EVOLUTION & DEVELOPMENT
16:4, 247–257 (2014)
DOI: 10.1111/ede.12087
Hidden genetic variation evolves with ecological specialization: the genetic basis of phenotypic plasticity in Arctic charr ecomorphs Eva Küttner,a,1 Kevin J. Parsons,2 Anne A. Easton,a Skuli Skúlason,b Roy G. Danzmann,a and Moira M. Fergusona * a b
Department of Integrative Biology, University of Guelph, Guelph, 50 Stone Road West, ON, Canada N1G 2W1 Hólar University College, Hólar, Hjaltadalur, 551, Saudarkrokur, Iceland
*Author for correspondence (e‐mail: [email protected]) Eva Küttner and Kevin J. Parsons contributed equally to this work. Present addresses: 1 Matís ohf, Haeyri 1, 550, Saudarkrokur, Iceland 2 College of Medical, Veterinary, and Life Sciences, Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom
SUMMARY The genetic variance that determines phenotypic variation can change across environments through developmental plasticity and in turn play a strong role in evolution. Induced changes in genotype–phenotype relationships should strongly influence adaptation by exposing different sets of heritable variation to selection under some conditions, while also hiding variation. Therefore, the heritable variation exposed or hidden from selection is likely to differ among habitats. We used ecomorphs from two divergent populations of Arctic charr (Salvelinus alpinus) to test the prediction that genotype–phenotype relationships would change in relation to environment. If present over several generations this should lead to divergence in genotype–phenotype relationships under common conditions, and to changes in the amount and type of hidden genetic variance that can evolve. We performed a
common garden experiment whereby two ecomorphs from each of two Icelandic lakes were reared under conditions that mimicked benthic and limnetic prey to induce responses in craniofacial traits. Using microsatellite based genetic maps, we subsequently detected QTL related to these craniofacial traits. We found substantial changes in the number and type of QTL between diet treatments and evidence that novel diet treatments can in some cases provide a higher number of QTL. These findings suggest that selection on phenotypic variation, which is both genetically and environmentally determined, has shaped the genetic architecture of adaptive divergence in Arctic charr. However, while adaptive changes are occurring in the genome there also appears to be an accumulation of hidden genetic variation for loci not expressed in the contemporary environment.
INTRODUCTION
Environmentally induced changes in development that expose “hidden” heritable variation are expected to provide a major source of variation for selection during adaptive radiations (Le Rouzic and Carlborg 2007; Barrett et al. 2009). Adaptive radiations are often defined by organisms invading new habitats and exposure to new environmental conditions (Schluter 2000). The premise that environment plays a key role during adaptive diversification within populations has motivated studies of phenotypic plasticity, whereby a single genotype can produce a range of phenotypes dependent upon environmental cues (Robinson and Parsons 2002; West‐ Eberhard 2003). Some models of adaptive divergence predict that increased ecological specialization may lead to a reduction in plasticity (Skulason and Smith 1995). However, greater degrees of specialization may also lead to increased levels of hidden genetic variation, as the same phenotypes are expressed and favoured by selection over generations (Le Rouzic and Carlborg 2007).
Natural selection has been established as a vital driver for evolution through the sorting of phenotypic variation (Mayr 1997) and it is becoming increasingly important to understand how that variation initially arises, and the underlying mechanisms involved. The production of new phenotypic variation occurs through alterations in development and does not necessarily involve new mutations (Barrett and Schluter 2008). Instead, new phenotypic variation can arise through changes in the environment, which alter development and lead to new phenotypes based on genetic variation that is normally neutral under common conditions, often termed “hidden” or “cryptic” genetic variation (Rutherford and Lindquist 1998; Le Rouzic and Carlborg 2007; Maharjan et al. 2014; Li et al. 2014). In fact, stable environments, by hiding this variation, may actually lead to evolved increases in “hidden” genetic variation by providing the same developmental conditions over generations. © 2014 Wiley Periodicals, Inc.
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Plastic responses can possess heritable variation and plasticity itself is a trait that can evolve in adaptive ways (Pigliucci 2005). However, there is limited understanding about the molecular basis of environmentally induced phenotypic variation and how it may change with ecological divergence (West‐Eberhard 2003). We know little about the loci involved in plastic responses, how persistent relationships between genotype and phenotype are across environments, or how the effect size of loci may change across environments (McGuigan et al. 2011; Zhai et al. 2014; Wang et al. 2014). These unknowns are ultimately aspects of development and will reflect evolutionary processes that have occurred (i.e., natural selection), as well as provide clues about future evolutionary potential in new environments by informing us about the levels and specific type of heritable variation that is available in a population. The concept of evolvabilty has been receiving increased attention in accordance with an increasing focus on sources of phenotypic variation for evolution (Hendrikse et al. 2007; Pigliucci 2008). Evolvability refers to the ability of a population to produce adaptive phenotypic variation (Pigliucci 2008; Parsons et al. 2011). Surprisingly, evolvability has rarely been considered conceptually in populations showing rapid intraspecific ecological divergence into different ecomorphs. Instead research has usually focused on determining the genetic basis of putatively adaptive traits (e.g., Colosimo et al. 2004; Rogers et al. 2007; Albert et al. 2008; Chan et al. 2010). Many of these studies have taken a quantitative trait locus (QTL) approach whereby backcross or F2 hybrids between different ecomorphs are created and reared in common garden experiments. While such QTL studies have proved invaluable for providing insight into the genomic regions and candidate genes that underlie phenotypic differences they have not addressed the potential sensitivity of QTL to environmental conditions, or the degree of ecological divergence. In other words most studies have typically identified loci that differ between ecomorphs. The determination of how evolutionary potential has been affected by ecological divergence and what the current levels of evolvability are in a subpopulation will require examinations of variation that is still segregating within subpopulations. Postglacial fishes have proven to be a particularly interesting group for the study of adaptive divergence (Skulason and Smith 1995; Taylor 1999; Robinson and Parsons 2002). This is due to the large but varying degrees of phenotypic divergence they exhibit across populations, typically between benthic and limnetic habitats where they form habitat associated ecomorphs (subpopulations). Although experiments are becoming increasingly utilized to study the molecular genetic basis (genetic architecture) and phenotypic plasticity of adaptive phenotypes (Peichel et al. 2001; Robinson and Parsons 2002; Parsons et al. 2010; Svanbäck and Schluter 2012), these have largely been treated as separate issues. Thus, there is an important opportunity to examine the genetic basis of plasticity in these systems, especially with respect to the
potential environmentally induced lability of the molecular basis of phenotypes. To explore these issues we focus on craniofacial variation in a postglacial fish the Arctic charr (Salvelinus alpinus), a species well known for its extreme levels of intraspecific divergence (Jonsson and Jonsson 2001; Klemetsen 2010). This model system provides lake populations with different degrees of phenotypic divergence in craniofacial traits, as well as ecomorphs within populations that differ in their degree of ecological specialization (Parsons et al. 2010). Lab studies suggest that levels of morphological plasticity differ among populations and ecomorphs, whereby more divergent populations are less plastic, as are more ecologically specialized ecomorphs (Parsons et al. 2010, 2011). Arctic charr are ecologically well characterized and a genetic linkage map based on microsatellite markers has been established for both Canadian (Woram et al. 2004) and Icelandic (Küttner et al. 2011) strains. Thus this species provides an excellent opportunity to build upon previous research by allowing us to study the potentially changing genetic basis of traits in response to environmental cues. We examine two lake populations of Arctic charr from Iceland that show evidence of intraspecific adaptive divergence of differing magnitudes. This includes an extremely divergent population from Thingvallavatn in the south of Iceland that hosts four distinct ecomorphs of Arctic charr (Sandlund et al. 1992), and a population from Vatnshlidarvatn in the north of Iceland which possesses two subtly divergent ecomorphs (Jonsson and Skulason 2000). The determination of these ecomorphs, as in other systems, is influenced by both genetic and environmental factors to varying degrees (Robinson and Parsons 2002). The two benthic ecomorphs of Thingvallavatn inhabit the shallow benthic zone of the lake where the larger benthic ecomorph (LB) lives in the epibenthic habitat feeding almost exclusively on large snails and the small benthivore (SB) occupies small crevices within the lava bed of the lake and feeds on a range of smaller invertebrates (Malmquist et al. 1992; Sandlund et al. 1992). The two limnetic ecomorphs of Thingvallavatn (planktivorous, PL and piscivorous, PI) inhabit the open pelagic zone of the lake, with one being predatory while the other feeds on a range of zooplankton prey. The two ecomorphs of Vatnshlidarvatn (brown, VB and silver, VS) have overlapping diets during part of the year and display subtle phenotypic differences (Jonsson and Skulason 2000). The VB ecomorph does however tend to have a more narrow range of prey, focusing more on benthic invertebrates. Within their respective lakes the LB and VB ecomorphs are hypothesized to be more phenotypically derived from their anadromous ancestors, as well as being more ecologically specialized due to the much lower diversity of prey they forage upon (Parsons et al. 2010, 2011). We aimed to determine how the molecular basis of phenotypic variation can shift in response to environment, and in turn impact the evolvability of Arctic charr ecomorphs. Therefore, the primary objective of this study was to examine the
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Genomics of Arctic charr craniofacial morphology
genetic architecture (quantitative trait loci, QTL) of craniofacial and body size traits in ecomorphs from Thingvallavatn and Vatnshlidarvatn reared under different diet treatments that induced changes in head shape. We performed genome scans with 133 microsatellite markers from 35 linkage groups on two full sib families from each of two ecomorphs in each lake (Thingvallavatn: LB, PL and Vatnshlidarvatn: VB, VS). We predicted (1) that evidence of divergent selection will be represented in the genome of different ecomorphs from the same lake through differences in the genetic architecture of craniofacial traits and body size. Within the context of evolvablity, we predicted (2) that more specialized ecomorphs, which are likely the product of stronger selection pressures, should show a reduction in the number of detectable QTL. This is because alleles important for increasing fitness are more likely to be fixed and no longer segregating in their respective subpopulations. Finally, we tested for the presence of QTL environment interactions by comparing QTL effects between full sibs reared on two diet treatments. We hypothesized that a substantial number of QTL for our traits would differ between rearing environments, thus altering the potential heritable variation
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exposed to selection. Because natural selection operates on expressed phenotypic variation we predicted that (4) novel rearing environments (i.e., limnetic diets for a benthic ecomorph) would expose “hidden” genetic variation through the detection of QTL.
MATERIALS AND METHODS
Phenotypic dataset In the fall of 2003, adult Arctic charr from lakes Thingvallavatn and Vatnshlidarvatn were collected and a male and female from the same ecomorph were crossed to produce a full‐sib family. Two full‐sibs families (different parents) were produced for each ecomorph and the progeny from each cross were split into two diet groups, one mimicking a benthic and the other a limnetic diet. Detailed description of collection and rearing of progeny on benthic and limnetic diets can be found in Parsons et al. (2010). A total of 730 progeny were available for genotyping. For the phenotypic analysis a reduced dataset of 563 progeny was available due to limitations of the bone staining process and
Table 1. Numbers progeny per family reared on benthic and limnetic diet treatments, linkage groups and loci analyzed in two sets of QTL analyses of six craniofacial traits and fork length in ecomorphs of Icelandic Arctic charr sampled from two Icelandic lakes (Vatnshildarvatn and Thingvallavatn) Each parent analyzed separately within families and diets Ecomorph Vatnshildarvatn Silver
Family
Diet
Ngenomic
NMultiQTL
No. LG
No. loci
NGridQTL
No. LG
No. loci
1
Limnetic Benthic Limnetic Benthic Limnetic Benthic Limnetic Benthic
45 45 46 46 46 46 46 45
39 42 42 46 43 41 39 35
34
84
23
63
36
88
34
87
22
59
36
90
38a 42 42 45a 43 40a 36a 29a
Limnetic Benthic Limnetic Benthic Limnetic Benthic Limnetic Benthic
48 33 46 57 45 45 45 46
23 30 45 40 37 34 27 0
36
74
53
56
35
82
31
67
16b 19
35b 52
33
63
20a 30 44a 39 37 32a 26a 0
0b
0b
2 Brown
1 2
Thingvallavatn Pelagic
1 2
Large benthic
Parents analyzed together within ecomorphs and diet treatments
1 2
a
For GridQTL an individual was excluded from the analysis if any of the traits had a missing value. Family 2 from the large benthic ecomorph (benthic treatment) was not included in the QTL analysis due to unavailability of phenotypic data. Thus a family specific map was used in the analysis resulting in fewer genetic markers than the merged map for the ecomorph. b
Ngenomic, number of progeny genotyped (total); NMultiQTL, number of progeny included in first approach of QTL analysis using the program MultiQTL (available at www.multiqtl.com); NGridQTL, number of progeny included in second approach of QTL analysis using the online software of GridQTL (www.gridqtl.org.uk).
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human error where specimens for only one family on the benthic diet were available for the LB ecomorph (Table 1). Two ecomorphs from Vatnshlidarvatn, the Silver (VS, N ¼ 169) and Brown (VB, N ¼ 158) and two ecomorphs from Thingvallavatn, Pelagic (PL, N ¼ 138) and large benthic (LB, N ¼ 98) were used. After rearing the fish for 160 days on different diets, experimental fish were anaesthetized and fixed in Bouin’s solution and stored in 75% ethanol. Heads underwent a clearing and staining protocol (see Küttner et al. 2013). Photographs were taken with a Nikon coolpix 4500 camera (Nikon Corp., Tokyo, Japan) with specimens lying on their left side. Eight homologous landmarks (Fig. 1) were collected from the digital photos using TPSdig2 (available at: http://life.bio.sunysb.edu/morph/) and coordinates superimposed in CoordGen6 (part of the Integrated Morphometrics Program [IMP] suite freely available at http:// www2.canisius.edu/sheets/morphsoft.html) using the generalized least squares Procrustes superimposition method which enables calculation of multivariate centroid size (the square root of the sum of squared distances in millimeters) between each landmark and a geometric central landmark, excluding landmark 1 due to variation in fixing of lower jaw position). Six linear measurements between pairs of landmarks were then calculated using tmorphgen6 from the IMP series (Fig. 1). The tip of the premaxilla to posterior end of the articular was used as measurement for upper jaw length because of challenges in identifying the dorsal end of the maxilla as it overlaps with the articular. Allometric effects were minimized for all traits using
Fig. 1. An example of a cleared and stained head of Silver ecomorph (VS) from Vatnshlidarvatn including the placement of the 8 homologous landmarks (1: anterior tip dentary, 2: pectoral girdle, postcleithrum/supracleithrum, 3: posterior end articular, 4: upper end joint articular/quadrate, 5: anterior tip premaxilla, 6: supraorbital#2/sphenotic, 7: pterotic, posterior point, connection to post‐ temporal, 8: pectoral fin proximal). Linear measurements taken between landmark pairs (UHL: LM2‐LM5 upper head length, LHL: LM1‐LM8 lower head length, HD: LM3‐lM6 head depth, LJL: LM1‐LM3 lower jaw length (ventral side of dentary and articular), UJL: LM3‐LM5 upper jaw length, AH: LM3‐LM4 articular height) are shown as white lines.
linear regressions against head centroid size with a common slope for the complete dataset and residuals used for all further analyses. Fork lengths for all fish (in millimeters) were available from a previous analysis of the same fish (Parsons et al. 2010).
Linkage and QTL analyses DNA was isolated from muscle tissue using a modified phenol‐ chloroform‐isoamyl alcohol method (Taggart et al. 1992) to perform whole genome scans (Moghadam et al. 2007) on progeny from all eight families. For this, genetic microsatellite marker selection was based on known linkage group assignments in mapping panels of North American (NA) Arctic charr (Woram et al. 2004) and aimed to cover all linkage groups. The low level of polymorphism in Icelandic Arctic charr necessitated that the parents be screened for the majority of the markers on the Arctic charr genetic linkage map (>500). The intense screening yielded 133 useable markers where the number of loci genotyped per ecomorph ranged from 63 to 90 (Table 1). The software package LINKMFEX (http://www.uoguelph. ca/rdanzman/software/) was used to analyze marker linkages to create sex‐specific linkage maps, which is necessary due to large differences in recombination rates between females and males (Woram et al. 2004). Markers were assigned to linkage groups using a threshold LOD score of 3.0, linkage order within linkage groups was established in MAPORD. Goodness‐of‐fit G tests were performed in SEGSORT to identify markers that show significant deviation (P < 0.05) from the expected 1:1 Mendelian expectation (segregation distortion). A Bonferroni corrected P‐value of 0.001 (0.05/number of linkage groups within respective family) was used to evaluate the significance of deviations from Mendelian expectations. Ten of 133 markers showed segregation distortion and each case was restricted to one family with one exception (BHMS417) where two families exhibited distortion (PL1: Omy38DU, BHMS417; PL2: OMM1238, Omi30TUF, Ots526NWFSC; LB1: BHMS417, LB2: OMM1228, BX080247; VB1: OMM5184, VB2: OkeSL, VS2: OMM5017). These were excluded from the final analysis. Phase‐corrected genotypes were generated with GENOVECTbatch prior to QTL analysis and phenotypic data was tested for normality using Shapiro–Wilk tests with PASW (Predictive Analytics SoftWare, IBM SPSS Statistics, New York, 2009). We took two approaches with the QTL analysis. First, QTL were located by interval analysis of the effects of one parent on a single trait within a family reared on a diet treatment. We used a single QTL per linkage group model using the software MultiQTL (available at www.multiqtl.com) where P‐values were generated with 1000 permutations of the phenotypic values against genetic data within families and proportion of phenotypic variation (PPV) were estimated. QTL with chromosome‐wide significance were identified by permutation tests within each linkage group. All QTL at or below the 0.01 threshold were defined as significant with chromosome‐wide effects. Significant
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QTL were further tested for genome‐wide effects at the 0.05 level using a false discovery rate (FDR) test (Benjamini and Hochberg 1995). In those cases where copies of duplicated markers could not be assigned to one of the two homeologous linkage group arms (i.e., linkage group arms arising from the most recent whole genome duplication in the salmonid ancestor), both linkage groups connected with an underscore are indicated as the location of the QTL (e.g., AC‐13_34 indicates a duplicated marker localized to the homeologous linkage groups AC‐13 and AC‐34). We then used a more conservative analysis where genetic and phenotypic data (all traits) of all parents within a ecomorph (by diet treatment) were analyzed simultaneously to test for QTL effects at the ecomorph level (Table 1). The GridQTL online software (http://www.gridqtl.org.uk/) utilizing the sib‐ pair analysis with all traits for both chromosome‐wide and experiment‐wide with 1000 permutations was used to detect QTL at the 0.01 significance levels. For this analysis, it was necessary to merge the linkage maps from each of the four parents into a single ecomorph‐specific map. Given the limited number of informative genetic markers in the parents (many hundreds of markers had been screened) and that parents differed in the identity of informative loci, many linkage groups from the genetic map were represented by multiple fragments. We trimmed the number of linkage groups (fragments) because of the capacity of the software and the need to know with certainty which copies of duplicated markers were homologous across parents. Thus, we eliminated linkage group fragments that were composed only of duplicated markers (often in unique combinations across parents) and others that showed homeologous affinities to these (AC‐1, ‐3, ‐10, ‐12, ‐13, ‐15, ‐20, ‐21, ‐24, ‐27, ‐34, ‐36, ‐43). Linkage group AC‐18 was removed from all analyses due to merging issues. Finally, three unlinked markers unique to the PL ecomorph were not included in the analysis.
Results Thirty‐five linkage groups could be identified and their marker arrangements (markers linked within linkage groups) were consistent with studies on cultured Icelandic charr (Küttner et al. 2011). Multiple QTL for craniofacial traits and fork length with chromosome‐wide effects were detected when each parent was analyzed separately (Tables 2 and 3; Fig. 2). No QTL with genome‐wide effects were detected for craniofacial traits, but one genome‐wide QTL for fork length on AC‐27 was detected in the LB2 family. Full‐sibs reared on different diets did not show consistent QTL effects. The number of linkage groups with QTL for a given trait ranged from 6 for upper jaw length (UJL) to 12 for lower head length (LHL) (Fig. 2). The PPV values were relatively high and ranged on average from 19.1% for UHL and 24.1% for AH (Table 2). The largest PPV value was 44% for AH in the PL1 female on AC‐18. The total numbers of linkage
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groups with QTL for all craniofacial traits counted together across diets and parents for each ecomorph were: VS‐23, VB‐11, PL‐19, and LB‐12 (Table 3). The count for LB is an underestimation given that that samples for the second family reared on the benthic diet treatment were unavailable. There was very limited overlap in QTL location for traits across parents: HD on AC‐1, LJL on AC‐22 and AH on AC‐18 (Table 2). There was only one case where QTL effects were detected in both progeny groups from a single parent reared on the two diet treatments (the male parent of family VS2 for UHL). Six QTL with experiment‐wide effects and six with chromosome‐wide effects were detected when parents from a particular ecomorph (separated by diet treatment) were analyzed together (Table 4). The experiment‐wide effects were detected for all craniofacial traits except for LJL where a QTL with chromosome‐wide effects was detected. The QTL were distributed across ecomorphs and traits with the LB ecomorph reared on the limnetic diet having detectable QTL for LHL, HD, UJL, LJL and AH. This finding mirrored the single parent analysis where a greater number of QTL were detected in the Thingvallavatn LB ecomorph reared on the less familiar diet. A single QTL with experiment‐wide effects was detected for fork length in the VS ecomorph reared on the limnetic diet.
DISCUSSION Our findings support the prediction that divergent natural selection on craniofacial morphology has altered the genetic architecture of these traits in Arctic charr ecomorphs. For example, ecomorphs differed in the average number of linkage groups with QTL for craniofacial traits. Notably, the ecomorphs hypothesized to be more ecologically specialized within their lake (VB and LB) had fewer linkage groups with QTL suggesting that divergent selection has fixed genetic variation in genomic regions related to craniofacial shape (Table 3). More specifically, the VB ecomorph had approximately half as many “significant” QTL as its sympatric counterpart VS with a similar trend between the LB and PL ecomorphs based on the analysis of individual traits and parents. This finding is in line with results from Parsons et al. (2010) that found reduced phenotypic variation for body shape over ontogeny in the more ecologically specialized LB and VB ecomorphs. Further, at the genetic level this suggestion is also in line with recent findings from a population genetic survey in Thingvallavatn that documented a reduced level of heterozygosity in the LB ecomorph (Kapralova et al. 2011), which in turn matches the reduced number of polymorphisms detected in the current genome scans of LB families (Küttner 2011). While this reduced phenotypic and genetic variation may limit the power to detect QTL it also suggests that less variation is currently available for selection. This pattern of differences in the number of QTL was not however detectable with the combined analysis across traits and
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Table 2. The location of significant QTL for six craniofacial traits and fork length in eight families produced with four ecomorphs of Icelandic Arctic charr from two Icelandic lakes and reared on two diet treatments LG
Parent
Upper head length (UHL) 4 VS2M 13 PL2F 14 VS1F 16 VB2M 18 VS1M 20 VS1M 26 VS2M VS2M 3_24 VS1F 20_43 VS2M Lower head length (LHL) 10 PL2M 12 VB2M 14 VB2F 20 PL1F 21 VS2M 22 PL1M 23 VB2F 28 PL2M 31 PL1F 37 LB1M 39 VS2M 10_15 VB2M 20_36 VB2M VS2F Head depth (HD) 1 PL1F VB1F 4 LB2M 18 VS2F 22 LB1F 27 VS1M 28 PL2F 37 LB1M 39 VS2F 3_24 PL1F Upper jaw length (UJL) 9 LB2M 13 PL1M 21 VS1F 6_12 VS1F 10_15 PL2F 20_43 VB2M Lower jaw length (LJL) 1 LB1F 11 VS2F 12 PL1F 13 PL1M 14 PL1M 15 LB1M 21 LB1F 22 PL1M LB1F
Diet
Marker/interval
P‐value
PPV
Lim Lim Ben Ben Lim Ben Ben Lim Lim Lim
Omy6DIAS Ssa85DU Sco19UBC/BHMS238 OMM5091/BHMS417 BX319197 OMM1274 SalD25SFU OMM1302 SalF41SFU OMM5184
0.005 0.006 0.008 0.001 0.005 0.006 0.004 0.003 0.005 0.007
0.18 0.15 0.28 0.27 0.15 0.18 0.18 0.17 0.15 0.20
Ben Lim Ben Lim Lim Lim Ben Ben Ben Lim Ben Ben Lim Lim
OMM1237/Omi187TUF OMM1236/OMM1345 Sco19UBC/One11ASC BX890355/OMM5184 SmaBFRO1/Omy21DU OkeSL BX873441 Ssa0033BSFU OMM1290 Omy1090UW Omi30TUF OMM1237/Omi187TUF CNE805‐786
0.007 0.001 0.002 0.001 0.007 0.003 0.002 0.002 0.007 0.007 0.004 0.006 0.004 0.007
0.14 0.21 0.20 0.20 0.18 0.17 0.21 0.16 0.25 0.20 0.17 0.21 0.18 0.22
Ben Ben Lim Lim Lim Lim Ben Ben Ben Lim
OMM1300 OMM1228 OMM1238 OkeSL Ogo4UW/CA383830 BHMS331 Omy1090UW Omi30TUF OMM1318
0.001 0.005 0.005 0.009 0.001 0.003 0.002 0.004 0.005 0.004
0.31 0.20 0.25 0.14 0.22 0.19 0.20 0.16 0.15 0.21
Lim Ben Ben Lim Ben Ben
OMM3067 Ssa85DU SmaBFRO1/OMM1302 Ots501 OMM1237 Sfo23LAV
0.004 0.001 0.008 0.006 0.005 0.001
0.31 0.29 0.22 0.21 0.16 0.21
Lim Lim Lim Ben Ben Ben Lim Lim Lim
OMM1300 SalF56SFU OMM1236 Ssa85DU Sco19UBC OmyRGT2TUF Omi70TUF OkeSL
0.001 0.001 0.008 0.001 0.009 0.003 0.007 0.005 0.001
0.22 0.17 0.17 0.30 0.29 0.17 0.14 0.17 0.21
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Table 2. (Continued) LG
26 32 43 20_36 20_43 Articular height (AH) 7 9 13 14 18 21 26 10_15 Fork length (FL) 1 4 8 11 13 18 20 22 27 30 32
Parent
Diet
Marker/interval
P‐value
PPV
VS1F VS2F PL1F PL2M VS1M VS2F VS2M
Lim Ben Ben Ben Ben Lim Ben
0.005 0.008 0.005 0.009 0.007 0.001 0.001
0.21 0.16 0.21 0.14 0.19 0.17 0.25
LB2M VS1F VS2F PL2F PL1F LB2F VB2M VB2M VB2M
Lim Lim Lim Ben Ben Lim Lim Ben Ben
BX302949 BX080247 OMM1194/Ssa85DU Omi127TUF BX319197 Omy27DY/SmaBFRO1 SalD25SFU OMM1237/Omi187TUF
0.001 0.002 0.009 0.005 0.004 0.006 0.005 0.009 0.005
0.24 0.40 0.20 0.16 0.44 0.16 0.17 0.19 0.21
VS2M PL1F VB2F VS2F LB1M VB2F VS2F PL1M PL2M LB2M PL1F VS1F VS2F
Ben Ben Ben Ben Lim Lim Lim Lim Lim Lim Lim Lim Lim
OMM1330 OMM1228 OMM1556 SalF56SFU Ssa85DU/OMM1174 OmyJTUF/BX870052 OmyRGT46TUF BX890355 OkeSL/Ssa0080BSFU Ogo4UW BHMS429 OMM1178/OMM1329 OMM1178/OMM1329
0.009 0.003 0.008 0.008 0.005 0.006 0.001 0.001 0.005 0.001q 0.001 0.007 0.005
0.15 0.31 0.17 0.19 0.29 0.21 0.24 0.24 0.22 0.37 0.31 0.15 0.17
Ssa0080SFU OMM1804 OMM1329 OMM5008 SalF56SFU OMM1379
QTL were detected with interval analysis within single families analyzed separately by parent. The proportion of phenotypic variance is given in the last column (PPV) and the QTL with experiment—wide significance are marked with.q (PL‐pelagic ecomorph from Thingvallavatn, LB, large benthic ecomorph from Thingvallavatn; VS, silver ecomorph from Vatnshlidarvatn, VB, brown ecomorph from Vatnshlidarvatn; Ben, groups raised on benthic diet treatment; Lim, groups raised on limnetic diet treatment; F‐female parent, M ‐male parent).
parents. Therefore, we suggest that our findings are preliminary and will require a higher number of families and a greater density of genetic markers to be conclusive. Nonetheless, our results from two independent populations suggest that within divergent populations more ecologically specialized ecomorphs exhibit fewer QTL.
Dissecting the evolutionary potential of craniofacial and body size traits The potential for further evolution of craniofacial morphology for charr ecomorphs will depend in large part on the degree of heritable variation available. As discussed above our findings suggest that the evolutionary potential for more ecologically specialized ecomorphs may be limited relative to more generalist ecomorphs. However, complex traits such as craniofacial shape are known to depend on a multitude of genes (Albertson
et al. 2003a, b; Parsons and Albertson 2009) which may cause greater difficulty for achieving allelic fixation within a subpopulation. This is because some alleles may present tradeoffs in fitness through epistatic interactions, antagonistic pleiotropy, or simply through the use multiple alternative developmental pathways that contribute toward phenotypes that increase fitness. Therefore, it is not surprising that our findings suggest that multiple loci underlie these traits in charr ecomorphs. Consistent with other studies QTL for body size were widely distributed across all linkage groups indicating the involvement of many genes. While only one trait for body size was examined (fork length) significant QTL for this trait were spread across eleven linkage groups (Table 2). This observation is in agreement with earlier QTL studies on body size traits like fork length and body weight in salmonids (Moghadam et al. 2007; Wringe et al. 2010; Küttner et al. 2011). This
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Table 3. The number of linkage groups with significant QTL for six craniofacial traits in four ecomorphs of Icelandic Arctic charr from Thingvallavatn and Vatnshlidarvatn raised on two diets Ecomorph Vatnshlidarvan Silver
Brown
Thingvallavatn Pelagic
Large benthic
Diet
UHL
LHL
HD
UJL
LJL
AH
Total
Benthic Limnetic Shared Benthic Limnetic Shared
3 5 1 1 0 0
1 2 0 3 2 0
1 2 0 1 0 0
1 1 0 1 0 0
2 3 1 0 0 0
0 2 0 2 1 0
8 15 2 8 3 0
Benthic Limnetic Shared Benthic Limnetic Shared
0 1 0 0 0 0
3 2 0 0 1 (1) 0
2 1 0 1 2 (1) 0
2 0 0 0 1 (0) 0
4 2 0 1 3 (3) 0
2 0 0 0 2 (0) 0
13 6 0 2 9 (5) 0
The number of linkage groups with QTL shared between the two diet groups within an ecomorph is also indicated. QTL were detected with interval analysis in individual analysis of 8 families (2 per ecomorph) by parent (UHL, upper head length; LHL, lower head length; HD, head depth; UJL, upper jaw length; LJL, lower jaw length; AH, articular height). Only one family available. Data for the single family reared under both diet treatments in parentheses.
finding coupled with moderate to large effect sizes for loci suggests that there is significant genetic variation and future evolutionary potential for this trait in both lake populations. Notably, some QTL appear to be consistent at a range of biological scales. For example, a comparison of fork length QTL based on all four parents per ecomorph from Thingvallavatn and Vatnshlidarvatn to the equivalent analysis with the cultured Icelandic Arctic charr (Küttner et al. 2011), implicates linkage group AC‐20, but with no overlap from other linkage groups (Table 3). Similarly, a QTL study on growth traits in Arctic charr originating from the Fraser strain in Canada (Moghadam et al. 2007) also detected “suggestive” QTL on linkage group AC‐20 for body weight and condition factor. At the species level the genetic markers associated with growth traits (OMM5184, OMM1274) in Icelandic and Canadian charr have also been implicated as markers of QTL for similar traits in rainbow trout (Oncorhynchus mykiss) and Atlantic Salmon (Salmo salar) (Reid et al. 2005; Wringe et al. 2010) as noted before (Küttner et al. 2011). These observations suggest that AC‐20 harbours one or multiple QTL for growth traits that are conserved across populations and even related species. Thus, macro and microevolution may involve the same mechanisms as the evolution of size differences within and among ecomorphs may be connected at the genomic level to size variation among salmonid species. The loci involved with craniofacial traits in charr may be especially visible to natural selection. This is suggested by the relatively large effect sizes for craniofacial trait QTL (average of single parent analysis about 20% PPV) than what has previously been found for life history and growth related traits (Moghadam
et al. 2007; Wringe et al. 2010; Küttner et al. 2011) in salmonids (i.e., most QTL explain less than 10% of variation, only 10% have effect sizes greater than 15%). At the more extreme end, studies on craniofacial morphology in teleosts (Albertson et al. 2005) have detected multiple QTL with even higher effect sizes (e.g. 40% þ). This is likely due to an experimental design involving an F2 generation of backcrosses that has increased power for detecting QTL. Thus, given our experimental design of intramorph crosses it is notable to find such large effect sizes. This may suggest a propensity for craniofacial variation to evolve rapidly relative to other traits. However, an alternative explanation for the large effect sizes detected in our study is that they are overestimated. This has been predicted for QTL studies with smaller samples sizes, which raise the detection limit (Otto and Jones 2000; Mackay et al. 2009). This explanation is supported by the observation that the effect sizes for fork length QTL in the current study are higher than those detected in two other studies with the same species and trait but based on larger sample sizes (Moghadam et al. 2007; Küttner et al. 2011). Moreover, a study of fork length in Atlantic salmon (Salmo salar) (Reid et al. 2005) with comparable sample sizes to those in the present study reported QTL with phenotypic effects in a similar range to what we found here.
Altering evolutionary potential through the environment: QTL interact with diet Because natural selection operates directly on phenotypic variation we examined whether the genetic variation used to
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Fig. 2. The linkage groups with significant QTL for six craniofacial traits and fork length in families from four ecomorphs of Icelandic Arctic charr from Thingvallavatn and Vatnshlidarvatn (PL‐pelagic ecomorph from Thingvallavatn, LB‐ large benthic ecomorph from Thingvallavatn, VS, silver ecomorph from Vatnshlidarvatn; VB, brown ecomorph from Vatnshlidarvatn) raised on benthic and limnetic diet treatments.
produce such phenotypic variation could change in response to ecologically relevant diet treatments. In accordance with our prediction there was a little consistency between diet treatments in terms of the location of QTL suggesting that different regions of the genome could be subjected to selection through the phenotypic response to environment. Further, all but two QTL were not present in both diet treatments for a given ecomorph. This suggests that strong diet QTL interactions are present in charr ecomorphs, such as has previously reported in lab model organisms (e.g. Cheverud et al. 2004; Gordon et al. 2008). However, in our case the environmental treatments mimic natural benthic and limnetic diets, which are thought to drive adaptive divergence in charr and several other species of postglacial fishes (Robinson and Parsons 2002). These findings also suggest that some environments can provide relaxed selection, which allows for the accumulation of hidden variation to evolve (Kawecki 1994; Snell‐Rood et al. 2010; Van Dyken and Wade 2010). Therefore, our results strongly suggest that the
genetic changes observed in divergent populations of these species could in large part be enabled by variation both exposed to and hidden from selection through environmentally determined phenotypes.
CONCLUSIONS This study provides evidence that selection on craniofacial and body size traits has played an important role in producing genetic differences among ecomorphs of Arctic charr. We observed only low levels of consistency between ecomorphs regarding their genetic architecture. Further, we found fewer linkage groups harbouring QTL for craniofacial traits in the ecomorphs hypothesized to be more ecologically specialized (VB and LB, based on the individual parents analysis) which suggests they have undergone stronger selection leading to a greater degree of allelic fixation. However, we also provided
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Table 4. The location of significant (chromosome‐wide) QTL for six craniofacial traits and fork length in four ecomorphs of Icelandic Arctic charr from two Icelandic lakes reared on two diet treatments based on a combined interval analysis of families within a ecomorph reared on a particular diet LG
Parent
Diet
Marker/interval
P‐value
Upper head length (UHL) 16 26
VB VS
Ben Ben
BHMS417/OMM5091 SalD25SFU
0.01 0.01
Lower head length (LHL) 37‐3 8‐1 6 or 23‐2
LB LB VB
Lim Ben Ben
Omy1090UW OMM1556 BX873441/i
0.01 0.01 0.01
Head depth (HD) 22
LB
Lim
OkeSL
0.01
Upper jaw length (UJL) 22 6 or 12 or 23
LB VS
Lim Lim
OkeSL Ots501NWFSC/i
0.01 0.01
Lower jaw length (LJL) 22
LB
Lim
OkeSL
0.01
Articular height (AH) 7‐2 9 19‐1
LB VS PL
Lim Lim Ben
BX0302949 BX080247/OMM1374 CA350064
0.01 0.01 0.01
Fork length (FL) 32
VS
Lim
OMMM1178/OMM1329
0.01
QTL with experiment—wide significance are also indicated ( P < 0.05; P < 0.01) (PL, pelagic ecomorph from Thingvallavatn; LB, large benthic ecomorph from Thingvallavatn; VS, silver ecomorph from Vatnshlidarvatn; VB, brown ecomorph from Vatnshlidarvatn; Ben, groups raised on benthic diet; Lim, groups raised on limnetic diet.
evidence that the selection that has caused this divergence, and the future evolutionary potential of charr ecomorphs is influenced by environmental conditions. Little overlap in QTL occurred between our ecologically relevant diet treatments. To our knowledge this also provides some of the first insights into the allelic basis of plasticity in an adaptive divergence. Among the most compelling aspects of these findings is that in some cases a novel diet treatment can elicit a greater number of QTL, suggesting that “hidden” genetic variation evolves in conditions where selection cannot “see” phenotypic variation due to developmental responses in surrounding conditions. This perhaps sheds light on a major paradox in evolution‐ how is variation present when selection is persistently removing it? Our findings suggest that a solution resides in the ability of developmental plasticity to “hide” variation, with its potential release occurring with the invasion of new environments. Acknowledgments This research was supported by the Natural Sciences and Engineering Research Council of Canada (RGD, MMF), Hólar University College (SK), a Madame Vigdis Finnbogadottir Scholarship (EK) and Ontario Graduate Scholarships (KJP). We thank Heather Fotherby for her outstanding technical support and Bjarni Kristófer Kristjánsson for assistance in the field.
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16:4, 247–257 (2014)
DOI: 10.1111/ede.12087
Hidden genetic variation evolves with ecological specialization: the genetic basis of phenotypic plasticity in Arctic charr ecomorphs Eva Küttner,a,1 Kevin J. Parsons,2 Anne A. Easton,a Skuli Skúlason,b Roy G. Danzmann,a and Moira M. Fergusona * a b
Department of Integrative Biology, University of Guelph, Guelph, 50 Stone Road West, ON, Canada N1G 2W1 Hólar University College, Hólar, Hjaltadalur, 551, Saudarkrokur, Iceland
*Author for correspondence (e‐mail: [email protected]) Eva Küttner and Kevin J. Parsons contributed equally to this work. Present addresses: 1 Matís ohf, Haeyri 1, 550, Saudarkrokur, Iceland 2 College of Medical, Veterinary, and Life Sciences, Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow G12 8QQ, United Kingdom
SUMMARY The genetic variance that determines phenotypic variation can change across environments through developmental plasticity and in turn play a strong role in evolution. Induced changes in genotype–phenotype relationships should strongly influence adaptation by exposing different sets of heritable variation to selection under some conditions, while also hiding variation. Therefore, the heritable variation exposed or hidden from selection is likely to differ among habitats. We used ecomorphs from two divergent populations of Arctic charr (Salvelinus alpinus) to test the prediction that genotype–phenotype relationships would change in relation to environment. If present over several generations this should lead to divergence in genotype–phenotype relationships under common conditions, and to changes in the amount and type of hidden genetic variance that can evolve. We performed a
common garden experiment whereby two ecomorphs from each of two Icelandic lakes were reared under conditions that mimicked benthic and limnetic prey to induce responses in craniofacial traits. Using microsatellite based genetic maps, we subsequently detected QTL related to these craniofacial traits. We found substantial changes in the number and type of QTL between diet treatments and evidence that novel diet treatments can in some cases provide a higher number of QTL. These findings suggest that selection on phenotypic variation, which is both genetically and environmentally determined, has shaped the genetic architecture of adaptive divergence in Arctic charr. However, while adaptive changes are occurring in the genome there also appears to be an accumulation of hidden genetic variation for loci not expressed in the contemporary environment.
INTRODUCTION
Environmentally induced changes in development that expose “hidden” heritable variation are expected to provide a major source of variation for selection during adaptive radiations (Le Rouzic and Carlborg 2007; Barrett et al. 2009). Adaptive radiations are often defined by organisms invading new habitats and exposure to new environmental conditions (Schluter 2000). The premise that environment plays a key role during adaptive diversification within populations has motivated studies of phenotypic plasticity, whereby a single genotype can produce a range of phenotypes dependent upon environmental cues (Robinson and Parsons 2002; West‐ Eberhard 2003). Some models of adaptive divergence predict that increased ecological specialization may lead to a reduction in plasticity (Skulason and Smith 1995). However, greater degrees of specialization may also lead to increased levels of hidden genetic variation, as the same phenotypes are expressed and favoured by selection over generations (Le Rouzic and Carlborg 2007).
Natural selection has been established as a vital driver for evolution through the sorting of phenotypic variation (Mayr 1997) and it is becoming increasingly important to understand how that variation initially arises, and the underlying mechanisms involved. The production of new phenotypic variation occurs through alterations in development and does not necessarily involve new mutations (Barrett and Schluter 2008). Instead, new phenotypic variation can arise through changes in the environment, which alter development and lead to new phenotypes based on genetic variation that is normally neutral under common conditions, often termed “hidden” or “cryptic” genetic variation (Rutherford and Lindquist 1998; Le Rouzic and Carlborg 2007; Maharjan et al. 2014; Li et al. 2014). In fact, stable environments, by hiding this variation, may actually lead to evolved increases in “hidden” genetic variation by providing the same developmental conditions over generations. © 2014 Wiley Periodicals, Inc.
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Plastic responses can possess heritable variation and plasticity itself is a trait that can evolve in adaptive ways (Pigliucci 2005). However, there is limited understanding about the molecular basis of environmentally induced phenotypic variation and how it may change with ecological divergence (West‐Eberhard 2003). We know little about the loci involved in plastic responses, how persistent relationships between genotype and phenotype are across environments, or how the effect size of loci may change across environments (McGuigan et al. 2011; Zhai et al. 2014; Wang et al. 2014). These unknowns are ultimately aspects of development and will reflect evolutionary processes that have occurred (i.e., natural selection), as well as provide clues about future evolutionary potential in new environments by informing us about the levels and specific type of heritable variation that is available in a population. The concept of evolvabilty has been receiving increased attention in accordance with an increasing focus on sources of phenotypic variation for evolution (Hendrikse et al. 2007; Pigliucci 2008). Evolvability refers to the ability of a population to produce adaptive phenotypic variation (Pigliucci 2008; Parsons et al. 2011). Surprisingly, evolvability has rarely been considered conceptually in populations showing rapid intraspecific ecological divergence into different ecomorphs. Instead research has usually focused on determining the genetic basis of putatively adaptive traits (e.g., Colosimo et al. 2004; Rogers et al. 2007; Albert et al. 2008; Chan et al. 2010). Many of these studies have taken a quantitative trait locus (QTL) approach whereby backcross or F2 hybrids between different ecomorphs are created and reared in common garden experiments. While such QTL studies have proved invaluable for providing insight into the genomic regions and candidate genes that underlie phenotypic differences they have not addressed the potential sensitivity of QTL to environmental conditions, or the degree of ecological divergence. In other words most studies have typically identified loci that differ between ecomorphs. The determination of how evolutionary potential has been affected by ecological divergence and what the current levels of evolvability are in a subpopulation will require examinations of variation that is still segregating within subpopulations. Postglacial fishes have proven to be a particularly interesting group for the study of adaptive divergence (Skulason and Smith 1995; Taylor 1999; Robinson and Parsons 2002). This is due to the large but varying degrees of phenotypic divergence they exhibit across populations, typically between benthic and limnetic habitats where they form habitat associated ecomorphs (subpopulations). Although experiments are becoming increasingly utilized to study the molecular genetic basis (genetic architecture) and phenotypic plasticity of adaptive phenotypes (Peichel et al. 2001; Robinson and Parsons 2002; Parsons et al. 2010; Svanbäck and Schluter 2012), these have largely been treated as separate issues. Thus, there is an important opportunity to examine the genetic basis of plasticity in these systems, especially with respect to the
potential environmentally induced lability of the molecular basis of phenotypes. To explore these issues we focus on craniofacial variation in a postglacial fish the Arctic charr (Salvelinus alpinus), a species well known for its extreme levels of intraspecific divergence (Jonsson and Jonsson 2001; Klemetsen 2010). This model system provides lake populations with different degrees of phenotypic divergence in craniofacial traits, as well as ecomorphs within populations that differ in their degree of ecological specialization (Parsons et al. 2010). Lab studies suggest that levels of morphological plasticity differ among populations and ecomorphs, whereby more divergent populations are less plastic, as are more ecologically specialized ecomorphs (Parsons et al. 2010, 2011). Arctic charr are ecologically well characterized and a genetic linkage map based on microsatellite markers has been established for both Canadian (Woram et al. 2004) and Icelandic (Küttner et al. 2011) strains. Thus this species provides an excellent opportunity to build upon previous research by allowing us to study the potentially changing genetic basis of traits in response to environmental cues. We examine two lake populations of Arctic charr from Iceland that show evidence of intraspecific adaptive divergence of differing magnitudes. This includes an extremely divergent population from Thingvallavatn in the south of Iceland that hosts four distinct ecomorphs of Arctic charr (Sandlund et al. 1992), and a population from Vatnshlidarvatn in the north of Iceland which possesses two subtly divergent ecomorphs (Jonsson and Skulason 2000). The determination of these ecomorphs, as in other systems, is influenced by both genetic and environmental factors to varying degrees (Robinson and Parsons 2002). The two benthic ecomorphs of Thingvallavatn inhabit the shallow benthic zone of the lake where the larger benthic ecomorph (LB) lives in the epibenthic habitat feeding almost exclusively on large snails and the small benthivore (SB) occupies small crevices within the lava bed of the lake and feeds on a range of smaller invertebrates (Malmquist et al. 1992; Sandlund et al. 1992). The two limnetic ecomorphs of Thingvallavatn (planktivorous, PL and piscivorous, PI) inhabit the open pelagic zone of the lake, with one being predatory while the other feeds on a range of zooplankton prey. The two ecomorphs of Vatnshlidarvatn (brown, VB and silver, VS) have overlapping diets during part of the year and display subtle phenotypic differences (Jonsson and Skulason 2000). The VB ecomorph does however tend to have a more narrow range of prey, focusing more on benthic invertebrates. Within their respective lakes the LB and VB ecomorphs are hypothesized to be more phenotypically derived from their anadromous ancestors, as well as being more ecologically specialized due to the much lower diversity of prey they forage upon (Parsons et al. 2010, 2011). We aimed to determine how the molecular basis of phenotypic variation can shift in response to environment, and in turn impact the evolvability of Arctic charr ecomorphs. Therefore, the primary objective of this study was to examine the
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genetic architecture (quantitative trait loci, QTL) of craniofacial and body size traits in ecomorphs from Thingvallavatn and Vatnshlidarvatn reared under different diet treatments that induced changes in head shape. We performed genome scans with 133 microsatellite markers from 35 linkage groups on two full sib families from each of two ecomorphs in each lake (Thingvallavatn: LB, PL and Vatnshlidarvatn: VB, VS). We predicted (1) that evidence of divergent selection will be represented in the genome of different ecomorphs from the same lake through differences in the genetic architecture of craniofacial traits and body size. Within the context of evolvablity, we predicted (2) that more specialized ecomorphs, which are likely the product of stronger selection pressures, should show a reduction in the number of detectable QTL. This is because alleles important for increasing fitness are more likely to be fixed and no longer segregating in their respective subpopulations. Finally, we tested for the presence of QTL environment interactions by comparing QTL effects between full sibs reared on two diet treatments. We hypothesized that a substantial number of QTL for our traits would differ between rearing environments, thus altering the potential heritable variation
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exposed to selection. Because natural selection operates on expressed phenotypic variation we predicted that (4) novel rearing environments (i.e., limnetic diets for a benthic ecomorph) would expose “hidden” genetic variation through the detection of QTL.
MATERIALS AND METHODS
Phenotypic dataset In the fall of 2003, adult Arctic charr from lakes Thingvallavatn and Vatnshlidarvatn were collected and a male and female from the same ecomorph were crossed to produce a full‐sib family. Two full‐sibs families (different parents) were produced for each ecomorph and the progeny from each cross were split into two diet groups, one mimicking a benthic and the other a limnetic diet. Detailed description of collection and rearing of progeny on benthic and limnetic diets can be found in Parsons et al. (2010). A total of 730 progeny were available for genotyping. For the phenotypic analysis a reduced dataset of 563 progeny was available due to limitations of the bone staining process and
Table 1. Numbers progeny per family reared on benthic and limnetic diet treatments, linkage groups and loci analyzed in two sets of QTL analyses of six craniofacial traits and fork length in ecomorphs of Icelandic Arctic charr sampled from two Icelandic lakes (Vatnshildarvatn and Thingvallavatn) Each parent analyzed separately within families and diets Ecomorph Vatnshildarvatn Silver
Family
Diet
Ngenomic
NMultiQTL
No. LG
No. loci
NGridQTL
No. LG
No. loci
1
Limnetic Benthic Limnetic Benthic Limnetic Benthic Limnetic Benthic
45 45 46 46 46 46 46 45
39 42 42 46 43 41 39 35
34
84
23
63
36
88
34
87
22
59
36
90
38a 42 42 45a 43 40a 36a 29a
Limnetic Benthic Limnetic Benthic Limnetic Benthic Limnetic Benthic
48 33 46 57 45 45 45 46
23 30 45 40 37 34 27 0
36
74
53
56
35
82
31
67
16b 19
35b 52
33
63
20a 30 44a 39 37 32a 26a 0
0b
0b
2 Brown
1 2
Thingvallavatn Pelagic
1 2
Large benthic
Parents analyzed together within ecomorphs and diet treatments
1 2
a
For GridQTL an individual was excluded from the analysis if any of the traits had a missing value. Family 2 from the large benthic ecomorph (benthic treatment) was not included in the QTL analysis due to unavailability of phenotypic data. Thus a family specific map was used in the analysis resulting in fewer genetic markers than the merged map for the ecomorph. b
Ngenomic, number of progeny genotyped (total); NMultiQTL, number of progeny included in first approach of QTL analysis using the program MultiQTL (available at www.multiqtl.com); NGridQTL, number of progeny included in second approach of QTL analysis using the online software of GridQTL (www.gridqtl.org.uk).
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human error where specimens for only one family on the benthic diet were available for the LB ecomorph (Table 1). Two ecomorphs from Vatnshlidarvatn, the Silver (VS, N ¼ 169) and Brown (VB, N ¼ 158) and two ecomorphs from Thingvallavatn, Pelagic (PL, N ¼ 138) and large benthic (LB, N ¼ 98) were used. After rearing the fish for 160 days on different diets, experimental fish were anaesthetized and fixed in Bouin’s solution and stored in 75% ethanol. Heads underwent a clearing and staining protocol (see Küttner et al. 2013). Photographs were taken with a Nikon coolpix 4500 camera (Nikon Corp., Tokyo, Japan) with specimens lying on their left side. Eight homologous landmarks (Fig. 1) were collected from the digital photos using TPSdig2 (available at: http://life.bio.sunysb.edu/morph/) and coordinates superimposed in CoordGen6 (part of the Integrated Morphometrics Program [IMP] suite freely available at http:// www2.canisius.edu/sheets/morphsoft.html) using the generalized least squares Procrustes superimposition method which enables calculation of multivariate centroid size (the square root of the sum of squared distances in millimeters) between each landmark and a geometric central landmark, excluding landmark 1 due to variation in fixing of lower jaw position). Six linear measurements between pairs of landmarks were then calculated using tmorphgen6 from the IMP series (Fig. 1). The tip of the premaxilla to posterior end of the articular was used as measurement for upper jaw length because of challenges in identifying the dorsal end of the maxilla as it overlaps with the articular. Allometric effects were minimized for all traits using
Fig. 1. An example of a cleared and stained head of Silver ecomorph (VS) from Vatnshlidarvatn including the placement of the 8 homologous landmarks (1: anterior tip dentary, 2: pectoral girdle, postcleithrum/supracleithrum, 3: posterior end articular, 4: upper end joint articular/quadrate, 5: anterior tip premaxilla, 6: supraorbital#2/sphenotic, 7: pterotic, posterior point, connection to post‐ temporal, 8: pectoral fin proximal). Linear measurements taken between landmark pairs (UHL: LM2‐LM5 upper head length, LHL: LM1‐LM8 lower head length, HD: LM3‐lM6 head depth, LJL: LM1‐LM3 lower jaw length (ventral side of dentary and articular), UJL: LM3‐LM5 upper jaw length, AH: LM3‐LM4 articular height) are shown as white lines.
linear regressions against head centroid size with a common slope for the complete dataset and residuals used for all further analyses. Fork lengths for all fish (in millimeters) were available from a previous analysis of the same fish (Parsons et al. 2010).
Linkage and QTL analyses DNA was isolated from muscle tissue using a modified phenol‐ chloroform‐isoamyl alcohol method (Taggart et al. 1992) to perform whole genome scans (Moghadam et al. 2007) on progeny from all eight families. For this, genetic microsatellite marker selection was based on known linkage group assignments in mapping panels of North American (NA) Arctic charr (Woram et al. 2004) and aimed to cover all linkage groups. The low level of polymorphism in Icelandic Arctic charr necessitated that the parents be screened for the majority of the markers on the Arctic charr genetic linkage map (>500). The intense screening yielded 133 useable markers where the number of loci genotyped per ecomorph ranged from 63 to 90 (Table 1). The software package LINKMFEX (http://www.uoguelph. ca/rdanzman/software/) was used to analyze marker linkages to create sex‐specific linkage maps, which is necessary due to large differences in recombination rates between females and males (Woram et al. 2004). Markers were assigned to linkage groups using a threshold LOD score of 3.0, linkage order within linkage groups was established in MAPORD. Goodness‐of‐fit G tests were performed in SEGSORT to identify markers that show significant deviation (P < 0.05) from the expected 1:1 Mendelian expectation (segregation distortion). A Bonferroni corrected P‐value of 0.001 (0.05/number of linkage groups within respective family) was used to evaluate the significance of deviations from Mendelian expectations. Ten of 133 markers showed segregation distortion and each case was restricted to one family with one exception (BHMS417) where two families exhibited distortion (PL1: Omy38DU, BHMS417; PL2: OMM1238, Omi30TUF, Ots526NWFSC; LB1: BHMS417, LB2: OMM1228, BX080247; VB1: OMM5184, VB2: OkeSL, VS2: OMM5017). These were excluded from the final analysis. Phase‐corrected genotypes were generated with GENOVECTbatch prior to QTL analysis and phenotypic data was tested for normality using Shapiro–Wilk tests with PASW (Predictive Analytics SoftWare, IBM SPSS Statistics, New York, 2009). We took two approaches with the QTL analysis. First, QTL were located by interval analysis of the effects of one parent on a single trait within a family reared on a diet treatment. We used a single QTL per linkage group model using the software MultiQTL (available at www.multiqtl.com) where P‐values were generated with 1000 permutations of the phenotypic values against genetic data within families and proportion of phenotypic variation (PPV) were estimated. QTL with chromosome‐wide significance were identified by permutation tests within each linkage group. All QTL at or below the 0.01 threshold were defined as significant with chromosome‐wide effects. Significant
Küttner et al.
QTL were further tested for genome‐wide effects at the 0.05 level using a false discovery rate (FDR) test (Benjamini and Hochberg 1995). In those cases where copies of duplicated markers could not be assigned to one of the two homeologous linkage group arms (i.e., linkage group arms arising from the most recent whole genome duplication in the salmonid ancestor), both linkage groups connected with an underscore are indicated as the location of the QTL (e.g., AC‐13_34 indicates a duplicated marker localized to the homeologous linkage groups AC‐13 and AC‐34). We then used a more conservative analysis where genetic and phenotypic data (all traits) of all parents within a ecomorph (by diet treatment) were analyzed simultaneously to test for QTL effects at the ecomorph level (Table 1). The GridQTL online software (http://www.gridqtl.org.uk/) utilizing the sib‐ pair analysis with all traits for both chromosome‐wide and experiment‐wide with 1000 permutations was used to detect QTL at the 0.01 significance levels. For this analysis, it was necessary to merge the linkage maps from each of the four parents into a single ecomorph‐specific map. Given the limited number of informative genetic markers in the parents (many hundreds of markers had been screened) and that parents differed in the identity of informative loci, many linkage groups from the genetic map were represented by multiple fragments. We trimmed the number of linkage groups (fragments) because of the capacity of the software and the need to know with certainty which copies of duplicated markers were homologous across parents. Thus, we eliminated linkage group fragments that were composed only of duplicated markers (often in unique combinations across parents) and others that showed homeologous affinities to these (AC‐1, ‐3, ‐10, ‐12, ‐13, ‐15, ‐20, ‐21, ‐24, ‐27, ‐34, ‐36, ‐43). Linkage group AC‐18 was removed from all analyses due to merging issues. Finally, three unlinked markers unique to the PL ecomorph were not included in the analysis.
Results Thirty‐five linkage groups could be identified and their marker arrangements (markers linked within linkage groups) were consistent with studies on cultured Icelandic charr (Küttner et al. 2011). Multiple QTL for craniofacial traits and fork length with chromosome‐wide effects were detected when each parent was analyzed separately (Tables 2 and 3; Fig. 2). No QTL with genome‐wide effects were detected for craniofacial traits, but one genome‐wide QTL for fork length on AC‐27 was detected in the LB2 family. Full‐sibs reared on different diets did not show consistent QTL effects. The number of linkage groups with QTL for a given trait ranged from 6 for upper jaw length (UJL) to 12 for lower head length (LHL) (Fig. 2). The PPV values were relatively high and ranged on average from 19.1% for UHL and 24.1% for AH (Table 2). The largest PPV value was 44% for AH in the PL1 female on AC‐18. The total numbers of linkage
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groups with QTL for all craniofacial traits counted together across diets and parents for each ecomorph were: VS‐23, VB‐11, PL‐19, and LB‐12 (Table 3). The count for LB is an underestimation given that that samples for the second family reared on the benthic diet treatment were unavailable. There was very limited overlap in QTL location for traits across parents: HD on AC‐1, LJL on AC‐22 and AH on AC‐18 (Table 2). There was only one case where QTL effects were detected in both progeny groups from a single parent reared on the two diet treatments (the male parent of family VS2 for UHL). Six QTL with experiment‐wide effects and six with chromosome‐wide effects were detected when parents from a particular ecomorph (separated by diet treatment) were analyzed together (Table 4). The experiment‐wide effects were detected for all craniofacial traits except for LJL where a QTL with chromosome‐wide effects was detected. The QTL were distributed across ecomorphs and traits with the LB ecomorph reared on the limnetic diet having detectable QTL for LHL, HD, UJL, LJL and AH. This finding mirrored the single parent analysis where a greater number of QTL were detected in the Thingvallavatn LB ecomorph reared on the less familiar diet. A single QTL with experiment‐wide effects was detected for fork length in the VS ecomorph reared on the limnetic diet.
DISCUSSION Our findings support the prediction that divergent natural selection on craniofacial morphology has altered the genetic architecture of these traits in Arctic charr ecomorphs. For example, ecomorphs differed in the average number of linkage groups with QTL for craniofacial traits. Notably, the ecomorphs hypothesized to be more ecologically specialized within their lake (VB and LB) had fewer linkage groups with QTL suggesting that divergent selection has fixed genetic variation in genomic regions related to craniofacial shape (Table 3). More specifically, the VB ecomorph had approximately half as many “significant” QTL as its sympatric counterpart VS with a similar trend between the LB and PL ecomorphs based on the analysis of individual traits and parents. This finding is in line with results from Parsons et al. (2010) that found reduced phenotypic variation for body shape over ontogeny in the more ecologically specialized LB and VB ecomorphs. Further, at the genetic level this suggestion is also in line with recent findings from a population genetic survey in Thingvallavatn that documented a reduced level of heterozygosity in the LB ecomorph (Kapralova et al. 2011), which in turn matches the reduced number of polymorphisms detected in the current genome scans of LB families (Küttner 2011). While this reduced phenotypic and genetic variation may limit the power to detect QTL it also suggests that less variation is currently available for selection. This pattern of differences in the number of QTL was not however detectable with the combined analysis across traits and
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Table 2. The location of significant QTL for six craniofacial traits and fork length in eight families produced with four ecomorphs of Icelandic Arctic charr from two Icelandic lakes and reared on two diet treatments LG
Parent
Upper head length (UHL) 4 VS2M 13 PL2F 14 VS1F 16 VB2M 18 VS1M 20 VS1M 26 VS2M VS2M 3_24 VS1F 20_43 VS2M Lower head length (LHL) 10 PL2M 12 VB2M 14 VB2F 20 PL1F 21 VS2M 22 PL1M 23 VB2F 28 PL2M 31 PL1F 37 LB1M 39 VS2M 10_15 VB2M 20_36 VB2M VS2F Head depth (HD) 1 PL1F VB1F 4 LB2M 18 VS2F 22 LB1F 27 VS1M 28 PL2F 37 LB1M 39 VS2F 3_24 PL1F Upper jaw length (UJL) 9 LB2M 13 PL1M 21 VS1F 6_12 VS1F 10_15 PL2F 20_43 VB2M Lower jaw length (LJL) 1 LB1F 11 VS2F 12 PL1F 13 PL1M 14 PL1M 15 LB1M 21 LB1F 22 PL1M LB1F
Diet
Marker/interval
P‐value
PPV
Lim Lim Ben Ben Lim Ben Ben Lim Lim Lim
Omy6DIAS Ssa85DU Sco19UBC/BHMS238 OMM5091/BHMS417 BX319197 OMM1274 SalD25SFU OMM1302 SalF41SFU OMM5184
0.005 0.006 0.008 0.001 0.005 0.006 0.004 0.003 0.005 0.007
0.18 0.15 0.28 0.27 0.15 0.18 0.18 0.17 0.15 0.20
Ben Lim Ben Lim Lim Lim Ben Ben Ben Lim Ben Ben Lim Lim
OMM1237/Omi187TUF OMM1236/OMM1345 Sco19UBC/One11ASC BX890355/OMM5184 SmaBFRO1/Omy21DU OkeSL BX873441 Ssa0033BSFU OMM1290 Omy1090UW Omi30TUF OMM1237/Omi187TUF CNE805‐786
0.007 0.001 0.002 0.001 0.007 0.003 0.002 0.002 0.007 0.007 0.004 0.006 0.004 0.007
0.14 0.21 0.20 0.20 0.18 0.17 0.21 0.16 0.25 0.20 0.17 0.21 0.18 0.22
Ben Ben Lim Lim Lim Lim Ben Ben Ben Lim
OMM1300 OMM1228 OMM1238 OkeSL Ogo4UW/CA383830 BHMS331 Omy1090UW Omi30TUF OMM1318
0.001 0.005 0.005 0.009 0.001 0.003 0.002 0.004 0.005 0.004
0.31 0.20 0.25 0.14 0.22 0.19 0.20 0.16 0.15 0.21
Lim Ben Ben Lim Ben Ben
OMM3067 Ssa85DU SmaBFRO1/OMM1302 Ots501 OMM1237 Sfo23LAV
0.004 0.001 0.008 0.006 0.005 0.001
0.31 0.29 0.22 0.21 0.16 0.21
Lim Lim Lim Ben Ben Ben Lim Lim Lim
OMM1300 SalF56SFU OMM1236 Ssa85DU Sco19UBC OmyRGT2TUF Omi70TUF OkeSL
0.001 0.001 0.008 0.001 0.009 0.003 0.007 0.005 0.001
0.22 0.17 0.17 0.30 0.29 0.17 0.14 0.17 0.21
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Table 2. (Continued) LG
26 32 43 20_36 20_43 Articular height (AH) 7 9 13 14 18 21 26 10_15 Fork length (FL) 1 4 8 11 13 18 20 22 27 30 32
Parent
Diet
Marker/interval
P‐value
PPV
VS1F VS2F PL1F PL2M VS1M VS2F VS2M
Lim Ben Ben Ben Ben Lim Ben
0.005 0.008 0.005 0.009 0.007 0.001 0.001
0.21 0.16 0.21 0.14 0.19 0.17 0.25
LB2M VS1F VS2F PL2F PL1F LB2F VB2M VB2M VB2M
Lim Lim Lim Ben Ben Lim Lim Ben Ben
BX302949 BX080247 OMM1194/Ssa85DU Omi127TUF BX319197 Omy27DY/SmaBFRO1 SalD25SFU OMM1237/Omi187TUF
0.001 0.002 0.009 0.005 0.004 0.006 0.005 0.009 0.005
0.24 0.40 0.20 0.16 0.44 0.16 0.17 0.19 0.21
VS2M PL1F VB2F VS2F LB1M VB2F VS2F PL1M PL2M LB2M PL1F VS1F VS2F
Ben Ben Ben Ben Lim Lim Lim Lim Lim Lim Lim Lim Lim
OMM1330 OMM1228 OMM1556 SalF56SFU Ssa85DU/OMM1174 OmyJTUF/BX870052 OmyRGT46TUF BX890355 OkeSL/Ssa0080BSFU Ogo4UW BHMS429 OMM1178/OMM1329 OMM1178/OMM1329
0.009 0.003 0.008 0.008 0.005 0.006 0.001 0.001 0.005 0.001q 0.001 0.007 0.005
0.15 0.31 0.17 0.19 0.29 0.21 0.24 0.24 0.22 0.37 0.31 0.15 0.17
Ssa0080SFU OMM1804 OMM1329 OMM5008 SalF56SFU OMM1379
QTL were detected with interval analysis within single families analyzed separately by parent. The proportion of phenotypic variance is given in the last column (PPV) and the QTL with experiment—wide significance are marked with.q (PL‐pelagic ecomorph from Thingvallavatn, LB, large benthic ecomorph from Thingvallavatn; VS, silver ecomorph from Vatnshlidarvatn, VB, brown ecomorph from Vatnshlidarvatn; Ben, groups raised on benthic diet treatment; Lim, groups raised on limnetic diet treatment; F‐female parent, M ‐male parent).
parents. Therefore, we suggest that our findings are preliminary and will require a higher number of families and a greater density of genetic markers to be conclusive. Nonetheless, our results from two independent populations suggest that within divergent populations more ecologically specialized ecomorphs exhibit fewer QTL.
Dissecting the evolutionary potential of craniofacial and body size traits The potential for further evolution of craniofacial morphology for charr ecomorphs will depend in large part on the degree of heritable variation available. As discussed above our findings suggest that the evolutionary potential for more ecologically specialized ecomorphs may be limited relative to more generalist ecomorphs. However, complex traits such as craniofacial shape are known to depend on a multitude of genes (Albertson
et al. 2003a, b; Parsons and Albertson 2009) which may cause greater difficulty for achieving allelic fixation within a subpopulation. This is because some alleles may present tradeoffs in fitness through epistatic interactions, antagonistic pleiotropy, or simply through the use multiple alternative developmental pathways that contribute toward phenotypes that increase fitness. Therefore, it is not surprising that our findings suggest that multiple loci underlie these traits in charr ecomorphs. Consistent with other studies QTL for body size were widely distributed across all linkage groups indicating the involvement of many genes. While only one trait for body size was examined (fork length) significant QTL for this trait were spread across eleven linkage groups (Table 2). This observation is in agreement with earlier QTL studies on body size traits like fork length and body weight in salmonids (Moghadam et al. 2007; Wringe et al. 2010; Küttner et al. 2011). This
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Table 3. The number of linkage groups with significant QTL for six craniofacial traits in four ecomorphs of Icelandic Arctic charr from Thingvallavatn and Vatnshlidarvatn raised on two diets Ecomorph Vatnshlidarvan Silver
Brown
Thingvallavatn Pelagic
Large benthic
Diet
UHL
LHL
HD
UJL
LJL
AH
Total
Benthic Limnetic Shared Benthic Limnetic Shared
3 5 1 1 0 0
1 2 0 3 2 0
1 2 0 1 0 0
1 1 0 1 0 0
2 3 1 0 0 0
0 2 0 2 1 0
8 15 2 8 3 0
Benthic Limnetic Shared Benthic Limnetic Shared
0 1 0 0 0 0
3 2 0 0 1 (1) 0
2 1 0 1 2 (1) 0
2 0 0 0 1 (0) 0
4 2 0 1 3 (3) 0
2 0 0 0 2 (0) 0
13 6 0 2 9 (5) 0
The number of linkage groups with QTL shared between the two diet groups within an ecomorph is also indicated. QTL were detected with interval analysis in individual analysis of 8 families (2 per ecomorph) by parent (UHL, upper head length; LHL, lower head length; HD, head depth; UJL, upper jaw length; LJL, lower jaw length; AH, articular height). Only one family available. Data for the single family reared under both diet treatments in parentheses.
finding coupled with moderate to large effect sizes for loci suggests that there is significant genetic variation and future evolutionary potential for this trait in both lake populations. Notably, some QTL appear to be consistent at a range of biological scales. For example, a comparison of fork length QTL based on all four parents per ecomorph from Thingvallavatn and Vatnshlidarvatn to the equivalent analysis with the cultured Icelandic Arctic charr (Küttner et al. 2011), implicates linkage group AC‐20, but with no overlap from other linkage groups (Table 3). Similarly, a QTL study on growth traits in Arctic charr originating from the Fraser strain in Canada (Moghadam et al. 2007) also detected “suggestive” QTL on linkage group AC‐20 for body weight and condition factor. At the species level the genetic markers associated with growth traits (OMM5184, OMM1274) in Icelandic and Canadian charr have also been implicated as markers of QTL for similar traits in rainbow trout (Oncorhynchus mykiss) and Atlantic Salmon (Salmo salar) (Reid et al. 2005; Wringe et al. 2010) as noted before (Küttner et al. 2011). These observations suggest that AC‐20 harbours one or multiple QTL for growth traits that are conserved across populations and even related species. Thus, macro and microevolution may involve the same mechanisms as the evolution of size differences within and among ecomorphs may be connected at the genomic level to size variation among salmonid species. The loci involved with craniofacial traits in charr may be especially visible to natural selection. This is suggested by the relatively large effect sizes for craniofacial trait QTL (average of single parent analysis about 20% PPV) than what has previously been found for life history and growth related traits (Moghadam
et al. 2007; Wringe et al. 2010; Küttner et al. 2011) in salmonids (i.e., most QTL explain less than 10% of variation, only 10% have effect sizes greater than 15%). At the more extreme end, studies on craniofacial morphology in teleosts (Albertson et al. 2005) have detected multiple QTL with even higher effect sizes (e.g. 40% þ). This is likely due to an experimental design involving an F2 generation of backcrosses that has increased power for detecting QTL. Thus, given our experimental design of intramorph crosses it is notable to find such large effect sizes. This may suggest a propensity for craniofacial variation to evolve rapidly relative to other traits. However, an alternative explanation for the large effect sizes detected in our study is that they are overestimated. This has been predicted for QTL studies with smaller samples sizes, which raise the detection limit (Otto and Jones 2000; Mackay et al. 2009). This explanation is supported by the observation that the effect sizes for fork length QTL in the current study are higher than those detected in two other studies with the same species and trait but based on larger sample sizes (Moghadam et al. 2007; Küttner et al. 2011). Moreover, a study of fork length in Atlantic salmon (Salmo salar) (Reid et al. 2005) with comparable sample sizes to those in the present study reported QTL with phenotypic effects in a similar range to what we found here.
Altering evolutionary potential through the environment: QTL interact with diet Because natural selection operates directly on phenotypic variation we examined whether the genetic variation used to
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Fig. 2. The linkage groups with significant QTL for six craniofacial traits and fork length in families from four ecomorphs of Icelandic Arctic charr from Thingvallavatn and Vatnshlidarvatn (PL‐pelagic ecomorph from Thingvallavatn, LB‐ large benthic ecomorph from Thingvallavatn, VS, silver ecomorph from Vatnshlidarvatn; VB, brown ecomorph from Vatnshlidarvatn) raised on benthic and limnetic diet treatments.
produce such phenotypic variation could change in response to ecologically relevant diet treatments. In accordance with our prediction there was a little consistency between diet treatments in terms of the location of QTL suggesting that different regions of the genome could be subjected to selection through the phenotypic response to environment. Further, all but two QTL were not present in both diet treatments for a given ecomorph. This suggests that strong diet QTL interactions are present in charr ecomorphs, such as has previously reported in lab model organisms (e.g. Cheverud et al. 2004; Gordon et al. 2008). However, in our case the environmental treatments mimic natural benthic and limnetic diets, which are thought to drive adaptive divergence in charr and several other species of postglacial fishes (Robinson and Parsons 2002). These findings also suggest that some environments can provide relaxed selection, which allows for the accumulation of hidden variation to evolve (Kawecki 1994; Snell‐Rood et al. 2010; Van Dyken and Wade 2010). Therefore, our results strongly suggest that the
genetic changes observed in divergent populations of these species could in large part be enabled by variation both exposed to and hidden from selection through environmentally determined phenotypes.
CONCLUSIONS This study provides evidence that selection on craniofacial and body size traits has played an important role in producing genetic differences among ecomorphs of Arctic charr. We observed only low levels of consistency between ecomorphs regarding their genetic architecture. Further, we found fewer linkage groups harbouring QTL for craniofacial traits in the ecomorphs hypothesized to be more ecologically specialized (VB and LB, based on the individual parents analysis) which suggests they have undergone stronger selection leading to a greater degree of allelic fixation. However, we also provided
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Table 4. The location of significant (chromosome‐wide) QTL for six craniofacial traits and fork length in four ecomorphs of Icelandic Arctic charr from two Icelandic lakes reared on two diet treatments based on a combined interval analysis of families within a ecomorph reared on a particular diet LG
Parent
Diet
Marker/interval
P‐value
Upper head length (UHL) 16 26
VB VS
Ben Ben
BHMS417/OMM5091 SalD25SFU
0.01 0.01
Lower head length (LHL) 37‐3 8‐1 6 or 23‐2
LB LB VB
Lim Ben Ben
Omy1090UW OMM1556 BX873441/i
0.01 0.01 0.01
Head depth (HD) 22
LB
Lim
OkeSL
0.01
Upper jaw length (UJL) 22 6 or 12 or 23
LB VS
Lim Lim
OkeSL Ots501NWFSC/i
0.01 0.01
Lower jaw length (LJL) 22
LB
Lim
OkeSL
0.01
Articular height (AH) 7‐2 9 19‐1
LB VS PL
Lim Lim Ben
BX0302949 BX080247/OMM1374 CA350064
0.01 0.01 0.01
Fork length (FL) 32
VS
Lim
OMMM1178/OMM1329
0.01
QTL with experiment—wide significance are also indicated ( P < 0.05; P < 0.01) (PL, pelagic ecomorph from Thingvallavatn; LB, large benthic ecomorph from Thingvallavatn; VS, silver ecomorph from Vatnshlidarvatn; VB, brown ecomorph from Vatnshlidarvatn; Ben, groups raised on benthic diet; Lim, groups raised on limnetic diet.
evidence that the selection that has caused this divergence, and the future evolutionary potential of charr ecomorphs is influenced by environmental conditions. Little overlap in QTL occurred between our ecologically relevant diet treatments. To our knowledge this also provides some of the first insights into the allelic basis of plasticity in an adaptive divergence. Among the most compelling aspects of these findings is that in some cases a novel diet treatment can elicit a greater number of QTL, suggesting that “hidden” genetic variation evolves in conditions where selection cannot “see” phenotypic variation due to developmental responses in surrounding conditions. This perhaps sheds light on a major paradox in evolution‐ how is variation present when selection is persistently removing it? Our findings suggest that a solution resides in the ability of developmental plasticity to “hide” variation, with its potential release occurring with the invasion of new environments. Acknowledgments This research was supported by the Natural Sciences and Engineering Research Council of Canada (RGD, MMF), Hólar University College (SK), a Madame Vigdis Finnbogadottir Scholarship (EK) and Ontario Graduate Scholarships (KJP). We thank Heather Fotherby for her outstanding technical support and Bjarni Kristófer Kristjánsson for assistance in the field.
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