself.

Evolution of size and size plasticity

Plasticity can be adaptive in that it helps populations cope with environmental heterogeneity, and it has even been argued that it can promote phenotypic and taxonomic diversification [20–22, 27]. Theoretical models highlight the ecological conditions that should influence the evolution of plasticity, such as the predictability of environmental fluctuations [87] and costs of plasticity [88, 89]. Plasticity is generally presumed to be costly and only selected for in predictably heterogeneous environments, such as seasons [90]. The absence of a correlation between our thermal plasticity measurements and various fitness-related traits measured for the same genotypes could not identify any such cost. These potential costs might involve traits that have not been considered here, or these same traits but under (environmental) conditions that were not those assayed.

The ability to respond or resist environmental perturbation, and the balance between both processes, can be crucial for fitness in variable environments. In the DGRP, even though some degree of environmental responsiveness is maintained, we found that the alleles contributing to increased levels of plasticity occur nearly always at lower frequencies (i.e. the genotypes with the minor allelic variant having steeper reaction norms than those with the major allele). It is unclear to what extent this is the result of natural selection by the thermal regime that the natural population from which the DGRP was derived was exposed to, and/or the result of the process of deriving the mapping panel in the laboratory. It is also unclear to what extent QTLs for size plasticity in the DGRPs are those under selection in other populations. While it is conceivable, if not likely, that different QTLs contribute to variation in plasticity (or other quantitative traits) in different populations, we did find that a number of our plasticity QTLs have been targets of selection in other populations. Specifically, some of our candidate genes for size plasticity appear to have been selected in experimental populations of D. melanogaster evolving under different fluctuating thermal regimes [91]. Among our 192 candidate QTLs for thermal plasticity, eight genes (including the validated btv) had changes in the populations evolving under hot and cold temperatures fluctuations [91], nine genes (including Men) had changes in populations evolving under hot fluctuations, and 25 genes had changes in the populations evolving under cold temperatures fluctuations (see all overlaps in S3 Table). Gene Eip75B has also been previously implicated in the response to artificial selection for body size [92] and in differentiation between clinal populations [93, 94], which typically represent different thermal environments.

Altogether, our results shed light onto the nature of inter-genotype variation in plasticity, necessary for the evolution of plasticity under heterogeneous environments. We showed that QTLs for size plasticity: 1) bear alleles for increased plasticity at low frequencies, 2) correspond to polymorphisms in different genomic regions and within genes of a multitude of functional classes, and 3) are mostly “private QTLs”, with little overlap between our various GWAS analysis. The latter underscores the potential for independent evolution of trait and trait plasticity (different QTLs for size plasticity and for within-environment size variation), plasticity of different body parts (different QTLs for size plasticity of thorax and of abdomen), and even properties of the environmental response (different QTLs for raw and absolute slopes of thermal reaction norms).

Fly stocks and rearing conditions

Data for the GWAS was collected from adult female flies of the Drosophila Genetic Reference Panel (DGRP) obtained from the Bloomington Stock Center. The DGRP is a set of fully sequenced inbred lines collected from a single population in Raleigh, NC, USA [46, 48]. The number and the details of the lines included in the GWAS for each trait can be found in S2 Table. Mutant stocks for the functional validations were: Hsp60A (stock 4689 from Bloomington), MenEx3 and MenEx55 (obtained from the T. Merritt lab). Control genetic backgrounds were w1118 (stock 5905, from Bloomington) and Canton-S (obtained from C. Mirth lab). UAS-Gal4 and UAS-RNAi lines used for validations were: stocks 6803 for bab-Gal4, 5138 for tub-Gal4, 28737 for btv-RNAi, and 35785 for mCherry-RNAi (all obtained from the Bloomington stock center), and stocks v108399 and v110813 for Eip75B and Optix-RNAi, respectively (obtained from the VDRC stock center).

Fly stocks were maintained in molasses food (45 gr. molasses, 75gr sugar, 70gr cornmeal, 20 gr. Yeast extract, 10 gr. Agar, 1100 ml water and 25 ml of Niapagin 10%) in incubators at 25°C, 12:12 light cycles and 65% humidity until used in this study. For the experiments, we performed over-night egg laying from ~20 females of each stock in vials with ad libitum molasses food. Eggs were then placed at either 17°C or 28°C throughout development. We controlled population density by keeping between 20 and 40 eggs per vial. We reared 200 DGRP lines and quantified thorax and abdomen size of 5 to 20 females per line, temperature and replicate. For 135 DGRP lines, we ran two replicates and for 33 lines we ran three replicates. The total number of flies used varied among DGRP lines due to differences in mortality at one or both of the temperatures. For some specimens, we could only quantify size of one body part if, for example, the individual was not properly positioned in the image or was damaged. Full details on the stocks used and the number of flies used per stock and temperature can be found in S1 Dataset and S2 Table. Rearing conditions for the validations of candidate QTLs were similar to those used for the DGRP lines.

Phenotyping body size and plasticity

Adult female flies (8–10 days after eclosion) were placed in 2ml Eppendorf and killed in liquid nitrogen followed by manual shaking to remove wings, legs and bristles. Bodies were mounted on Petri dishes with 3% Agarose, dorsal side up, and covered with water to avoid light reflections. Images containing 10 to 20 flies were collected with a LeicaDMLB2 stereoscope and a Nikon E400 color camera under controlled imaging conditions of light and white-balance. Images were later processed with a customized Mathematica macro to extract size measurements. For this purpose, we drew two transects per fly, one in the thorax and one in the abdomen, using body landmarks (as shown in S1A Fig). Size of each body part was initially quantified as the number of pixels in the transect and later converted to millimeters. For abdominal transects, when necessary, we performed an additional step that involved the removal of pixels corresponding to the membranous tissue that is sometimes visible between segments.

Genome-Wide association study

For each body part (thorax and abdomen), we performed four independent Genome-Wide Analyses (GWAS): two for thermal plasticity (raw and absolute values of the slopes of the reaction norms), and two for within-environment variation (length at 17°C and length at 28°C). Slopes of the reaction norm were calculated as the slope of the linear model lm (Size ~ Temperature) for each body part and DGRP line. We used linear mixed-effects models (lmer) in lme4 R package to test which polymorphisms explained variation in size plasticity or in size. The GWAS analyses for variation in thermal plasticity tested for effects of fixed factor Alelle (corresponding to each polymorphic site), and random factors Wolb (corresponding to Wolbachia status (0 or 1) of each DGRP line [7,8]) and DGRP (i.e. genetic background) to variation in the dependent variable Slope (i.e. raw or absolute values of the reaction norms). This corresponds to notation lmer (Slope ~ Allele + (1|Wolb/DGRP)) in lme4 R package [95]. The GWAS analyses for within-environment variation tested the model lmer (Size ~ Allele + (1|Wolb/DGRP), where Size is the dependent variable (thorax or abdomen length from flies reared at 17°C or at 28°C) and all the other terms are the same as described above for the plasticity GWAS. All the GWAS were performed by using polymorphisms where we had information for at least ten DGRP lines per allele. We did not find an effect of Wolbachia in any of our GWAS analyses.

For each of the GWAS, we annotated the SNPs with an arbitrary and commonly used p-value < 10e-5 using the FlyBase annotation (FlyBase release FB2017_05; [73]). For the same SNPs, we performed first, a gene ontology enrichment analysis using the publicly available GOrilla Software [96, 97] and second, a network enrichment analysis using gene-enrichment and pathway-enrichment analyses were done using the publicly available NetworkAnalyst Software [98, 99]; using all nodes from first order network generated with IrefIndex Interactome settings.

Analyses of the overlap in significant QTLs (p-value < 10e-5) from different GWAS analyses was done for individual polymorphic sites (considered to be the same based on genomic position and type) and for the genes those sites were annotated to (using FlyBase release FB2017_05; [73]).

We tested for the effect of the chromosomal inversions (In_3R_K, In_3R_P, In_2L_t, In_2R_NS and In_3R_Mo) on our thorax and abdomen traits by using linear models (lm (Mean Size ~ Inversion) for within-environment size variation and lm (Slope ~ Inversion) for size plasticity variation).

Genetic distance matrix for the DGRPs was obtained from http://dgrp2.gnets.ncsu.edu/data.html and was used to perform a cluster hierarchical dendogram using ape and phylobase R packages. We estimated the phylogenetic signal and statistical significance for each of our traits using Blomberg’s K [100] and Pagel’s λ [101] metrics with the phylosig function in the phytools R package [102].

Functional validations

The subsample of significant QTLs to be validated was taken from a first list of candidates (S3 and S4 Figs) selected based on p-value and corresponding peaks in the Manhattan plots (clear peaks prioritized), putative effect (missense and regulatory variants prioritized over intergenic variants), associated genes (annotated and known function prioritized). We used three methods for validation, depending on QTL properties: null mutants and RNAi (Gal4/UAS system) for genes containing several significant SNPs and/or containing SNPs corresponding to missense variants, and Mendelian Randomization (MR) for SNPs in genes with little or no information available. Mutant and RNAi test that no or low levels of peptide affect variation in the quantitative trait for which the gene was identified as a candidate QTL while MR tests for sufficiency and independence from genetic background of the specific allele. Following these criteria we tested a total of 11 candidate SNPs/genes.

Validations by null mutants were done by comparing the phenotype in the heterozygous mutant stock with its respective genetic background. Validations by RNAi were done by comparing, for each Gal4 driver line, the phenotype of the gene of interest knockdown with the corresponding control cross using UAS-mCherryRNAi as well as with the corresponding control genetic background for the UAS line. We always used two different driver lines for our validations by RNAi: tub-Gal4 and bab-Gal4. Gene tub is ubiquitously expressed throughout development and gene bab is expressed in multiple tissues during different stages of development. Both genes include expression in developing thoraxes and abdomens (see FlyBase reports FBgn0003884 and FBgn0004870, respectively). However, for all candidate genes selected for RNAi validation, except Nmdmc, the crosses between RNAi line and tub-Gal4 were lethal.

The identity of the SNPs tested by MR is given by their annotation with Genome Release v6. For each candidate SNP, we first selected 10 DGRP lines to make a population with the minor allele fixed and 10 others to make a population with the major allele fixed. The 10 DGRP lines used to create each population were checked for having only one of the significant QTLs (p-value < 10e-5) fixed and not the others. These lines were used to generate four populations, two fixed for the major allele and two for the minor allele (S2 Table). Each population was established by crossing 8 virgin females from each of 5 of the same-allele lines to 8 males of the other 5 lines. Reciprocal crosses were used to set two independent populations per allele. These populations were allowed to cross for eight generations to randomize genetic backgrounds. We confirmed by Sanger sequencing that those populations had our candidate allele fixed. Primer sequences used to confirm the allele in each population were:

1. Gene CG43902—forward primer: ACCACCAACATCAGCGTTTC; reverse primer: TGGTTTCGGCGTAGTTGTTG.
2. Gene ACC–forward primer: TGGGAAAAACCGGCCTAAGA; reverse primer: ATTTGTGGCTGTGGATTGCG.
3. Gene CG43117 –forward primer: TAAGCAAAATGTGGCGTGCA; reverse primer: TTAACATGGATCCTGCGCAC
4. Gene CG14688 –forward primer: CATACTTTGACAGACGGCCG; reverse primer: CGGCTACATTGTCATCGAGG

Statistical analyses

All statistical analyses were performed with R Statistical Package version 3.3.1 [103]. For each body part and temperature, we used linear mixed-effects model (lmer) in lme4 R package [95] to test for the effect of replicate on size (model lmer (Size ~ Replicate + (1|DGRP/Replicate))), that were found to be non-significant (p-value > 0.05). Individuals from different replicates were pooled for all other analyses. We then used linear models (lm) to test for the within-environment effect of genotype (DGRP) on size (model lm (Size ~ DGRP)). We also used linear models to test for plasticity in size (i.e. the interaction between genotype (DGRP) and temperature (model lm (Size ~ DGRP*Temperature))). Reaction norms for each DGRP line were calculated by using the linear model lm (Size ~ Temperature). We extracted two properties of the reaction norms per DGRP line and body part: the absolute value of the slope as a measurement of thermal sensitivity, describing only the magnitude of the response to temperature, and the raw value of the slope as a measurement which describes also the direction of that response. We defined plastic genotypes as those DGRP lines whose reaction norm slope was significantly different from zero (p-value < 0.05).

We used the total number of phenotyped individuals for each DGRP and temperature to perform the calculations of the summary statistics in S2 Table. We used Pearson correlations (alpha = 0.99) to test for linear correlation in size between body parts, controlling for DGRP lines. We also used Pearson correlations to test for linear correlations among our measured traits and between those and other available datasets for the DGRPs. For this, we used the mean value per DGRP line for each trait and the corrplot R package [104]. We report both correlation coefficient and significance levels. Available DGRP phenotypes that were used to correlate with our traits were: size measurements at 25°C [51], longevity [49], starvation resistance, chill coma recovery [46], tolerance to infection with Providencia rettgeri bacteria [56] and resistance to infection with Metarhizium anisopliae fungi or with Pseudomonas aeruginosa bacteria [50].

Broad sense heritability for size at each temperature was estimated as H² = σ²_A/(σ²_A + σ²_W) where σ²_A and σ²_W are the among-line and within-line variance components, respectively. Heritability of plasticity was calculated as H² = σ²_G*E/σ²_TOTAL where σ²_G*E and σ²_TOTAL are the variance associated with the genotype by environment interaction and total variance components, respectively, as proposed in Scheider and Lyman (1989). Variance components were extracted using varcomp R package.

For the functional validations of within-environment SNPs and genes we used the linear models lm (Size ~ Allele) and lm (Size ~ Genotype), respectively. For the validations of plasticity SNPs and genes we used the models lm (Size ~ Genotype*Temperature) and lm (Size ~ Allele*Temperature), respectively. In all cases, significant differences among groups were estimated by post hoc comparisons (Tukey’s honest significant differences).