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Copyright 2008 by the Genetics Society of AmericaDOI: 10.1534/genetics.107.084251

The Genetic Basis of Smoltification-Related Traits in Oncorhynchus mykiss

Krista M. Nichols,*,,,1 Alicia Felip Edo,*,2 Paul A. Wheeler* and Gary H. Thorgaard*

*School of Biological Sciences and Center for Reproductive Biology, Washington State University, Pullman, Washington 99164, NationalOceanic and Atmospheric Administration, National Marine Fisheries Service, Northwest Fisheries Science Center, Conservation

Biology Division, Seattle, Washington 98112 and Departments of Biological Sciences and Forestry and Natural Resources,

Purdue University, West Lafayette, Indiana 47907Manuscript received November 6, 2007Accepted for publication May 1, 2008

ABS TRACT

The timing and propensity for migration between fresh- and seawater is a key theme in the diversity oflife histories within the salmonid fishes. Across salmonid species, life-history strategies range from whollyfreshwater-resident populations, to migratory and nonmigratory variation within populations, topopulations and species that are primarily migratory. Despite the central theme of migration to theevolution of these fishes, the genetic architecture of migration-related processes is poorly understood.Using a genetic cross of clonal lines derived from migratory and nonmigratory life-history types ofOnchorhynchus mykiss(steelhead and rainbow trout, respectively), we have dissected the genetic architec-ture of the complex physiological and morphological transformation that occurs immediately prior to

seaward migration (termed smoltification). Quantitative trait loci (QTL) analyses were used to identify thenumber, effects, and genomic location of loci associated with smoltification-related traits, including growthand condition factor, body coloration, morphology, and osmoregulatory enzymes during the smoltificationperiod. Genetic analyses revealed numerous QTL, but one locus in particular is associated with multipletraits in single and joint analyses. Dissecting the genetic architecture of this highly complex trait hasprofound implications for understanding the genetic and evolutionary basis of life-history diversity withinand among migratory fishes.

MIGRATION is a pervasive theme in the diversifi-cation and evolution of animals. Migration hasbeen described as a syndrome or a complex suite ofintegrated physiological, morphological, and behav-ioral traits that together with environment promotesthe drive to move over long distances (Dingle 2006).

Across all animal taxa, proximate mechanisms or featuressuch as energetic and morphological changes are intri-cately tied to the ultimate decision to migrate (Dingle2006). The genetic architecture of migration and the evo-lutionary forces that have shaped polyphenism in migra-tion and residency have not been elucidated for anyspecies. In this study, we describe the genetic architectureof proximate physiological and morphological traits asso-ciated with migrationvs.residency inOncorhynchus mykiss,a species that consists of both migratory (steelhead trout)

and nonmigratory (rainbow trout) life-history types.

Migratory or anadromous salmonids rear in fresh-water, migrate to the ocean as juveniles, and return tofreshwater to spawn (Groot andMargolis 1991). Priorto seaward migration, juvenile salmonids transformfrom dark-colored parr, adapted to life in a freshwaterstream environment, to silvery smolts, physiologicallyand morphologically adapted to life in the ocean (Hoar1976). This physiological transformation, called smolti-fication, is regulated by environmental cues and com-plex physiological processes and ultimately culminatesin downstream migration (FolmarandDickhoff 1980;Hoar1988). Smoltification is generally characterizedby an increase in lipid metabolism and protein synthe-sis, increased growth in length relative to weight, andincreased hypo-osmoregulatory ability relative to pre-migratory and nonmigratory individuals (Folmarand

Dickhoff 1980; Hoar 1988; Dickhoff et al. 1997).These processes are modulated by numerous, integratedendocrine changes involving at least three different hor-mone axes, including the growth hormone, thyroidhormone, and corticosteroid hormone axes (Folmarand Dickhoff 1980; Hoar1988; Dickhoff et al.1997).Increased protein synthesis is associated with the depo-sition of the metabolic by-products guanine and hypo-xanthine in the skin and scales of smolts, giving smolts asilvery appearance (Folmarand Dickhoff1980; Hoar1988). An increase in growth rate in length relative to

Sequence data from this article have been deposited with the EMBL/GenBank Data Libraries under accession nos. EU084717, EU084718,EU084727, EU084728, EU084733, EU084734, EU084737, EU084738,and EU084741EU084744.

1Corresponding author:Departments of Biological Sciences, 915 W. StateSt., Purdue University, West Lafayette, IN 47907.E-mail: [email protected]

2Present address:Instituto de Acuicultura de Torre de la Sal (IATS),Consejo Superior de Investigaciones Cientficas (CSIC), 12595 Castellon,Spain.

Genetics179: 15591575 ( July 2008)

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weight has profound influences on the body morphol-ogy of smolts (Beemanet al.1994, 1995; Gorbmanet al.1982), rendering the fish more streamlined for a migra-tory life history. An increase in hypo-osmoregulatoryability is most notably associated with biochemical andmorphological changes in the gill and intestines, lead-ing to an increased expression of Na1-K1ATPase duringthe transformation (Folmarand Dickhoff 1980; Hoar

1988).These rapid growth, metabolic, morphological, andbehavioral changes in smolts do not occur at the samemagnitude in nonmigrant fish. The decision to undergosmoltification and migrate is tightly associated withgrowth trajectories. Physiological thresholds of bodysize and growth are often discussed as a mechanismpromoting divergence in alternative migratory andnonmigratory life-history trajectories (Thorpe 1994;Dickhoff et al. 1997; Thorpe and Metcalfe 1998;Thorpe et al. 1998). In freshwater, sexual maturationprecludes seawardmigration (Foote et al. 1994; Thorpe1994; Thorpeand Metcalfe1998). Thorpe and others

(Thorpe 1994; Thorpeand Metcalfe 1998; Thorpeet al. 1998) have hypothesized that smoltification is adevelopmental decision made in individuals that havefailed to sexually mature in freshwater. Indeed, a nega-tive genetic correlation or genetic trade-off betweensexual maturation and smoltification has been observed(Throweret al. 2004). Although the physiology and theendocrinology of smoltification have been well studied,the ultimate genetic and molecular regulatory mecha-nisms responsible for alternative migratory and residentlife-history types and the genetic trade-offs betweenthese divergent life-history decisions are poorly un-derstood. In this study, we test the hypothesis that thesemultiple physiological and morphological changes asso-ciated with smoltification are triggered by one or fewmajor genome regions or that one or few major generegions work to integrate the complex physiological andmorphological changes occurring during smoltification.

The origin of anadromy in salmonid fishes has longbeen debated (McDowall 1997). Some suggest thatanadromy is a derived character arising from whollyfreshwater ancestors (Hoar1976; Stearley 1992). Thebasis for this theory was originally predicated upon thebroad freshwater distribution of salmonid species, par-allels among species in the physiological basis of sea-

water adaptation, and the fact that all species spawn andspend at least part of their life cycle in freshwater(Tchenavin 1939; Quinnand Myers 2004). Phyloge-netic relationships among salmonid species constructedfrom morphological data are in agreement with thedegree of anadromy. Historically, the mapping ofmigratory life-history type upon a coarse, morphology-derived phylogenetic tree of salmonids showed a grada-tion in anadromyfrom optionally anadromous (speciesconsisting of either freshwater or anadromous forms) tothe most derived obligately anadromous (species con-

sisting exclusively of anadromous forms) (Hoar1976;Oakley andPhillips 1999). However, more recent mo-lecular Salmonidae and Salmonine phylogenies suggestmultiple origins of anadromy when mapped to thesetrees (Stearley 1992; Stearley and Smith 1993;Oakleyand Phillips 1999). On a finer level, observa-tions within species suggest plasticity in the trait andmultiple parallel accounts of both the gain and the loss

of anadromy in both natural and introduced popula-tions (Quinn et al. 2000, 2001; Unwin et al. 2000;Pascualet al.2001; Stockwell et al.2003; Riva-Rossiet al. 2007). While many of the above studies support thetheory that timing of and propensity for anadromy isa derived character, having evolved from a freshwaterancestor, alternative views suggest that anadromy is an-cestral and that extant salmonids evolved from a marineancestor (McDowall1997).

The inheritance of nonmigratory and migratory life-history types and associated traits has been examined inseveral salmonid species (Refstie et al. 1977; Clarkeet al.1992, 1994; Footeet al.1992; Johnssonet al.1994;

Thrower et al. 2004; Duston et al. 2005). Althoughplasticity exists within and across populations (Quinnand Myers 2004; McPhee et al. 2007), inheritancestudies suggest that both timing of and propensity forsmoltification are also under genetic control. In crossesbetween life-history variants of Chinook salmon thatdiffer in smoltification timing, Clarke et al. (1994)found that early smolting (smolting during the first yearof life, also called ocean type) was dominant andapparently controlled by few genetic loci; the authorsused body morphology and coloration to discriminatebetween dark, stream-dwelling individuals and silvery,premigratory smolts to determine if Chinook salmon intheir study were smolting in their first or second year oflife. In species for which both freshwater resident andmigratory forms exist, studies evaluating smoltificationin intercrosses between resident and anadromous formsindicate that there is also a genetic basis for thepropensityto undergo smoltification (Footeet al.1992; Johnssonet al.1994). In these studies, hybrid progeny were madefrom crosses between anadromous sockeye salmon andfreshwater resident kokanee (O. nerka) (Foote et al.1992) and between anadromous steelhead and freshwa-ter resident rainbow trout (O. mykiss) (Johnsson et al.1994); in both studies, hybrids were intermediate be-

tween freshwater and migratory forms in their ability tohypo-osmoregulate in seawater, suggesting that pro-pensity for smoltification is under additive rather thandominant genetic control in these species.

To dissect the genetic basis of alternative migratoryand nonmigratory life-history types, we have conducteda comprehensive quantitative trait loci (QTL) analysisof smoltification-related traits inO. mykiss(rainbow andsteelhead trout). We evaluate the joint segregation ofmolecular markers, ordered in the genome, with thesegregation of quantitative or polygenic traits associated

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with smoltification in a cross between nonmigratoryrainbow trout and migratory steelhead trout lines. Wetest the hypotheses that multiple traits are controlled bya few loci of large effect and that multiple, correlatedtraits of smoltification colocalize to the same regions inthe genome.

MATERIALS AND METHODS

Genetic crosses: Genetic analyses of smoltification-relatedcharacters were conducted in doubled-haploid progeny pro-duced from a cross between the Oregon State University(OSU) rainbow trout and Clearwater (CW) steelhead troutclonal lines; clonal line origin and production is describedelsewhere (Nichols et al. 2007). Briefly, eggs from one OSUfemale were fertilized with sperm from one CW male toproduce a family of F1progeny. Sperm from the resulting F1male hybrid clones was used to produce doubled haploidprogeny by androgenesis, as described by Young et al.(1998).Doubled haploids were produced in October and reared for3 years at the Washington State University research hatchery.Embryos were maintained in recirculating stack egg incuba-

tors at a constant 11 until swim-up (complete yolk absorp-tion). At swim-up, fish were transferred to 5-gallon tanksmaintained in a recirculating system at similar densities. At 6months of age, all fish were transferred to a single 200-gallontank and were fed by automatic feeder, which was replenishedonce daily. Temperatures in the recirculating systems rangedfrom 11to 18throughout this study.

Genotyping: Genotypes of doubled-haploid progeny wereassessed at multiple loci, using amplified fragment lengthpolymorphic (AFLP), microsatellite, and known genemarkers. Dominant AFLP markers are unambiguous in thedoubled-haploid progeny, as all individuals are homozygous.Methods for AFLP genotyping are described elsewhere(Nicholset al.2007). Seventeen AFLP primer sets were used

with EcoRI- and MseI-digested genomic DNA. AFLP markers

are named according to EcoRI 1 3 selective bases, MseI 1 3selectivebases, andeither an arbitrary numberor base pair sizeof fragment, in that order. Microsatellite markers were chosenon the basis of known placement on existing rainbow troutmaps (Sakamotoet al.2000; Nicholset al.2003; Danzmannet al.2005; Guyomardet al.2006). At least one microsatellitelocus per chromosome waschosen to enable theidentificationof syntenic linkage groups among salmonid linkage maps.Potential candidate genes were chosen on the basis of theirputative involvement in the smoltification process, as part of aparallel study aimed to map genes whose products play a rolein the endocrine, osmoregulatory, and other physiologicalchanges that occur during smoltification. Primer sets formicrosatellite and known genes are provided in supplementalTable S1.

Phenotyping:Because smoltification is a temporal process,we chose to sample phenotypes over a time course fromFebruary through June 2003 over which the physiologicaltransformation or smoltification was anticipated to occur. In2003, fish were age 11, and all fish were sampled again inMarch 2004 at age 21 to determine if fish that had not gonethrough smoltification at 11 were doing so at age 21. Sincerepeated samples or data were taken from the same individ-uals, fish were sampled nonlethally and with limited invasive-ness to minimize handling stress effects. Data on phenotypes

were collected February 24, March 17, April 7, April 21, May 5,May 18, and June 9, 2003. At sampling, fish were anesthetized

with clove oil and phenotypic data were then collected.

Phenotypes collected over this time course included bodymorphology and skin reflectance from digital photographs,body size (length and weight), gill sodium-potassium ATPase(Na1/K1 ATPase), body condition factor, and instantaneousgrowth rate in length and weight.

Body morphology:The change in body morphology from amore deep-bodied, stream-dwelling fish to a more fusiformshape has been well documented in smolting salmonids(Gorbman et al. 1982; Beeman et al. 1994, 1995). We usedmorphometric shape analysis to quantify variability in body

shape among phenotypically divergent doubled-haploid prog-eny. From digital photographs, 13 landmarks along the leftlateral side of the fish were digitized for each time point(Figure 1). Two-dimensional coordinates for landmarks wereobtained by digitizing each point using the software tpsDig2(Rohlf2005a).

Skin reflectance:As smolting salmonids are readied for out-migration and adaptation to life in the sea, the metabolic by-products guanine and hypoxanthine are deposited in the skinand scales of smolts, rendering the fish silver in color andhighly reflective (Folmarand Dickhoff1980; Hoar1988).The concentration of skin guanine has been shown to behighly correlated with color or the reflectance from the skin(Haneretal. 1995). Subjective, qualitative assessments of bodysilvering have long been used to identify smolts, but more

recently, noninvasive measures of skin color or reflectancehave been developed to objectively quantify body silver coloror reflectance (Duston1995; Andoet al.2005). In this study,skin reflectance wasquantified by calculating theaverage pixelintensity within an area directly above the lateral line andbelow thedorsal fin (Figure 1). To do this, digital photographs

were taken in an enclosed box with constant, bright, diffuselight provided by four full-spectrum bulbs. Fish were placed ina shallow, V-shaped aquarium that was affixed with black and

white calibration tabs. Digital photographs were taken in thiscontrolled-light environment, using manual camera settings

with constant exposure settings for all photos. SigmaScanPro(SysStat Software) software was used to acquire average pixelintensity from this area. Briefly, intensity was calibrated foreach image, choosing a two-point calibration: one calibration

point on the black tab (scaled at 0) and one calibration pointon the white tab (scaled at 100). Using the trace tool, the areamentioned above was defined and calibrated average pixelintensity measured (on a scale from 0 to 100).

Gill Na1/K1 ATPase:During smoltification, salmonids expe-rience a spike in gill Na1/K1 ATPase, enabling hypo-osmo-regulation in seawater (Folmarand Dickhoff1980; Hoar1988; McCormickand Bradshaw2006). At sampling, threeto four gill filaments were clipped from each fish nonlethally,placed in 100 ml SEI buffer (150 mmsucrose, 10 mmEDTA,50 mm imidazole, pH 7.3) at 4, and then frozen within 4 hr at80 for storage. Na1/K1 ATPase was quantified in thesesamples according to McCormick(1993).

Body condition factor:Condition factor commonly decreasesin smolting salmonids relative to nonsmolts (Folmar and

Dickhoff1980; Hoar1988). Condition factor was quantifiedusing weight (W in grams) and standard length (L inmillimeters) with the formula (W/L3 3 100,000).

Growth rates: Growthrateswere calculated from both weightsand standard lengths for spring interval from February to June2003. Relative to nonsmolts, smolts experience a greaterincrease in growth in length during the spring of smolting(Folmar and Dickhoff 1980; Hoar 1988; Dickhoff et al.1997). Instantaneous growth rates were calculated using[ln(L2) ln(L1)]/[t2 t1] 3 100, where L1 and L2 arestandard lengths or weights at times 1 and 2 (t1and t2in days).

Binary smolt phenotype: From each sampling point, inaddition to the above quantitative data, individuals were

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scored as smolts (1) or nonsmolts (0) for QTL analyses. Thebinary smolt phenotype was assigned on the basis of the digitalphotographs taken for reflectance and morphology. Individ-uals were categorized as smolts if they were streamlined,reflective, silvery individuals characteristic of this life-historytype. Individuals with distinctive parr marks along the lateralline through the duration of the study are typical of stream-dwelling parr and were categorized as nonsmolts. Individualsthat were more deeply bodied, yellow, and spotted in color aremore characteristic of larger freshwater-dwelling rainbow

trout and were also categorized as nonsmolts. Fish that couldnotbe reliably categorized were scoredas missing or unknown.

Sex phenotype:The phenotypic sex of the fish surviving toMarch 2004 was determined by internal examination ofgonads. Fish were euthanized with a lethal dose of anesthetic(clove oil) prior to dissection. For summary statistical analyses,males were coded as 1 and females as 0.

Statistical analyses:Body morphology, thin plate spline analysis:Morphometric shape analysis was conducted using a thin platespline analysis, using tpsRelw software (Rohlf2005b). Land-marks from all fish from all time points were superimposed tocalculate the generalized least-squares Procrustes average orconsensus shape, eliminating size differences (Rohlf andSlice1990). From the consensus shape, each individual sam-pled at each time point was evaluated for deformation from

the consensus shape, and variation captured in partial warpscores (Rohlf and Bookstein2003). The covariance matrixof partial warp scores for all individuals at all time points wasused in a principal-components analysis to distill partial warpscores into fewer orthogonal dimensions describing variationin shape (Bookstein1991). Principal components of partial

warp scores are called relative warps, and these relative warpswere taken as metrics for shape for use in QTL analyses.

All traits, summary statistics: Traits were organized over thetime course of sampling. With longitudinal sampling of eachindividual, one couldconceivably conduct genotypephenotypeanalyses of the trajectories of individual traits using fitting oforthogonal polynomials (Yang et al.2006). However, becausethese methods are not readily implemented for intervalmapping in non-growth-related traits, we chose to use metrics

that were informative on whether or not thefish were smoltingduring the time course of the spring, based upon our bio-logical knowledge of the physiological and morphologicalchanges that occur during smoltification. Using time-coursedata, we chose to use (1) the minimum condition factor, sincesmolts have lower condition factors than nonsmolts; (2) themaximum and empirical range of skin reflectance values,since smolts will have larger values and may change mostdramatically during smoltification; (3) the maximum Na1/K1ATPase value, since smolts achieve a higher peak ATPaseduring smoltification, returning to baseline levels if they donot migrate to the ocean; (4) maximum values of relative

warps obtained from thin plate spline analyses (along sixorthogonal axes); and (5) spring growth rates (taken as thegrowth from February to June) in length and weight. Individ-

uals with incomplete time-course data were given missing datavalues for QTL analyses. Using the metrics chosen for eachtrait above, data were tested for outliers and assumptions ofnormality and homogeneity of variance prior to QTL analyses.Data that were nonnormal in distribution were transformed toachieve normality. Correlations between trait values used inQTL analyses were conducted using Pearsons correlations inthe case where data were normal and Spearmans correlationsin the case where data were not normally distributed. One-wayanalysis of variance was conducted (PROC GLM; SAS Statisti-cal Software, Cary, NC) to examine whether means for con-tinuous traits were significantly different between the sexesand life-history categories. The model used to test the null

hypothesis that the means for each trait (y) were the same formales and females (mm mf) wasyij m1 sexi1 eij, whereyijis the phenotype of individualjof sexi. For tests of whethercontinuous-trait means were the same for the binary life-history category (LHbin), or for smolts and nonsmolts(ms mns), themodelyij m1 LHbini1 eijwas used, whereyijis the continuous-trait phenotype of individualjwith LHbincategoryi. Finally, to test whether continuous-trait means weresignificantly different among the life-history categories (LHcat),smolt, rainbow, parr, and unknowns (ms mR mP mu), the

model yij m1 LHcati1 eij was evaluated. When overallmodels were significant, TukeyKramer tests were used todetermine which pairs of means were significantly different. Inall tests, statistical significance was determined with a type I errorrate ofa 0.05.

Genetic analyses: Linkage map construction:Prior to linkagemap construction, markers were evaluated for segregationdistortion, or deviation from the expected 1:1 genotype ratioin doubled-haploid progeny. Markers showing significantdistortion were checked for reliability and genotyping errorsand were removed from the analysis if unreliable. Onlymarkers for which .80% of individuals were genotyped wereused in subsequent analyses. The genetic linkage map wasconstructed using Mapmaker/EXP (Lander et al. 1987) asdescribed by Nichols et al.(2007).

QTL analyses: Quantitative trait loci analyses were con-ducted using interval and composite-interval mapping (Zeng1994) in R/qtl (Broman et al. 2003); multiple-trait intervalmapping (Kaoet al. 1999) was conducted using QTL Cartog-rapher (Basten et al.1994, 2004). In all analyses, permutationtests (Churchill and Doerge 1994; Doerge and Churchill1996) were performed (n 1000 permutations for each test)to determine the threshold for log of the odds (LOD) orlikelihood-ratio (LR) scores, using a type I error rate ofa0.05. To control the overall familywise error rate (FWER) inthe case of multiple-QTL analyses for single traits, the highest

value of the 95% LOD thresholds for all single-trait interval-mapping permutation tests was chosen as the cutoff forinclusion of QTL in the second step of composite-intervalmapping, which is outlined below. This is more conservative

than using false discovery rate (FDR) methods for simpleinterval mapping (Benjamini and Yekutieli2005). The issueof controlling for multiple tests for subsequent single-traitcomposite-interval mapping (CIM) becomes more difficult,since the models for each QTL scan in CIM are different foreach trait; in this case, choosing an overall FWER might be toostringent if chosen among models that range from theinclusion of one additional background cofactor to multiplebackground cofactors. In this case, significant QTL identifiedin step 2 below were those that exceeded the95% permutationthreshold for that trait.

Single-trait interval mapping:R/qtl does not include a modelfor doubled haploids; thus a backcross model coding for thedoubled haploids was used since models for both cross typesutilize two genotypic classes for tests of significant QTL effects.

In the first step, single QTL were identified with intervalmapping using the EM algorithm in R, scanning for QTL atmarker positions and every 2 cM in the genome. In this firstinterval-mapping step, the modelyij bo1b

*1

x*i 1 eij(model1) was used test the null hypotheses that QTL additive effectsare zero (b*

1 0), where yij is the phenotype for individualj

with marker genotype i; and x*i is the marker or calculatedQTL genotype (given flanking marker genotypes), where x*i0 for the OSU genotype and x*i 1 for the CW genotype. Inthe second step,ksignificant QTL, identified in the first step,

were used as cofactors in a search for additional QTL in thegenome, using the model yijk bo1b

*1x

*i 1Pk

i1bkxk1 eijk

(model 2),testing again for significant QTL effects (b*1 0)at


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every 2-cM position. All terms in model 2 are the same asdescribed above, andxk 0 for the OSU genotype andxk 1for the CW genotype at the k significant QTL positionsidentified in model 1. New QTL identified in model 2 wereadded to the set ofkQTL, and model 2 was tested again atevery 2-cM position in the genome until no new QTL wereidentified in the context of this multiple-QTL model. Two-locus epistasis was tested at all markers and at every 5-cMposition in the genome; here, epistasis was tested comparingthe likelihood of the model that includes an interaction

between two QTL positions (x1 and x2), yij bo1b1xi1 1b2xi21b3xi1xi21 eij(model 3), to the likelihood of the modelincluding only main effects (where b3 0). QTL positions

were refined with all significant terms (single and interactingQTL) included in the model, as described by Nicholset al.(2007). Briefly, refining the QTL position involved the use ofmodel 2 above, with the addition of significant interactingQTL from tests of model 3; refining the position of each QTL

was accomplished by iteratively testing the likelihood of eachQTL position within a chromosome in the context of themultiple-QTL model, until the most likely position of eachQTL remained unchanged. Once positionswere refined, QTLeffects and percentage of variation explained (PVE) wereestimated in the context of the multiple-QTL model, using adrop-one-term analysis as described by Nicholset al.(2007).

Two-LOD support intervals were estimated for each identifiedQTL and have been shown to be conservative estimatesof 95%confidence intervals for QTL position (Visscheret al.1996).

Multiple-trait interval mapping:Multiple-trait QTL mappingwas performed using interval mapping with a doubled-haploiddesign in QTL Cartographer (Basten et al. 2004). Multiple-trait analyses were performed (a)with alltraits used to quantifymigration-related physiology and morphology and (b) withtraits that colocalized to QTL in the genome. Multiple-traitpermutation tests were performed by shuffling traits for eachindividual as a block, maintaining the correlation structureamong traits while shuffling traits across marker genotypes.

RESULTS

Phenotypic traits: A single family of 110 doubled-haploid individuals was measured for physiological andmorphological traits related to smoltification. Pheno-typic trait values used in QTL analyses are summarizedbelow by life-history category assigned to each fish. Ofthe 110 fish used for QTL analyses, 22 (20%) werecategorized as smolts, 17 (15%) as parr, and 23 (21%) asrainbow; 48 fish (44%) were intermediate in phenotypeand were scored as unknowns at age 11. Continuous-trait data are summarized below by life-history category(including unknowns) to illustrate the differences among

the life-history types used to categorize the binary trait.For the binary trait, all smolts were categorized assmolts, while parr and rainbow were categorized asnonsmolts; intermediate fish were treated as missingdata for the binary trait.

Phenotypes used for QTL analyses and summarizedbelow include maximum relative warp values for six axesdescribing body morphology (maxrelw1maxrelw6),maximum and range of skin reflectance values (maxrefland rangerefl, respectively), maximum gill Na1/K1

ATPase (natural log-transformed, ln_maxatpase), min-

imum condition factor (minkfact), growth in standardlength and weight from January through June (sprgrsland sprgrwt, respectively), binary smolt phenotype (bi-nary), and sex phenotype (sex). With the exception ofcategorical traits (sex and binary phenotype) and gillNa1/K1ATPase, all traits conformed to the assumptions

of normality and homogeneity of variance.Body morphology: Thirteen landmarks digitized along

the left side of the fish (Figure 1) revealed significantvariation in body morphology (Figure 2). Thin platespline analyses of all doubled-haploid progeny at alltime points (n 677) revealed that the first six relative

warps explained 77% of the variation in body shape;each of these relative warps explained from 5% (relw6)to 24% (relw1) of the variation in shape. Some individ-uals or time points were missing from the analysis dueeither to mortality before the end of the study or topoor-quality photographs for accurate digitization oflandmarks. Examination of the shape variability cap-tured by each of the relative warps(Figure2) reveals thatthis analysis has readily captured quantitative shape

variability within the doubled-haploid family. Much ofthe variability, looking at the extreme values for eachrelative warp in relation to the consensus (Figure 2),appears to be differences in (1) dorso-ventral bodydepth (landmarks 3, 4, and 11), (2) snout morphology(landmarks 1, 2, 12, and 13), and (3) caudal peduncledepth and length (landmarks 59). Relative warp 1explained 24% of variation in shape; although rainbowtrout appear to have lower maximum relw1 (Figure 3A),the differences among categories are not statistically

significant [F 2.43, d.f. 3,P 0.0719 (categorical);F 0.6, d.f. 1,P 0.4437 (binary)]. Relative warp 2accounted for 17% of shape variation. Fish with anundetermined life-history category had the greatestmaximum relw2 values, while rainbow trout had thesmallest (Figure 3A). Unknowns were significantlydifferent from rainbow for maxrelw2, but no other pairs

were statistically significant (F 5.42, d.f. 3, P0.0020). When life-history type is coded as a binary trait,not surprisingly from the similar means among smolt,parr, and rainbow, there was no statistical difference

Figure 1.Digitized landmarks for morphology and areameasured for skin reflectance. Skin reflectance was made inthe shaded area below the dorsal fin and was quantified as av-erage pixel intensity.


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between smolts and nonsmolts for maxrelw2 (F 0.34,d.f. 1,P 0.5648). Relative warp 3 explained 12% ofthe variation in morphology and most obviously cap-

tured the shape differences among migratory and non-

migratory life-history types (Figure 3A). Relative warp 3appears to explain differences in dorso-ventral bodydepth and in caudal peduncle length and depth (Figure

2). Rainbow trout had lower average maximum relw3

Figure 2.Relative warps from thinplate spline analysis of morphology. (AF) The first six relative warps, which ex-plain 77% of the shape variation. Extreme

positive (blue) and negative (red) values ofeach relative warp are presented in com-parison to the consensus or average shape(black).

Figure3.Smoltification phenotype trait means (plus or minus standard error of the mean) for traits used in QTL analyses. (A)Maximum relative warp means for morphology (maxrelw1maxrelw6). (B) Range (rangerefl) and maximum (maxrefl) reflectancemeans. (C) Mean maximum Na1/K1 ATPase (ln_ATPase; ln transformed). (D) Mean minimum condition factor (minkfact). (E)Mean growth rates during the spring (February through June) in standard length (SPRGRSL) and weight (SPRGRWT).


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TABLE1

Pairwisecorrelations

amongphenotypictraitvaluesusedforQTLanalyses

maxrefl

ln_a

tpase

sprgrsl

sprgrwt

m

axrelw1

maxrelw2

maxrelw3

maxrelw4

maxrelw5

maxrelw6

minkfact

Binarya

rangerefl

0.6

0646

0.18508

0.02974

0.00596

0.00741

0.00561

0.20075

0.2

6422

0.01706

0.03007

0.2

4561

0.4

2154

,0.0

001

0.088

0.7833

0.956

0.9497

0.9619

0.0842

0.0

22

0.8845

0.7979

0.0

226

0.0

021

89

8

6

88

88

75

75

75

75

75

75

86

51

maxrefl

0.18276

0.04239

0.06724

0.0

7931

0.2

2756

0.18437

0.04546

0.11197

0.15286

0.02022

0.6

5298

0.0921

0.695

0.5336

0.4988

0.0

496

0.1133

0.6986

0.3389

0.1904

0.8534

,0.0

001

8

6

88

88

75

75

75

75

75

75

86

51

ln_atpase

0.3

9227

0.3

112

0.16369

0.12654

0.15658

0.06972

0.16223

0.06343

0.04988

0.24205

,0.0

001

0.0

02

0.1605

0.2

794

0.1

798

0.5523

0.1643

0.5887

0.633

0.0

723

96

96

75

75

75

75

75

75

94

56

sprgrsl

0.9

4194

0.04153

0.11395

0.3

3521

0.18303

0.13708

0.08308

0.3

0377

0.06681

,0.0

001

0.7199

0.3237

0.0

029

0.1111

0.2345

0.4

725

0.0

026

0.6215

98

77

77

77

77

77

77

96

57

spgrwt

0.06042

0.13523

0.3

8475

0.1

7854

0.09179

0.05493

0.3

8342

0.10733

0.6017

0.2409

0.0

006

0.1203

0.4272

0.6351

0.0

001

0.4268

77

77

77

77

77

77

96

57

maxrelw1

0.06516

0.16817

0.13076

0.3

5169

0.09101

0.2

4676

0.11689

0.5

734

0.1437

0.257

0.0

017

0.4312

0.0

316

0.4391

77

77

77

77

77

76

46

maxrelw2

0.0

713

0.19152

0.1359

0.15552

0.3

7472

0.12033

0.5377

0.0952

0.2386

0.1

768

0.0

009

0.4257

77

77

77

77

76

46

maxrelw3

0.01411

0.11061

0.05611

0.5

8458

0.5

2602

0.9031

0.3382

0.6279

,0.0

001

0.0

002

77

77

77

76

46

maxrelw4

0.047

0.04327

0.0225

0.12377

0.6848

0.7086

0.847

0.4125

77

77

76

46

maxrelw5

0.06061

0.05524

0.02063

0.6005

0.6355

0.8918

77

76

46

maxrelw6

0.15719

0.01719

0.1

751

0.9097

76

46

minkfact

0.4

0.0

025

55

Abbreviationsfortraitsared

escribedinthetext.Significantcorrelationsareinitalics.Foreachpairoftraits,correlationcoefficients,

P-valuesfortestsofH

o:r

0,and

samplesizeareshown.UnderlinedpairsaretraitsforwhichQTLcolocalize.

a

Spearmanscorrelationcoefficients;allothercorrelationcoefficie

ntsarePearsonscoefficients.


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in both standard length and weight was negatively corre-lated with relative warp 3 (maxrelw3) and positivelycorrelated with condition factor (minkfact). Individuals

with lower growth rates in both length and weight gen-erally had higher maxrelw3 values (were more fusiformin shape) and lower minimum condition factors. Atthe same time, individuals with higher growth rates inlength and weight had lower maxrelw3 values (were

more deep bodied) and higher minimum conditionfactors. Additional axes of body morphology, aside fromthose mentioned above, were also correlated withminimum condition factor. The first three relative warps

were negatively correlated with condition factor; indi-viduals with higher values for these relative warps gen-erally had lower minimum condition factors. The binarysmolt phenotype was positively correlated with reflec-tance (both maximum and range) and maxrelw3 andnegatively correlated with minimum condition factor.Individuals that were scored as smolts had greatermaximum and range of reflectance, greater values formaxrelw3 (more fusiform in shape), and lower mini-

mum condition factors.Genetic linkage map: Twenty-nine major linkage

groups were identified with linkage analysis in 110doubled-haploid individuals, using 263 markers andthe sex phenotype. The number of major groups corre-sponds to the number of chromosome pairs revealed bykaryotyping in the OSU and CW clonal lines and hybrids(Phillips et al.2005). Synteny among linkage groups inthis study and other rainbow trout maps (Sakamotoet al.2000; Nichols et al. 2003; Danzmann et al. 2005;Guyomard et al. 2006) is established by mapped syn-tenic microsatellite markers; linkage groups are namedaccording to the cross (OC) and the linkage groupnumber defined in prior maps (Figure 4; only groups withsignificant QTL are shown). The average intermarkerdistance within linkage groups, including markers with0-cM distances, was 5.8 cM; excluding markers that werecompletely linked (0-cM distance), the average inter-marker distance was 7.5 cM. The sex phenotype mappedto linkage group OC1 and was perfectly linked to markerSsa408UoS (0-cM distance). This map was the frameworkfor our QTL analysis of smoltification-related traits.

QTL analyses: With composite-interval mapping forsingle traits, QTL were identified for each trait with theexception of morphology metrics maxrelw2 and max-

relw6 and gill Na1

/K1

ATPase (Figure 4, Table 2). QTLare named according to the trait and the linkage groupto which they localized. In all cases, the genotypic effectof a QTL refers to the estimated phenotypic differencebetween the CW genotype and the OSU genotypeattributed to the additive effects of alleles at that QTL.

A positive genotypic effect would indicate that individ-uals with a CW genotype at a particular QTL would havea greater phenotypic trait value, attributed to that locus,than individuals with the OSU genotype. Note thatalthough determining statistical thresholds for signifi-

cance in the context of multiple tests is not a simple oneto address in the context of IM and CIM for thedetection of QTL, most all QTL, in the context of themultiple-QTL models, were significant atP, 0.0001.

Although a Bonferroni correction for the 13 single traitsmay not be correct in the case of multiple noninde-pendent traits, even in the most conservative case thatthe traits are independent, the nominal P-value for

significance would be a 0.05/13 or 0.004. All exceptfor one QTL for body morphology (maxrelw3-OC8) weresignificantly below this threshold in the multiple-QTLmodels.

Body morphology:Ten QTL for maximum relative warpvalues for four axes of shape were identified (Figure 4and Table 2). For maxrelw1, 2 QTL were identified ontwo linkage groups, OC13 and OC31, explaining 11.6and 18.5% of the phenotypic variation in maxrelw1. TheQTL on linkage group OC31 colocalizes with QTL formaxrelw5, another axis of morphometric shape. NoQTL were identified for maxrelw2, despite the signifi-cant variation observed among the fish sampled. For

maxrelw3, the descriptor that captured dorso-ventralvariability in body depth and caudal peduncle shape, 4main-effect QTL were identified. These QTL explained3.014.1% of the phenotypic variability along this shapeaxis. The QTL on linkage group OC8 was statisticallysignificant in a single-QTL interval-mapping scan, butdid not significantly contribute to the overall, multiple-QTL model when all other QTL were included (Table2). Maxrelw3 QTL on linkage groups OC8 and OC20colocalized with QTL for other traits in this analysis.Each of the 4 QTL for maxrelw3 had a positive genotypiceffect, indicating that individuals with a CW genotype(paternal) at these loci had larger maximum values ofrelw3 than those with an OSU genotype at this locus; thisdifference in genotypic values at these loci exhibits amore dorso-ventrally flattened or fusiform fish, charac-teristic of smolts, in those with the CW genotype relativeto those with the OSU genotype. For maxrelw4, 3 QTL

were identified, explaining 10.418.1% of variation inshape along this axis. The two QTL with the greatesteffects were found on OC9 and OC20, and both hadnegative genotypic effects. The QTL for maxrelw4 onOC20 (maxrelw4-OC20) colocalized to the same regionas maxrelw3-OC20and other smoltification traits. Oneadditional QTL was identified for body morphology

with maxrelw5. The QTL for this morphological shapedescriptor explained 14.1% of shape variation and waslocalized to OC31, the same region as that for maxrelw1-OC31.

Skin reflectance:A total of four QTL were identifiedfor the two metrics of skin reflectance, maximum re-flectance (maxrefl) and range of reflectance (ranger-efl) (Table 2, Figure 4). Two QTL were detected forrangerefl,rangerefl-OC16and rangerefl-OC20; these QTLaccounted for 11.2 and 24.3% of the variation in therange of reflectance, respectively. Two QTL were iden-


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tified for maxrefl, maxrefl-OC17and maxrefl-OC20, whichexplained 14.4 and 11.1% of the variation in maximum

reflectance, respectively. QTL for reflectance on OC16and OC17 did not colocalize with QTL for other traits;however, reflectance QTL on OC20 did colocalize withQTL for other traits on this linkage group (Figure 4).Each of the reflectance QTL had positive genotypiceffects (Table 2), indicating that individuals with a CWgenotype at each locus had a greater maximum or rangeof reflectance than individuals with an OSU genotype.

Gill Na1/K1 ATPase: No QTL were detected formaximum log-transformed gill Na1/K1ATPase, despitethe variation observed among life-history types.

Body condition factor: One QTL for minimum bodycondition factor (minkfact) was identified on OC20

(Table 2); this QTL colocalizes to the same region asmany other traits, including reflectance and bodymorphology QTL (Figure 4). The minkfact-OC20QTLaccounted for 13.8% of the variation in minimumcondition factor and had a negative genotypic effect.Individuals with a CW genotype at this locus had asmaller minimum condition factor compared to those

with an OSU genotype.Growth rate:QTL for spring growth rate in standard

length (sprgrsl) and weight (sprgrwt) colocalized tolinkage groups OC8 and OC20 (Table 2). It is clear that

Figure 4.Quantitative trait loci (shown as 2-LOD support intervals) for smoltification traits from single-trait and multiple-traitinterval mapping. Only linkage groups with significant QTL are shown. Linkage groups are named according to the cross used inthis study (OSU 3 CW: OC) and the linkage group number defined by syntenic markers (in boldface type) with previously pub-lished rainbow trout maps.


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sprgrsl-OC8 and sprgrwt-OC8 colocalize to the sameregion, but 2-LOD support intervals for sprgrsl-OC20

and sprgrwt-OC20do not overlap on linkagegroup OC20(Figure 4). One additional QTL (sprgrsl-OC14) and two-locus epistasis were revealed for spring growth rate instandard length (Table 2). Main-effect QTL for springgrowth rate in standard length each had positivegenotypic effects; individuals with a CW genotype hadgreater spring growth rate in standard length comparedto those with an OSU genotype with differences rangingfrom 0.025 6 0.017% (sprgrsl-OC20) to 0.060 6 0.014%(sprgrsl-OC8) growth in standard length per day (Table2). Epistasis betweensprgrsl-OC14and sprgrsl-OC20had

the opposite effect on growth rate; individuals with CWgenotypes at both of these loci had 0.140 6 0.034%

lower growth rate in standard length than individualswith OSU genotypes at both loci (Figure 5). The twoQTL for spring growth in weight were opposite in theireffects. Forsprgrwt-OC8, individuals with a CW genotypehad larger growth rates in weight relative to those withan OSU genotype (genotypic effect: 0.172 6 0.044%growth in weight per day); for sprgrwt-OC20, individuals

with a CW genotype exhibited a slower spring growthrate in weight compared to those with the OSUgenotype (genotypic effect: 0.174 6 0.044% growthin weight per day).

Figure4.Continued.


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cofactors does not identify main-effect QTL at these loci(data not shown). Complete penetrance (100%, n11/11) was observed as a result of epistasis betweenOSU genotypes at binary_smolt-OC14 and binary_smolt-OC25; on the other hand, no individuals with CW andOSU genotypes at binary_smolt-OC14 and binary_smolt-OC25, respectively, were smolts (n 0/11; Figure 6C).The remaining two joint genotypic classes producedintermediate proportions of smolts: 25% (n 4/12) ofindividuals with CW, CW genotypes and 36% (n 5/14)

with OSU, CW genotypes at binary_smolt-OC14 andbinary_smolt-OC25were smolts (Figure 6C).

Multiple-trait analysis: Joint- or multiple-trait QTLanalyses were conducted using (a) all 12 traits used toquantify physiological and morphological metrics ofsmoltification and (b) only the 7 quantitative traits thatcolocalized to OC20 (underlined in Table 1). For eachof these scenarios, correlations were not significant for

all pairs of traits, but each trait was significantly cor-related with at least one other trait (Table 1), with theexception of maxrelw6. A single-QTL interval-mappingscan for all 12 traits revealed a total of 4 QTL (Figure 4).

Approximate P-values for significance were obtainedfrom 1000 permutations of the phenotypes (shuffledas a block) across genotypes. Joint QTL for the 12-traitdata set were found on linkage groups OC8 (LOD 10.45, P, 0.01 from 1000 permutations), OC14 (LOD9.32, 0.01 , P, 0.05), OC20 (LOD 16.70,P, 0.01),and OC31 (LOD 9.02, 0.01 , P, 0.05). When the12-trait joint QTL on OC20 was included as a back-ground cofactor in the model, all main-effect QTL

remained significant except the QTL on OC14 (LOD1.96, P? 0.05); no additional main-effect QTL wereidentified. Joint QTL for the 7-trait data set (includingtraits colocalizing to OC20) also revealed 4 QTL, butnot all in the same positions as those identified in the12-trait data set (Figure 4). Joint QTL for the 7-traitdata set were found on linkage groups OC8 (LOD 7.35,P, 0.01), OC20 (LOD 13.35,P, 0.01), OC21(LOD 7.09,P, 0.01), and OC30 (LOD 7.73,P,0.01). All joint QTL in the 7-trait data set remainedsignificant when the QTL on OC20 was included asa background cofactor in a genome scan, and noadditional joint QTL were identified. Not surprisingly,

joint QTL for both analyses colocalize to regions

Figure 6.Binary-trait

proportions for binary-traitloci with significant maineffects (A and B) and signif-icant two-locus epistasis (C).

Figure5.Joint genotypic effects or two-locus epistasis forspring growth rate in standard length. Phenotypic values(points) and means (plus or minus standard error of the mean)for each multilocus genotype are shown. Red symbols were cal-culated from imputed genotypes in missing individuals.


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where multiple single traits localize, most notably forOC-8 and OC-20 (Figure 4).

DISCUSSION

We have identified several genome regions associatedwith variation in multiple morphological and physiolog-

ical indexes of smoltification inO. mykiss, a species thatconsists of both resident and anadromous or migratorylife-history types. This is the first study to reveal genomicloci associated with migration physiology in any fish. Ourresults provide a foundation for a deeper understandingof the genes and regulatory mechanisms underlyingsmoltification, or the decision to migrate or stay, in thisspecies. As we reveal the genomic regions and genesinvolved in this important life-history decision, we maybegin to understand the evolutionary history of smoltifi-cation within O. mykissandgain a betterunderstanding ofhow genes and environment influence the decision tomigrate or maintain residency. Ultimately, this study lays

the foundation for parallel studies in this and othersalmonid species to begin asking questions about theorigins and evolution of migrationvs.residency.

Our findings indicate that a few genomic loci contrib-ute to variation in the individual trait phenotypes andthat one genome region is strongly associated withmultiple smoltification traits. The distribution of QTLeffects in this study is not unlike the skewed distributionof QTL effects in QTL analyses of complex traits ob-served across many taxa, and with the limitations ofsample size in this study, it is likely that small-effect loci

were not detected (Lynch and Walsh 1998; Roff 2007).The fact that all traits for which significant differences

were observed among life-history types are associatedwith a common genome region suggests that either asingle locus or multiple linked loci may play a role in thephysiology associated with migration vs. residency. TheQTL on OC20 are associated with body morphology, skincoloration or silvering, condition factor, and growth rate;not surprisingly, when these traits are considered to-gether either subjectively in binary trait analysis or objec-tivelyin joint analysis, the same major region is identified.The fact that multiple single-trait QTL underlie thebinary smoltification QTL further corroborates the ideathat migration vs. residency is a threshold trait, regulated

by underlying continuous traits that contribute to thedecision to migrate or stay. Does the colocalization ofcontinuous and binary traits suggest that a masterregulatory region or gene may play a significant role inthe integrated cascade of events that promote smoltifi-cation or residency? It cannot be ruled out that a singlegene upstream of the complex, integrated endocrineand growth cascades is responsible for regulating theswitch that determines whether an individual willundergo smoltification. Despite the long history ofstudies on smolt physiology, no studies have yet detected

the molecular processes triggering and integrating thesecomplex endocrine and physiological processes involvedin smoltification. Dissection of the genome region onOC20 together with functional studies will provide clueson whether a single trigger controls many of the phys-iological differences between smolts and residents.

For most of the detected QTL in this study, the major-ity of additive effects are unidirectional for individual

quantitative traits. For example, for traits significantlysegregating between smolt and nonsmolt categories(relative warps 3 and 4, reflectance (maximum andrange), and minimum condition factor) there is a pre-ponderance of genotypic effects with the same sign orinfluencing the trait in the same direction. Under neu-tral expectations, the signs or directions of effects areexpected to be random for a given phenotype (Orr1998). Our results suggest that additive effects for prox-imate physiology associated with smolt status may be aresult of directional selection. In other words, genesassociated with smoltification physiology and morphol-ogy are primarily found only in the steelhead line (CW).

This result, however, does not hold for the binary-traitQTL. In binary-trait analysis, one locus, found on thelinkage group where most other quantitative traits lo-calized (OC20), influences the trait as expected underdirectional selection and fixation of steelhead allelesin the steelhead line (and nonsmolt alleles in the rain-bow trout line). However, the other binary trait locusdetected (on OC6) has an opposite effect; individualspossessing the steelhead (CW) allele at this locus havea lower propensity for smolting than OSU. Taken to-gether, there is clear evidence that the multiple genesfor being steelhead or rainbow trout in smoltificationphysiology have been fixed for the most part in the OSUand CW lines, but there is still some evidence forrainbow trout genes in the steelhead line and vice

versa. What does this mean for adaptive genetic varia-tion for smoltification in the populations from whichthe lines were derived? Depending on allele frequenciesin the source populations, this may mean that even

where one life-history strategy is predominant, theremay be adaptive potential to respond to environmentalchange.

In this study, we revealed genomic loci associated withlife-history diversity in an evolutionarily divergent crossof O. mykiss (between a rainbow trout derived from

California and a steelhead trout derived from Idaho),and it is unknown whether loci associated with smolti-fication in this wide cross would be the same betweensympatricO. mykisslife-history types. Across the naturalrange ofO. mykiss, there is considerable variability in thepropensity to migrate. Landlocked and the most north-erly populations are wholly resident, while populations

with connections to the sea exhibit variable proportionsof migratory individuals ranging from wholly anadro-mous to partially anadromous (a mixture of anadro-mous and resident life-history types) (Quinn and


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Myers 2004; McPhee et al. 2007). The degree of geneticdifferentiation between resident and anadromous pop-ulations ofO. mykissin sympatry is variable. Most studiesexamining genetic differentiation between sympatriclife-history types ofO. mykissreveal that alternative life-history forms within the same freshwater system aremore closely related to one another than are the samelife-history types across river systems (Docker and

Heath 2003; Narum et al. 2004; Olsen et al. 2006;McPheeet al.2007); however, there are some instanceswhere significant reproductive isolation has been ob-served between sympatric O. mykiss life-history forms(Dockerand Heath 2003; Narum et al.2004). In other

words, resident and anadromousO. mykissin sympatryexhibit some level of gene flow, and there is evidence forplasticity in the expression of the alternative lifehistories. The close genetic relationship between resi-dent and anadromous life-history types in sympatry isnot unique toO. mykiss, but is found in other salmonidspecies that exhibit variation in anadromyvs.residency(Hindar et al. 1991; Jones et al. 1997; Quinn 2005).

With possible multiple parallel examples of life-historydiversification within and across salmonid species, thequestion remains whether the same or different sets ofgenes play a role in the diversification of (1) anadro-mous and resident salmonid life histories and (2)timing of migration in anadromous species. Continuedstudies examining the genetic architecture of thesealternative life-history strategies within and amongpopulations and species will address these questions.

The salmonid lineage experienced a whole-genomeduplication by autotetraploidization 25100 MYA(Allendorf and Thorgaard 1984). The recent ge-nome duplication within salmonids affords the oppor-tunity to ask questions regarding how duplicatedgenome regions play a role in the evolution of complexphenotypes. Genomic resources for the salmonid line-age are more extensive than for any other major groupof fishes and provide the opportunity to understandcomplex trait evolution following a genome duplicationin multiple, closely related species. Homeologous rela-tionships among duplicated chromosome arms havebeen identified for several salmonid species (Danzmannet al. 2005; Gharbi et al. 2006; Guyomard et al. 2006). Inour study, we find direct evidence that at least one pairof duplicated, homeologous genome regions is associ-

ated with smoltification-related traits. The duplicatedEST-linked microsatellite,OMM5017is linked to a QTLidentified in the 12-trait joint analysis and to a lesserdegree with QTL for spring growth rate in standardlength on homeologous linkage groups OC14 andOC20 (Figure 4). For homeologous chromosomes (for

which .1 marker has previously identified homeo-logues) from priorO. mykissmapping studies (Nicholset al. 2003; Danzmann et al. 2005; Guyomard et al.2006), there is indirect evidence that QTL for bodymorphology (maxrelw4) localize to homeologous link-

age groups OC9 and OC20. We cannot ascertain whetherthe duplicate genes or closely linked loci are funda-mentally associated with smoltification trait variation inthis study. However, this and other studies (OMalleyet al. 2002; Moghadam et al. 2007) suggest that dupli-cate copies could play a role in the expression of quan-titative traits in salmonid taxa. The role of duplicationand subsequent duplicate gene evolution remains an

important question as the genetic architecture of lifehistories and other quantitative traits is revealed in thismajor group of fishes.

To summarize, we have identified genome regionsassociated with smoltification or the physiological andmorphological transition that occurs prior to seawardmigration. In particular, one region that is associated

with multiple traits lends insight into the genetic basis ofmultiple correlated traits. We emphasize that in theabsence of functional tests for migratory displacement,

we cannot ascertain whether genome regions associatedwith the ultimate decision to migrate are the same asthat associated with these proximate metrics of smolti-

fication. The question of whether this same region isassociated with smoltification in other salmonids re-mains unanswered, but this study lays the foundation forparallel studies in this and other species. Ultimately,

with parallel accounts of the genetic architecture ofsmoltification and migration in multiple salmonid spe-cies, we can begin to understand the evolution of anad-romy on both coarse and fine scales.

The authors thank Kyle Sundin, Kyle Martin, Steve Patton, AlanInderrieden, and Lee Suksdorf for assistance in fish sampling at

Washington State University. Trout eggs for the production of clonal

linesand doubled haploids were provided by Troutlodge. Bench space

for ATPase quantification was kindly provided by Penny Swanson and

Brian Beckman, Northwest Fisheries Science Center (NWFSC), andlaboratory assistance was provided by Kathy Cooper. Philip Haner washelpful in discussions on quantification of body reflectance. The

authors thank Gary Winans and James Rohlf for discussions onmorphometric statistical analyses. Later molecular work was com-

pleted at the NWFSC in the Conservation Biology DNA laboratoryunder the direction of Linda Park and Michael Ford, with the kind

assistance of Elizabeth Heeg. Julia Sharp, Montana State University,was helpful with general statistical discussions. Rodney McPhail,

Purdue University, was helpful in the production of scientificillustrations and figures. Karl Broman, Johns Hopkins University,

kindly provided code for refining QTL positions in R/qtl and washelpful in discussions on statistical issuesin QTLanalyses. Theauthors

thank Michael Ford and Ted Morgan and anonymous reviewers andthe editor for comments that improved the final manuscript. This

research was funded by the National Science Foundation to G.H.T.(IBN-0082773) and by a National Research Council postdoctoral

fellowship to K.M.N.

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