Comparison of Migratory and Resident Populations of Brown Trout Reveals Candidate Genes for Migration Tendency

Comparison of Migratory and Resident Populations of Brown Trout Reveals Candidate Genes for... Candidate genes associated with migration have been identified in multiple taxa: including salmonids, many of whom perform migrations requiring a series of physiological changes associated with the freshwater–saltwater transition. We screened over 5,500 SNPs for signatures of selection related to migratory behavior of brown trout Salmo trutta by focusing on ten differentially migrating freshwater populations from two watersheds (the Koutajoki and the Oulujoki). We found eight outlier SNPs potentially associated with migratory versus resident life history using multiple (3) outlier detection approaches. Comparison of three migratory versus resident population pairs in the Koutajoki watershed revealed seven outlier SNPs, of which three mapped close to genes ZNF665-like, GRM4-like, and PCDH8-like that have been previously associated with migration and smoltification in salmonids. Two outlier SNPs mapped to genes involved in mucus secretion (ST3GAL1-like) and osmoregulation (C14orf37-like). The last two strongly supported outlier SNPs mapped to thermally induced genes (FNTA1-like, FAM134C-like). Within the Oulujoki, the only consistent outlier SNP mapped close to a gene (EZH2) that is associated with compensatory growth in fasted trout. Our results suggest that a relatively small yet common set of genes responsible for physiological functions associated with resident and migratory life histories is evolutionarily conserved. Key words: salmonids, smoltification, RADseq, migratory behavior, life-history strategy. Introduction Salmonids can adopt migratory, partially migratory or res- Diverse migration patterns are almost ubiquitous within the ident life history strategies, with feeding migrations directed animal kingdom. Migrations have evolved to maximize fitness to sea (anadromy), lakes (adfluvial), or larger river sections in heterogeneous environments (Gross et al. 1988; Dingle and (potamodromy), and spawning migrations back to natal Drake 2007; Dingle 2014). Although these migrations are freshwater stream habitats (Chapman, Skov, et al. 2012; proximately induced by a combination of environmental fac- Dodson et al. 2013). Whether migratory salmonids originate tors, changes in physiology, morphology, and/or behavior from marine or freshwater ancestors have been under a long (Chapman et al. 2011; Chapman, Hulthen, et al. 2012), mul- debate (McDowall 2002). Nor it is known whether migration, tiple studies have documented the genetic underpinnings of as a behavioral trait, drives evolution of physiology required migration propensity (e.g., Pulido 2007; Zhu et al. 2008). for migration or vice versa. Ancestral diadromy might have Nonetheless, comparative studies across a wide range of induced the evolution of physiological adaptations that could organisms are needed to understand the mechanisms of evo- drive migratory behavior even in currently landlocked species lution leading to adaptive migrations (Liedvogel et al. 2011). and populations (Piironen et al. 2013). Salmonid migrations The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Genome Biol. Evol. 10(6):1493–1503. doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1493 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE are proximately influenced by individual condition and a num- We predicted that comparison of populations pairs would ber of environmental factors (e.g., Olsson et al. 2006; reveal putative signs of selection related to migratory behav- Wysujack et al. 2009; Vainikka et al. 2012). Dichotomy be- ior and adaptation to different environments. We further tween migratory and resident life histories has a significant predicted that comparison of the two distant watersheds heritable component in the Oncorhynchus (e.g., Hecht et al. could potentially reveal parallel genomic signatures associ- 2012; Hu et al. 2014), Salvelinus (Theriault et al. 2007), and ated with migration tendency, whereas watershed-specific Salmo (Naslund 1993; Palm and Ryman 1999) genera. signs might be linked with adaptation to particular environ- Genome scans and environmental association analyses mental conditions. have been increasingly used to detect signature of selection (e.g., Berg et al. 2015; Babin et al. 2017). These methods Materials and Methods detect markers that deviate from neutrality (i.e., are outliers) and are associated with particular environmental variables (for Sampling areview, see Ahrens et al. 2018). Recent studies investigating Koutajoki Watershed the molecular mechanisms of migratory behavior in salmonids have identified a long list of candidate loci (e.g., Moore et al. Six naturally connected brown trout populations were studied 2017; Veale and Russello 2017). Migration tendency has also within the Koutajoki (K) watershed in North-East Finland been linked with differential gene expression (McKinney et al. (66 17’N 29 53’E [WGS84], fig. 1b); three migratory: 2015) and multiple quantitative trait loci (QTL; Nichols et al. Kuusinkijoki (KM1), Oulankajoki (KM2), and Kitkajoki (KM3) 2008; Hecht et al. 2012). However, only a small proportion of and three resident: (Juumajoki, KR1, flowing into the identified QTL regions and genes appear to be shared Kuusinkijoki; Maaninkajoki, KR2, flowing into Oulankajoki; among populations and species. One particular genomic re- and Pesospuro, KR3, flowing into Kitkajoki). These represent gion (Omy5) has been highlighted by several studies in the a subset of populations that were previously studied by rainbow trout/steelhead Oncorhynchus mykiss complex as the Lemopoulos et al. (2017). most influential genetic component for migration propensity In short, three resident populations (KR1–KR3) in headwa- (Hechtetal. 2012; Pearse et al. 2014; Leitwein et al. 2017). ter streams were sampled by electrofishing in 2015 (Reid et al. However, this association has not been observed across all O. 2009; Luhta et al. 2012). The length of the sampled sections mykiss populations (Hale et al. 2013). Thus, there is little ev- varied between streams from 300 to 800 m. Based on the idence for a master locus for a life-history switch, whereas stream-specific length frequency distributions and scale read- ample evidence exists for family, population, and species- ings the sampled fish belonged to the age groups of 0–3, specific genetic effects on phenotypic migration decisions including mainly parr but also some mature fish. Although (e.g., Narum et al., 2011; Nichols et al., 2016). This suggests the residency of individuals could not be confirmed through that the molecular mechanisms behind migratory-resident telemetry, residency was inferred from the observed repro- life-history variation may vary between species and even pop- ductive isolation (Lemopoulos et al. 2017). ulations or that only a subset of relevant causative genes has The landlocked adfluvial populations (M1–M3) were sam- been identified and characterized. pled by capturing upstream-migrating adults during their Brown trout Salmo trutta is one of the most diverse species spawning migration from the feeding lake Pyaozero in terms of migratory behavior (Olsson et al. 2006). Brown (659 km ) in 2014. The river Oulankajoki samples (scales, see trout shows a wide variety of migration strategies from strictly Saraniemi et al. 2008) were from taken at the Kiutako¨ngas Falls anadromous to complex and plastic feeding and spawning on the river. These natural waterfalls form a partial migration migrations in freshwater (Hindar et al. 1991; Charles et al. obstacle, and local fisheries managers have caught trout 2005; Aarestrup et al. 2017). However, it is not clear to annually since 1965 at the falls and transferred them upstream. what extent the migratory behavior is driven by genetic The fish from the other two rivers were sampled using a fyke effects and to what extent it is plastic and relying on environ- net further downstream in River Olanga in Russia, and the river mental clues. Migration-related life-history variation in brown of origin was inferred from radiotelemetry data. Right after trout have been extensively characterized from different per- capture, 149 individuals were equipped with external (100 spectives (e.g., Boel et al. 2014; Jones et al. 2015). Classic individuals) or internal (49 fish were surgically implanted) radio- studies have also shown that propensity for migration in tags. The fish that were tracked by automated recording brown trout must have a genetic component (Naslund stations and manual tracking of the radiotag signals into the 1993; Palm and Ryman 1999). rivers Kitkajoki and Kuusinkijoki at the spawning time were In this study, we screened single nucleotide polymorphisms used as representative individuals of these river-specific popu- (SNPs) to explore and characterize signatures of selection as- lations. Based on scale readings, the sampled fish from the sociated with migration and residency. We focused on multiple main stems were 6–8 years old, being on their first or second migratory versus resident population pairs from river systems spawning run (Huusko et al. 2017). All captured brown trout in two distinct watersheds (see Lemopoulos et al. 2017). were anaesthetized with clove oil (40 mgl in 1:9 water: 1494 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE FIG.1.—(a) General map of Finland and of the two watersheds locations. (b) Koutajoki watershed. Three resident populations (R) and three migratory populations (M) were sampled in corresponding tributaries and main stems. (c) Oulujoki watershed. Three resident populations (R) and one migratory populations (M) were sampled in corresponding tributaries and lake. ethanol solution, Oy Anders Meder Ab, Helsinki, Finland), mea- et al. unpublished manuscript). The quality of total DNA was sured for total length, sampled for 2–3 scales for age determi- controlled with fluorometric measurements using Qubit 2.0 nation and a small piece of pectoral fin that was preserved in (Qubit dsDNA HS Assay Kit, ThermoFisher Scientific). pure ethanol, and released after recovery. Genotyping Oulujoki Watershed Libraries for double digest restriction-site associated DNA Four populations were sampled in the Oulujoki (O) watershed (ddRADseq) were taken from two previous studies above the main feeding area Lake Oulujarvi (887 km ) (Lemopoulos et al. 2017; Prokkola et al., unpublished manu- (fig. 1c). Wild trout were caught by electrofishing from three script). The protocol used was adapted from (Peterson et al. putatively resident brown trout populations in rivers Pohjajoki 2012; Pukk et al. 2014). Briefly, DNA was digested using two (16.9.–17.9.2015, OR1), Tuhkajoki (17.9.2013, OR2), and 0 0 0 enzymes (PstI-HF [5 CTGCAG 3 ]and BamHI-HF [5 GGATCC Vaarainjoki (28.9.–30.9.2010, 15.9.–11.10.2011, and 3 ]) and then ligated using unique individual barcodes to the 2.10.2012, OR3). Lake Oulujarvi stock (OM1) was originally forwards ends. We pooled the individuals into five libraries. established by breeding adfluvial brown trout from two pop- Each library was purified with a PCR purification kit and frag- ulations, River Varisjoki and River Kongasjoki (fig. 1c). All of ments were size selected at 280–320 bp on e-gel. They were these populations except for River Tuhkajoki are naturally then amplified with PCR and purified with SPRI-Beads. Finally, connected. we examined the concentrations (Qubit 2.0) and quantities using Agilent 2100 Bioanalyzer. Sequencing was conducted DNA Samples at commercial provider Turku Centee for Biotechnology (BTK), DNA samples were extracted from individuals of each popu- Turku, Finland (www.btk.fi) with an Illumina HiSeq 2500 lation (table 1, see also Lemopoulos et al. [2017] and Prokkola system. Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1495 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE Table 1 Summary Information of the Studied Populations and Samples Sample Location River Type Putative Life-History Number of Samples Average Length (TL and SD) [mm] Accronym Juumajoki Tributary Resident 30 70.83 6 12.25 KR1 Maaninkajoki Tributary Resident 30 86.42 6 33.21 KR2 Pesospuro Tributary Resident 30 146.20 6 24.64 KR3 Kuusinkijoki Main stem Migratory 30 699.13 6 73.42 KM1 Oulankajoki Main stem Migratory 30 604.33 6 56.88 KM2 Kitkajoki Main stem Migratory 30 767.73 6 77.32 KM3 Pohjajoki Tributary Resident 11 248.5 6 60.7 OR1 Tuhkajoki Tributary Resident 12 422.1 6 55.7 OR2 Vaarainjoki Tributary Resident 29 459.3 6 79.1 OR3 Migratory stock Main stem Migratory 28 526.9 6 67.1 OM1 We used Stacks v1.40 (Catchen et al. 2013) process_radtags methods (LFMM: [Frichot and Franc¸ois 2015] and BayScEnv: function to demultiplex, quality filter, clean, and trim all reads [Villemereuil and Gaggiotti 2015]. P values were corrected to 85 bp. Since Atlantic salmon is the evolutionarily closest rel- (False Discovers Rate, FDR) and SNPs were identified under ative to brown trout with an accessible genome (GenBank: an alpha threshold of 0.05 for genome scans, whereas more GCA_000233375.4) (Cr^ ete-Lafrenie ` re et al. 2012; Lien et al. relaxed thresholds (0.1) were applied environmental associa- 2016), Bowtie v. 2.3.0 (Langmead and Salzberg 2012)was tion methods in order to reduce type II error. SNPs identified used to create an index of the Atlantic salmon genome and as outliers in at least three of the methods were examined in to align all the reads against it (bowtie2 –p 2 –s/–summary – detail for gene proximity and putative functions. sensitive –end, rest of parameters to default). Reads that were aligned to several locations were assessed separately, and only Bayescan the ones presenting unique best scores were retained. In total, Bayescan (v2.1) (Foll and Gaggiotti 2008) was runwitha 70% of the original reads were retained, and the average cov- burn-in of 50,000 and 50,000 iterations. Individuals were erage after filtering was 53X. Genotype error rate (single nu- pooled according to populations, and the rest of the param- cleotide polymorphism [SNP] level) was 6%. We used the eters were set to defaults. refmap and population functions for SNP calling. SNPs with a minimum coverage of three (i.e., each SNP position was cov- Pcadapt ered by at least three reads), with loci that were present in all our populations (–p 6 and –p 4, respectively) and in 60% of the Pcadapt (v3.0.4) (Luu et al. 2017)was run with a K value of six individuals retained. Further, SNPs with a minor allele frequency (v3.3.2). SNPs q-values were obtained with the qvalue pack- of at least 0.05 (Roesti et al. 2012) and maximum heterozygos- age (Dabney and Storey 2015). All the other parameters were ity of 0.5 (Hohenlohe et al. 2011) were retained (supplemen- set to default values. tary table S4, Supplementary Material online). All other parameters were set to default values. Afterwards, we filtered Latent Factor Mixed Modeling (LFMM) the SNPs for Hardy–Weinberg equilibrium using the Using the LEA package (v1.6.0) (Frichot and Franc¸ois 2015)in “adegenet” (v2.01) (Jombart 2008) package (hw.test func- R version 3.3.2 (R Core Team 2016), we ran the lfmm func- tion, with 1,000 repetitions) in R version 3.2.0 (R Core Team tion. The environment file was implemented using “0” for 2015). Only loci that were in equilibrium in at least 50% of the resident populations and “1” for migratory populations. 30 populations were kept. In total, out of 3.46 10 and repetitions were conducted with K¼ 6/K¼ 4,60,000 itera- 1.19 10 retained reads, respectively (supplementary tables tions and a burn-in of 30,000 and by allowing missing data. S1–S3, Supplementary Material online), 5,519 SNPs for the The rest of the parameters were set to defaults. Adjusted P Koutajoki watershed and 5,670 for the Oulujoki watershed values were obtained using the genomic inflation factor (k) were retained for the final analyses. and Benjamini–Hochberg correction. BayeScEnv Outlier Analyses Potential candidate SNPs influenced by divergent selection In the environment file of BayeScEnv (v1.1) (Villemereuil and related to migratory-residency were identified using two ge- Gaggiotti 2015), we assigned the same distance from the nome scans (Bayescan [Foll and Gaggiotti 2008] and Pcadapt: origin for both ecotypes, using “0.5” for resident individuals [Luu et al. 2017]) and two environmental association analysis and “0.5” for migratory. 20 pilot runs of 2,000 iterations 1496 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE FIG.2.—Overlap among outlier approaches (a) Koutajoki watershed: consistent outliers identified using multiple genome approaches are presented within white circles. UPGMA tree compiled based on 5,519 SNPs. Heatmap of the major allele frequencies of the seven most consistent outlier SNPs. (b) Oulujoki watershed: consistent outlier identified using multiple genome approaches is presented within white circle. UPGMA tree compiled based on 5,670 SNPs. Heatmap of the major allele frequencies of the most consistent outlier SNPs. were ran prior to a burn-in phase of 50,000 and 1,00,000 between migratory or resident populations. The seven outlier iterations. The rest of the parameters were set to defaults. SNPs mapped against or close to different genes coding for proteins. As such, a zinc finger protein, a metabotropic glu- tamate receptor, a protocadherin, a sialyltransferase, an Allele Frequency and Multivariate Analysis uncharacterized protein, a Farnesyltransferase and a FAM were associated to these outliers (table 2). Allele frequencies for the identified outliers were computed In general, PCA revealed similar patterns as UPGMA clus- using makefreq function in adegenet package. Principle com- tering, where migratory populations grouped together, and ponent analysis (PCA) using the dudi.pca function of ade4 resident populations were more diverged (see Lemopoulos package (v1.7.4; Chessel et al. 2004) was conducted based et al. 2017). PCA based on 5,519 SNP data set (fig. 3a)and on 1) the whole data and 2) solely on the seven outliers iden- on seven outlier SNPs (fig. 3b) showed comparable but more tified in the Koutajoki watershed. Missing values were treated diffused patterns in the second casee. In contrast, PCA of using the tab function of adegenet (v2.0.1; Jombart 2008) random subsets showed weaker distinction between popula- using the “mean” parameter; that is, replacing missing values tions (supplementary fig. S1, Supplementary Material online). by the mean allele frequencies. Three individuals were trimmed out from the second PCA, as they did not contain any genotype data on these seven loci. Oulujoki Based on the analysis of 5,670 SNPs, Bayescan identified 20, Results Pcadapt 133, LFMM 17, and BayeScEnv zero putative outliers (fig. 2b). Overall, six outliers were identified by two methods Koutajoki (supplementary table S6, Supplementary Material online) and Out of 5,519 SNPs, Bayescan detected 44, Pcadapt 105, only one was identified by at least three methods and was LFMM 22, and BayesScEnv eight putative outliers (fig. 2a). located within the coding region of Histone-lysine N-methyl- Five outliers were identified by two methods (supplementary transferase isoform gene (table 2). This SNP did not show fixa- table S5, Supplementary Material online). In addition, six out- tion of alternative alleles between migratory and resident lier loci were identified by three and one by all methods populations (fig. 2b). (fig. 2a and table 2). Two of the outliers were found within the coding region of the corresponding genes while five were Overlap between Watersheds located from 11,597 to 62,804 bp from the adjacent gene based on the Atlantic salmon reference genome. None of the None of the outliers detected by any number of methods seven outliers showed fixed allele frequency differences (table 2, supplementary tables S5 and S6, Supplementary Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1497 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE FIG.3.—Principal component analysis of River Koutajoki samples based on (a) 5519 SNPs and (b) 7 outlier SNPs. Resident populations (KR) are plotted in blue while migratory populations (KM) are plotted in green. Material online) were shared between the two watersheds. Because anadromous salmonids face similar physiological However, some gene families were identified within two and environmental challenges during their life cycle, it is rea- watersheds by a limited number of methods (supplementary sonable to expect that shared biological pathways related to table S7, Supplementary Material online). ancestral diadromy have contributed to the evolution of migrations also in currently landlocked salmonid (McDowall 1997; Bloom et al. 2014). In the Koutajoki watershed, with Discussion historical connectivity to the White Sea, the most consistent To date, no single locus of major effect has been reported to outlier SNP between migratory and resident populations was explain the resident migratory life-history dichotomy in salmo- found adjacent to the ZNF665-like gene. Proteins that contain nids. Our study in brown trout agrees with the available liter- zinc finger motifs have shown to be differentially expressed ature in having identified multiple outlier SNPs that mapped during smoltification in Atlantic salmon (Seear et al. 2010) close to genes that may play a role in migration propensity by and coho salmon Onchorhynchus kisutch (Gallagheretal. affecting osmoregulation, and growth. Most of the involved 2008), but also in the brain between progeny of experimen- functions of outlier genes are likely associated with smoltifi- tally bred offspring in the O.mykiss complex (McKinney et al. cation in salmonids. In addition, the genes associated with 2015). The next two outlier SNPs mapped adjacent to gluta- thermal processes and growth may be related to adaptations mate receptor and cadherin genes (table 2). The metabotropic required to live in small boreal brooks. glutamate receptor gene was identified to be under 1498 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE Table 2 Annotation of the Eight Most Consistent Outlier SNPs SNP ID Watershed Number of CHROMOSOME Distance (bp) Closest Predicted Biological Methods (S. salar) from Closest Protein process/ Supporting Predicted (Corresponding coexpression Outlier Gene Gene Symbol) support Status of a SNP 12184_69 Koutajoki 4 11 11,597 Zinc finger protein 665- Gene family is differentially like (ZNF665-like) expressed during smoltification andindifferent ecotypes progeny in several salmonids (Gallagher et al., 2008; Seearetal., 2010; McKinney et al., 2015) 23320_30.3 Koutajoki 3 22 15,068 metabotropic glutamate Gene family involved in in rainbow receptor 4-like (GRM4- trout migratory behavior (Hale like) et al., 2013; Baerwald et al., 2016) 15271_41 Koutajoki 3 14 59,757 protocadherin-8-like Gene family involved in rainbow (PCDH8-like) trout migratory behavior (Hale et al., 2013; Baerwald et al., 2016) 24980_62.1 Koutajoki 4 25 0 acetylneuraminate-beta- Involved in mucus secretion in brown galactosamide-alpha- trout (Malachowicz et al., 2017) 2,3-sialyltransferase 1- like (ST3GAL1-like) 6823_74.2 Koutajoki 3 6 15,936 Uncharacterized protein Differentially expressed between C14orf37-like freshwater and saltwater Japanese eel. (Gu, 2014) 9048_26 Koutajoki 3 9 62,804 Farnesyltransferase/gera- Differentially expressed in low tem- nylgeranyltransferase perature in two species of gobies type-1 subunit alpha- (Wellband and Heath 2017) like (FNTA1-like) 16463_64 Koutajoki 3 3 0 FAM134C-like Differentially expressed according to temperature in a goby species (Logan and Somero 2010) 18093_80 Oulujoki 3 17 0 Histone-lysine N-methyl- Differentially expressed in trout transferase isoform muscle according to feeding (EZH2-isoform) treatment. (Rescan et al. 2017) diversifying selection in resident and anadromous O. mykiss Among many functions including acting as a mechanical bar- (Hale et al. 2013), whereas another recent work showed that rier (Desseyn et al. 2000) and protection from pathogens metabotropic glutamate receptors are differentially methyl- (Padra et al. 2014), mucus is known to be important for os- ated between O. mykiss ecotypes (Baerwald et al. 2016). moregulation (Shephard 1994; Tacchi et al. 2015). Because G-protein-coupled glutamate receptors are involved Interestingly, a related sialyltransferase protein is overex- in central nervous system transmission (Yin and Niswender pressed in migratory individuals of the partially migrating 2014), synaptic plasticity, learning, and memory (Ohtani European blackbird Turdus merula (Franchini et al. 2017). et al. 2014), these receptors may play an important role in Another outlier (table 2)mapped close to the C14orf37-like the migration and homing of salmonids. Similarly, cadherins gene that is differentially expressed between freshwater and representing calcium-dependent cell adhesion proteins have marine-phase Japanese eel Anguilla japonica (Gu 2014). Thus, been identified as putative targets of migration-driven diver- evidence from gene expression studies supports the putative gent selection in O. mykiss (Hale et al. 2013), while also dis- functional link between osmoregulation and both the playing differential methylation patterns between its two ST3GAL1-like and C14orf37-like genes. Even though the ecotypes (Baerwald et al. 2016). Koutajoki trout is currently landlocked, there has been a his- Two other outlier SNPs mapped close to genes involved in torical connection to the White Sea (Koutaniemi 1999), and osmoregulation (table 2). ST3GAL1-like gene belongs to pro- smoltification most likely still involves genes that originally fa- tein glycosylation pathway and is implicated in mucus produc- cilitated migration to a marine environment, as suggested by tion in anadromous brown trout (Malachowicz et al. 2017). recent studies on landlocked Atlantic and Pacific salmon Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1499 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE species (Piironen et al. 2013; Leitwein et al. 2017). exhaustive list of candidate genes associated with migration Evolutionary consequences of the transition from saline to (Ahrens et al. 2018). Also, while reducing Type I errors (false freshwater has been extensively investigated in nonsalmonid positive), focusing on outliers supported by multiple method- species such as three-spined stickleback (e.g., Hohenlohe ologies may increase the frequency of Type II errors (i.e., fail- et al. 2010; Jones et al. 2012). These studies have led to the ing to identify real outliers under selection). In addition, we identification of several candidate genes potentially involved cannot exclude the possibility that at least some headwater in saltwater–freshwater transition adaptation. For example, streams contain low proportions of migratory individuals, as among candidate genes identified by Ferchaud et al. (2014), measuring individual migration patterns in natural population FAM70A (family with sequence similarity 70), GRID1 (gluta- is extremely challenging. Therefore, it is possible that some mate receptor, ionotropic, delta 1) and CDH20 (cadherin 20) gene families identified in the two watersheds (supplementary all belong to gene families also represented among outlier loci table S7, Supplementary Material online) contain additional in this study (table 2). The detection of these genes in land- variants related to migratory behavior not identified by our locked populations may hint that physiological changes—in- analysis. However, the limited overlap of migration-associated duced by genetic components—could be driving the migratory outliers between studies may also have a biological rationale. behavior (Boel et al. 2014), rather than the opposite. Migratory and resident life history strategies may be influ- In addition, while a majority of the consistent outliers enced by both population-specific effects, such as migration appeared to be associated with migratory behavior the pos- timing (Cauwelier et al. 2017; Prince et al. 2017), or available sibility that some of the identified outliers reflect selection on standing genetic variation (e.g., Barrett and Schluter, 2008). other traits cannot be ruled out. Because resident brown trout Migratory tactics in salmonids are considered threshold traits inhabited cold, small headwater streams, they may experience (Dodson et al. 2013) with switches occurring at certain values strong temperature-driven selection; two other outlier genes for heritable characteristics such as body length (Paez et al. (FNTA1-like; FAM134-like) have shown differential expression 2010) or body mass (Martyniuk et al. 2003). It is not surprising in different temperature conditions in gobies Gillichthys mira- that a large extent of the genetic variation between migration bilis, Neogobius melanostomus and Proterorhinus semilunaris types could be unique to each population, as parallelism in (Logan and Somero 2010; Wellband and Heath 2017). ecological differentiation is not always reflected through uni- In contrast to Koutajoki watershed, only a single outlier form genotype patterns (Frazer and Russello 2013; Nichols was identified by at least three different methods in the et al. 2016). Oulujoki watershed. The outlier occurred near a gene The evolution of migration is not phylogenetically con- (EZH2) that has been shown to be differentially expressed in strained, as alternative migration strategies exist across relation to compensatory muscle growth in rainbow trout evolutionary distant taxa point to a parallel evolution of (Rescan et al. 2017; table 2). Food availability is known to key biological pathways (Dingle 2006). Our results provide have a crucial role in brown trout smoltification (Jones et al. indirect evidence that migration in brown trout, despite 2015), such that fasted trout are more likely to migrate than being considered highly plastic (Olsson et al. 2006), is trout provided with abundant food (Wysujack et al. 2009; influenced by a set of putative candidate genes that ap- Bergman et al. 2013). Thus, it is possible that compensatory pear to be shared with O. mykiss and potentially other muscle growth in brown trout is linked to migration and, Pacific salmonids. Further studies combining individual therefore, EZH2 may have functional consequences for movement information within whole genome-wide asso- migratory-resident life-histories. In addition, epigenetic mod- ciation frameworks are needed to validate the role of the ifications of DNA (i.e., methylation) have been associated with identified migration-related candidate loci. Nevertheless, different life-history strategies in rainbow trout (Baerwald this work, to the best of our knowledge, identifies for the et al. 2016) and also with saltwater adaptation in brown trout first time several promising candidate genes associating (Moran et al. 2013). Therefore, histone-lysine N-methyltrans- with the migratory behavior of brown trout. Thus, the ferase isoform (EZH2) could be involved in such processes in genes we identified represent interesting targets to fur- brown trout. However, the observed outliers may also result ther understand the evolution of migratory behavior. from unintended domestication and mixed origin that may have influenced the genetic make-up of the studied hatchery- Supplementary Material reared migratory stock (OM1). Supplementary data are available at Genome Biology and Interestingly, and resembling previous O. mykiss studies Evolution online. (e.g., Hale et al. 2013; Hecht et al. 2014), none of the outliers overlapped between the two watersheds (supplementary tables S5 and S6, Supplementary Material online). Technical Ethics issues such as suboptimal genome coverage intrinsic to No animal experiments were performed. All sampled fish RADseq, low number of studied populations and low sample (License: 1013/5713-2012 by Center for Economic sizes are some potential factors preventing us from getting an 1500 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE Chapman BB, Bro ¨ nmark C, Nilsson J-A, Hansson L. 2011. The ecology and Development, Transport, and the Environment) were released evolution of partial migration. Oikos 120(12):1764–1775. doi: after sampling. 10.1111/j.1600-0706.2011.20131.x. Charles K, Guyomard R, Hoyheim B, Ombredane D, Baglinie ` re J. 2005. Lack of genetic differentiation between anadromous and resident sympatric brown trout (Salmo trutta) in a Normandy population. Acknowledgments Aquat Living Resour. 18(1):65–69. doi: 10.1051/alr. Chessel D, Dufour AB, Thioulouse J. 2004. The ade4 package - I: one-table The Academy of Finland (#286261) funded the study (A.L., methods. R News 4:5–10. A.Vai.). AXA Research Fund funded S.U.H. A.Vas. was funded Crete-Lafrenie ` re A, Weir LK, Bernatchez L. 2012. Framing the Salmonidae by the Estonian Ministry of Education and Research family phylogenetic portrait: a more complete picture from increased taxon sampling. PLoS One 7(10):e46662. (SF1080022s07 and IUT8-2) and the Academy of Finland Dabney AA, Storey JD. 2015. 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Comparison of Migratory and Resident Populations of Brown Trout Reveals Candidate Genes for Migration Tendency

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Abstract

Candidate genes associated with migration have been identified in multiple taxa: including salmonids, many of whom perform migrations requiring a series of physiological changes associated with the freshwater–saltwater transition. We screened over 5,500 SNPs for signatures of selection related to migratory behavior of brown trout Salmo trutta by focusing on ten differentially migrating freshwater populations from two watersheds (the Koutajoki and the Oulujoki). We found eight outlier SNPs potentially associated with migratory versus resident life history using multiple (3) outlier detection approaches. Comparison of three migratory versus resident population pairs in the Koutajoki watershed revealed seven outlier SNPs, of which three mapped close to genes ZNF665-like, GRM4-like, and PCDH8-like that have been previously associated with migration and smoltification in salmonids. Two outlier SNPs mapped to genes involved in mucus secretion (ST3GAL1-like) and osmoregulation (C14orf37-like). The last two strongly supported outlier SNPs mapped to thermally induced genes (FNTA1-like, FAM134C-like). Within the Oulujoki, the only consistent outlier SNP mapped close to a gene (EZH2) that is associated with compensatory growth in fasted trout. Our results suggest that a relatively small yet common set of genes responsible for physiological functions associated with resident and migratory life histories is evolutionarily conserved. Key words: salmonids, smoltification, RADseq, migratory behavior, life-history strategy. Introduction Salmonids can adopt migratory, partially migratory or res- Diverse migration patterns are almost ubiquitous within the ident life history strategies, with feeding migrations directed animal kingdom. Migrations have evolved to maximize fitness to sea (anadromy), lakes (adfluvial), or larger river sections in heterogeneous environments (Gross et al. 1988; Dingle and (potamodromy), and spawning migrations back to natal Drake 2007; Dingle 2014). Although these migrations are freshwater stream habitats (Chapman, Skov, et al. 2012; proximately induced by a combination of environmental fac- Dodson et al. 2013). Whether migratory salmonids originate tors, changes in physiology, morphology, and/or behavior from marine or freshwater ancestors have been under a long (Chapman et al. 2011; Chapman, Hulthen, et al. 2012), mul- debate (McDowall 2002). Nor it is known whether migration, tiple studies have documented the genetic underpinnings of as a behavioral trait, drives evolution of physiology required migration propensity (e.g., Pulido 2007; Zhu et al. 2008). for migration or vice versa. Ancestral diadromy might have Nonetheless, comparative studies across a wide range of induced the evolution of physiological adaptations that could organisms are needed to understand the mechanisms of evo- drive migratory behavior even in currently landlocked species lution leading to adaptive migrations (Liedvogel et al. 2011). and populations (Piironen et al. 2013). Salmonid migrations The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Genome Biol. Evol. 10(6):1493–1503. doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1493 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE are proximately influenced by individual condition and a num- We predicted that comparison of populations pairs would ber of environmental factors (e.g., Olsson et al. 2006; reveal putative signs of selection related to migratory behav- Wysujack et al. 2009; Vainikka et al. 2012). Dichotomy be- ior and adaptation to different environments. We further tween migratory and resident life histories has a significant predicted that comparison of the two distant watersheds heritable component in the Oncorhynchus (e.g., Hecht et al. could potentially reveal parallel genomic signatures associ- 2012; Hu et al. 2014), Salvelinus (Theriault et al. 2007), and ated with migration tendency, whereas watershed-specific Salmo (Naslund 1993; Palm and Ryman 1999) genera. signs might be linked with adaptation to particular environ- Genome scans and environmental association analyses mental conditions. have been increasingly used to detect signature of selection (e.g., Berg et al. 2015; Babin et al. 2017). These methods Materials and Methods detect markers that deviate from neutrality (i.e., are outliers) and are associated with particular environmental variables (for Sampling areview, see Ahrens et al. 2018). Recent studies investigating Koutajoki Watershed the molecular mechanisms of migratory behavior in salmonids have identified a long list of candidate loci (e.g., Moore et al. Six naturally connected brown trout populations were studied 2017; Veale and Russello 2017). Migration tendency has also within the Koutajoki (K) watershed in North-East Finland been linked with differential gene expression (McKinney et al. (66 17’N 29 53’E [WGS84], fig. 1b); three migratory: 2015) and multiple quantitative trait loci (QTL; Nichols et al. Kuusinkijoki (KM1), Oulankajoki (KM2), and Kitkajoki (KM3) 2008; Hecht et al. 2012). However, only a small proportion of and three resident: (Juumajoki, KR1, flowing into the identified QTL regions and genes appear to be shared Kuusinkijoki; Maaninkajoki, KR2, flowing into Oulankajoki; among populations and species. One particular genomic re- and Pesospuro, KR3, flowing into Kitkajoki). These represent gion (Omy5) has been highlighted by several studies in the a subset of populations that were previously studied by rainbow trout/steelhead Oncorhynchus mykiss complex as the Lemopoulos et al. (2017). most influential genetic component for migration propensity In short, three resident populations (KR1–KR3) in headwa- (Hechtetal. 2012; Pearse et al. 2014; Leitwein et al. 2017). ter streams were sampled by electrofishing in 2015 (Reid et al. However, this association has not been observed across all O. 2009; Luhta et al. 2012). The length of the sampled sections mykiss populations (Hale et al. 2013). Thus, there is little ev- varied between streams from 300 to 800 m. Based on the idence for a master locus for a life-history switch, whereas stream-specific length frequency distributions and scale read- ample evidence exists for family, population, and species- ings the sampled fish belonged to the age groups of 0–3, specific genetic effects on phenotypic migration decisions including mainly parr but also some mature fish. Although (e.g., Narum et al., 2011; Nichols et al., 2016). This suggests the residency of individuals could not be confirmed through that the molecular mechanisms behind migratory-resident telemetry, residency was inferred from the observed repro- life-history variation may vary between species and even pop- ductive isolation (Lemopoulos et al. 2017). ulations or that only a subset of relevant causative genes has The landlocked adfluvial populations (M1–M3) were sam- been identified and characterized. pled by capturing upstream-migrating adults during their Brown trout Salmo trutta is one of the most diverse species spawning migration from the feeding lake Pyaozero in terms of migratory behavior (Olsson et al. 2006). Brown (659 km ) in 2014. The river Oulankajoki samples (scales, see trout shows a wide variety of migration strategies from strictly Saraniemi et al. 2008) were from taken at the Kiutako¨ngas Falls anadromous to complex and plastic feeding and spawning on the river. These natural waterfalls form a partial migration migrations in freshwater (Hindar et al. 1991; Charles et al. obstacle, and local fisheries managers have caught trout 2005; Aarestrup et al. 2017). However, it is not clear to annually since 1965 at the falls and transferred them upstream. what extent the migratory behavior is driven by genetic The fish from the other two rivers were sampled using a fyke effects and to what extent it is plastic and relying on environ- net further downstream in River Olanga in Russia, and the river mental clues. Migration-related life-history variation in brown of origin was inferred from radiotelemetry data. Right after trout have been extensively characterized from different per- capture, 149 individuals were equipped with external (100 spectives (e.g., Boel et al. 2014; Jones et al. 2015). Classic individuals) or internal (49 fish were surgically implanted) radio- studies have also shown that propensity for migration in tags. The fish that were tracked by automated recording brown trout must have a genetic component (Naslund stations and manual tracking of the radiotag signals into the 1993; Palm and Ryman 1999). rivers Kitkajoki and Kuusinkijoki at the spawning time were In this study, we screened single nucleotide polymorphisms used as representative individuals of these river-specific popu- (SNPs) to explore and characterize signatures of selection as- lations. Based on scale readings, the sampled fish from the sociated with migration and residency. We focused on multiple main stems were 6–8 years old, being on their first or second migratory versus resident population pairs from river systems spawning run (Huusko et al. 2017). All captured brown trout in two distinct watersheds (see Lemopoulos et al. 2017). were anaesthetized with clove oil (40 mgl in 1:9 water: 1494 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE FIG.1.—(a) General map of Finland and of the two watersheds locations. (b) Koutajoki watershed. Three resident populations (R) and three migratory populations (M) were sampled in corresponding tributaries and main stems. (c) Oulujoki watershed. Three resident populations (R) and one migratory populations (M) were sampled in corresponding tributaries and lake. ethanol solution, Oy Anders Meder Ab, Helsinki, Finland), mea- et al. unpublished manuscript). The quality of total DNA was sured for total length, sampled for 2–3 scales for age determi- controlled with fluorometric measurements using Qubit 2.0 nation and a small piece of pectoral fin that was preserved in (Qubit dsDNA HS Assay Kit, ThermoFisher Scientific). pure ethanol, and released after recovery. Genotyping Oulujoki Watershed Libraries for double digest restriction-site associated DNA Four populations were sampled in the Oulujoki (O) watershed (ddRADseq) were taken from two previous studies above the main feeding area Lake Oulujarvi (887 km ) (Lemopoulos et al. 2017; Prokkola et al., unpublished manu- (fig. 1c). Wild trout were caught by electrofishing from three script). The protocol used was adapted from (Peterson et al. putatively resident brown trout populations in rivers Pohjajoki 2012; Pukk et al. 2014). Briefly, DNA was digested using two (16.9.–17.9.2015, OR1), Tuhkajoki (17.9.2013, OR2), and 0 0 0 enzymes (PstI-HF [5 CTGCAG 3 ]and BamHI-HF [5 GGATCC Vaarainjoki (28.9.–30.9.2010, 15.9.–11.10.2011, and 3 ]) and then ligated using unique individual barcodes to the 2.10.2012, OR3). Lake Oulujarvi stock (OM1) was originally forwards ends. We pooled the individuals into five libraries. established by breeding adfluvial brown trout from two pop- Each library was purified with a PCR purification kit and frag- ulations, River Varisjoki and River Kongasjoki (fig. 1c). All of ments were size selected at 280–320 bp on e-gel. They were these populations except for River Tuhkajoki are naturally then amplified with PCR and purified with SPRI-Beads. Finally, connected. we examined the concentrations (Qubit 2.0) and quantities using Agilent 2100 Bioanalyzer. Sequencing was conducted DNA Samples at commercial provider Turku Centee for Biotechnology (BTK), DNA samples were extracted from individuals of each popu- Turku, Finland (www.btk.fi) with an Illumina HiSeq 2500 lation (table 1, see also Lemopoulos et al. [2017] and Prokkola system. Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1495 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE Table 1 Summary Information of the Studied Populations and Samples Sample Location River Type Putative Life-History Number of Samples Average Length (TL and SD) [mm] Accronym Juumajoki Tributary Resident 30 70.83 6 12.25 KR1 Maaninkajoki Tributary Resident 30 86.42 6 33.21 KR2 Pesospuro Tributary Resident 30 146.20 6 24.64 KR3 Kuusinkijoki Main stem Migratory 30 699.13 6 73.42 KM1 Oulankajoki Main stem Migratory 30 604.33 6 56.88 KM2 Kitkajoki Main stem Migratory 30 767.73 6 77.32 KM3 Pohjajoki Tributary Resident 11 248.5 6 60.7 OR1 Tuhkajoki Tributary Resident 12 422.1 6 55.7 OR2 Vaarainjoki Tributary Resident 29 459.3 6 79.1 OR3 Migratory stock Main stem Migratory 28 526.9 6 67.1 OM1 We used Stacks v1.40 (Catchen et al. 2013) process_radtags methods (LFMM: [Frichot and Franc¸ois 2015] and BayScEnv: function to demultiplex, quality filter, clean, and trim all reads [Villemereuil and Gaggiotti 2015]. P values were corrected to 85 bp. Since Atlantic salmon is the evolutionarily closest rel- (False Discovers Rate, FDR) and SNPs were identified under ative to brown trout with an accessible genome (GenBank: an alpha threshold of 0.05 for genome scans, whereas more GCA_000233375.4) (Cr^ ete-Lafrenie ` re et al. 2012; Lien et al. relaxed thresholds (0.1) were applied environmental associa- 2016), Bowtie v. 2.3.0 (Langmead and Salzberg 2012)was tion methods in order to reduce type II error. SNPs identified used to create an index of the Atlantic salmon genome and as outliers in at least three of the methods were examined in to align all the reads against it (bowtie2 –p 2 –s/–summary – detail for gene proximity and putative functions. sensitive –end, rest of parameters to default). Reads that were aligned to several locations were assessed separately, and only Bayescan the ones presenting unique best scores were retained. In total, Bayescan (v2.1) (Foll and Gaggiotti 2008) was runwitha 70% of the original reads were retained, and the average cov- burn-in of 50,000 and 50,000 iterations. Individuals were erage after filtering was 53X. Genotype error rate (single nu- pooled according to populations, and the rest of the param- cleotide polymorphism [SNP] level) was 6%. We used the eters were set to defaults. refmap and population functions for SNP calling. SNPs with a minimum coverage of three (i.e., each SNP position was cov- Pcadapt ered by at least three reads), with loci that were present in all our populations (–p 6 and –p 4, respectively) and in 60% of the Pcadapt (v3.0.4) (Luu et al. 2017)was run with a K value of six individuals retained. Further, SNPs with a minor allele frequency (v3.3.2). SNPs q-values were obtained with the qvalue pack- of at least 0.05 (Roesti et al. 2012) and maximum heterozygos- age (Dabney and Storey 2015). All the other parameters were ity of 0.5 (Hohenlohe et al. 2011) were retained (supplemen- set to default values. tary table S4, Supplementary Material online). All other parameters were set to default values. Afterwards, we filtered Latent Factor Mixed Modeling (LFMM) the SNPs for Hardy–Weinberg equilibrium using the Using the LEA package (v1.6.0) (Frichot and Franc¸ois 2015)in “adegenet” (v2.01) (Jombart 2008) package (hw.test func- R version 3.3.2 (R Core Team 2016), we ran the lfmm func- tion, with 1,000 repetitions) in R version 3.2.0 (R Core Team tion. The environment file was implemented using “0” for 2015). Only loci that were in equilibrium in at least 50% of the resident populations and “1” for migratory populations. 30 populations were kept. In total, out of 3.46 10 and repetitions were conducted with K¼ 6/K¼ 4,60,000 itera- 1.19 10 retained reads, respectively (supplementary tables tions and a burn-in of 30,000 and by allowing missing data. S1–S3, Supplementary Material online), 5,519 SNPs for the The rest of the parameters were set to defaults. Adjusted P Koutajoki watershed and 5,670 for the Oulujoki watershed values were obtained using the genomic inflation factor (k) were retained for the final analyses. and Benjamini–Hochberg correction. BayeScEnv Outlier Analyses Potential candidate SNPs influenced by divergent selection In the environment file of BayeScEnv (v1.1) (Villemereuil and related to migratory-residency were identified using two ge- Gaggiotti 2015), we assigned the same distance from the nome scans (Bayescan [Foll and Gaggiotti 2008] and Pcadapt: origin for both ecotypes, using “0.5” for resident individuals [Luu et al. 2017]) and two environmental association analysis and “0.5” for migratory. 20 pilot runs of 2,000 iterations 1496 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE FIG.2.—Overlap among outlier approaches (a) Koutajoki watershed: consistent outliers identified using multiple genome approaches are presented within white circles. UPGMA tree compiled based on 5,519 SNPs. Heatmap of the major allele frequencies of the seven most consistent outlier SNPs. (b) Oulujoki watershed: consistent outlier identified using multiple genome approaches is presented within white circle. UPGMA tree compiled based on 5,670 SNPs. Heatmap of the major allele frequencies of the most consistent outlier SNPs. were ran prior to a burn-in phase of 50,000 and 1,00,000 between migratory or resident populations. The seven outlier iterations. The rest of the parameters were set to defaults. SNPs mapped against or close to different genes coding for proteins. As such, a zinc finger protein, a metabotropic glu- tamate receptor, a protocadherin, a sialyltransferase, an Allele Frequency and Multivariate Analysis uncharacterized protein, a Farnesyltransferase and a FAM were associated to these outliers (table 2). Allele frequencies for the identified outliers were computed In general, PCA revealed similar patterns as UPGMA clus- using makefreq function in adegenet package. Principle com- tering, where migratory populations grouped together, and ponent analysis (PCA) using the dudi.pca function of ade4 resident populations were more diverged (see Lemopoulos package (v1.7.4; Chessel et al. 2004) was conducted based et al. 2017). PCA based on 5,519 SNP data set (fig. 3a)and on 1) the whole data and 2) solely on the seven outliers iden- on seven outlier SNPs (fig. 3b) showed comparable but more tified in the Koutajoki watershed. Missing values were treated diffused patterns in the second casee. In contrast, PCA of using the tab function of adegenet (v2.0.1; Jombart 2008) random subsets showed weaker distinction between popula- using the “mean” parameter; that is, replacing missing values tions (supplementary fig. S1, Supplementary Material online). by the mean allele frequencies. Three individuals were trimmed out from the second PCA, as they did not contain any genotype data on these seven loci. Oulujoki Based on the analysis of 5,670 SNPs, Bayescan identified 20, Results Pcadapt 133, LFMM 17, and BayeScEnv zero putative outliers (fig. 2b). Overall, six outliers were identified by two methods Koutajoki (supplementary table S6, Supplementary Material online) and Out of 5,519 SNPs, Bayescan detected 44, Pcadapt 105, only one was identified by at least three methods and was LFMM 22, and BayesScEnv eight putative outliers (fig. 2a). located within the coding region of Histone-lysine N-methyl- Five outliers were identified by two methods (supplementary transferase isoform gene (table 2). This SNP did not show fixa- table S5, Supplementary Material online). In addition, six out- tion of alternative alleles between migratory and resident lier loci were identified by three and one by all methods populations (fig. 2b). (fig. 2a and table 2). Two of the outliers were found within the coding region of the corresponding genes while five were Overlap between Watersheds located from 11,597 to 62,804 bp from the adjacent gene based on the Atlantic salmon reference genome. None of the None of the outliers detected by any number of methods seven outliers showed fixed allele frequency differences (table 2, supplementary tables S5 and S6, Supplementary Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1497 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE FIG.3.—Principal component analysis of River Koutajoki samples based on (a) 5519 SNPs and (b) 7 outlier SNPs. Resident populations (KR) are plotted in blue while migratory populations (KM) are plotted in green. Material online) were shared between the two watersheds. Because anadromous salmonids face similar physiological However, some gene families were identified within two and environmental challenges during their life cycle, it is rea- watersheds by a limited number of methods (supplementary sonable to expect that shared biological pathways related to table S7, Supplementary Material online). ancestral diadromy have contributed to the evolution of migrations also in currently landlocked salmonid (McDowall 1997; Bloom et al. 2014). In the Koutajoki watershed, with Discussion historical connectivity to the White Sea, the most consistent To date, no single locus of major effect has been reported to outlier SNP between migratory and resident populations was explain the resident migratory life-history dichotomy in salmo- found adjacent to the ZNF665-like gene. Proteins that contain nids. Our study in brown trout agrees with the available liter- zinc finger motifs have shown to be differentially expressed ature in having identified multiple outlier SNPs that mapped during smoltification in Atlantic salmon (Seear et al. 2010) close to genes that may play a role in migration propensity by and coho salmon Onchorhynchus kisutch (Gallagheretal. affecting osmoregulation, and growth. Most of the involved 2008), but also in the brain between progeny of experimen- functions of outlier genes are likely associated with smoltifi- tally bred offspring in the O.mykiss complex (McKinney et al. cation in salmonids. In addition, the genes associated with 2015). The next two outlier SNPs mapped adjacent to gluta- thermal processes and growth may be related to adaptations mate receptor and cadherin genes (table 2). The metabotropic required to live in small boreal brooks. glutamate receptor gene was identified to be under 1498 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE Table 2 Annotation of the Eight Most Consistent Outlier SNPs SNP ID Watershed Number of CHROMOSOME Distance (bp) Closest Predicted Biological Methods (S. salar) from Closest Protein process/ Supporting Predicted (Corresponding coexpression Outlier Gene Gene Symbol) support Status of a SNP 12184_69 Koutajoki 4 11 11,597 Zinc finger protein 665- Gene family is differentially like (ZNF665-like) expressed during smoltification andindifferent ecotypes progeny in several salmonids (Gallagher et al., 2008; Seearetal., 2010; McKinney et al., 2015) 23320_30.3 Koutajoki 3 22 15,068 metabotropic glutamate Gene family involved in in rainbow receptor 4-like (GRM4- trout migratory behavior (Hale like) et al., 2013; Baerwald et al., 2016) 15271_41 Koutajoki 3 14 59,757 protocadherin-8-like Gene family involved in rainbow (PCDH8-like) trout migratory behavior (Hale et al., 2013; Baerwald et al., 2016) 24980_62.1 Koutajoki 4 25 0 acetylneuraminate-beta- Involved in mucus secretion in brown galactosamide-alpha- trout (Malachowicz et al., 2017) 2,3-sialyltransferase 1- like (ST3GAL1-like) 6823_74.2 Koutajoki 3 6 15,936 Uncharacterized protein Differentially expressed between C14orf37-like freshwater and saltwater Japanese eel. (Gu, 2014) 9048_26 Koutajoki 3 9 62,804 Farnesyltransferase/gera- Differentially expressed in low tem- nylgeranyltransferase perature in two species of gobies type-1 subunit alpha- (Wellband and Heath 2017) like (FNTA1-like) 16463_64 Koutajoki 3 3 0 FAM134C-like Differentially expressed according to temperature in a goby species (Logan and Somero 2010) 18093_80 Oulujoki 3 17 0 Histone-lysine N-methyl- Differentially expressed in trout transferase isoform muscle according to feeding (EZH2-isoform) treatment. (Rescan et al. 2017) diversifying selection in resident and anadromous O. mykiss Among many functions including acting as a mechanical bar- (Hale et al. 2013), whereas another recent work showed that rier (Desseyn et al. 2000) and protection from pathogens metabotropic glutamate receptors are differentially methyl- (Padra et al. 2014), mucus is known to be important for os- ated between O. mykiss ecotypes (Baerwald et al. 2016). moregulation (Shephard 1994; Tacchi et al. 2015). Because G-protein-coupled glutamate receptors are involved Interestingly, a related sialyltransferase protein is overex- in central nervous system transmission (Yin and Niswender pressed in migratory individuals of the partially migrating 2014), synaptic plasticity, learning, and memory (Ohtani European blackbird Turdus merula (Franchini et al. 2017). et al. 2014), these receptors may play an important role in Another outlier (table 2)mapped close to the C14orf37-like the migration and homing of salmonids. Similarly, cadherins gene that is differentially expressed between freshwater and representing calcium-dependent cell adhesion proteins have marine-phase Japanese eel Anguilla japonica (Gu 2014). Thus, been identified as putative targets of migration-driven diver- evidence from gene expression studies supports the putative gent selection in O. mykiss (Hale et al. 2013), while also dis- functional link between osmoregulation and both the playing differential methylation patterns between its two ST3GAL1-like and C14orf37-like genes. Even though the ecotypes (Baerwald et al. 2016). Koutajoki trout is currently landlocked, there has been a his- Two other outlier SNPs mapped close to genes involved in torical connection to the White Sea (Koutaniemi 1999), and osmoregulation (table 2). ST3GAL1-like gene belongs to pro- smoltification most likely still involves genes that originally fa- tein glycosylation pathway and is implicated in mucus produc- cilitated migration to a marine environment, as suggested by tion in anadromous brown trout (Malachowicz et al. 2017). recent studies on landlocked Atlantic and Pacific salmon Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 1499 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Lemopoulos et al. GBE species (Piironen et al. 2013; Leitwein et al. 2017). exhaustive list of candidate genes associated with migration Evolutionary consequences of the transition from saline to (Ahrens et al. 2018). Also, while reducing Type I errors (false freshwater has been extensively investigated in nonsalmonid positive), focusing on outliers supported by multiple method- species such as three-spined stickleback (e.g., Hohenlohe ologies may increase the frequency of Type II errors (i.e., fail- et al. 2010; Jones et al. 2012). These studies have led to the ing to identify real outliers under selection). In addition, we identification of several candidate genes potentially involved cannot exclude the possibility that at least some headwater in saltwater–freshwater transition adaptation. For example, streams contain low proportions of migratory individuals, as among candidate genes identified by Ferchaud et al. (2014), measuring individual migration patterns in natural population FAM70A (family with sequence similarity 70), GRID1 (gluta- is extremely challenging. Therefore, it is possible that some mate receptor, ionotropic, delta 1) and CDH20 (cadherin 20) gene families identified in the two watersheds (supplementary all belong to gene families also represented among outlier loci table S7, Supplementary Material online) contain additional in this study (table 2). The detection of these genes in land- variants related to migratory behavior not identified by our locked populations may hint that physiological changes—in- analysis. However, the limited overlap of migration-associated duced by genetic components—could be driving the migratory outliers between studies may also have a biological rationale. behavior (Boel et al. 2014), rather than the opposite. Migratory and resident life history strategies may be influ- In addition, while a majority of the consistent outliers enced by both population-specific effects, such as migration appeared to be associated with migratory behavior the pos- timing (Cauwelier et al. 2017; Prince et al. 2017), or available sibility that some of the identified outliers reflect selection on standing genetic variation (e.g., Barrett and Schluter, 2008). other traits cannot be ruled out. Because resident brown trout Migratory tactics in salmonids are considered threshold traits inhabited cold, small headwater streams, they may experience (Dodson et al. 2013) with switches occurring at certain values strong temperature-driven selection; two other outlier genes for heritable characteristics such as body length (Paez et al. (FNTA1-like; FAM134-like) have shown differential expression 2010) or body mass (Martyniuk et al. 2003). It is not surprising in different temperature conditions in gobies Gillichthys mira- that a large extent of the genetic variation between migration bilis, Neogobius melanostomus and Proterorhinus semilunaris types could be unique to each population, as parallelism in (Logan and Somero 2010; Wellband and Heath 2017). ecological differentiation is not always reflected through uni- In contrast to Koutajoki watershed, only a single outlier form genotype patterns (Frazer and Russello 2013; Nichols was identified by at least three different methods in the et al. 2016). Oulujoki watershed. The outlier occurred near a gene The evolution of migration is not phylogenetically con- (EZH2) that has been shown to be differentially expressed in strained, as alternative migration strategies exist across relation to compensatory muscle growth in rainbow trout evolutionary distant taxa point to a parallel evolution of (Rescan et al. 2017; table 2). Food availability is known to key biological pathways (Dingle 2006). Our results provide have a crucial role in brown trout smoltification (Jones et al. indirect evidence that migration in brown trout, despite 2015), such that fasted trout are more likely to migrate than being considered highly plastic (Olsson et al. 2006), is trout provided with abundant food (Wysujack et al. 2009; influenced by a set of putative candidate genes that ap- Bergman et al. 2013). Thus, it is possible that compensatory pear to be shared with O. mykiss and potentially other muscle growth in brown trout is linked to migration and, Pacific salmonids. Further studies combining individual therefore, EZH2 may have functional consequences for movement information within whole genome-wide asso- migratory-resident life-histories. In addition, epigenetic mod- ciation frameworks are needed to validate the role of the ifications of DNA (i.e., methylation) have been associated with identified migration-related candidate loci. Nevertheless, different life-history strategies in rainbow trout (Baerwald this work, to the best of our knowledge, identifies for the et al. 2016) and also with saltwater adaptation in brown trout first time several promising candidate genes associating (Moran et al. 2013). Therefore, histone-lysine N-methyltrans- with the migratory behavior of brown trout. Thus, the ferase isoform (EZH2) could be involved in such processes in genes we identified represent interesting targets to fur- brown trout. However, the observed outliers may also result ther understand the evolution of migratory behavior. from unintended domestication and mixed origin that may have influenced the genetic make-up of the studied hatchery- Supplementary Material reared migratory stock (OM1). Supplementary data are available at Genome Biology and Interestingly, and resembling previous O. mykiss studies Evolution online. (e.g., Hale et al. 2013; Hecht et al. 2014), none of the outliers overlapped between the two watersheds (supplementary tables S5 and S6, Supplementary Material online). Technical Ethics issues such as suboptimal genome coverage intrinsic to No animal experiments were performed. All sampled fish RADseq, low number of studied populations and low sample (License: 1013/5713-2012 by Center for Economic sizes are some potential factors preventing us from getting an 1500 Genome Biol. Evol. 10(6):1493–1503 doi:10.1093/gbe/evy102 Advance Access publication May 29, 2018 Downloaded from https://academic.oup.com/gbe/article-abstract/10/6/1493/5020727 by Ed 'DeepDyve' Gillespie user on 20 June 2018 Comparison of Migratory and Resident Populations of Brown Trout GBE Chapman BB, Bro ¨ nmark C, Nilsson J-A, Hansson L. 2011. 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