ABSTRACT Most organisms experience variable and sometimes suboptimal environments in their lifetime. While stressful environmental conditions are normally viewed as a strong selective force,

they can also impact directly on the genetic basis of traits such as through environment-dependent gene action. Here, we used the _Drosophila melanogaster_ Genetic Reference Panel to

investigate the impact of developmental temperature on variance components and evolutionary potential of cold tolerance. We reared 166 lines at five temperatures and assessed cold tolerance

of adult male flies from each line and environment. We show (1) that the expression of genetic variation for cold tolerance is highly dependent on developmental temperature, (2) that the

evolvability decrease with increasing developmental temperatures. Our results show that the quantitative genetic basis of low temperature tolerance is environment specific. This conclusion

is important for the understanding of evolution in variable thermal environments and for designing experiments aimed at pinpointing candidate genes and performing functional analyses of

thermal resistance. You have full access to this article via your institution. Download PDF SIMILAR CONTENT BEING VIEWED BY OTHERS CHILL COMA ONSET AND RECOVERY FAIL TO REVEAL TRUE VARIATION

IN THERMAL PERFORMANCE AMONG POPULATIONS OF _DROSOPHILA MELANOGASTER_ Article Open access 25 May 2021 EVOLUTION OF CROSS-TOLERANCE IN _DROSOPHILA MELANOGASTER_ AS A RESULT OF INCREASED

RESISTANCE TO COLD STRESS Article Open access 14 November 2022 SPECIES-SPECIFIC EFFECTS OF THERMAL STRESS ON THE EXPRESSION OF GENETIC VARIATION ACROSS A DIVERSE GROUP OF PLANT AND ANIMAL

TAXA UNDER EXPERIMENTAL CONDITIONS Article 06 July 2020 INTRODUCTION Stressful environmental conditions not only act as a strong selective force, but also directly affect the genetic

architecture of traits in several ways. Firstly, stressful conditions can influence the nature of environmental variation, _V_E (Hoffmann and Merilä 1999; Ørsted et al. 2018a). Secondly,

these conditions can directly affect expression of additive genetic variation, _V_A (e.g., Czesak et al. 2006; Wilson et al. 2006). Both changes in _V_E and _V_A will influence heritability

estimates (Gebhardt-Henrich and van Noordwijk 1991; Charmantier and Garant 2005; Wilson et al. 2006; Visscher et al. 2008; Dingemanse et al. 2009; Kristensen et al. 2015). Thirdly,

environment-dependent gene action may exist, where the function of genes or gene networks, rather than overall genetic variation, change under stressful conditions (Telonis-Scott et al.

2009). This can lead to a situation where genes influencing a trait in one environment may not have much impact on that trait in a different environment (Sgrò and Hoffmann 2004), which in

turn will alter genetic correlations between environments (de Jong 1990; Stearns et al. 1991; Vieira et al. 2000; Stinchcombe et al. 2010). The impact of these environmental effects on

genetic variation can be important for evolutionary trajectories (Hoffmann and Merilä 1999; Hercus and Hoffmann 2000), but these mechanisms are rarely studied, as they are typically regarded

as a nuisance in quantitative genetic models (but see Wilson et al. 2006; Husby et al. 2011; Wood and Brodie 2016). Environmental conditions are likely to have particularly large effects on

the genetic architecture of ecological traits critical for responding to variable environmental conditions. For this class of traits, the environment can influence _V_A, _V_E and gene

action (Sgrò and Hoffmann 2004; Kristensen et al. 2015), affecting the extent to which organisms respond to selection and hence persist in the long run. Tolerance to stressful temperatures

in ectotherms, especially in _Drosophila_, provides examples of traits whose genetic architecture is strongly affected by environmental variation, such as the impact of developmental

temperature on the resistance to cold extremes (Schou et al. 2017; Ørsted et al. 2017). The ways in which the environment influences trait expression across genotypes is often characterised

through genotype-by-environment (GxE) interactions, where the direction and magnitude of GxE can depend on genotype (Rohde et al. 2017; Ørsted et al. 2018a) and/or as genetic correlations of

a trait measured in several environments (Vieira et al. 2000; Sgrò and Hoffmann 2004; Agrawal and Stinchcombe 2009). Genotypic responses to environmental variation are usually expressed as

differences in phenotypic plasticity, which is commonly quantified with linear reaction norms, i.e., the slope of trait values under continuous variation in an environmental variable

(Valladares et al. 2006). However, while environmental effects on gene action have typically been investigated by comparing two environments (Telonis-Scott et al. 2009; Ellers and Driessen

2011; Levine et al. 2011; Ketola et al. 2012; Gerken et al. 2015), patterns across environmental gradients are not well established. If the slopes of different genotypes along a gradient

vary, there will be GxE interactions and genetic correlations will be less than one. Under a continuous environmental gradient, a polygenic trait might be expected to show declining genetic

correlations (increasing GxE interactions) as environments become more dissimilar, assuming that there is an increase in variance due to environment-specific gene expression (Via and Lande

1985; Sgrò and Blows 2004; Spichtig and Kawecki 2004). This could lead to a linear or non-linear decrease in genetic correlations with the environmental variable. Surprisingly, this rather

simple prediction has rarely been studied experimentally. Knowledge of the effects of environmental similarity on genetic correlations of stress resistance traits can prove valuable when

trying to predict adaptive responses in heterogeneous environments (Sheldon et al. 2003; Sgrò and Blows 2004), such as those arising from anthropogenic climate change (Etterson and Shaw

2001; Agrawal and Stinchcombe 2009). Environment-dependent gene action also raises the question of how plasticity is linked to stress tolerance in a particular environment. Phenotypic

plasticity can be costly, because it requires energy and flexibility of the organism on a number of biological levels (Auld et al. 2010; Tonsor et al. 2013; Murren et al. 2015), and might

become maladaptive if environments change rapidly and in unpredictable ways (Kristensen et al. 2008). Several studies have found an inverse association between stress resistance in one

environment (here defined as “basal” tolerance) and the capacity for plasticity (Stillman 2003; Nyamukondiwa et al. 2011; Gerken et al. 2015; Comte and Olden 2017; Noh et al. 2017),

suggesting a trade-off between the ability to cope with current conditions and performance in an altered environment in the future (Murren et al. 2015). However, other studies have not found

evidence for such trade-offs (Kellett et al. 2005; Calosi et al. 2008; Franke et al. 2012; Gunderson et al. 2015; Ørsted et al. 2018a). Thus, it remains unclear if plasticity constrains

basal thermal tolerance and _vice versa_. Adding to the ambiguity, some studies have found that trade-offs are species-specific even across related taxa (Nyamukondiwa et al. 2011; Strachan

et al. 2011; Comte and Olden 2017), while others have found seasonal variation in trade-offs (Noh et al. 2017), highlighting the influence of environmental conditions on these patterns.

Here, we investigate the effect of developmental temperature on the genetic architecture of cold tolerance in _Drosophila melanogaster_. The aims were to explore the environmental dependency

of quantitative genetic parameters of cold tolerance, and potential trade-offs between cold tolerance in each environment and plasticity in this trait. We used the _Drosophila melanogaster_

Genetic Reference Panel (DGRP; Mackay et al. 2012; Huang et al. 2014), which was established from a natural population of _D. melanogaster_ from Raleigh, USA. The DGRP consists of a

collection of lines that has been inbred to an expected inbreeding coefficient of F ~1, resulting in extremely high homozygosity throughout the genome within each line. Thus, by assessing

cold tolerance of the same DGRP genotype reared in different thermal environments, knowledge about the genetic aspects of cold tolerance, and the association between cold tolerance at

individual temperatures and plasticity, can be obtained. Low temperatures limit the geographical distribution of many species more than warm temperatures (Sunday et al. 2011; Williams et al.

2014), and cold tolerance is therefore a good predictor of present and future distributions of species (Kimura 2004; Overgaard et al. 2011; Araújo et al. 2013). The temperature regimes

employed in this study were well within the range of what the cosmopolitan _D. melanogaster_ will experience in its natural habitats (Hoffmann et al. 2003), thus providing ecological

relevance to our experimental setup. However, it can be difficult to ascertain which of the thermal environments are optimal and which imposes a level of stress, as this is highly dependent

on the biological context and traits investigated (David et al. 1997; Hoffmann 2010). We reared 166 DGRP lines at five different developmental temperatures and quantified cold tolerance in

adults from each developmental temperature. We performed quantitative genetic analyses to estimate genetic and environmental variance components, heritabilities, evolvabilities and

phenotypic/genetic correlations of cold tolerance in each developmental environment and for (slope-based) plasticity. This allowed three main questions to be answered. (1) To what extent is

cold tolerance phenotypically and genetically correlated between developmental environments, and are these correlations dependent on environmental similarity? (2) Do the developmental

temperatures impact on variance components, heritability and evolvability of cold tolerance within developmental temperatures, and on plastic cold tolerance? (3) Is there a trade-off between

cold tolerance at individual developmental temperatures and the plasticity of cold tolerance, and is this trade-off dependent on the environment? MATERIALS AND METHODS EXPERIMENTAL SETUP

This study included 166 lines from the _D. melanogaster_ Genetic Reference Panel (DGRP) obtained from Bloomington _Drosophila_ Stock Centre (NIH P40OD018537), maintained at 23 °C, 50%

relative humidity and a 12:12 h photoperiod on a standard oatmeal–sugar–agar–yeast medium for two generations before starting the experiment. The experimental procedure follows the setup

previously described in Ørsted et al. (2018a). In summary, we reared each DGRP line at five different temperatures: 17, 20, 23, 26 and 29 °C, from eggs to adults in vials containing 7 mL

standard medium (for details on medium composition see Ørsted et al. 2018b). Eggs were laid by approximately 20 adult flies in three 12 h periods. Vials were checked daily and eclosed flies

were sexed under CO2 anaesthesia. Males were transferred to vials containing fresh medium, and kept at the respective developmental temperature for 48 h. At age 60 ± 12 h, the cold tolerance

of around 10 males (for exact numbers see Table S1) per DGRP line per temperature were assessed using the dynamic measure Critical Thermal Minimum (CTmin; Overgaard et al. 2012). In this

standardised procedure, individual flies were placed in glass vials in a water bath pre-set to 23 °C. Water in the bath was gradually cooled at a rate of 0.1 °C/min. The temperature at which

a fly enters chill coma, i.e., when all movement ceases, was recorded and defines CTmin. In total, 7690 flies were scored for CTmin by the same person to reduce potential scoring bias. We

chose males because virginity is not likely to impact on males to the same extent as it does to females (Goenaga et al. 2012), and we were not able to assure that all flies were virgins when

assessed. Phenotypic plasticity of cold tolerance was estimated as the regression coefficient of a linear regression, i.e., slope of CTmin on rearing temperature. Linear regression analysis

provided the best fit compared to polynomial regression, as assessed by the coefficient of determination (results not shown). In addition, linear norms-of-reaction are the most commonly

used in studies of phenotypic plasticity (Valladares et al. 2006), and especially for the relationship between CTmin and developmental temperature, which is typically linear in _Drosophila_

(Schou et al. 2017; Ørsted et al. 2018a). QUANTITATIVE GENETIC ANALYSES Genetic analyses was performed with the average information restricted maximum-likelihood (AI-REML) procedure (Madsen

et al. 1994) utilising the R package QGG (http://psoerensen.github.io/qgg/) that links to DMU (http://dmu.agrsci.dk/DMU/). We estimated variance components, genetic correlations and broad

and narrow sense heritabilities (_H_2 and _h_2, respectively) based on multi-trait linear mixed models. For CTmin we fitted a five-trait multi-trait model including all developmental

temperatures, and to estimate genetic correlations between CTmin at individual temperatures and plasticity we fitted a bivariate model. The multi-trait model including _i_-traits was

generalised as: $$\left[ {\begin{array}{*{20}{c}} {{\mathbf{y}}_1} \cr \vdots \cr {{\mathbf{y}}_i} \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} {{\mathbf{X}}_1} \cr \vdots \cr

{{\mathbf{X}}_i} \end{array}\begin{array}{*{20}{c}} {{\mathbf{b}}_1} \cr \vdots \cr {{\mathbf{b}}_i} \end{array}} \right] + \left[ {\begin{array}{*{20}{c}} {{\mathbf{Z}}_1} \cr \vdots \cr

{{\mathbf{Z}}_i} \end{array}\begin{array}{*{20}{c}} {{\mathbf{g}}_1} \cr \vdots \cr {{\mathbf{g}}_i} \end{array}} \right] + \left[ {\begin{array}{*{20}{c}} {{\mathbf{e}}_1} \cr \vdots \cr

{{\mathbf{e}}_i} \end{array}} \right]$$ where, Y_i_ was a vector of phenotypic observations (i.e., either temperature specific CTmin or plasticity), X_i_ and Z_i_ are design matrices linking

fixed and random genetic effects to the phenotypes. The B_i_ is a vector of fixed effects (experimental day, water bath, time on day (for CTmin), _Wolbachia_ infection status, and five

major chromosomal inversions (only when estimating the additive genetic variance), and _E__i_ is a vector of random residuals (\(\boldsymbol {e}_i\sim N\left( {0, \boldsymbol {I}\sigma

_{e_i}^2} \right)\)). Estimates of the genetic variance components (and therefore _H_2) were obtained by assuming the DGRP lines to be independent, which we modelled by an identity block

matrix (_I__L_) as (co)variance structure of the genetic values, \(g{\mathrm{\sim }}N\left( {0,{{\boldsymbol {I}}}_L\sigma _g^2} \right)\). To estimate the additive genetic variance

components (including _h_2) we estimated the realised relationship among DGRP lines from publicly available single nucleotide polymorphisms (SNPs). Thus _h_2 was the proportion of phenotypic

variance captured by common segregating SNPs within the DGRP. We used standard filtering for the DGRP, i.e., only considering biallelic SNPs with minor allele frequencies ≥0.05, and for

which the Phred scaled variant quality was greater than 500, and the genotype call ≥0.8 (Mackay et al. 2012; Huang et al. 2014). This yielded 1,725,755 genetic markers distributed on the six

chromosome arms (2L, 2R, 3L, 3R, 4 and X). The notation is similar as above, however, here we included five major polymorphic inversions present in the DGRP system; _I2Lt_, _I2Rns_, _I3Rp_,

_I3Rk_ and _I3RMo_), _G__i_ was a vector of random genetic effects, \({\boldsymbol{g}}_i{\mathrm{\sim }}N\left( {0,{\boldsymbol{G}}\sigma _{g_i}^2} \right)\), where _G_ was the additive

genomic relationship matrix, and _E__i_ was a random residual, \({\boldsymbol{e}}_i{\mathrm{\sim }}N\left( {0,{\boldsymbol{I}}\sigma _{e_i}^2} \right)\). The _G_ matrix was computed as _G_ =

 _WW_′/_m_ (VanRaden 2008), where _m_ was the number of genetic markers, and _W_ was a centred and scaled genotype matrix. Each column vector of _W_, \({\boldsymbol{w}}_i =

\frac{{{\boldsymbol{a}}_i - 2p_i}}{{\sqrt {2p_i\left( {1 - p_i} \right)} }}\), _pi_ was the frequency of the minor allele at locus _i_, and _A__i_ was the _i_th column vector of the allele

count matrix, _A_, with the genotypes encoded as 0, 2, referring to the number of the minor allele. The SNP data, including karyotype inversions and _Wolbachia_ infection status, can be

obtained from the DGRP2 website (http://dgrp2.gnets.ncsu.edu). To achieve a replicated structure of the plasticity measures, which was needed to obtain accurate estimates of the genetic

parameters, we grouped the CTmin data within each DGRP line into three groups of individuals according to day of assay, time of day, and water bath. Based on these replicate groups, we

obtained three replicates measures of slope per DGRP line. The slope estimates were robust against re-sampling of random individuals into groups of the same size (for details see Ørsted et

al. 2018a). Phenotypic correlations were computed as Pearson’s correlation using within DGRP line means, and standard errors were estimated as: \({\mathrm{SE}}_{r} = \sqrt {\frac{{1 -

{r}^2}}{{n - 2}}}\), where _n_ was the sample size. We also computed correlations between the rank order of the DGRP lines (_r__r_), in which lines are ranked within rearing regimes from 1

to 166. All variance components were tested under the null hypothesis of being zero using Wald’s test. In addition, we tested if correlations differed from zero, i.e., the estimate deviated

more than 1.645 × SE from 0 (_p_ < 0.05). We performed pairwise comparisons of genetic correlations, _r__g_ among CTmin in the five rearing temperatures (e.g., _r__g_ (17, 20 °C) vs.

_r__g_ (17, 23 °C)), as well as between correlations between CTmin in each environment and plasticity across environments. These comparisons were based on Fisher’s _z_ transformation.

Evolvability (_I_A) was computed at each developmental temperature as: \({I}_{\mathrm{A}} = \widehat {V}_{\mathrm{A}}/\left( {\overline {{\mathrm{CT}}} _{{\mathrm{min}}}} \right)^2 \times

100\), where \(\overline {{\mathrm{CT}}} _{{\mathrm{min}}}\) is the mean CTmin across all DGRP lines in a given environment (Houle 1992; Hoffmann et al. 2016). All analyses were done within

the R programming environment (R Core Team 2017). RESULTS HERITABLE VARIATION FOR COLD TOLERANCE AT THE INDIVIDUAL REARING TEMPERATURES AND PLASTICITY We observed a significant increase in

line mean CTmin, i.e., a decrease in cold tolerance, with increasing rearing temperature (Fig. 1, Table 1, _F_4,804 = 3956; _p_ < 0.001). Average CTmin (±SE) across DGRP lines spanned

from 2.81 ± 0.03 °C for flies reared at 17 to 7.80 ± 0.04 °C for flies reared at 29 °C, with considerable variation among DGRP lines within rearing environment (Fig. 2, Table 1 and Table

S1). The estimates of _H_2 and _h_2 for CTmin were consistent across the developmental temperatures, with estimates in the range of 0.41–0.48 and 0.27–0.34 for _H_2 and _h_2, respectively

(Table 2). Plasticity of cold tolerance was estimated as the slope of a linear regression of CTmin as a function of developmental temperature. The slope was significantly different from zero

in all DGRP lines (_t_(3–21) > 2.13; _p_ < 0.001). A significant interaction between line and developmental temperature (two-way ANOVA; _F_(165,477) = 2.22, _p_ < 0.001) indicated

a GxE interaction, reflecting differences among DGRP lines in slope ranging from 0.32 to 0.57 °C in CTmin per 1 °C increase in developmental temperature. The heritability estimates for

plasticity of CTmin were 0.65 and 0.51 for _H_2 and _h_2, respectively. We observed a higher total genetic variance \(\left( {\widehat {V}_{\mathrm{G}}} \right)\), additive genetic variance

\(\left( {\widehat {V}_{\mathrm{A}}} \right)\) and environmental variance \(\left( {\widehat {V}_{\mathrm{E}}} \right)\) of CTmin at 29 °C than at 26 °C (Table 2). At 17, 20 and 23 °C, we

observed a decrease in all variance components as compared to 29 °C (Table 2). In contrast, for evolvability (_I_A; mean corrected \(\widehat {V}_{\mathrm{A}}\)) of CTmin, we observed higher

values at lower temperatures, i.e., from 0.67 at 17 to 0.18 at 29 °C (Table 2). CORRELATIONS BETWEEN CTMIN FROM DIFFERENT DEVELOPMENTAL TEMPERATURES INCREASED WITH ENVIRONMENTAL SIMILARITY

We found strong positive genetic correlations (_r__g_) between CTmin for DGRP lines at the different rearing temperatures (Fig. 2). The _r__g_ values were inversely proportional to the

difference between developmental temperatures, i.e., correlations between similar developmental temperatures were larger than between dissimilar developmental temperatures. This pattern was

confirmed by statistical pairwise comparisons of _r__g_. The phenotypic correlations (_r__p_) followed the same pattern, decreasing with increasing environmental dissimilarity (Fig. 2).

Similarly, the rank order of the DGRP lines was more positively correlated across thermal environments that were more similar to each other (Fig. 2). TRADE-OFFS BETWEEN CTMIN AT INDIVIDUAL

DEVELOPMENTAL TEMPERATURES AND PLASTIC COLD TOLERANCE DEPENDED ON THE ENVIRONMENT The phenotypic (_r__p_) correlations between CTmin at the individual temperatures and slope of CTmin across

temperatures depended on the environment. At 17 °C, the correlation was significantly negative (Fig. 3), i.e., in this environment genotypes with a high cold tolerance (low CTmin) tended to

have a high plasticity of cold tolerance. However, we did not find this association at 20 °C. At higher temperatures, correlations were significantly positive (Fig. 3), thus genotypes with a

high cold tolerance (low CTmin) tended to have low plasticity of cold tolerance, suggesting a trade-off between inherent cold tolerance at these developmental temperatures and plasticity.

The genetic correlations (_r__g_) followed the same pattern, particularly when involving CTmin scores following development at 17, 26 and 29 °C (Fig. 3). DISCUSSION In the present study, we

investigated the effects of developmental temperature on quantitative genetic parameters of cold tolerance, which is a key stress tolerance trait. We showed that environmental conditions can

directly influence the quantitative genetic basis of cold tolerance by affecting variance components, especially \(\widehat {V}_{\mathrm{A}}\). It is expected that if standing genetic

variation is environmentally dependent, selection responses will also be affected (Roff 2002; Bijlsma and Loeschcke 2005; Wilson et al. 2006; Husby et al. 2011; Wood and Brodie 2016). This

has been demonstrated in recent studies showing that genetic variation and evolutionary potential of stress resistance, morphological traits and life-history traits depend on the

environmental conditions (van Heerwaarden and Sgrò 2011; Bubliy et al. 2012; Kristensen et al. 2015; Bastide et al. 2016; Zhao et al. 2016; Feldman et al. 2017). Such findings can be

explained by environment-specific gene expression, i.e., the impact of genes on a trait can depend on prior environmental exposures. This highlights the need to perform experiments and field

studies under environmental conditions that are relevant to organisms at the time traits are under selection (Hoffmann and Parsons 1991; Hoffmann and Merilä 1999). This is important not

only from an ecological and evolutionary perspective (Bay et al. 2017) but also for animal and plant breeding, in which effects of the environment on the genetic basis of production traits

pose a challenge, such as for genomic selection in heterogeneous environments (Kadarmideen et al. 2006; Zhao et al. 2016). It has long been debated whether some types of environmental

conditions, and particularly stressful or benign conditions, affect heritable variation in distinct ways (Hoffmann and Parsons 1991; Møller and Swaddle 1998). Here we showed an increase in

\(\widehat {V}_{\mathrm{A}}\) of cold resistance with increasing developmental temperature, and heritability estimates being similar across developmental temperatures (Table 2). However, for

mean corrected \(\widehat {V}_{\mathrm{A}}\), (i.e., evolvability (_I_A)), we found higher values at 17 and 20 °C than in the range 23–29 °C, suggesting that the evolutionary capacity for

increasing cold tolerance might be more restricted in warm than in cold environments. This is supported by recent _Drosophila_ studies showing that the potential for evolution is lower in

relatively warmer conditions (Schou et al. 2014; Kristensen et al. 2015). In nature, individuals reared under cold conditions are more likely to encounter subsequent extreme cold conditions,

therefore stronger selection for cold tolerance might be expected in natural populations experiencing low developmental temperatures compared to in hotter environments (Ayrinhac et al.

2004). Thus, in the case of cold tolerance, developmental environments with low temperatures might be considered more optimal than those with high temperatures. However, because we did not

have a direct measure of stress in each thermal environment, we cannot confidently determine whether the patterns in \(\widehat {V}_{\mathrm{A}}\) and _I_A are associated with unfavourable

environmental conditions (Hoffmann and Merilä 1999; Swindell and Bouzat 2006; Talloen et al. 2009). The environment can also affect genetic variation via environment-dependent gene action,

where genes influencing a certain trait in one environment may not be important for that trait in a different environment. In such cases selection responses can be slowed or even become

maladaptive (Steinger et al. 2003). This will often be expressed as altered genetic correlations either between different traits in one environment or between the same trait in a range of

environments (Vieira et al. 2000; Sgrò and Hoffmann 2004; Agrawal and Stinchcombe 2009). Here we found positive genetic and phenotypic correlations between cold tolerance across

environments, i.e., genotypes with high cold tolerance when developed at one temperature also tended to have high cold tolerance when developed at other temperatures, implying an overlap in

the sets of alleles influencing cold tolerance across developmental thermal environments (Via and Lande 1985; Falconer and Mackay 1996; Agrawal and Stinchcombe 2009). However, we also showed

that the magnitude of correlations between thermal environments decreased as developmental temperatures diverged. This suggests a shared genetic basis for cold tolerance between rearing

environments, which contributes less to genetic variation as environments become more dissimilar. Although evidence for such patterns has rarely been investigated, a few studies have found

similar results; Sgrò and Blows (2004) found declining genetic correlations with environmental dissimilarity in three life-history traits in _Drosophila serrata_, while Stinchcombe et al.

(2010) found declining genetic correlations with environmental distance for growth rate in jewelweed. Our findings are in line with other studies suggesting that the genetic architecture of

fitness components is specific to an environment (Bourret and Garant 2015; Rellstab et al. 2017). This represents a challenge when testing for adaptation at specific loci which often seem to

be environment and population specific (Zhao et al. 2011; Manenti et al. 2016). Knowledge about the genetic architecture of many complex traits has been rapidly accumulating recently, aided

by developments in genomics and quantitative genetics. Typically, such studies identify genes, variance components and heritabilities for a given trait in one environment to predict

evolutionary trajectories. In animal and plant breeding, such knowledge may be used to predict selection responses and devise genomic selection procedures. However, our results show that the

architecture of an important stress resistance trait is highly dependent on environmental conditions, challenging the notion that there are candidate genes and a particular architecture

applicable across a gradient of environmental conditions. An important issue for the evolution of thermal resistance is whether an organism’s thermotolerance is constrained by plasticity,

particularly in response to anthropogenic climate change (Stillman 2003; Chown et al. 2010; Levine et al. 2011; Gunderson et al. 2015; Comte and Olden 2017). Some studies point to a negative

association between an organism’s thermal tolerance and plasticity in tolerance (Hoffmann et al. 2003; Kellett et al. 2005; Nyamukondiwa et al. 2011; Gerken et al. 2015; Noh et al. 2017).

Here we showed that any association between cold tolerance and plasticity depend on the environment; both the phenotypic and genetic correlations shifted in sign as developmental

temperatures shifted from 17 to 29 °C (Fig. 3). Given that the lowest CTmin occurred at the lowest developmental temperature, genotypes with a flat slope would likely have a fitness

advantage when exposed to low temperatures, as they would remain the most cold tolerant (i.e., maintain relatively lower CTmin at higher developmental temperatures). However, as indicated by

the negative correlation between slope and CTmin at 17 °C, DGRP lines with low CTmin reared at this temperature, typically had a high CTmin (low cold tolerance) when they developed at

higher temperatures, indicative of a trade-off (Fig. 3a). At low developmental temperatures, lines with low CTmin and a steep slope could be considered “specialists”; i.e., they performed

relatively better in one environment, but relatively poorly across environments. At higher developmental temperatures (23, 26 and 29 °C), we found a positive correlation between CTmin and

the plastic response, suggesting that high cold tolerance (low CTmin) at high developmental temperatures was associated with low plasticity (Fig. 3c–e). Thus, genotypes with low CTmin at

higher developmental temperatures could also be considered as the “specialists” because they performed relatively better in one environment. This shift in sign of the correlations reflects a

reversal in the ordering of genotypes for cold resistance across the thermal gradient, with a tipping point at 20 °C, when there was no correlation between cold tolerance and plasticity.

These genotypic differences in constraints may reflect differential environmental sensitivity and suggests that the fitness effect of plasticity depends heavily on the environment where

genotypes are reared. Thus, the fitness landscape of cold tolerance needs to be considered in the context of environmental frequencies, i.e., how likely are individuals reared under one set

of thermal conditions to encounter subsequent extreme cold conditions (Ayrinhac et al. 2004)? Our findings support those of Levine et al. (2011) who suggested a “directionality” of

plasticity in gene expression depending on the thermal environment commonly encountered in the native range of populations of _D. melanogaster_. This has implications for understanding

developmental processes in variable environments (Stearns et al. 1991), and also for understanding local adaptation in thermal tolerances in populations with a broad geographic distribution

along an environmental cline (Ayrinhac et al. 2004; Levine et al. 2011; Cooper et al. 2012; Kellermann et al. 2012; Kristensen et al. 2015). Our results indicate that the quantitative

genetic basis of basal cold tolerance and plastic cold tolerance is highly environment specific, which is important for predicting selection responses in natural and variable environments

(Sgrò and Hoffmann 2004; Agrawal and Stinchcombe 2009). Most studies investigating phenotypic and genetic correlations and/or candidate genes between thermal environments focus on only two

temperatures (Guerra et al. 1997; Ellers and Driessen 2011; Ketola et al. 2012) or investigate two settings such as field _vs_. lab conditions (Kristensen et al. 2008; Overgaard et al.

2010), and thus do not provide a comprehensive picture of changing genetic correlations and architectures across environments. A meta-analysis by Charmantier and Garant (2005) specifically

called for approaches that consider genetic variability under multiple conditions, and ideally along a continuous environmental gradient such as in the present study. Because correlations

within and between traits can change between environments or even shift in sign (Sgrò and Hoffmann 2004; Agrawal and Stinchcombe 2009) as evident in this study, we reiterate this call and

caution about genetic constraints and trade-offs deduced from studies that consider only one or two environments. In conclusion, we showed that the expression of additive genetic variation

for cold tolerance was environment dependent, and that evolvability of cold resistance decreased with increasing developmental temperatures. We also showed that the relationship between

basal and plastic cold tolerance was affected by the environment, and even switched sign across a gradient of developmental temperatures. We further provided evidence for an evolutionary

trade-off between cold tolerance in one environment and cold tolerance across environment. It will be interesting to test for these patterns in relevant ecological settings, such as in

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7:40132 Article  PubMed  PubMed Central  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS DGRP stocks were obtained from the Bloomington _Drosophila_ Stock Centre (NIH P40OD018537).

We thank Iben R. Jensen, Helle Blendstrup, Susan M. Hansen, and Iben V. Nielsen for laboratory assistance. This research was financed by the Danish Natural Science Research Council through

a Sapere aude stipend to TNK (DFF—4002-00036), the Science Industry Endowment Fund and a grant from the Velux Visiting Professor programme to AAH, and by the Danish Strategic Research

Council (GenSAP: Centre for Genomic Selection in Animals and Plants, contract 12-132452) to PS. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Chemistry and Bioscience, Section

of Biology and Environmental Science, Aalborg University, Aalborg E, 9220, Denmark Michael Ørsted, Ary Anthony Hoffmann & Torsten Nygaard Kristensen * Department of Bioscience, Section

of Genetics, Ecology and Evolution, Aarhus University, Aarhus C, 8000, Denmark Michael Ørsted & Torsten Nygaard Kristensen * School of Biosciences, Bio21 Molecular Science and

Biotechnology Institute, The University of Melbourne, Parkville, Victoria, 3010, Australia Ary Anthony Hoffmann * Department of Molecular Biology and Genetics, Center for Quantitative

Genetics and Genomics, Aarhus University, Tjele, 8830, Denmark Palle Duun Rohde & Peter Sørensen Authors * Michael Ørsted View author publications You can also search for this author

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ARTICLE CITE THIS ARTICLE Ørsted, M., Hoffmann, A.A., Rohde, P.D. _et al._ Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait. _Heredity_

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