Abstract
Finding the specific nucleotides that underlie adaptive variation is a major goal in evolutionary biology, but polygenic traits pose a challenge because the complex genotype–phenotype relationship can obscure the effects of individual alleles. However, natural selection working in large wild populations can shift allele frequencies and indicate functional regions of the genome. Previously, we showed that the two most common alleles of a complex amino acid insertion–deletion polymorphism in the Drosophila insulin receptor show independent, parallel clines in frequency across the North American and Australian continents. Here, we report that the cline is stable over at least a five-year period and that the polymorphism also demonstrates temporal shifts in allele frequency concurrent with seasonal change. We tested the alleles for effects on levels of insulin signaling, fecundity, development time, body size, stress tolerance, and life span. We find that the alleles are associated with predictable differences in these traits, consistent with patterns of Drosophila life-history variation across geography that likely reflect adaptation to the heterogeneous climatic environment. These results implicate insulin signaling as a major mediator of life-history adaptation in Drosophila, and suggest that life-history trade-offs can be explained by extensive pleiotropy at a single locus.
Many organisms display intraspecific variation in life-history traits, including differences in reproductive timing and allocation, development, body size, stress tolerance, and life span. Alternative life-history strategies have long been hypothesized to represent adaptive responses to variable selection (Stearns 1992), but examples with known causal nucleotides are rare (Flatt and Heyland 2011). However, sequence polymorphisms have been identified recently for flowering time in Arabidopsis thaliana (Méndez-Vigo et al. 2013) and adaptation to freshwater environments by marine stickleback fish, which includes changes in life history as well as in physiology and morphology (Jones et al. 2012). One reason that identification of molecular targets of selection is valuable is because it can illuminate mechanisms of multitrait correlation. For example, the gene Catsup in Drosophila melanogaster affects longevity, locomotor behavior, and bristle number, but individual polymorphisms within the locus act on the traits independently and do not show pleiotropic effects (Carbone et al. 2006). Likewise, coat color in deer mice is a multiphenotype trait associated with the Agouti locus, but mutations within the gene appear to have been targeted independently by selection with minimal pleiotropy (Linnen et al. 2013). These examples suggest that fitness-related traits, even those affected by the same gene, may be functionally independent such that natural populations might achieve any genetic and phenotypic combination. Alternatively, life-history traits routinely exhibit strong genetic correlations, including negative associations between life span and reproduction and positive associations between life span and stress resistance (Reznick 1985; Stearns 1991; Partridge et al. 2005; Harshman and Zera 2007; Vermeulen and Loeschcke 2007; Toivonen and Partridge 2009). These associations mirror pleiotropic effects of laboratory-derived mutations and can be difficult to break in artificial selection experiments (Patridge et al. 1999; Leroi et al. 2005; Anderson et al. 2011a; but see Khazaeli and Curtsinger 2010; Khazaeli and Curtsinger 2013). Consequently, pleiotropy, and specifically antagonistic pleiotropy, in which a genetic element encodes both positive- and negative-fitness phenotypes, remains an important component in the discussion of how life histories evolve (Williams 1957; Flatt and Heyland 2011).
Drosophila melanogaster are distributed across environments that range from temperate to tropical, and exhibit variation in life history that appears adaptive. At high latitudes, populations exhibit higher incidence of reproductive diapause, larger body size, higher cold stress tolerance, and sometimes longer life span and lower fecundity, relative to low-latitude populations (Capy et al. 1993; Mitrovski and Hoffmann 2001; De Jong and Bochdanovits 2003; Schmidt et al. 2005a; Trotta et al. 2006). There is substantial genetic variance for these traits and pervasive genetic correlations among them, indicating that selection in the local environment may act on some phenotypes but drive expression of others through trade-offs (David and Bocquet 1975; Anderson et al. 2003; De Jong and Bochdanovits 2003; Schmidt et al. 2005b; Rako et al. 2007; Schmidt and Paaby 2008). Tolerance to environmental stress may be especially important, and the ability to resist desiccation, starvation, and temperature stress correlates with climatic environment (Hoffmann and Harshman 1999; Hoffmann et al. 2001, 2005, 2007). This framework suggests a hypothetical selection regime: at high latitude, cold winters impose seasonal stress and favor genotypes that confer stress tolerance and overwintering ability; correlated traits such as larger body size, longer life span, slower development, and lower fecundity may evolve as coadapted responses to the same selection regime or by indirect selection via pleiotropy (Paaby and Schmidt 2009).
Few loci have been found to explain the observed genetic variance for D. melanogaster life history (De Luca et al. 2003; Carbone et al. 2006; Paaby and Schmidt 2008; Schmidt et al. 2008; Bergland et al. 2012; Remolina et al. 2012; Sgrò et al. 2013). Genes in the insulin/insulinlike growth factor signaling (IIS) pathway are good candidates because experimental manipulation of IIS mirrors the life-history trade-offs observed in natural populations (Clancy et al. 2001; Tatar et al. 2001, 2003; Partridge and Gems 2002; Giannakou and Partridge 2007; Grönke et al. 2010), and some IIS pathway members show evidence of adaptive response in the wild (Dantzer and Swanson 2012; Jovelin et al. 2014). We previously evaluated sequence variation at the Insulin-like Receptor (InR) in wild populations of D. melanogaster and observed striking reciprocal clines in the northern and southern hemispheres for the common alleles of a complex amino acid indel polymorphism (Paaby et al. 2010). The polymorphism disrupts a region of glutamine–histidine repeats in the first exon (Guirao-Rico and Aguadé 2009). In both North America and Australia, the most common allele is at low frequency in tropical and subtropical populations and increases in frequency with latitude, showing highest frequency in temperate populations; the second most common allele exhibits the inverse cline. These patterns appear to be responses to similar but independent selection pressures between the continents: fly populations in North America and Australia were founded at different times and from different source populations (Bock and Parsons 1981; David and Capy 1988), and nucleotide variants on either side of the polymorphism show neutral patterns across geography.
Initial tests showed that the two common alleles, which differ in length by two amino acids and are herein designated InRshort and InRlong, demonstrated significant effects on fitness traits. InRshort, the allele common at high latitudes, was associated with greater stress tolerance; InRlong, which is common at low latitudes, was associated with greater fecundity (Paaby et al. 2010). Here, we show that the allele frequency cline in North America persists five years later and that temporal changes in allele frequency within a single population correlate with fluctuations in the seasonal environment, which mimic the temperature and resource availability differences associated with latitudinal climate. We also find that the InR locus in general exhibits elevated patterns of clinality and seasonality relative to the rest of the genome. We more thoroughly tested the functional effects of the complex polymorphism by measuring levels of IIS and a spectrum of life-history phenotypes in InRshort and InRlong lines with randomized genetic backgrounds. We find that the alleles associate predictably with the tested traits, suggesting that the complex amino acid indel polymorphism at InR is an important target of contemporaneous selection in wild D. melanogaster populations, is highly pleiotropic, and contributes to observed trade-offs in D. melanogaster life history.
Methods
ALLELE FREQUENCY ESTIMATES
As described in Bergland et al. (2014), between 50 and 200 isofemale lines were established from six populations spanning Florida to Maine along the North American east coast (latitudes 25.5°N, 30.9°N, 33.0°N, 35.5°N, 39.9°N, 44.1°N). The population from Linvilla Orchard in Media, Pennsylvania (39.9°N, 74.4°W) was additionally sampled in the spring and fall in 2009, 2010, and 2011. Within five generations, a single male from each line was pooled into population samples, which were sequenced on an Illumina (San Diego, CA) HiSeq2000 using 100-bp paired-end reads. Reads were aligned to the D. melanogaster reference genome version 5.39 using bwa version 0.5.9-r16 (Li and Durbin 2009). The indel polymorphism at InR was called using UnifiedGenotyper from GATK version 3.1–1 (McKenna et al. 2010), which identified multiple discrete polymorphisms within the complex indel haplotypes that we previously characterized (Paaby et al. 2010). That characterization identified many haplotypes of different lengths, two of which were common (herein InRshort and InRlong); the reference genome to which the new pooled samples were mapped has a haplotype of intermediate length (previously referred to as InR251). Thus, the complex haplotypes, previously identified by their lengths, were decomposed into sets of shared or distinct alleles at discrete polymorphic sites.
We used our prior Sanger sequence data (see GenBank accessions GQ927177–GQ927244) to assign alleles of the discrete polymorphisms to the InRshort and InRlong allele classes. Here, we report those classifications (polymorphisms are denoted by their position in the D. melanogaster reference genome version 5.48). Three discrete polymorphisms in the pooled sequencing data discriminate unambiguously between InRshort and InRlong: 17405631–7, 17405651–4, and 17405784. In each case, the reference genome allele matches InRshort. Only one of the discrete polymorphisms, SNP 17405614, was variable within an allele class; the alternate and reference alleles are both associated with InRshort such that the association between the discrete allele identities and the larger haplotypes in which they are embedded (InRshort and InRlong) are approaching equilibrium (R2 = 0.45). SNP 17405619 segregates at low frequency and was invariant in our Sanger sequencing data such that the InRshort and InRlong haplotypes we observed shared the same discrete allele. Both the alternate allele of SNP 17405631 and the alternate allele of 17405634–7 match both InRshort and InRlong. For polymorphism 17405637, two alternate alleles were called; the six-nt insertion matches InRlong, but the three-nt insertion is confounded with the alternate allele of 17405634–7 (which is called consistently). Thus, we ignored the three-nt insertion allele at 17405637.
Allele frequencies of SNPs at InR and across other sites on chromosome 3R were called from the resequencing data using CRISP (Bansal 2010) as described in Bergland et al. (2014). To test whether the InR locus demonstrates elevated clinality or seasonality relative to the rest of the genome, we calculated the proportion of clinal and seasonal SNPs at InR and compared them to random genome regions matched for length, chromosome, and inversion status. Clinality and seasonality was estimated by regressing allele frequencies on latitude or season (“spring” vs. “fall”), respectively; technical details of this analysis are available in the Supporting Information.
GENOTYPING
To genotype flies for the experimental assays, the identity of the complex InR indel was determined by measuring its amplified fragment length following polymerase chain reaction (PCR) with a fluorescent-tagged primer on an Applied Biosystems (Foster City, CA) 3100 capillary sequencer. PCR conditions, including primer sequences, are described in Paaby et al. (2010). That paper reported the distribution of alleles across geography and referred to the alleles by their PCR fragment lengths, which we erroneously stated as 248 bases for the high-latitude allele and 254 bases for the low-latitude allele. The actual fragment lengths using the cited primers are five bases longer. To avoid confusion, in this article we refer to these two alleles as InRshort (previously InR248) and InRlong (previously InR254). It should be noted that although fragment-length genotyping does not discriminate between the SNP alleles we characterized in or near the complex indel polymorphism (described above), only one of those SNPs (17405614) appears to be independent of the InRshort and InRlong allele classes. All evidence to date indicate that fragment-length genotyping unambiguously discriminates between consistent, but complex, haplotype classes (see Fig. 1A, B).
From pooled sequencing data, we identified eight discrete polymorphisms associated with the complex indel in the first exon of InR (A). Each polymorphism is labeled according to position on chromosome 3R relative to the published D. melanogaster reference genome (version 5.48). The discrete polymorphisms describe the complex indel haplotypes we previously characterized, which include two alleles, InRshort and InRlong, that are common at high and low latitudes, respectively (B). Polymorphisms that unambiguously discriminate between the InRshort and InRlong alleles are denoted with a large dot; in each case, the allele present in the reference genome identifies InRshort. (See Methods for relationships between the other discrete polymorphisms and InRshort and InRlong.) All eight of the discrete polymorphisms demonstrate some degree of seasonality, but four exhibit especially strong patterns in which the plotted allele increases in frequency by approximately 20% over the winter for three consecutive years (C). Alleles assigned to the InRshort allele class (large dots) generally show higher frequency in the spring and lower frequency in the fall, suggesting that overwintering may impose selection pressures consistent with high latitudes. As in our earlier report (Paaby et al. 2010), alleles assigned to the InRshort allele class also increase in frequency with latitude (D). The plotted alleles are all reference genome alleles.
FLY STOCKS
To determine whether InRshort and InRlong alleles have different effects on phenotype, we generated stocks with InRshort or InRlong on the third chromosome, and for which the X and second chromosomes were replaced so that they were isogenic within and across lines. The X chromosome was derived from stock 2475 and the second chromosome was derived from stock 6326 from the Bloomington Stock Center. The genome regions surrounding InR on the third chromosome were randomized, from lines originally genotyped as InRshort and InRlong, across lines. To generate the stocks, we selected InRshort and InRlong parental strains without the In(3R)Payne inversion, crossed them, and allowed the offspring to recombine for four generations so that genetically variable regions from the parental third chromosomes were distributed as randomized blocks across lines. Fourteen isogenic stocks were subsequently established per allele by homozygosing the third chromosome via balancer extraction and genotyping the InR indel polymorphism. This entire scheme was performed twice, using lines derived from two independent populations: Mount Sinai, New York (40.95°N, 72.84°W) and Bowdoin, Maine (44.01°N latitude, 69.90°W longitude). Replicating across populations provided two sets of randomized genetic backgrounds in which to test the InR genotypes. Although four generations of laboratory-imposed recombination are unlikely to have isolated the InR alleles completely from the parental backgrounds, the rapid decay of linkage disequilibrium surrounding the polymorphism (Paaby et al. 2010) means that genetic variation on 3R is already randomized in natural populations.
InRshort and InRlong alleles were evaluated as homozygotes and heterozygotes, as well as with InR mutant and wild-type alleles derived from a laboratory strain. The InR mutant was the hypomorphic InRp5545 allele generated by P-element insertion in exon 1 (Tatar et al. 2001) balanced over the TM3 chromosome (stock 11661 from the Bloomington Stock Center, ry506 P{PZ}InRp5545/TM3, ryRK Sb1 Ser1). The TM3 balancer contains a wild-type InR allele (herein InRTM3) that is intermediate in length between InRshort and InRlong: it is three nucleotides longer than InRshort, corresponding to an additional histidine, and three nucleotides shorter than InRlong, which contains an additional glutamine, as determined by Sanger sequencing.
FLIES FOR ASSAYS
Once the stocks were established, we used three methods to generate flies for the phenotype assays (derived from one or both of the independent populations). The first two tested the effects of InRshort and InRlong in a wild-type background; the third tested InRshort and InRlong effects while paired with either the InR hypomorphic allele or a wild-type allele in the TM3 balancer chromosome. In the first method, we exerted nominal control over the background variation in the stocks by crossing 14 InRshort and 14 InRlong stocks in a round-robin design to generate a total of 42 lines from which we collected flies for phenotyping (14 each of InRshort/InRshort, InRlong/InRlong, and InRshort/InRlong). The crosses were conducted in standard media vials with seven virgin females (two- to five-day old) and four young males (three- to eight-day old). This design produced nonindependent replication within each genotypic class (in that chromosomes were shared across lines), and because test stocks were never mated to themselves, also ensured that no individuals were strictly isogenic at the third chromosome. In the second method, we combined stocks (minimum of five) carrying the same allele (InRshort or InRlong) but otherwise randomized variation on the third chromosome in bottles, permitting the stocks to continue recombining freely. To rear flies for the assays, 40 virgin females (two- to five-day old) and 20 young males (three- to eight-day old) were collected and mated in fresh bottles, either within or across genotype class, to generate the two homozygous and the heterozygous genotypes. Unless otherwise specified, the phenotype assays employing this method were replicated via 10 mating bottles for each genotypic class. In the third method, we crossed 12 InRshort stocks and 12 InRlong stocks to the hypomorphic InRp5545/TM3 stock (in vials as in method 1). From these 24 lines, we collected progeny carrying both the InR hypomorphic allele and wild-type allele in the balancer chromosome for phenotyping.
PHENOTYPE ASSAYS
For all assays, flies were reared and assays were performed on standard cornmeal-molasses media at room temperature and subject to ambient light cycles. Larval density was kept low to limit overcrowding. We tested InRshort and InRlong alleles derived from Mount Sinai in all assays; we tested InRshort and InRlong alleles from Bowdoin in a subset of the assays. All InRshort and InRlong alleles tested in the presence of the InR hypomorph (InRp5545) or wild-type balancer (InRTM3) were from Mount Sinai. All statistics were performed using JMP version 7 (SAS Institute, Cary, NC). Within analyses, we used planned comparisons to test for significant differences between InRshort and InRlong genotypes. We measured 11 different traits, including life span, lifetime fecundity, early fecundity, development time, body weight, lipid content, body size, chill coma recovery, cold shock tolerance, starvation resistance, and heat shock resistance; assay details can be found in the Supporting Information.
QUANTITATIVE PCR
To test whether InR alleles affect IIS, we used quantitative PCR (qPCR) to determine the relative abundance of seven transcriptional targets of dFOXO, a central transcription factor within the IIS pathway (Jünger et al. 2003; Puig et al. 2003; Wang et al. 2005; Casas-Tinto et al. 2007; Flatt et al. 2008; Vihervaara and Puig 2008; Matilla et al. 2009). Total RNA was prepared from flies generated by method two from flies derived from Mount Sinai. Thirty virgin females from each of the three genotypic classes were collected over 24 h, aged on standard media, and snap frozen. RNA was extracted using RNeasy (Qiagen) and reverse transcribed using iScript cDNA synthesis kit (Bio-Rad), following the manufacturer's protocol. Relative abundance of transcript levels was determined using an ABI 7500 Fast Real-Time PCR machine and SYBR Green PCR Master Mix (Applied Biosystems) by the DDCT relative quantitation method. Four technical replicates were used for each sample and relative abundance was normalized by using GAPDH2 as an endogenous control. Other work has demonstrated that GAPDH2 is an appropriate control for measuring relative levels of IIS (Hwangbo et al. 2004; Flatt et al. 2008). Primer sequences for the qPCR reaction are listed in the Supporting Information.
Results
CLINAL AND SEASONAL ESTIMATES OF ALLELE FREQUENCY
Previously, we showed that InR allele frequencies change in association with latitude on two continents, and hypothesized that this was in response to climate-driven selection (Paaby et al. 2010). Now, we ask whether the allele frequency cline in North America persists five years later and whether allele frequencies fluctuate seasonally in a single temperate population. Our previous survey identified haplotypes; here, we interrogated allele frequencies for the discrete polymorphisms that comprise those haplotypes using resequencing data. We find strong evidence of both clinality and seasonality for the indel polymorphism at InR, and support for a high degree of complexity in this system given that discrete sites within the haplotypes appear to respond differently to geographical and seasonal pressures (Fig. 1).
We identified all variants (SNPs or small indels) that comprise the complex indel itself and three SNPs –11, –16, and +130 nucleotides from the indel that segregate at frequencies above 5% (Fig. 1A, B). Four of these discrete polymorphisms show clear seasonality, with repeating fluctuations in allele frequency of approximately 20% between spring and fall time points over three consecutive years (Fig. 1C). The other four discrete polymorphisms are suggestive of seasonality: they demonstrate either comparable changes in amplitude but a deviation in the frequency pattern at a single time point, or a repetitive pattern at low amplitude. The discrete polymorphisms also show clines in allele frequency across latitude (Fig. 1D). We were able to unambiguously assign allele identities for three of the discrete polymorphisms to the InRshort or InRlong allele classes (Table S1), which exhibited strong allele frequency clines previously (Paaby et al. 2010). In each of these cases, changes in allele frequency across latitude replicated our earlier observations, and seasonal fluctuations in frequency for these discrete polymorphisms support the hypothesis that alleles favored at high latitude are favored during the winter.
Compared to the rest of the genome, the InR locus exhibits elevated patterns of allele frequency change across latitude and seasonal time. The region surrounding InR contains significantly more clinal polymorphisms relative to other regions of chromosome 3R matched for size and inversion status (42% vs. 20%, respectively; Pr(%InR > %control) = 0.986). Relative to matched control regions, the InR locus also contains slightly more seasonal polymorphisms (6% vs. 4.7%; Pr(%InR > %control) = 0.8). Previous examination of linkage disequilibrium across InR (Paaby et al. 2010) indicates that the complex indel polymorphism is largely independent of variation elsewhere in the gene, which implies that many sites within InR may be targets of spatially and temporally varying selection.
ALLELE EFFECTS
We evaluated the effects of InR alleles on levels of IIS and multiple phenotypes and found that individuals carrying the InRshort allele, which is prevalent in high-latitude, temperate environments, exhibited lower levels of IIS, better survived cold temperature stresses and starvation, and showed sex-specific evidence for longer life span. Flies carrying the InRlong allele, which is common in low-latitude, warm environments, were associated with increased signaling and exhibited higher rates of fecundity, larger body mass, faster development time, and better survival of heat shock. These phenotypic differences are consistent with experimental reduction of IIS as well as life-history trade-offs observed in natural populations, and suggest that a single locus can act pleiotropically on a suite of fitness-related traits. We replicated some of the assays by using InRshort and InRlong alleles derived from two independent populations (Bowdoin and Mount Sinai); the results are reported separately. We also evaluated the effects of InRshort and InRlong in an InR hypomorphic background with reduced InR kinase activity. We found that the effects of InRshort versus InRlong are generally subtler than those of the wild-type versus hypomorph, and are consistent with our other observations of InRshort and InRlong and with prior work describing InR mutant alleles; a summary of these findings, including additional figures and tables (Figs. S2–S4 and Table S2), is available in the Supporting Information.
LEVELS OF INSULIN SIGNALING
To test whether the observed phenotypic differences between InRshort and InRlong are mediated by different levels of IIS, we used qPCR to measure relative abundance of seven transcriptional targets of dFOXO, a central transcription factor in the IIS pathway that is repressed by InR activity. Decreased InR activity increases target abundance (Puig et al. 2003), and transcripts for five of these (4E-BP, l(2)efl, ac76e, dLip4, and InR T1) were highest in InRshort, intermediate in the heterozygote, and lowest in InRlong; all but 4E-BP showed statistical significance (P < 0.05) between InRshort and InRlong (Fig. 2). This pattern is robust considering that the InRshort genotype exhibited at most twofold higher levels relative to InRlong: a difference that may be biologically meaningful, and realistic for wild-type genotypes, but close to the limit of detection by qPCR methods. Two other targets, transcripts of InR itself, show no difference in abundance level between the homozygote genotypes, but significantly higher abundance in the heterozygote.
Relative abundance of seven transcriptional targets of dFOXO, a central transcription factor in the IIS pathway that is repressed by InR activity. Increased abundance of these targets indicates reduced IIS; transcript abundance for l(2)eft, ac76e, dLip4, and InR T1 were significantly greater (P < 0.05) in InRshort samples compared to InRlong. These results are consistent with the expectation that InRshort is associated lower IIS than InRlong. Error bars show 95% confidence intervals.
FECUNDITY
Surprisingly, the homozygous InR genotypes showed no significant differences in total fecundity: a fly carrying the InRlong allele laid on average only 5.3 more eggs over her lifetime than a fly carrying InRshort (P = 0.6949, Table 1). This was unexpected because we routinely observed substantially greater population numbers in our InRlong bottle cultures, and previous results showed that the InRlong genotype is more fecund (Paaby et al. 2010). Examination of the eggs laid per day, however, revealed that the InRlong genotype lays more eggs early in life (Fig. 3A). We performed another assay to explicitly measure early fecundity, or how many eggs young mated females laid upon first access to fresh food. Here, we found that the InRlong genotype is nearly six times more fecund than InRshort in the first 12 h of egg laying (P < 0.0001, Table 1 and Fig. 3B). This result was again observed in a replicate assay using flies derived from the second population (P = 0.0005, Table 1 and Fig. 3C).
Statistical results for planned comparisons and other analyses explicitly testing for significant differences between InRshort and InRlong
| DF | SS | F | P | |
| Mount Sinai | ||||
| Lifetime fecundity | 1, 39 | 200.18 | 0.1561 | 0.6949 |
| Early fecundity | 1, 21 | 299.77 | 22.79 | 0.0001 |
| Development time | 1, 3006 | 17049.77 | 113.23 | <0.0001 |
| Dry body weight (f) | 1, 45 | 1.10 × 10−6 | 16.73 | 0.0002 |
| Dry body weight (m) | 1, 44 | 2.22 × 10−7 | 14.99 | 0.0004 |
| Lipid weight (f) | 1, 44 | 0.0013 | 0.1361 | 0.7140 |
| Lipid weight (m) | 1, 43 | 0.0303 | 1.4110 | 0.2414 |
| L2 wing vein (f) | 1, 230 | 0.0157 | 9.0749 | 0.0029 |
| L2 wing vein (m) | 1, 212 | 0.0005 | 0.2759 | 0.5999 |
| Chill coma (f) | 1, 187 | 246,318.61 | 2.14 | 0.1448 |
| Chill coma (m) | 1, 207 | 534,075.8 | 16.31 | <0.0001 |
| Bowdoin | ||||
| Early fecundity | 1, 27 | 324.82 | 15.55 | 0.0005 |
| Development time (f) | 1, 244 | 9383.14 | 9.07 | 0.0029 |
| Development time (m) | 1, 278 | 1652.35 | 1.42 | 0.2341 |
| Dry body weight (f) | 1, 37 | 1.42 × 10−6 | 103.96 | <0.0001 |
| Dry body weight (m) | 1, 39 | 4.03 × 10−7 | 63.17 | <0.0001 |
| Lipid weight (f) | 1, 36 | 0.0767 | 11.0854 | 0.0020 |
| Lipid weight (m) | 1, 38 | 0.3759 | 27.3649 | <0.0001 |
| L2 wing vein (f) | 1, 117 | 0.3969 | 297.3210 | <0.0001 |
| L2 wing vein (m) | 1, 123 | 0.3281 | 35.8582 | <0.0001 |
| Wing:thorax ratio (f) | 1, 117 | 0.0599 | 35.8776 | <0.0001 |
| Wing:thorax ratio (m) | 1, 123 | 0.0627 | 5.4002 | 0.0218 |
| Mount Sinai | DF | χ2 | P | Risk ratio |
| Lifespan (f, het excluded) | 1 | 0.0188 | 0.8909 | 0.9870 |
| Lifespan (m, het excluded) | 1 | 6.7336 | 0.0095 | 0.7665 |
| Mount Sinai | Odds ratio | Reciprocal | ||
| Cold shock (f) | 0.2613 | 3.8275 | ||
| Cold shock (m) | 0.4231 | 2.3636 | ||
| Starvation (f) | 0.7493 | 1.3346 | ||
| Starvation (m) | 0.5503 | 1.8173 | ||
| Heat shock | 15.2938 | 0.0654 | ||
| DF | SS | F | P | |
| Mount Sinai | ||||
| Lifetime fecundity | 1, 39 | 200.18 | 0.1561 | 0.6949 |
| Early fecundity | 1, 21 | 299.77 | 22.79 | 0.0001 |
| Development time | 1, 3006 | 17049.77 | 113.23 | <0.0001 |
| Dry body weight (f) | 1, 45 | 1.10 × 10−6 | 16.73 | 0.0002 |
| Dry body weight (m) | 1, 44 | 2.22 × 10−7 | 14.99 | 0.0004 |
| Lipid weight (f) | 1, 44 | 0.0013 | 0.1361 | 0.7140 |
| Lipid weight (m) | 1, 43 | 0.0303 | 1.4110 | 0.2414 |
| L2 wing vein (f) | 1, 230 | 0.0157 | 9.0749 | 0.0029 |
| L2 wing vein (m) | 1, 212 | 0.0005 | 0.2759 | 0.5999 |
| Chill coma (f) | 1, 187 | 246,318.61 | 2.14 | 0.1448 |
| Chill coma (m) | 1, 207 | 534,075.8 | 16.31 | <0.0001 |
| Bowdoin | ||||
| Early fecundity | 1, 27 | 324.82 | 15.55 | 0.0005 |
| Development time (f) | 1, 244 | 9383.14 | 9.07 | 0.0029 |
| Development time (m) | 1, 278 | 1652.35 | 1.42 | 0.2341 |
| Dry body weight (f) | 1, 37 | 1.42 × 10−6 | 103.96 | <0.0001 |
| Dry body weight (m) | 1, 39 | 4.03 × 10−7 | 63.17 | <0.0001 |
| Lipid weight (f) | 1, 36 | 0.0767 | 11.0854 | 0.0020 |
| Lipid weight (m) | 1, 38 | 0.3759 | 27.3649 | <0.0001 |
| L2 wing vein (f) | 1, 117 | 0.3969 | 297.3210 | <0.0001 |
| L2 wing vein (m) | 1, 123 | 0.3281 | 35.8582 | <0.0001 |
| Wing:thorax ratio (f) | 1, 117 | 0.0599 | 35.8776 | <0.0001 |
| Wing:thorax ratio (m) | 1, 123 | 0.0627 | 5.4002 | 0.0218 |
| Mount Sinai | DF | χ2 | P | Risk ratio |
| Lifespan (f, het excluded) | 1 | 0.0188 | 0.8909 | 0.9870 |
| Lifespan (m, het excluded) | 1 | 6.7336 | 0.0095 | 0.7665 |
| Mount Sinai | Odds ratio | Reciprocal | ||
| Cold shock (f) | 0.2613 | 3.8275 | ||
| Cold shock (m) | 0.4231 | 2.3636 | ||
| Starvation (f) | 0.7493 | 1.3346 | ||
| Starvation (m) | 0.5503 | 1.8173 | ||
| Heat shock | 15.2938 | 0.0654 | ||
Where sex was significant in the full model (see Tables S2 and S3), females and males were evaluated separately.
Statistical results for planned comparisons and other analyses explicitly testing for significant differences between InRshort and InRlong
| DF | SS | F | P | |
| Mount Sinai | ||||
| Lifetime fecundity | 1, 39 | 200.18 | 0.1561 | 0.6949 |
| Early fecundity | 1, 21 | 299.77 | 22.79 | 0.0001 |
| Development time | 1, 3006 | 17049.77 | 113.23 | <0.0001 |
| Dry body weight (f) | 1, 45 | 1.10 × 10−6 | 16.73 | 0.0002 |
| Dry body weight (m) | 1, 44 | 2.22 × 10−7 | 14.99 | 0.0004 |
| Lipid weight (f) | 1, 44 | 0.0013 | 0.1361 | 0.7140 |
| Lipid weight (m) | 1, 43 | 0.0303 | 1.4110 | 0.2414 |
| L2 wing vein (f) | 1, 230 | 0.0157 | 9.0749 | 0.0029 |
| L2 wing vein (m) | 1, 212 | 0.0005 | 0.2759 | 0.5999 |
| Chill coma (f) | 1, 187 | 246,318.61 | 2.14 | 0.1448 |
| Chill coma (m) | 1, 207 | 534,075.8 | 16.31 | <0.0001 |
| Bowdoin | ||||
| Early fecundity | 1, 27 | 324.82 | 15.55 | 0.0005 |
| Development time (f) | 1, 244 | 9383.14 | 9.07 | 0.0029 |
| Development time (m) | 1, 278 | 1652.35 | 1.42 | 0.2341 |
| Dry body weight (f) | 1, 37 | 1.42 × 10−6 | 103.96 | <0.0001 |
| Dry body weight (m) | 1, 39 | 4.03 × 10−7 | 63.17 | <0.0001 |
| Lipid weight (f) | 1, 36 | 0.0767 | 11.0854 | 0.0020 |
| Lipid weight (m) | 1, 38 | 0.3759 | 27.3649 | <0.0001 |
| L2 wing vein (f) | 1, 117 | 0.3969 | 297.3210 | <0.0001 |
| L2 wing vein (m) | 1, 123 | 0.3281 | 35.8582 | <0.0001 |
| Wing:thorax ratio (f) | 1, 117 | 0.0599 | 35.8776 | <0.0001 |
| Wing:thorax ratio (m) | 1, 123 | 0.0627 | 5.4002 | 0.0218 |
| Mount Sinai | DF | χ2 | P | Risk ratio |
| Lifespan (f, het excluded) | 1 | 0.0188 | 0.8909 | 0.9870 |
| Lifespan (m, het excluded) | 1 | 6.7336 | 0.0095 | 0.7665 |
| Mount Sinai | Odds ratio | Reciprocal | ||
| Cold shock (f) | 0.2613 | 3.8275 | ||
| Cold shock (m) | 0.4231 | 2.3636 | ||
| Starvation (f) | 0.7493 | 1.3346 | ||
| Starvation (m) | 0.5503 | 1.8173 | ||
| Heat shock | 15.2938 | 0.0654 | ||
| DF | SS | F | P | |
| Mount Sinai | ||||
| Lifetime fecundity | 1, 39 | 200.18 | 0.1561 | 0.6949 |
| Early fecundity | 1, 21 | 299.77 | 22.79 | 0.0001 |
| Development time | 1, 3006 | 17049.77 | 113.23 | <0.0001 |
| Dry body weight (f) | 1, 45 | 1.10 × 10−6 | 16.73 | 0.0002 |
| Dry body weight (m) | 1, 44 | 2.22 × 10−7 | 14.99 | 0.0004 |
| Lipid weight (f) | 1, 44 | 0.0013 | 0.1361 | 0.7140 |
| Lipid weight (m) | 1, 43 | 0.0303 | 1.4110 | 0.2414 |
| L2 wing vein (f) | 1, 230 | 0.0157 | 9.0749 | 0.0029 |
| L2 wing vein (m) | 1, 212 | 0.0005 | 0.2759 | 0.5999 |
| Chill coma (f) | 1, 187 | 246,318.61 | 2.14 | 0.1448 |
| Chill coma (m) | 1, 207 | 534,075.8 | 16.31 | <0.0001 |
| Bowdoin | ||||
| Early fecundity | 1, 27 | 324.82 | 15.55 | 0.0005 |
| Development time (f) | 1, 244 | 9383.14 | 9.07 | 0.0029 |
| Development time (m) | 1, 278 | 1652.35 | 1.42 | 0.2341 |
| Dry body weight (f) | 1, 37 | 1.42 × 10−6 | 103.96 | <0.0001 |
| Dry body weight (m) | 1, 39 | 4.03 × 10−7 | 63.17 | <0.0001 |
| Lipid weight (f) | 1, 36 | 0.0767 | 11.0854 | 0.0020 |
| Lipid weight (m) | 1, 38 | 0.3759 | 27.3649 | <0.0001 |
| L2 wing vein (f) | 1, 117 | 0.3969 | 297.3210 | <0.0001 |
| L2 wing vein (m) | 1, 123 | 0.3281 | 35.8582 | <0.0001 |
| Wing:thorax ratio (f) | 1, 117 | 0.0599 | 35.8776 | <0.0001 |
| Wing:thorax ratio (m) | 1, 123 | 0.0627 | 5.4002 | 0.0218 |
| Mount Sinai | DF | χ2 | P | Risk ratio |
| Lifespan (f, het excluded) | 1 | 0.0188 | 0.8909 | 0.9870 |
| Lifespan (m, het excluded) | 1 | 6.7336 | 0.0095 | 0.7665 |
| Mount Sinai | Odds ratio | Reciprocal | ||
| Cold shock (f) | 0.2613 | 3.8275 | ||
| Cold shock (m) | 0.4231 | 2.3636 | ||
| Starvation (f) | 0.7493 | 1.3346 | ||
| Starvation (m) | 0.5503 | 1.8173 | ||
| Heat shock | 15.2938 | 0.0654 | ||
Where sex was significant in the full model (see Tables S2 and S3), females and males were evaluated separately.
Average eggs laid per female over lifetime and in the first 12 h after mating. Lifetime fecundity was not significantly different between any genotypes except between InRshort and the heterozygote (A). However, InRlong females laid many more eggs in the first 12 h after mating than InRshort females, in comparisons from both populations (C). Error bars show 95% confidence intervals.
DEVELOPMENT TIME
From egg to emerging adult, flies carrying the InRlong allele developed faster than flies carrying InRshort. This result was replicated in flies derived from two populations: in the first, males and females carrying InRlong developed an average of 6.0 h faster than those carrying InRshort (P < 0.0001); in the second population, development time was not significantly different among male genotypes, but InRlong females emerged an average of 18.2 h ahead of InRshort females (P = 0.0029, Table 1 and Fig. 4).
Average development time, from the time the egg was laid to eclosion of the adult. Flies derived from the Mount Sinai population showed no significant differences in development time by sex, and overall InRlong flies developed faster than InRshort flies (A). However, InRlong females from Bowdoin developed faster than InRshort females (B), whereas male genotypes were not significantly different (C). Error bars show 95% confidence intervals.
BODY WEIGHT AND SIZE
Because reduction in IIS reduces body size (Clancy et al. 2001; Tatar et al. 2001), we predicted that individuals carrying the InRlong allele would be larger than individuals carrying InRshort. Indeed, the average dry body weights of InRlong flies were heavier than InRshort flies. This was true for alleles tested from both populations: InRlong females and males derived from Bowdoin were 26.1% and 18.6% heavier, respectively (P < 0.0001 for both), whereas InRlong females and males derived from Mount Sinai were 15.5% and 10.9% heavier (P = 0.0002 and P = 0.0004, Table 1 and Figs. 5, S1). The higher mass associated with the InRlong genotype is partially explained by lipid content. For both populations, the InRlong allele was associated with greater lipid mass than InRshort (Figs. 5, S1); however, only alleles derived from Bowdoin demonstrated a statistically significant difference when nonlipid mass was included as a covariate (P = 0.0066 for females, P < 0.0001 for males, Table 1). Wings of InRlong flies were also larger than those for InRshort, at least in the Bowdoin population. In that comparison, the average L2 wing vein length was 0.24 mm longer in InRlong females (P < 0.0001) and 0.12 mm longer in InRlong males (P < 0.0001, Table 1 and Fig. 5). In flies derived from Mount Sinai, InRshort females had the larger wings, but only by 0.02 mm (P = 0.0029), and InRshort and InRlong males were not different from each other (P = 0.5999, Table 1 and Fig. S1).
Average dry body weight, lipid weight, length of the L2 wing vein, and wing:thorax ratio. InRshort flies had lower dry weight (A) and lower lipid content (B) than InRlong flies. InRshort flies were also smaller, as measured by the L2 wing vein (C), and had a smaller wing:thorax ratio (D). These results are from flies derived from Bowdoin; similar findings, but of lower magnitude, were observed in flies derived from Mount Sinai (Figure S1). Error bars show 95% confidence intervals.
An association between large size and InRlong, the low-latitude allele, is predictable in terms of insulin signaling, but unpredictable given the observation that Drosophila are larger at higher latitude (Huey et al. 2000; De Jong and Bochdanovits 2003). Others have shown that proportionally larger wings relative to thorax size are associated with better flying in Drosophila (Hoffmann et al. 2007) and that the wing:thorax ratio increases with latitude (Karan et al. 1998), possibly because muscles in colder temperatures generate less power and proportionally larger wings can compensate for this (Gilchrist and Huey 2004). We measured thorax width to see if bigger wing:thorax ratios were associated with the high-latitude InRshort allele, but they were not. InRshort flies averaged smaller wing:thorax ratios relative to InRlong flies (P < 0.0001 for females, P = 0.0218 for males, Table 1 and Fig. 5). For all samples in which we measured the L2 wing vein, we also measured the L3 vein, and observed qualitatively identical results (data not shown).
STRESS TOLERANCE
Reduction in IIS confers increased resistance to stress (Giannakou and Partridge 2007), and results for three of our four stress assays accord with these findings and our hypothesis that the high-latitude InRshort allele mediates increased stress tolerance via reduced signaling (Fig. 6). Females carrying the InRshort allele recovered from chill coma on average 89 sec faster than females carrying InRlong; InRshort males recovered 123 sec faster (although this comparison was only statistically significant for males; P = 0.1448 and P < 0.0001, respectively, Table 1 and Fig. 6). Likewise, InRshort females and males exhibited 3.8 and 2.4 times the odds of surviving cold shock and 1.3 and 1.8 times the odds of surviving starvation, respectively, compared to InRlong flies. However, the InRshort allele did not correlate with higher tolerance of heat shock: instead, InRshort flies showed 15.3 times the odds of dying in the assay compared to InRlong flies (Table 1 and Fig. 6). All four of these outcomes are consistent with presumed selection pressures in natural populations, as flies in high-latitude, temperate environments, where the InRshort allele is common, likely face cold temperatures and scarce food availability during the winter, whereas flies in low-latitude, semitropical environments, where InRlong is common, should face thermal stress from higher temperatures. However, if levels of IIS mediate these stress phenotypes, the heat shock outcome provides a unique example of correlation reversal between signaling and stress. Nevertheless, the phenotypic correlations across these traits, and the correlations between the phenotypes and latitude, are patterns consistent with other examples of reciprocal tolerance for environmentally imposed thermal stresses (Hoffmann et al. 2001, 2005).
Average time to recovery from chill coma and proportion of flies surviving cold shock, starvation, and heat shock. Although InRshort females and males both recovered faster from chill coma than did InRlong flies, the effect was only significant for males (A). InRshort flies also better survived cold shock (B) and starvation (C), but more InRlong flies survived heat shock (D). Error bars show 95% confidence intervals.
LIFE SPAN
Insulin signaling has been established as a major regulator of longevity in many animals, including Drosophila: hypomorphic alleles of InR increase life span relative to wild-type controls (Tatar et al. 2001), as do other manipulations that reduce IIS (Clancy et al. 2001; Hwangbo et al. 2004; Grönke et al. 2010). Consequently, we hypothesized that InRshort might increase life span relative to InRlong. Flies carrying the InRshort and InRlong alleles showed little difference in rates of aging over the majority of life span, although InRshort males exhibited reduced mortality late in life (Fig. 7). The lifetime survivorship of InRshort males was significantly longer than that of InRlong males, with a mortality risk ratio of 1.3 for InRlong over InRshort (P = 0.0095). The female mortality risk ratio for InRlong over InRshort was only 1.01, with no significant difference in longevity between these genotypes (P = 0.8909). Interestingly, the heterozygote of both sexes lived significantly longer (P < 0.0001) than either of the homozygote genotypes (Table S3). We hypothesize that the heterozygote longevity is a result of heterosis across the extended InR-embedded locus, a consequence of the fact that the homozygotes carried identical alleles with haplotypes extending beyond InR. Any general increase in fitness associated with greater heterozygosity was probably not mediated by IIS, as the heterozygotes were associated with intermediate levels of gene expression for the majority of IIS targets.
Survivorship curves, for females (A) and males (B). In both sexes, the heterozygote lived significantly longer than either homozygote genotype. In males, the InRshort genotype exhibited a reduced rate of aging late in life to produce a significant lifespan extension relative to InRlong, but there was no difference in longevity between InRshort and InRlong females.
Discussion
Our results indicate that the complex indel polymorphism in the first exon of InR is likely a direct target of natural selection in wild populations of D. melanogaster. Allele frequencies exhibit persistent latitudinal clines, change rapidly and cyclically over seasonal time scales, and the two common alleles are pleiotropically associated with multiple traits that correspond to expected pressures in distinct climatic environments. The alleles are also associated with predicted up- and downregulation of IIS, strongly suggesting that they mediate signaling behavior to effect differential trait expression. The fact that the alleles encode different amino acid sequences suggests that this occurs through changes in protein function. Overall, our findings provide an example of life-history trade-offs influenced by a complex, strongly pleiotropic polymorphism.
Although the phenotypes associated with InRshort and InRlong were specific and predictive, our assays did not precisely test the effects of the glutamine and histidine changes that account for the length differences in the complex indel polymorphism. This is for two reasons. First, the InRshort and InRlong alleles themselves contain several other SNPs, each of which exhibits nonrandom patterns of allele frequency across latitude and seasonal time (Fig. 1). Only one of these (a nonsynonymous SNP at position 17405614) has the potential to confound our functional results, however, as the other SNPs did not vary between our InRshort and InRlong lines. Allele frequencies at 17405614 exhibited substantial seasonal and clinal patterns, although not as strongly as several of the discrete indel variants that comprise the complex indel polymorphism (Fig. 1). Second, genome regions flanking the complex indel polymorphsim were not fully randomized due to the limits of experimentally imposed recombination. Consequently, in our test lines, the InRshort and InRlong alleles were embedded in haplotypes that extended beyond the InR locus. Based on prior estimates of linkage disequilibrium, however, flanking variants should be unlinked to the focal indel polymorphism (Paaby et al. 2010). Although these other variants may be targets of selection—a conclusion supported by our observation that the entire InR locus exhibits elevated clinality and seasonality relative to the rest of the genome—the fact that the indel polymorphism exhibits such strong changes in allele frequency across climatic environments provides complementary evidence for allele functionality. This accords with other reports of high recombination and rapid decay of linkage disequilibrium (Mackay et al. 2012), and evidence that selection precisely targets functional loci, including InR (Fabian et al. 2012), in natural populations of D. melanogaster.
Two of the discrete polymorphisms associated with the complex indel polymorphism, 17405631 and 17405634–7, do not discriminate between InRshort and InRlong but rather discriminate between the published reference genome genotype (which is of intermediate amino acid length) and InRshort or InRlong. Both of these discrete polymorphisms show strong seasonal patterns of allele frequency, which suggests that the intermediate allele may be a target of selection as well. The discrete 17405631 and 17405634–7 alleles segregate at substantial frequencies in all populations, and in both North America and Australia the intermediate-length haplotype is the third most common allele class (Paaby et al. 2010). In our previous survey, the intermediate allele showed no cline in North America and a weak cline in Australia. Here, the discrete 17405631 and 17405634–7 alleles again show little evidence of clinality in North America (Fig. 1D and Table S1). These observations support the conclusion that seasonal and geographical environments may impose different evolutionary forces, including aspects of demography and selection, even in the face of generally concordant responses to shared climatic selection pressure (Bergland et al. 2014).
Because flies derived from high latitudes live longer than those from low latitudes (Schmidt et al. 2005a), and reduction in IIS extends life span (Clancy et al. 2001; Tatar et al. 2001; Hwangbo et al. 2004; Grönke et al. 2010), we predicted that the InRshort allele might be associated with longer life span than the InRlong allele. InRshort males did live significantly longer than InRlong males, but the magnitude of difference was small compared to other traits and there were no differences between the alleles in females. The comparatively weak longevity effect may reflect decoupling between life span and reproduction that has been observed elsewhere (Khazaeli and Curtsinger 2010)—or it may represent true functional variation. Laboratory-derived InR mutations have shown a dramatic effect on life span, but only the heterozygous combination of two hypomorphic alleles, of all genotypes tested, significantly extended life span (Tatar et al. 2001). This suggests that IIS-mediated traits may be determined by precise titration of IIS levels; it may also be the case that within the InR locus, some polymorphisms affect some traits more than others (Stern 2000). For both sexes, the heterozygote lived significantly longer than either homozygote genotype, which we interpret as a function of excess homozygosity on chromosome 3R independent of InR genotype. Drosophila melanogaster suffers severe inbreeding depression, which negatively affects longevity (Swindell and Bouzat 2006); as life span is a highly polygenic, quantitative trait, it stands to reason that it is the most sensitive to heterozygosity of all the traits we evaluated.
In addition to longer male life span, the InRshort allele was also associated with better cold and starvation tolerance and lower fecundity in females, a classic example of antagonistic pleiotropy (Williams 1957). The differences in fecundity were limited to early life, and represent variation in egg-laying rate, because total lifetime fecundity was not significantly different between the alleles. Modest differences in egg laying in early life could have massive fitness consequences in the wild, particularly on patchy substrates with larval competition, yet the competitive advantage may be undetectable in the laboratory if fecundity is measured over more than one day. Consequently, we emphasize that estimates of reproduction and other quantitative fitness traits should be examined carefully, particularly in the context of decoupling correlated traits. We also note that although the high-latitude InRshort allele is associated with smaller body size, in general high-latitude flies are bigger (Huey et al. 2000; De Jong and Bochdanovits 2003). If InR contributes to selection-mediated differences in body size, it either acts epistatically with other body-size loci or suffers antagonistic selection pressures across multiple fitness axes. This finding, like observations of the pleiotropic polymorphism at neurofibromin 1 in which the high-latitude haplotype is negatively associated with wing size (Lee et al. 2013), demonstrates the complexity of selection dynamics in natural populations.
One hypothesis for strong genetic correlations among life-history traits, including the trade-off between life span and reproduction, is the presence of pleiotropic alleles. (Williams 1957; Flatt and Heyland 2011). If many genetic variants for life history are pleiotropic, then correlations among traits can never be completely dissolved. Our results implicate extensive pleiotropy, but the complexity of the InR indel polymorphism limits our ability to map phenotype to specific nucleotides. The two alleles we tested, InRshort and InRlong, differ at four amino acids across a span of 16 residues (Paaby et al. 2010). Even if in isolation these sites could break pleiotropy by functioning independently, over short time scales the distinction between pleiotropy and close linkage may be inconsequential (Paaby and Rockman 2013). However, this region at InR clearly experiences sufficient recombination (or mutation) over longer time scales to generate a hypervariable set of alleles on which selection may act. Tests on additional InR alleles would help to resolve the functional roles of individual nucleotides. Moreover, our findings in support of pleiotropy may not represent the majority of life-history alleles segregating in natural populations. Other work has shown that recombination can generate genotypes with positively correlated effects for both life span and reproduction, which suggests that life-history variation in nature is likely determined by both pleiotropic and recombining nonpleiotropic loci (Khazaeli and Curtsinger 2013). Sampling genotypes from nature may represent blocks of alleles that produce the commonly observed correlations, but which can be broken under the right circumstances. Finally, although the two major alleles of the InR indel polymorphism showed canonical trade-offs in the laboratory, the role of these alleles in the natural environment is unknown. Whether and how they affect trait expression might vary across different environments (Fournier-Level et al. 2013), with the possibility that under some conditions, pleiotropy may present as conditional neutrality (Anderson et al. 2011b). Thus, true understanding of the role of this polymorphism in adaptive response, including fitness consequences and degree of pleiotropy, requires investigation rooted in the natural environment.
ACKNOWLEDGMENTS
We thank K. O'Brien for assistance in measuring the fly wings and L. Noble for lending expertise on the pooled sample analyses. We also thank L. Yang and N. Bonini for sharing expertise and resources for qPCR. This work was supported by National Science Foundation (NSF) DEB 0921307, National Institutes of Health (NIH) F32 GM090557, NIH F32 GM097837, and the Charles H. Revson Foundation. The authors declare no conflicts of interest.
DATA ARCHIVING
The doi for our data is 10.5061/dryad.057r9.
LITERATURE CITED
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Associate Editor: Dr. Jay Storz
