# Genetic basis of latitudinal adaptation in Chinook salmon

Jonathan Rolland, Kris Christensen, Yaël Assassa, Tom Booker, Robert H.

Devlin, Maëva Gabrielli, Kathleen G. O'Malley, Natacha Nikolic, Ruth E.

Withler, Ben F. Koop, et al.

► To cite this version:

Jonathan Rolland, Kris Christensen, Yaël Assassa, Tom Booker, Robert H. Devlin, et al. Genetic basis of latitudinal adaptation in Chinook salmon. 2025. hal-05425316

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Distributed under a Creative Commons Attribution 4.0 International License
1 Genetic basis of latitudinal adaptation in Chinook salmon

2 Jonathan Rolland¹*, Kris A. Christensen², Yaël Assassa¹, Tom Booker³, Robert H. Devlin⁴,

3 Maëva Gabrielli¹, Kathleen G. O'Malley⁵, Natacha Nikolic¹,⁶,⁷, Ruth E. Withler⁸, Ben F. Koop²,

4 Dolph Schlüter⁹

5

6 ¹Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS,

7 IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3), Toulouse, France.

8 ²Department of Biology, University of Victoria, 3800 Finnerty Rd., Victoria, B.C. - Canada.

9 ³Department of Forest and Conservation Science, University of British Columbia, #3041-2424 Main

10 Mall, Vancouver, B.C. – Canada

11 ⁴Fisheries and Ocean Canada, 4160 Marine Drive, West Vancouver, B.C., Canada

12 ⁵State Fisheries Genomics Lab, Coastal Oregon Marine Experiment Station, Department of Fisheries,

13 Wildlife, and Conservation Sciences, Hatfield Marine Science Center, Oregon State University,

14 Newport, Oregon, USA

15 ⁶Université de Pau et des Pays de l'Adour, INRAE, AQUA, ECOBIOP, Saint-Pée-sur-Nivelle, France

16 ⁷ARBRE – Agence de Recherche Pour la Biodiversité à La Réunion, Saint-Gilles, France

17 ⁸Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Rd., Nanaimo, B.C. -

18 Canada (emertitus).

19 ⁹Department of Zoology, University of British Columbia, #4200-6270 University Blvd., Vancouver,

20 B.C. – Canada

21

22 *Corresponding author

23 Authors are ranked alphabetically except the first, the second and the two last authors.

24

25 Email addresses

26 Jonathan Rolland (ORCID ID: 0000-0002-7141-0870): jonathan.rolland@univ-tlse3.fr

27 Kris Christensen (ORCID ID: 0000-0003-0021-0326): kris.christensen@wsu.edu

28 Yaël Assassa: yael.assassa@yahoo.fr

29 Tom Booker (ORCID ID: 0000-0001-8403-6219): thomas.booker@ubc.ca

30 Robert Devlin: devlinrh@hotmail.com

31 Maëva Gabrielli: maeva.gab@hotmail.fr

32 Kathleen O'Malley (ORCID ID: 0000-0003-0995-599X): Kathleen.OMalley@oregonstate.edu

33 Natacha Nikolic: natacha.nikolic@inrae.fr

34 Ruth Withler: rwithler@shaw.ca

35 Ben Koop (ORCID ID: 0000-0003-0045-5200): bkoop@uvic.ca

36 Dolph Schlüter (ORCID ID: 0000-0003-1683-7836): dolph.schlüter@ubc.ca
37

**Abstract**

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**Introduction**

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Organisms can experience very different selective pressures across their latitudinal range, yet the genetic changes that underlie phenotypic differences across latitudes are poorly understood. Pacific salmon are renowned for their extreme life history variation and local adaptation to specific environments. We sequenced 137 whole-genomes of Chinook salmon (*Oncorhynchus tshawytscha*) sampled along the Pacific Coast of north America to study how adaptation across latitudes has evolved. We tested latitudinal gradients in five genes previously linked to polymorphisms in timing of migration and rate of maturation in salmonids: *CLOCK1A*, *CLOCK1B*, *VGLL3*, *SIX6*, *ROCK1/GREB1L*. Allelic variation in all five candidate genes was steeply associated with latitude. Many of the SNPs in *ROCK1/GREB1L* overlapped with those previously linked to a polymorphism in timing of adult migration in the south of the range. Additionally, we found that many other genes having strong correlations with latitude interact with the gene pathways previously linked to migration or maturation (including circadian clock and Hippo signaling pathways) and in multiple organs (especially the hypothalamic-pituitary-gonadal axis). We suggest that changes in functional clusters of interacting genes rather than a small number of disconnected changes influence life history adaptations to latitude. This has implications for the conservation of this iconic species and its adaptation to future climates.

Identifying the genetic mechanisms by which organisms adapt to different environments has been a central research topic in evolutionary biology [1-3]. Numerous genes have been discovered in the last few decades that have evolved in association with drastic environmental changes in temperature, photoperiod, and seasonality across latitudes [4-6]. Genetic adaptation to latitude is of newfound interest due to poleward shifts in phenotypic natural selection caused by global temperature changes, which will likely shift the geographical distributions of underlying genotypes within species, especially under ‘evolutionary rescue’ [7]. Identifying loci exhibiting strong latitudinal clines with the current changes in temperature provides a framework for predicting which alleles are likely to be on the move in the coming decades [8].

Local adaptation has been studied for some traits in salmonids, identifying some large-effect genes associated with different run time ecotypes within rivers [9-13] and between anadromous and landlocked populations of both Atlantic salmon and Pacific sockeye salmon [14,15]. Here,
70 we examine latitudinal variation in several genes that we consider strong life history candidates
71 because they have previously been associated with one of two classes of phenotypic traits in
72 salmonids that vary greatly with latitude in Chinook salmon: adult migration timing and
73 maturation rate. For example, in Chinook salmon, high latitude individuals have slightly bigger
74 body size [16] and earlier spawning times (June/July) than fish at low latitudes
75 (November/December [17]). High latitude fish also take longer to mature [18]. Chinook salmon
76 spend a longer time at sea at higher latitudes before migrating to rivers to spawn: 3 years on
77 average (and up to 8 years) in Alaska, compared to two years on average in California [20].
78 Although temperature exhibits substantial variation across latitude, little is yet known about the
79 genetic basis of latitudinal variation in thermal tolerance in salmonids [21].

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81 The genes examined in the present study include three candidates previously linked to the
timing of migration in salmon. Variation in the length of the PolyQ domain within the
82 CLOCK1B locus is correlated with spawn timing, age at maturity and latitude in Chinook and
83 chum salmon [22-24]. In rainbow trout, the frequency of a large genomic inversion that includes
84 CLOCK varies strongly with latitude [25]. Chinook salmon also possess CLOCK1A, a paralog
85 to CLOCK1B [22], which we considered as our second candidate for adaptation to latitude.
86 Third, the intergenic region between the GREB1L and ROCK1 genes has been shown to be
87 strongly associated with spring-run versus fall-run ecotypes in Chinook salmon in California
88 and Washington state [10,12,26,27] and summer-run and winter-run ecotypes in steelhead in
89 Oregon and California [9,10,28,29].
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92 Our additional candidates include two large-effect genes, vestigial-like 3 (*VGLL3*) and sine
93 oculis-related homeobox (*SIX6*), which have been previously implicated in Atlantic salmon's
94 variation in maturation rate [30-39] and also in the age at menarche in humans [40-41]. *VGLL3*
95 acts as a regulator of adiposity, explaining 39% of the age at maturity in Norwegian populations
96 of Atlantic salmon [31]. *SIX6* is linked to stream versus lake-shore spawning ecotypes in
97 sockeye salmon [42-43], as well as age at maturity in both sockeye salmon and steelhead [44].
98 It is also associated with run timing in Atlantic salmon [45]. Latitudinal variation in these two
99 genes remains unexplored.

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101 In the first stage of our analysis, we tested the relationship between latitude and allelic variation
102 at these five "candidate" genes: three related to clock rhythm and timing (*OTSCLOCK1A*,
103 *OTSCLOCK1B*, *GREB1L/ROCK1* intergenic region) and two associated with growth and
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maturation (*VGLL3*, *SIX6*). We used whole-genome re-sequencing of 137 Chinook salmon (*Oncorhynchus tshawytscha*) across its latitudinal range along the Pacific Coast of north America, between California and Alaska, to test all SNPs in these five genes and their adjacent intergenic regions. In a second, discovery, stage of our analysis we tested the relationship between all genomic SNPs and latitude to identify new genes underlying local adaptation to latitude. This *a priori* candidate approach reduces the probability of false positives, while the discovery phase identifies new candidates for local adaptation that could be explored in future research.

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## Methods

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### *Sampling*

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A total of 137 Chinook salmon from 29 populations were sampled between 2001 to 2017 across their North American range, from California (Sacramento River) to Alaska (Cook Inlet, Table S1, Figure 1). Individuals were sampled at various dates (Table S1, Figure S1), with more samples in the most abundant run for each river based on expert knowledge (often the summer/fall-run populations). Based on count data, we checked that our sampling was representative of the main run, and consistently our dates of sampling followed a cline from summer-run in June in Alaska to late fall-run in February in California (Table S1, Figure S1), following the well-documented latitudinal trend in spawning times, which occur mainly in June/July in Alaska and November/December in California [17]. Diversity of migration timing varies with latitude, with more variability in California (winter, spring, fall and late fall runs), than Washington state (spring and fall runs) and Alaska (mainly one summer run, Figure S1, [13,46-47]). Thus, not all run diversity is represented in our sample design. To test whether some alleles vary in their relationship to latitude depending on the run sampled, we carried out additional analyses adding (low-coverage) Californian samples from the winter and spring run from [11]. Given that salmon were not always caught at their spawning site, we used the longitude and latitude corresponding to the junction of the source river and the sea in our analyses. This procedure ensured that all salmon from the same river sampled at different locations, or at a different stage of their migration, are assigned the same latitude (Table S1).

### *Sequencing*
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DNA extractions were performed on tissues stored either in 100% ethanol or RNAlater (ThermoFisher) using the protocol "Genomic DNA Preparation from RNAlater™ Preserved Tissues - CA". NxSeq AmpFREE Low DNA Library Kits and NxSeq Adaptors (Lucigen) were used at McGill University and Génome Québec Innovation Centre (now the Centre d'expertise et de services Génome Québec) to generate shotgun DNA libraries. These libraries were then sequenced on an Illumina HiSeq X (PE150).

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Variant calling

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Paired-end reads were aligned to the Chinook salmon genome assembly Otsh_v2.0 [48] (NCBI RefSeq assembly GCF_018296145.1) with bwa mem (version 0.7.15, parameter: -M) (49,50). Alignments were sorted with samtools sort (version 1.9, with default parameters, [51]), read group information was added with Picard (version 2.18.9, parameter: VALIDATION_STRINGENCY=LENIENT), and PCR duplicates were identified with Picard using the same parameters. Samples sequenced on multiple Illumina lanes were merged during PCR duplicate identification with Picard.

The HaplotypeCaller command of GATK (version 3.8 [52-54]) was used to generate .gvcf files with the --genotyping_mode DISCOVERY and --emitRefConfidence GVCF parameters. GenotypeGVCFs from GATK was subsequently used to generate genotypes in 10 Mbp intervals (see [15] for Python scripts). The intervals were merged with the GATK CatVariants command using default parameters. Nucleotide variants were recalibrated using both a "truth" (GATK nomenclature) and a training dataset with the GATK VariantRecalibrator.

The truth dataset was generated from a published genetic map [55] by converting the variant positions from 0-based positions to 1-based positions manually and mapping them to the genome assembly. The training dataset was created by filtering the newly generated nucleotide variants using the VariantFiltration command and the following parameters: QD < 2.0, FS > 60.0, SOR > 3.0, MQRankSum < -12.5, and ReadPosRankSum < -8.0. The following options were used during recalibration: -an FS, -an SOR, -an DP, -an InbreedingCoeff, and -mode SNP. The truth dataset was given a prior score of 15.0 and the training dataset was given a prior score of 12.0. Recalibration was then applied to the variants using the ApplyRecalibration command with the mode set to SNP and a filter (--ts_filter_level) set to 99.5.
The recalibrated nucleotide variants were then filtered using *vcftools* [56]. We parameterized *vcftools* to keep all SNP with a PASS tag (`--remove-filtered-all`), for which at least 90% of the individuals had data (`--max-missing 0.9`), with a minimum frequency of the minor allele of 0.05 (`--maf 0.05`), and a mean depth between 5 and 200 (`--min-meanDP 5 --max-meanDP 200`). We also removed indels (`--remove-indels`), and kept only biallelic SNPs (`--min-alleles 2 --max-alleles 2`). SNPs of poor quality (QUAL <= 20) were also dropped and we transformed the vcf file into a genotype table with all SNP using the function *extract.gt* from the package *vcfR* (parameter= 'GT'). We obtained a total of 8,055,508 SNPs, with a mean depth of 20.3 per individual. Average counts of missing genotypes was on average 69,062 per individual.

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## Population Structure

We estimated genetic variation among Chinook salmon samples with *Admixture* software version 1.3.0 [57] and principal component analysis (PCA) using *PLINK* version 1.9 [58]. These analyses were used to identify population structure from the 1,756,159 nucleotide variants remaining after filtering for linkage disequilibrium. Regions of the genome with increased linkage disequilibrium can disproportionately influence clustering otherwise. Hence, we pruned variant sites in linkage disequilibrium using the function *indep* of *PLINK* with a window size of 50Kb. This step removed 6,299,349 of the 8,055,508 initial variants. To convert the nucleotide variants to an appropriate format for *Admixture* analysis, we used a Python script [15] to replace chromosome names with numbers and then generated .bed files using *PLINK* (parameters used: --chr-set 34 no-xy, unplaced scaffolds were excluded). An optimal cluster number was chosen based on the lowest cross-validation error value output from the *Admixture* software (default settings, clusters from K=1 to 10 were tested). The PCA was generated using *PLINK* with the following parameters: --pca, --allow-extra-chr.

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## First stage: Candidate gene selection

In the first stage of our analysis, we tested for associations between allele frequencies and latitude at all SNPs of the five candidate genes for timing and maturation. We hypothesized that these five would be likely to vary between high and low latitude environmental conditions in association with known phenotypic variation in migration timing and maturation rate. For each candidate gene, we included the region spanned by the exons ("coding region", including introns) and the adjacent intergenic regions (in the case of the candidate intergenic region between *GREB1L* and *ROCK1*, we included the two adjacent coding regions). One gene,
CLOCK1B, had previously been shown to vary with latitude [23] and we also included its paralog CLOCK1A. GREB1L/ROCK1 was previously associated with a spring-fall polymorphism in migration timing [12,13,24,26,28]. The SNPs differentiating fall- and spring-run Chinook salmon are mainly regulatory and are associated with gene expression level [59], but the precise mechanism regulating migration timing is unknown. VGLL3 and SIX6 have been associated with population variation in growth and maturation rate in Atlantic salmon [31,35-37,43]. The genomic positions of each gene were identified using the information in the GCA_018296145.1_Otsh_v2.0_genomic_wMito.gff annotation file and data from the gene section of the NCBI website www.ncbi.nlm.nih.gov for Chinook salmon. SNPs within the coding region of the gene included those located between the first base of the first exon and the last base of the last exon, and SNPs in intergenic regions fell in-between coding regions.

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## Association between latitude and candidate gene allele frequencies

We tested associations between allele frequencies in 29 populations and their river latitude at all SNPs at the five candidate genes (and adjacent regions) using two modeling methods. First, we modeled the association between the occurrences of ALT alleles (0 or 1, where 0 indicates a reference (REF) allele), in all individuals genotyped in each river using generalized linear mixed-effects models (GLMM) with a logit link function. River was included as a random effect. We used the *bglmer()* function of the R package *blme*, which is similar to *glmer()* of the *lme4* package but uses a penalized likelihood to prevent boundary estimates of 0 for the variance among random groups (equivalent to using a weakly informative prior [60]). Second, we use the method *BayPass* [61] to estimate the strength of the association between the latitude and the occurrence of ALT alleles. We first ran the core model that is covariate-free to estimate the covariance matrix of population allele frequencies Ω resulting from gene flow or shared population history (Figure S2). We then ran a standard covariate model testing for a linear association between latitude and the occurrence of ALT alleles at each SNP accounting for Ω. The slope parameter *β* was estimated using default parametrization: importance sampling approximation and a uniform prior distribution. The results presented below are the Bayes Factor (BF) estimated from the comparison between the covariate and the core model, measuring the degree of support for an association between allele frequency and latitude at each SNP.

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We evaluated statistically significant associations by combining results for all SNPs within the coding or intergenic region of a given candidate gene. We calculated the average $-log_{10}(p-value)$ from the *bglmer()* analysis and the mean BF from the *BayPass* analysis of all SNPs within the gene or intergenic region. This was repeated for all candidate coding and intergenic regions. Statistical significance was determined by carrying out the same analyses on all other genes in the genome (see below). We then ranked all the regions in the genome according to each average metric (69,374 regions for *bglmer()* and 71,003 for *BayPass*). Candidate gene significance was then determined by whether the gene region of interest fell in the 5% tail of the ranked metrics from the whole genome, in the top 3,469 regions for the mean $-log_{10}(p-value)$ of *bglmer()*, and the top 3,550 regions for mean BF of *BayPass*.

SNPs falling in the top 5% of $-log_{10}(p-value)$ and BF values were often clustered within candidate genes or intergenic regions (Figure 2). To locate these clusters, we adopted a “peakfinder” approach. Peaks in $-log_{10}(p-value)$ and BF were identified by sliding a 50-SNPs window across the genome (step size 1 base) and calculating the average value of each metric for all SNPs within the window. We then kept one (the best) window for each gene coding region or intergenic/regulatory region. This was repeated for all other genes in the genome (see below). All regions were then ranked and those candidate genes falling in the top 5% were deemed statistically significant. For each SNP in candidate genes and neighboring regions, we ran *SnpEff* [62] to identify the synonymous and non-synonymous mutations in the genic regions.

We then tested if the SNPs in the candidate genes showing strong latitudinal gradients were the same as those known to distinguish spring and fall-run Chinook salmon within the Klamath and Sacramento Basins in California [12], the only populations for which whole genomes of spring run individuals were available. We recalculated allele frequency differences between salmon samples from the two run types using information provided by E. Anderson from [12] in the files *wgs-chinook-samples.csv* and *Cal-Chinook-160.vcf.gz* (File S4-S5). This latter file contains the whole genome sequences from 160 individuals previously aligned on the reference genome *Otsh_v1.0* (GenBank accession number GCA_002872995.1), so we had to convert the coordinates to the reference *Otsh_v2.0* used in our study. To do this, we first mapped the two assemblies using *nucmer* V.4.0.1 [63] with default settings except the minimum length of a cluster of matches that was set to 100. We then converted the resulting *delta* file to the *maf* format using *MUMmer* V.3.20 [64] and to a chain file using the *maf-convert.py* script from
LAST [65]. We finally lifted all coordinates of Thompson *et al.* vcf from Otsh_v1.0 to Otsh_v2.0 vcf using *liftvcf* from *transanno* ([https://github.com/informationsea/transanno](https://github.com/informationsea/transanno), resulting in 9,657,610 SNPs successfully lifted out of the 14,299,815 original SNPs. We then merged the two vcf files using *bcftools merge* V.1.20 [63], and filtered for missing data using *vcftools* V0.1.17 [56] (max-missing 0.7, no filter on MAF, no filter on DEPTH) to only keep SNPs shared between the two datasets, resulting in a total of 2,928,302 SNPs for 297 individuals.

The procedure detected no SNPs in *CLOCK1A* and *SIX6* within the coding and adjacent intergenic regions and yielded 178 SNPs in *CLOCK1B*, 461 SNPs in *GREB1L/ROCK1* and 109 SNPs in *VGLL3*. Allele frequency differences between spring and fall run samples of at least 0.95 were found only at 76 SNPs in the *GREB1L/ROCK1* genic and intergenic region. For those positions, we assessed the frequency of spring alleles in the population across the latitudinal gradient.

## Second stage: Discovery genes from whole genome scan

In the discovery stage of our analysis, we performed a hypothesis-free scan at every SNP of the whole genome to detect strong associations between allele frequency and latitude. Subsequently, we identified the genes in which groups of significant SNPs were located. Tests of association between SNP allele frequency and latitude were carried out using the R function *bglmer()* and *BayPass* as described above. Also, as described previously, we computed the mean -log₁₀(*p*) and BF values per genomic region, and applied our “peakfinder” approach with a 50-SNP window. We did not compute the statistical significance of latitudinal associations in the discovery stage of our analysis, but rather ranked the genes according to their mean and median -log₁₀(*p*) and BF scores. Regions falling in the top 5% were deemed statistically significant. Annotation was done using the online NCBI *blastn* tool with default settings to annotate the genes and intergenic regions overlapping peak windows. We also looked for well-studied pathways in the salmonid literature connecting candidate genes and other functional genes. We found genetic pathways of interest related to *CLOCK* and *VGLL3*. *CLOCK* genes are known to be central in transcription feedback loops involving other genes, such as *BMAL1*, *PER*, *CRY*, *RORα* and *REV-erb*, and regulating the circadian rhythm and the seasonal clock, with consequences for biological rhythms, phenology and migration timing [66-67]. *VGLL3* binds to *TEADs* transcription factor implicated in the Hippo pathway, regulating cell
proliferation and organ size [68]. We tested whether some other genes in the clock feedback loop pathways and the Hippo pathway were ranked in the top 5% for *bglmer()* and *BayPass*.

## Results

### Population structure

The first axis of the principal component analysis on genetic data was strongly associated with latitude (Figure 1; Pearson correlation: r = -0.90, P < 1×10⁻²⁵), and the second axis was associated with longitude (r = -0.417, P < 1×10⁻⁶). Individuals were also clearly structured by river. We found little evidence that Chinook salmon populations sampled were divided into a small number of discrete groups. Statistical support for a single large group of admixed populations was as strong as support for two and three clusters, as indicated by Admixture cross-validation (CV) scores (Figure S3). The CV score was slightly lower for two clusters (K = 2) than for other values, but improvement in support over K = 1 was weak and the estimate of FST between putative clusters was relatively low (FST = 0.072). These results suggest that allele frequencies of populations vary approximately continuously over space without large breaks (Figure S4).

### Candidate gene association between latitude and allele frequency

All five candidate genes previously linked to adult migration timing and maturation rate in salmonids showed strong relationships between allele frequency and latitude, with all ranking in the top 5% of all genomic regions — both “coding” and intergenic — on the basis of at least one of the four criteria : mean *-log₁₀(p-value)*, mean Bayes Factor (BF), or a 50-SNP sliding window peak in *-log₁₀(p-value)* or BF values (Table 1, Figure 2 & S5). Finding the five candidate genes in the top 5% just by chance would be highly unlikely (0.05⁵= 3.125×10⁻⁷). The intergenic region between *SIX6* and *PP1A*, the coding region of *CLOCK1A*, and the region between *GREB1L* and *ROCK1*, showed among the most significant latitudinal gradients in the sampled Chinook salmon genomes according to either or both of the *bglmer()* and *BayPass* analyses (Table 1, Figure 2 & S5). Allele frequencies at SNPs ranking in the top 5% of all SNPs in the genome in *CLOCK1A* and *CLOCK1B* change continuously across latitude, whereas those in the other three genes show the steepest changes in frequency between 50 and 60 degrees of latitude (Figure 2, Figure S6-S10). We conclude that variation at all these candidate genes is likely involved in local adaptation to the environment across latitudes. These genes have previously been linked to polymorphisms in migration timing and maturation, and our clines
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confirm allelic variation associated with latitude at *CLOCK1B* in Chinook salmon that is paralleled in chum (but not coho and pink salmon [23]). We find that a similar gradient is seen here in its paralog, *CLOCK1A*.

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*SnpEff* analysis identified synonymous and non-synonymous mutations in each genic region ranking in the top 5% of the SNPs by at least one of the *bglmer()* or *BayPass* methods (Table S4-S8). Several non-synonymous missense variants (causing a change in amino acid) were associated with latitude: 1 missense variant in *CLOCK1A* (65,588,588, Table S4), 3 missense variants in *CLOCK1B* (at positions 30,349,361; 30,360,940 and 30,364,770, Table S5), and 1 stop retained variant in *GREB1L* (13422276, Table S6). We found no non-synonymous missense variants associated with latitude in *ROCK1*, *SIX6* or *VGLL3* (Table S7-S8). All other SNPs varying significantly with latitude were identified by the method either as synonymous or hitting a non-coding feature, e.g. an intron or a non-translated transcript.

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The intergenic region between *GREB1L* and *ROCK1* is of particular interest because some of the SNPs showing strong latitudinal gradients in the present sample also show almost fixed differences between Klamath and Sacramento basin spring- and fall-run Chinook salmon [12]. *GREB1L* expression levels within the hypothalamus are known to differ between run times [59]. From a total of 76 SNPs with an allele frequency difference > 0.95 in [12] and that also lifted over to the Otsh_v2 genome, 33 SNPs ranked in the top 5% of all SNPs in *BayPass* or *bglmer()* analyses (Figure S11). Whereas the spring/fall-run associated SNPs straddle the coding regions of both genes as well as the intergenic region, those with the strongest latitudinal gradients detected here were intergenic and occurred mainly close to *ROCK1* (Figure 2, S5 & S11). Thus, variation across latitudes is associated with different but overlapping genetic regions as a run time polymorphism within rivers (Figure S11). The overlapping SNPs change gradually in frequency, rather than suddenly, from south to north in our sample of late-migrating salmon (Figure S12). The comparison suggests that more northern salmon, which enter rivers earlier than those in our southern samples (Figure S1), increasingly resemble southern, spring-run salmon in part but not all of the *GREB1L/ROCK1* region (Figure S13 & S14). Such similarities and differences in sequences between and within latitudes might help to identify causal variants underlying run timing differences. Additional analysis of read depth variation in our samples suggests that this intergenic region might also include a sizable duplication in some populations (from position 13,536,082 to 13,541,889, Figure S15), at an additional location than the putative duplication identified in [24].

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Associations between latitude and allele frequency in discovery genes

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Associations between allele frequency and latitude discovered in the whole-genome analysis are likely to contain a higher false positive rate than candidate genes, because they were not hypothesized *a priori*. Several noteworthy patterns nevertheless emerged. The most striking observation is that other loci interacting with the five candidate genes fell in the extreme tail of the distribution of scores from *BayPass* and *bglmer()*. Based on the literature, we have identified these genes and summarized their potential connections in Figure 3, though functional connections remain to be established.

First, several discovery genes associated with latitude are components of the circadian clock pathway, interacting with *CLOCK* in well-described transcriptional feedback loops [66](Figure 3). Among the genes implicated in the feedback loop, *CRY-DASH*, *CRY1*, *REV-ERBα* (*NR1D1*) and *REV-ERBβ* (*NR1D2A*) were ranked in the top 5% of the regions (Figure 3, Table S2). *RORα* (*NR1F1*) was only marginally significant (in the top 5.9%). Multiple genes in pathways regulating these circadian feedback loops were also associated with latitude (Table S2). These include *MTNR1B*, coding for the receptor to melatonin, a hormone interacting with light in the pineal gland and with the *CLOCK* feedback loop (particularly with *RORα* [69]), and the *CLOCK*-interacting pacemaker (*CIPC*), favoring the phosphorylation of *CLOCK* through *BMAL1* [70]. We also found the retinoic acid receptor alpha (*RARA*) to be associated with latitude (Table S2). Retinoic acid is produced in the eye and is a key regulator of the genes *CLOCK* and *BMAL1* [71]. Although the function of these SNPs has yet to be determined, their association in these well-known pathways suggests the possibility of a suite of adaptive changes linked to day length variation across latitudes.

Second, multiple discovery genes related to latitude, like the candidate genes *VGLL3* and *ROCK1*, are connected to the Hippo signaling pathway [68] involved in cell proliferation and organ growth (Figure 3, Table S2). These genes are either found upstream of *RHOA* such as genes in the *ARHGAP* gene family (*ARHGAP* 6, 18 and 20b [68,72]), *AKAP11* and *PKA* [72], or downstream, such as *TEAD1* (*TEF-1*) [68] and *ROCK1* (not itself part of the Hippo pathway but interacting with *RHOA* [73]). *VGLL3* acts as a cofactor for *TEAD1*, which is also known to interact with *ROCK1* [73]. We also detected *CASP3A*, which mediates the cleavage of *ROCK1* [74], and *COFILIN* which mediates the interaction between *ROCK1* and actin [75], a central component of the Hippo pathway.
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Other discovery genes plausibly linked to *CLOCK* or the Hippo pathway include cyclic adenosine monophosphate (*cAMP*) receptor-like protein A (*CRLA* or *si:dkey-100n23.5*, Table S3), a regulator of *CLOCK* [76], *PDE10a* which regulates *cAMP* levels [77] and the *cAMP*-dependent protein kinase (*PRKAR1a*, subunit of *PKA*). *cAMP* and *PKA* play critical roles in cellular signal transduction pathways including Hippo [68]. *AKAP11* also interacts directly with *cAMP* and *PKA* [77-78]. Four genes have been previously been associated with variation in migratory behavior in birds, namely *CLOCK*, Citrate Synthase, *PEAK1* and *ADCYAP1R1B* (*ADCYAP1* is also regulator of cAMP in human [79]). *CLOCK* and *ADCYAP1* have also shown to be part of the hypothalamic-pituitary-gonadal axis, an important physiological regulatory circuit synchronizing photoperiod and several life-history traits such as migration and reproduction [67,80-81]. The intergenic region between *SIX6* and *PP1A* may also be connected to the hippo pathway, as *PP1A* has been shown to dephosphorylate *YAP* and *TAZ* [82](Figure 3, Table S2).

We lacked *a priori* candidate genes linked to phenotypic variation in thermal tolerance in salmonids. Nevertheless, our discovery list suggests several genes worthy of investigation, including heat shock proteins, *HSP4L* (from the *HSP70* family [83]), *HSPB11* (also known as *HSP30*), and *HSP90*. *HSP70* and *HSP90* proteins are known to mitigate cellular damage and stress in plants [84], and *HSP70* is upregulated at high temperature in ray-finned fish [85]. Two other important genes to investigate are *HIF-1* (hypoxia-inducible factor 1-alpha) and *PDE10A* potentially providing adaptation to low-oxygen waters at high temperature, as they were respectively found to regulate oxygen homeostasis during hypoxia in high elevations Andean and Tibetan human populations [86] and associated to breath-holding capacity of Bajau “sea nomads” [87].

## Discussion

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Allele frequency and latitude were strongly associated for all candidate genes previously identified in salmonids as being linked to polymorphisms in life history timing and maturation (such as *CLOCK1A/B*, *ROCK1/GREB1I*, *VGLL3* and *SIX6*). This suggests that both clock genes and genes linked to migration timing and maturation rate are important components of local adaptation to latitude in Chinook salmon. Moreover, our list of “discovery” genes based on genome scans identified multiple genes strongly varying with latitude that interact with these
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candidates. This raises the possibility that a suite of changes in functional clusters of interacting genes underlies life-history adaptations to latitude rather than being driven by a small number of disconnected changes.

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Our study supported previous results showing that variation at the clock gene *CLOCK1B* is associated with latitude in salmonids [23] as seen in other species such as *Drosophila* and birds [8]. We also found that SNPs that varied strongly with latitude within the intergenic region between *GREB1L* and *ROCK1* partially overlapped with those previously shown to differ between migration-time ecotypes in Chinook salmon (spring run and fall run) within rivers in California [12]. We hypothesize that latitudinal SNP variation at this locus contributes to local adaptation in migration timing. Earlier run-timing of late-migrating salmon at northern latitudes could be adaptive because eggs need longer to develop in cold water before emerging at an advantageous time [88]. In contrast, summer migration in the southern part of the range would be disadvantageous because of reduced river flows or because of low oxygen concentration in warm water [89-90]. This opposite (divergent) selection along the latitudinal cline might maintain polymorphism at intermediate latitudes (Figure 2 & S5).

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We found strong associations between latitude and the genes *SIX6* and *VGLL3*. *SIX6* encodes for a sine oculis homeobox protein, a transcription factor found in insects and vertebrates [91], and is known to be associated with eye development [92] (also called *OTX2B* [93]), circadian timing in the pituitary of mammals and ray-finned fish [94], maturation in Atlantic salmon [35] and age at menarche in humans [40-41]. *VGLL3* was previously shown to be related to maturation, growth and body condition in Atlantic Salmon [31,95-96] and inflammation [97]. We found a large number of SNPs associated with latitude in the regulatory region between *VGLL3* and the neighboring gene *AKAP11* on chromosome 3, which was already identified as a potential important regulator of maturation [31,96] has been shown to be important in the maturation of sperm [98] and is also connected to the Hippo signaling pathway [68]. Additionally, we found a large number of SNPs in the intergenic region between *SIX6* and its neighboring gene *PP1A* on chr 11, also known to be an activator of *YAP* and *TAZ* in the Hippo pathway [82-99].

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Our discovery stage of analysis revealed many other genetic variants that might play a role in adaptation to different latitudes. Most of these variants were synonymous or located in intergenic regions (Table S4-S8) and further functional genomics and long-read sequencing would be needed to identify the regulatory elements correlated with changes in gene expression. The number of genes identified confirmed previous expectations that adaptation to latitude is highly polygenic [4] (Figure S16-S18). More importantly, many of the genes most strongly associated with latitude were connected to at least two genetic pathways that included our candidate genes: clock feedback loops and the Hippo signaling pathway, which are both in the hypothalamus/pituitary/gonadal axis previously identified as a key regulator of maturation in Atlantic salmon [18]. The potential functional links between these discovery genes and known genes discovered in previous studies increase the likelihood that many of the associations found with latitude are real. We suggest that significant changes to pathways of interacting genes underlies life-history adaptation to latitude. The regulation of gene expression across these pathways may influence several life-history traits such as timing of migration and age at maturity (age at sea). The polygenic nature of these adaptations can facilitate adaptation on standing genetic variation in a wide variety of environments across the latitudinal cline through small allele frequency changes at multiple loci, even during a single generation [100].

Our PCA analysis confirmed previous studies demonstrating strong population structure in Chinook salmon at a local scale, mostly following river basins ([46, 99, 101-102], Figure S19). Across the Pacific Coast, our ADMIXTURE results suggest that the support for populations being grouped into a small number of discrete genetic clusters was weak, and our PCA indicated that a substantial proportion of all genetic variation was associated to latitude. This is consistent with previous results from smaller genetic datasets proposing a complex biogeographical history with multiple coastal refugia and subsequent mixing [103].

Given that our sampling followed a latitudinal distribution, we were expecting that a large number of genomic regions would be associated with latitude by chance, following an isolation-by-distance pattern. For instance, neutral demographic processes, such as allele surfing [104], can lead to an increase in frequency along a spatial gradient even if allelic variation is not adaptive. Our discovery stage of analysis is likely more prone to false positives than the candidate gene analysis, but many of our results should hold for four main reasons. First, we used the *BayPass* analysis to take into account this genetic structure. Second, our discovery analysis focused on those genetic regions most strongly associated with latitude rather than on
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statistical significance *per se*. Third, many genes on the discovery list interact directly with the candidate genes, which is unlikely to be produced by chance. Fourth, some associations with latitude are also found in other organisms, i.e. such as circadian clock genes in *Drosophila* and birds [8].

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Although our sampling undoubtedly misses some genetic variation in this species, our results should also be robust to adding more populations, especially adding more spring-run individuals across latitudes. Given that *GREB1L/ROCK1* is the only region to differ between spring and fall-run [12-13], the association with latitude found in the other candidate genes should be found in all runs similarly. This also indicates that genetic variation at *GREB1L/ROCK1* does not strictly follow a latitudinal cline, and the within-latitude variation is only partly described herein. In our sample including mostly late-migrating individuals, we found that most SNPs associated to latitude were located in the intergenic region close to *ROCK1* (Figure 2) and only few in *GREB1L* (Table 1). The association found with latitude followed the natural temporal cline in migration timing and spawning timing across latitudes [17]. We found that earlier run timing in northern populations was linked to changes at many of the same SNPs previously found to underlie a spring-fall run time polymorphism within rivers in the southern part of the range (Figure S13 & S14). Consistently, allele frequency of 76 spring-run SNPs (identified from an absolute difference in frequency >0.95 between Californian fall and spring run from [12] data) increased gradually from California to Alaska populations in the late-migrating populations (Figure S13). This relationship was particularly steep for the SNPs falling in the intergenic region between *GREB1L* and *ROCK1*, going from a frequency of 0 in California to 0.88 in Alaska (Figure S13). This potentially indicates allele migration among spring/summer-run populations across latitude – between Alaskan and Californian populations – that should be investigated in further studies.

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Identifying the genetic architecture of locally adapted phenotypes can have strong implications for conservation [13]. Our work is a snapshot of adaptive and neutral variation at the present time. Frequencies of alleles of major large-effect genes related to timing of migration or maturation have already been shown to change drastically in frequency over the past few decades in relation to major environmental and anthropic change [11]. *VGLL3* and *SIX6* have been linked to a decrease in body mass during a major change of water flow regime in Atlantic salmon [36]. If the genes associated with latitude in the present study play a role in life-history differences, then monitoring changes in frequencies associated with environmental changes will
be useful to indicate whether populations are adapting to environmental changes. Our results
suggest that many potentially adaptive alleles share genetic pathways with large-effect genes,
which will also be shifting latitudinally in the coming decades if the climate warms and
associations between water flows, temperature, and light regimes change across latitudes. Our
study indicates that adaptation to latitude is highly polygenic, and current
management/translocations should be attempted with this in mind, taking into account the
complexity of impacts across the genome, e.g., by considering all genetic regions potentially
associated with latitude and not just a few large-effect genes [13].

538

539 Acknowledgments

540 We thank David Minkley for helping with the bioinformatic analyses. We thank Yann Czorlich
541 and Robin Waples for useful discussions. We also thank John Carlos Garza (SWFSC), Todd
542 Confer (ODFW), Todd Seamons (WDFW), and Chris Habicht (DFG) for providing samples,
543 and Eric Anderson and Carlos Garza for providing information on [11]. We would like to thank
544 McGill University and Génome Québec Innovation Centre (now the Centre d'expertise et de
545 services Génome Québec) for their work in sample preparation and sequencing. We thank
546 Digital Research Alliance of Canada (formerly Compute Canada) (alliancecan.ca) for the
547 computing resources that make this work possible. We thank Stephen N. Lyle for copy-editing
548 an early version of the manuscript. Finally, we would like to acknowledge the personnel at
549 Fisheries and Oceans Canada and the University of Victoria for the essential resources and for
550 sample collection. For the purpose of Open Access, a CC-BY public copyright licence has been
551 applied by the authors to the present document and will be applied to all subsequent versions
552 up to the Author Accepted Manuscript arising from this submission.

553

554 Funding

555 J.R. received funding from the European Union's Horizon 2020 Research and Innovation
556 Program under the Marie Skłodowska-Curie (grant no. 785910) and from Investissement
d'Avenir grants managed by the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-
557 25-01; TULIP: ANR-10-LABX-0041, JCJC: ANR-23-CE02-0005-01). D.S. received funding
558 from Genome B.C. Genome sequencing was funded by a Genome BC grant SIP003 to D
559 Schluter. Funding was provided to R. Devlin by the Canadian Regulatory System for
560 Biotechnology (Fisheries and Oceans Canada). The funding for B. Koop was provided by the
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Natural Sciences and Engineering Research Council of Canada. We also thank John Carlos
Garza, Todd Confer, Todd Seamons and Chris Habicht for their help for collecting the samples.

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Animal care

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Tissues were collected from post-euthanized adult fish by Fisheries and Oceans hatchery staff during their routine operations propagating the hatchery population in compliance with the Canadian Council on Animal Care Guidelines.

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Contribution

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J.R. and D.S. designed the study. K. G. O., R. W., R. H. D. and B. K. collected the data. J.R., K.C., M.G. and D.S. ran the analyses. J.R. wrote the first draft of the manuscript with substantial edits by D.S.. All authors commented on the final version of the manuscript.

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Data sharing plans

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Fastq files for all individuals are available here:
https://www.ncbi.nlm.nih.gov/bioproject/PRJNA694998. The reference genome is available
here:

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https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/018/296/145/GCF_018296145.1_Otsh_v2.0/GCF_018296145.1_Otsh_v2.0_genomic.gff.gz . Scripts used to perform the analyses and generate the figure are available in https://github.com/jonathanrolland/Genomics and supplementary files are available here: (https://zenodo.org/records/15114333?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjBiMTQ5NDY5LTEyNDYtNGZjNi1hYTA3LWM4OWY4YzkyZjgyZiIsImRhdGEiOnt9LCJyYW5kb20iOiI3MjBjMGFjZWEyODE4ZWEyZjIzZDVhMDAyMGUwMTYxNSJ9.7t8ezM1JynkD5aLviB8746bMafdzEZb2CEDmPXA3h3C4W3QQSRYEt-aQpg4OfLOdBA3PVmGNpAthRZcxaJnwdQ, doi:10.5281/zenodo.15114333).

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**Figure 1.** Population structure of Chinook salmon. (A) Chinook salmon individuals plotted against the two first axes of the genetic PCA. PC1 is shown on the Y-axis to highlight its relationship with latitude. AK stands for Alaska, BC for British Columbia, WA for Washington, OR for Oregon and CA for California. (B) Latitudinal distribution of our samples. The sizes of
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<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

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Figure 2. Manhattan plots (left) and alleles frequencies (right) for the 5 candidate genes associated with latitude. The regions correspond to (A) *CLOCK1A*, (B) *CLOCK1B*, (C) *GREB1L/ROCK1*, (D) *VGLL3* and (E) *SIX6*. The results are only shown for the *BayPass* results; *bglmer()* results are shown in Figure S5. The SNPs shown in red have Bayes factor values above the threshold of the best 5% of all SNPs (BF=3.25). The others are shown in grey. SNPs with negative Bayes factor values are not shown. Left side panels have variable axes indicating different amounts of support for the SNPs found at each of the 5 loci. Right side panels show trends in frequencies smoothed using the *smooth.spline()* function with df=5. Blue tick marks on the x-axis indicate latitudes of river mouth for the sampled populations.

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<figure><img src="image_4.png" /><figcaption>A</figcaption></figure>

CLOCK1A

<figure><img src="image_5.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_6.png" /></figure>

CLOCK1B

<figure><img src="image_7.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_8.png" /></figure>

GREB1L - ROCK1

<figure><img src="image_9.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_10.png" /></figure>

VGLL3

<figure><img src="image_11.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_12.png" /></figure>

SIX6

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /><figcaption>**Figure 3. Genes tested and found to be associated with latitude in the (A) clock and (B) Hippo pathways.** The figure mostly follows the schematics described in the literature: Hastings et al. [66] for the clock pathway and (B) Kurko et al. [68] for the Hippo pathway. *P*-values and Bayes Factor, and detailed connections between genes are shown in Table S2.</figcaption></figure>

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<caption>Table 1. Associations with latitude for the 5 candidate genes identified from the salmonid literature. The regions of interest have been ranked against all the other genetic regions (shown in %) according to their mean -log10(pvalue) and their best 50-SNPs sliding window for bglmer() and BayPass. Regions in the top 5% of all regions are shown in bold. The exact ranking is given when the region falls in the top 0.5%.</caption>

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<table><thead><tr><th>Category</th><th>Gene</th><th>Organism</th><th>Reference</th><th>Name of the genetic region</th><th>Chromosome and positions</th><th>bglmer<br>mean -log10(pvalue) (%)</th><th>bglmer<br>sliding window of 50 snps (%)</th><th>BayPass<br>mean BF (%)</th><th>BayPass<br>sliding window of 50 snps (%)</th></tr></thead><tbody><tr><td rowspan="3">Timing</td><td rowspan="3">OTSCLOCK1B<br>(CLOCKA)</td><td rowspan="3">Chinook, chum,<br>coho and pink salmon</td><td>O'Malley et al. 2008</td><td>CLOCK1B</td><td>Chr1: 30,346,551 - 3,0407,903</td><td>16.72</td><td><strong>3.87</strong></td><td>16.63</td><td>5.12</td></tr><tr><td>O'Malley et al. 2010</td><td>region between CLOCK1B and NMU</td><td>Chr1: 30,407,904 - 30,419,389</td><td>34.66</td><td>46.91</td><td>81.33</td><td>59.23</td></tr><tr><td></td><td>region between TMEM165 and CLOCK1B</td><td>Chr1: 30,337,921 - 30,346,550</td><td><strong>1.31</strong></td><td>7.46</td><td><strong>3.72</strong></td><td>12.28</td></tr><tr><td rowspan="3">Timing</td><td rowspan="3">OTSCLOCK1A</td><td rowspan="3">Chinook, chum, coho<br>and pink salmon</td><td>O'Malley et al. 2008</td><td>CLOCK1A</td><td>Chr5: 65,581,551 - 65,632,197</td><td><strong>3.55</strong></td><td><strong>0.17 (rank=116)</strong></td><td>6.97</td><td><strong>3.33</strong></td></tr><tr><td>O'Malley et al. 2010</td><td>region between CLOCK1A and NMU</td><td>Chr5: 65,632,198 - 65,642,249</td><td>13.56</td><td>15.75</td><td>19.43</td><td>12.79</td></tr><tr><td></td><td>region between TMEM165 and CLOCK1A</td><td>Chr5: 65,574,213 - 65,581,550</td><td>21.38</td><td>9.30</td><td>21.35</td><td>14.43</td></tr><tr><td rowspan="3">Timing</td><td rowspan="3">Region between<br>GREB1L and ROCK1</td><td rowspan="3">Chinook salmon<br>and steelhead trout</td><td>Narum et al. 2018</td><td>region between GREB1L and ROCK1</td><td>Chr28: 13,496,979 - 13,548,901</td><td>11.49</td><td><strong>3.72</strong></td><td><strong>4.17</strong></td><td><strong>0.83</strong></td></tr><tr><td>Prince et al. 2017</td><td>ROCK1</td><td>Chr28: 13,548,902 - 13619775</td><td>42.20</td><td>9.82</td><td>21.79</td><td><strong>1.76</strong></td></tr><tr><td>Thompson et al. 2021</td><td>GREB1L</td><td>Chr28: 13,415,735 - 13,496,978</td><td>60.77</td><td>28.20</td><td>35.16</td><td>6.00</td></tr><tr><td rowspan="3">Maturation</td><td rowspan="3">VGLL3</td><td rowspan="3">Chinook, coho, sockeye and<br>Atlantic salmon and steelhead trout</td><td>Sinclair-Waters et al. 2020</td><td>VGLL3</td><td>Chr3: 57,887,979 - 57,892,088</td><td><strong>3.78</strong></td><td>16.21</td><td>12.33</td><td>31.85</td></tr><tr><td></td><td>region between VGLL3 and AKAP11</td><td>Chr3: 57,892,089 - 57,939,896</td><td><strong>4.31</strong></td><td><strong>1.27</strong></td><td>9.03</td><td><strong>3.49</strong></td></tr><tr><td></td><td>region between CHMP2B and VGLL3</td><td>Chr3: 57,876,958 - 57,887,978</td><td>9.93</td><td>11.41</td><td>18.92</td><td>29.12</td></tr><tr><td rowspan="3">Maturation</td><td rowspan="3">SIX6<br>(OPTX2)</td><td rowspan="3">Chinook, coho, sockeye and<br>Atlantic salmon and steelhead trout</td><td>Pritchard et al. 2017</td><td>SIX6</td><td>Chr11: 30,454,760 - 30,459,932</td><td><strong>4.22</strong></td><td><strong>2.34</strong></td><td>12.55</td><td>12.18</td></tr><tr><td></td><td>region between SIX6 and PP1A</td><td>Chr11: 30,459,933 - 30,489,762</td><td><strong>0.69</strong></td><td><strong>0.18 (rank=128)</strong></td><td><strong>0.77 (rank=549)</strong></td><td><strong>0.17 (rank=123)</strong></td></tr><tr><td></td><td>region between SIX1B and SIX6</td><td>Chr11: 30,434,666 - 30,454,759</td><td><strong>3.78</strong></td><td><strong>2.87</strong></td><td><strong>3.71</strong></td><td><strong>4.85</strong></td></tr></tbody></table>

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