Parallel selection in domesticated Atlantic salmon from divergent founders
including on whole genome duplication-derived homeologous regions

Pauline Buso¹, Célian Diblasi², Domniki Manousi², Jun Soung Kwak², Arturo Vera Ponce de Leon²,
Kristina Stenløkk²³, Nicola Barson²*, Marie Saitou²*

¹IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan Via Domitia, Montpellier,
France

²Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, As, Norway

³Present address: Department of Forensic Sciences, Oslo University Hospital, 0424 Oslo, Norway

Nicola Barson: nicola.barson@nmbu.no

Marie Saitou: marie.saitou@nmbu.no

ABSTRACT

Domestication and artificial selection for desirable traits have driven significant phenotypic changes and left detectable genomic footprints in farmed animals. Since the 1960s, intensive breeding has led to the rapid domestication of Atlantic salmon (*Salmo salar*), with multiple independent events that make it a valuable model for studying early domestication stages and the parallel evolution of populations of different origins subjected to similar selection pressures. Some aquatic species, including Atlantic salmon, have undergone whole-genome duplication (WGD), raising the possibility that genetic redundancy resulting from WGD has contributed to adaptation in captive environments, as seen in plants. Here, we examined the genomic responses to domestication in Atlantic salmon, focusing on potential signatures of parallel selection, including those associated with WGD. Candidate genomic regions under selection were identified by comparing whole-genome sequences from aquaculture and wild populations across two independently domesticated lineages (Western Norway and North America) using a genome-wide scan that combined three statistical methods: allele frequencies (F<sub>ST</sub>), site-frequency (Tajima's D), and haplotype differentiation (XP-EHH). These analyses revealed shared selective sweeps on identical SNPs in major histocompatibility complex (MHC) genes across aquaculture populations. This suggests that a combination of long-term balancing selection and recent human-induced selection has shaped MHC gene evolution in domesticated salmon. Additionally, we observed selective sweeps on a small number of gene pairs in homeologous regions originating from WGD, offering insights into how historical genome duplication events may intersect with recent selection pressures in aquaculture species.

KEYWORDS: domestication, MHC, artificial selection, aquaculture, ohnolog

SIGNIFICANCE STATEMENT

Domestication rapidly alters the genomic architecture of farmed animals to enhance favorable traits, yet the early genetic responses to domestication are incompletely understood in many systems. By analyzing two independently domesticated Atlantic salmon lineages, this study identified shared genomic regions under selection across continents, particularly in immune-related genes. This suggests that independent domestication events can lead to similar genetic changes under artificial selection, highlighting the role of immune-related genes in Atlantic salmon evolution.

<footnote>*Corresponding authors:</footnote>
INTRODUCTION

Domestication marks one of the key shifts in human history, tracing back to the enduring bond between early hunter-gatherers and wolves over 15,000 years ago (Vigne, 2011). Domesticated plants and animals became essential to human societies for social, nutritional, symbolic, or economic purposes, reflecting the development of multicultural societies that emerged at the end of the last ice age (Larson et al., 2014). Domestication refers to a small, selected fraction from a wild population adapting to a human-controlled environment. This process can change the genetic composition and phenotypes of farmed individuals in response to both intentional and unintentional selection (Wang et al., 2014). Small initial founder populations result in a strong bottleneck and limited genetic diversity. Consequently, the genomic evolution of domesticated species is influenced by strong genetic drift, as well as intentional artificial selection for favorable production traits (Innan and Kim, 2004), and unintentional domestication selection resulting from adaptation to the captive environment and human contact (Mignon-Grasteau et al., 2005; Frantz et al., 2020; Huang et al., 2022). Thus, domesticated species carry signatures of these processes in their genomes, providing valuable insights into their domestication history and make them good models for genetic studies of evolution (Wiener and Wilkinson, 2011).

Conventional investigations into the evolutionary changes that occurred after domestication began with archaeological records (Diamond, 2002). Recent advances using 'Omics approaches have enabled researchers to trace the geographical and temporal origins of domestication and its early stages. These approaches have revealed the genomic basis of favored traits and selected loci in various species (Frantz et al., 2020), while also shedding light on the unintended consequences of these processes (Glover et al., 2017a; Fernandez et al., 2021; Izawa, 2022). Domestication processes are often associated with selection pressures on key production traits such as growth, immunity, and age of sexual maturity (Gjederm, 1979; Milla et al., 2021; Podgorniak et al., 2022). Artificial selection for such traits and domestication selection may lead to parallel evolution between independent populations exposed to similar selection pressures. This refers to the independent development of genetic adaptations within different populations that affect the organism's phenotype in the same way (Wood, Burke and Rieseberg, 2008; Bailey et al., 2017). For instance, high-altitude adaptation has been observed within independent human populations and their domesticated animals such as dogs, cows, horses, sheep, pigs, and chickens, highlighting a shared genetic response to environmental pressures (Witt and Huerta-Sánchez, 2019). Similarly, mammals that have adapted to high-starch diets, including various domesticated animals, have an increased copy number of the amylase gene (amy) (Pajic et al., 2019).

Compared to the long history of terrestrial livestock domestication, the domestication of most aquaculture species is relatively recent. Finfish aquaculture expanded rapidly in the early 1980s, while domestication timelines vary across aquaculture species (Houston et al., 2020; Teletchea, 2021). Unlike established terrestrial livestock species, aquaculture species present a varied landscape of domestication success, with some species achieving breakthroughs in captive breeding while others continue to rely on wild stock imports. Successful domestications in aquaculture systems are often observed in certain fish families, such as sturgeons, cyprinids (including carp), and salmonids. Notably, some of these families have undergone whole genome duplication (WGD), which has been linked to increased genomic redundancy, allelic diversity, heterozygosity, and meiotic recombination (Lien et al., 2016; Gundappa et al., 2022). These genomic features may be one factor to enhance tolerance to new mutations and facilitate adaptation to human-mediated environments, while WGD is not essential for successful domestication (Salman-Minkov, Sabath and Mayrose, 2016; Baduel et al., 2019; Mihajlovic et al., 2023; Wang et al., 2024). Many successful aquaculture species, including catfish, sea bass, and flatfishes, among others, have achieved domestication without lineage-specific WGD events. Beyond fish, domesticated aquaculture species also include mollusks (e.g., oysters, mussels) and crustaceans (e.g., shrimp, crabs), which do not have known histories of lineage-specific WGD (Houston et al., 2020; Naylor et al., 2021). While WGD-derived genetic redundancy may still contribute to adaptation in some lineages, multiple genomic pathways facilitate domestication across diverse taxa.
The Atlantic salmon (*Salmo salar*) genome underwent a whole-genome duplication event (Ss4R) approximately 100 million years ago, leading to the retention of a substantial fraction of duplicated genes (Macqueen and Johnston, 2014; Gundappa *et al.*, 2022). Unlike a random loss of duplicate genes, rediploidisation followed a structured trajectory, resulting in a non-random spectrum of ohnolog divergence levels. These ohnologs are organized into collinear blocks across the genome, reflecting the evolutionary history of this process (Lien *et al.*, 2016; Robertson *et al.*, 2017). In this study, we use 'homeolog' to refer to corresponding genomic regions and 'ohnolog' to refer to duplicated genes arising from whole-genome duplication events. While many duplicated genes were subsequently lost, a considerable proportion has been maintained as ohnolog pairs, contributing to functional diversification. Some ohnologs retained their ancestral function, while others underwent subfunctionalization or neofunctionalization, leading to functional differentiation and specialization. In addition to structural retention, the expression of Ss4R-derived ohnologs has evolved under selective pressures, leading to differential expression patterns. Some ohnolog pairs exhibit complementary expression across tissues, while others have diverged functionally (Lien *et al.*, 2016). Additionally, understanding these expression dynamics is crucial for interpreting how Ss4R-derived variation contributes to domestication and aquaculture adaptation, particularly in traits related to metabolism, immune function, and stress response.

Among the aquaculture species with duplicated genomes, Atlantic salmon, has been subject to
strong artificial selection and has seen growing economic importance since the 1960s (Cross and
King, 1983; Ford and Myers, 2008; Glover et al., 2017b). Atlantic salmon is a widely distributed
anadromous species characterized by a strong tendency to reproduce in the rivers of their birth,
which encourages genetic divergence among populations (Klemetsen et al., 2003; Lehnert et al.,
2018). Wild populations of Atlantic salmon have been split into four main phylogeographical lineages,
three in Europe (East Atlantic, Barents/White Sea, Baltic) and one in North America (West Atlantic)
(Bourret et al., 2013; Rougemont and Bernatchez, 2018). These lineages exhibit karyotypic variation,
with the North American populations displaying a polymorphism in chromosome numbers due to
fusions (Brenna-Hansen et al., 2012). Atlantic salmon in Europe and North America have been
independently domesticated multiple times from different source populations, with repeated artificial
selection directed at similar traits under human-induced environments. This suggests that parallel
evolution has likely occurred in domesticated Atlantic salmon populations across both continents,
driven by comparable selective pressures.

Despite its recent domestication, studies of Atlantic salmon have revealed rapid genetic changes in
response to artificial selection targeting metabolism, disease resistance, and faster growth (Debes
et al., 2012; Bicskei et al., 2014; Harvey et al., 2018; Houston and Macqueen, 2019; Jin et al., 2020;
Bull et al., 2022). Moreover, signatures of domestication selection have also been observed in
neurological genes (Bertolotti et al., 2020), causing behavioral alterations, such as increased
sensitivity to predation and increased tolerance to stress compared with the wild (Solberg et al.,
2020). Especially, the probability of parallel evolution is dependent on the richness of genetic
variation, standing genetic variation when this occurs over short time periods (Wood, Burke and
Rieseberg, 2008; Bailey et al., 2017; Ebadi et al., 2023). Several studies have investigated parallel
selection in Atlantic salmon across different geographic lineages. López et al. (2019) observed
parallel evolution at four genes (e.g ubiquitin-conjugating enzyme E2F putative (ube2f), collagen
alpha-1XIII chain (coda1), autism susceptibility 2 protein-like (auts2-like), and transient receptor
potential cation channel subfamily M member 3-like (trpm3-like) between Scottish and North
American populations (i.e Canada). However, Naval-Sanchez et al. (2020) did not observe any
overlap among selected loci between populations of European and North American origin,
highlighting the need for further research to clarify the role of parallel selection in Atlantic salmon
domestication.

Here, we investigated the genomic responses of independent Atlantic salmon lineages to recent
domestication and artificial selection. By examining genomic divergence between wild and aquaculture
populations in Western Norway and North America, we aimed to: (1) identify loci and pathways involved
in the early stages of domestication, (2) explore parallel evolution across lineages, and (3) assess the
impact of the salmonid-specific WGD on domestication responses. Using whole genome sequencing and multiple selection scan methods ($F_{ST}$, Tajima's D, XP-EHH), we identified loci under selection, including shared loci between the two lineages, with a few examples of parallel selection on ohnolog pairs (i.e. genes retained in duplicate from the salmonid-specific WGD).

# RESULTS

## Population genetic structure

We first examined the genetic structure of the sequenced populations. We compared the genetic patterns of aquaculture samples from MOWI (Norwegian) with their wild counterparts from 17 rivers in West Norway, the region from where this domesticated lineage originates. Similarly, we analyzed aquaculture samples from three independently domesticated North American lineages and wild samples from eight rivers in the Gulf of St. Lawrence for comparison (Figure 1 (A) & (B)). Principal Component Analysis (PCA) (Figure 1 (C)) based on 148,433 SNPs derived from whole genome sequencing showed four distinct clusters: (1) Norwegian aquaculture, (2) wild Norwegian, (3) North American aquaculture from the St John and Penobscot rivers and wild North American, and (4) North American aquaculture from Gaspe. The first two principal components (PCs) jointly accounted for 64.59% of the total variance, with PC1 (53.85% of variance) separating the East Atlantic (North American) and West Atlantic (West Norwegian) lineages, and PC2 (10.74%) distinguishing between wild and aquaculture populations in Norway, while we did not observe a clear separation between wild and aquaculture populations in North America (Figure S2). In agreement with the PCA, ADMIXTURE analysis (Alexander, Novembre and Lange, 2009; Alexander and Lange, 2011) also highlighted a clear separation between the two geographical regions. At $K=6$, where the cross-validation error plateaued (Figure S3), each phylogeographical lineage showed distinct ancestry, with varying extents of shared ancestry among wild and aquaculture individuals from the same lineage (Figure 1D). Despite recent domestication from wild populations (Gjedrem, Gjøen and Gjerde, 1991; Glover et al., 2009; Bradbury et al., 2022), the Norwegian aquaculture population demonstrated higher genetic heterogeneity than its wild counterpart, possibly reflecting the admixed origin from multiple wild populations. Conversely, North American aquaculture and wild populations were genetically more similar, except for the Gaspe individuals, which were largely distinct, with limited representation in wild populations. This reflects more recent co-ancestry among North American individuals resulting from the later development of aquaculture in Eastern North America. We also observed a small amount of admixture from the wild East Atlantic group in both wild and aquaculture North American populations, reflecting known past hybridization events that have occurred both through natural secondary contact and as a result of escaped aquaculture individuals with admixed European and North American ancestry (Bradbury et al., 2022).

We delineated our study populations into four main groups for the following analysis: West Norwegian Aquaculture Group, Wild West Norwegian Group, North American Aquaculture Group, and Wild North American Group. This grouping was based on the tight PCA clustering of all North American populations and the smaller variance among wild Norwegian populations on PC2 than within the MOWI aquaculture strain. The groups, therefore, reflect both the continent-scale geographical origin, Western Norway versus North America, and the selective environment, those that are wild versus those that have undergone domestication.

## Identification of putatively selected regions, genes and SNPs

The genome scans to identify potential genomic regions under artificial selection in domestic strains were carried out on 1,139,312 SNPs for Tajima's D and $F_{ST}$ and 1,053,324 SNPs for XP-EHH, depending on the filtering requirements (see Methods). Tajima's D revealed 1053 windows in the Norwegian aquaculture population and 1032 windows in the North American aquaculture populations that are putatively under selection. Regarding $F_{ST}$, the analysis identified 3,177 windows in the West Norwegian pair and 3,088 in the North American pair as potentially under divergent selection. Finally,
XP-EHH identified 1914 SNPs in Western Norway and 1982 in North America that are under putatively recent directional selection (Figure 2).

We then asked if there were any regions or SNPs suggested as under selection by multiple methods. For the pairs in Western Norway and North America, we identified 8 and 2 windows, respectively, where regions were suggested to be non-neutral by both the F<sub>ST</sub> and Tajima's D metrics. Similarly, there are 104 and 339 shared SNPs suggested to be non-neutral by both F<sub>ST</sub> and XP-EHH in Western Norway and North America, respectively. Meanwhile, no SNPs overlapped between Tajima's D results and XP-EHH results on either continent. The limited overlap among loci detected as outliers between different methods could be because each method is sensitive to detecting selection over different timescales (Oleksyk, Smith and O'Brien, 2010). Lack of overlapped loci among methods has been reported previously for domesticated Atlantic salmon (López et al., 2019).

To assess the statistical significance of overlapping genomic sweep regions identified by multiple metrics, we performed random size-matched window sampling simulations (n = 1000). For Tajima's D and F<sub>ST</sub> comparisons, the observed overlap fell below the bottom 1% of the simulated distribution. In contrast, for XP-EHH and F<sub>ST</sub>, the observed overlap exceeded the maximum value in the simulations (Figure S4). These findings suggest that Tajima's D and F<sub>ST</sub> capture selection signatures at different evolutionary timescales, leading to lower-than-expected overlap, whereas XP-EHH and F<sub>ST</sub> tend to highlight regions where selective sweeps and resulting population differentiation co-occur.

To infer which functional groups of genes have been subjected to selection, we identified genes that overlap with windows or SNPs suggested to be non-neutral by multiple methods, using the genes from the Ensembl v111 annotation of the Atlantic salmon reference genome. For these genes, we conducted Gene Ontology (GO) enrichment analysis (Table 1). The "Focal adhesion (FDR = 0.015)" category has been detected in Western Norway from the intersect F<sub>ST</sub>/Tajima's D. No Gene Ontology categories were detected in other groups of genes detected by multiple methods. Therefore, we expanded our investigation to include results from XP-EHH alone, which can detect higher resolution and more recent directional selection. For the genes that contain SNPs identified by XP-EHH, the categories "Semaphorin receptor binding (FDR = 0.01)" and "Organelle organization (FDR = 0.013)" were enriched in Western Norway, while the category "Intracellular protein-containing complex (FDR = 0.0077)" was enriched in North America. A complete list of genes overlapping with XP-EHH outliers is available in Supplementary material (Table S3).

**Parallel selective sweeps on the shared SNPs in the MHC locus in two Atlantic salmon lineages**

We assessed parallel genomic patterns by comparing candidate positively selected XP-EHH SNPs of aquaculture groups across continents, as this method was likely to be most specific to recent divergent selection. We found 82 SNPs with extended haplotypes in both aquaculture lineages (Figure 3A, Table 2). Of these, 58 SNPs fell within the *tapbp* gene (ENSSSAG00000040723, 14:64331687-64338464) and the rest, 24 SNPs fell within the adjacent *daxx-chr14* gene (ENSSSAG00000040701, 14:64304883-64330023) on chromosome 14. Both genes are located into the Major Histocompatibility Complex (MHC) class IA region. Notably, five of the shared SNPs are missense SNPs (Table 2). To check if this result reflected genotyping errors owing to the high diversity and duplicated nature of the MHC region, we manually inspected the SNPs using the BAM files in IGV (Robinson et al., 2011). We assessed that these SNPs are likely real (Figure S6) and are reliably genotyped.

Variation in the MHC region is known to be maintained by long-term balancing selection as observed as trans-species polymorphisms (Tsukamoto et al., 2012; Teixeira et al., 2015). Given that variation in the MHC region has been maintained since before the divergence of the lineages, we would expect high Tajima's D values in this region. To investigate it, we conducted haplotype analysis and calculated Tajima's D on this MHC region on chromosome 14 and random, size-matching 500 control regions in the four groups. Haplotype clustering using the 125 MHC SNPs on chromosome 14 (chr14:64326415-64339381) suggested that there are two independent selective sweeps in the West Norwegian aquaculture group and North American Aquaculture group and that high diversity in the haplotypes has been maintained in Atlantic salmon since before the lineage divergence between the lineages of North
America and Europe (Figure 3B). In the wild North American group, the Tajima's D value for this region was at the 1st percentile compared to the control regions, while the Tajima's D value for this region was in the top 56% in the North American aquaculture group. In Western Norway, the Tajima's D value for this region was at the 43rd percentile in the aquaculture group and at the 10 percentile in the wild group (Figure 3C). These results suggest that the wild groups show signs of long-term balancing selection in the MHC region on chr14, while the aquaculture groups appear to have undergone selective sweeps since domestication began. Similar trends were observed in the two continents; however, likely due to demographic influences, the distribution of Tajima's D values in the control regions is generally higher in Western Norway compared to those in North America (Figure 3C).

### Parallel signatures of selection in alternate ohnolog copies across lineages

To further investigate if and how the salmonid-specific WGD contributes to the domestication of Atlantic salmon genetically, we examined whether one of the gene pairs from salmonid-specific WGD has adaptive signatures in the Western Norway aquaculture group while the other gene pair has adaptive signatures in the North American aquaculture group. By investigating XP-EHH outlier SNPs in genic regions, we detected four loci with ohnologous genes (i.e. genes duplicated from the WGD) under aquaculture-specific selective sweeps, one containing potentially multiple sweep signals. In addition to the *daxx* gene on chromosome 14, which was identified as a gene under selection in both lineages, its ohnolog on chromosome 27 (ENSSSAG00000077402) was identified as under selection in the Western Norwegian aquaculture group. In addition, we identified three ohnolog gene pairs under parallel domestication-related sweeps across the two distinct geographical regions: the bromodomain-containing 2a (*brd2a*) and proteasome 20S subunit beta 13a (*psmb13*) genes within 100kb of *daxx* on chromosome 14 in the North American aquaculture group and chromosome 27 in the Western Norwegian aquaculture group, and ABR activator of RhoGEF and GTPase (*abr*) on chromosome 4 in the North American aquaculture group and chromosome 13 in the Western Norwegian aquaculture group (Figure 2 & 3A, Table 3). The *brd2* gene has previously been reported as under positive selection in Atlantic salmon in aquaculture in Canada compared to their wild counterparts (López *et al.*, 2019), which aligns with its identification as part of the selective sweep in this study.

In addition to the shared genes above, the MHC class I region (Lukacs *et al.*, 2007, 2010) also contains salmonid-lineage-specific gene copies with overlapping XP-EHH outliers. In the Western Norwegian aquaculture group, these include *uba*, *vhsv*, *col11A2*, *tcf19*, *zbtb22b*, *psmb8a*, and *vsp52* on chromosome 27. In contrast, the *psmb8b* and *tap2a* genes on the homeolog chromosome 14 show selection signatures in the North American aquaculture group (Figure 2 & 3A, Table 3). These lineage-specific selection patterns on homeolog regions suggest parallel selection targeting the MHC class I region.

To estimate the degree of selection signals in ohnolog genes expected by random chance, we performed a simulation analysis by randomly placing putative genes with outlier XP-EHH values across the genome 10000 times. For cases where "one gene exhibited selection in one population while its duplicate showed selection in the other population," we observed four such pairs in empirical data, with a simulated probability of 0.03 of observing four or more by chance. Similarly, for cases where "the same gene exhibited selection in both populations," two such genes were observed, with a simulated probability of 0.027. These results indicate that while the observed selection patterns in duplicated genes are not extremely rare, they are also not entirely expected by chance.

### Expression pattern of the genes under putative selection

We leveraged publicly available RNA-seq datasets spanning various developmental stages and tissues. This allowed us to infer the potential functions of these putatively adaptive genes and regulatory difference of homeolog gene copies.

Both *daxx* homeolog gene copies, showed high expression in the ovary, testis, and during early blastulation stages (Figure 4), suggesting a role in both reproduction and early development.
Additionally, *daxx_chr27* is also highly expressed in the gill, spleen, and head kidney, indicating its role in immunity. This pattern suggests that both copies may contribute to multiple physiological processes.

The *tapbp* gene, showed low expression in most tissues in the healthy fish. This gene was, though, moderately expressed in the gill (**Figure 4**), which serves multiple important functions, including gas exchange, osmoregulation, and acting as a gateway for potential pathogens and its expression pattern suggests a potential role in the immune response (Amill *et al.*, 2024). It is reported that *tapbp* expression is upregulated in a rainbow trout monocyte/macrophage cell line in response to viral infection (Sever *et al.*, 2014).

Both homeologous copies of *brd2* show high expression in the brain and gill, while *brd2_chr27* is also expressed in ovary and *brd2_chr14* is expressed in head kidney, kidney and nose (**Figure 4**). As with *daxx*, this pattern indicates that each copy may have common and divergent roles. Both copies also display high expression levels during the early stages of development, particularly in early blastulation (**Figure 4**). This is consistent with the established roles of *brd2* in reproductive and developmental processes, including spermatogenesis and folliculogenesis (Rhee *et al.*, 1998), and egg polarity in zebrafish (*Danio rerio*) (DiBenedetto *et al.* 2008). *Brd2* is also responsible for nervous system development, morphogenesis and differentiation of the digestive tract in zebrafish (Murphy *et al.* 2017). In the Gene Ontology analysis, both *daxx* and *brd2* appear in the organelle organization category that is enriched in genes under selection in Norwegian aquaculture (**Table 1**).

Another duplicate pair *Psmb8*, codes for a beta subunit of the type 8 proteasome, which facilitates endopeptidase activity and proteasomal protein catabolism (Arima *et al.*, 2011). *Psmb8a_chr14* is highly expressed in many tissues, including immune-related tissues such as the gill, head kidney, and spleen, while *psmb8a_chr27* did not show such high expression, suggesting possible regulatory difference (**Figure 4**). Both copies of *abr*, which activate cerebellar development in multiple vertebrates (Chuang *et al.*, 1995; Mulherkar *et al.*, 2014; Guo, Yang and Shi, 2020), were highly expressed in the brain (**Figure 4**), also suggesting a neurological function in Atlantic salmon.

## DISCUSSION

Domestication often involves unintentional selection for traits such as tameness, reduced stress, and changes in neurological and behavioral responses, enabling species to better coexist with humans (Fleming *et al.*, 1996; Islam *et al.*, 2020). In contrast, artificial selection deliberately targets desirable traits linked to dietary, economic, or social factors. Over the past 50 years (~15 generations), independent Atlantic salmon populations have undergone both domestication and artificial selection for key production traits, resulting in specialized phenotypes significantly distinct from their wild counterparts (Milla *et al.*, 2021b). Parallel evolution across different populations is expected due to similar captive environments and selection pressures (Bourret *et al.*, 2013; Elmer *et al.*, 2014). Moreover, aquaculture species that experienced WGD, exemplified with salmonids, may have benefited from this genomic event, potentially hosting the genetic resources to adapt to novel, artificial environments and intensive selection of aquaculture (Lien *et al.*, 2016; Chen *et al.*, 2019; Xu *et al.*, 2019; Du *et al.*, 2020; Redmond *et al.*, 2023; Rondeau *et al.*, 2023).

In this study, we investigated how domestication and artificial selection have shaped the genomic landscapes of farmed populations compared to the wild. Genome-wide scans using whole-genome sequencing data from Atlantic salmon populations in North America and Western Norway revealed evidence of genetic divergence between farmed and wild populations. We also identified parallel selection sweeps driven by positive selection in the two farmed strains, addressing a knowledge gap in previous research. Among these, our findings highlight parallel ohnologs—a rare case where WGD-derived homeologous regions show shared selection signals during independent domestication events.

**Parallel selective sweeps in MHC genes in aquaculture across continents: Likely retention of variation under long-term balancing selection**
Our comparative study identified 82 SNPs within the *daxx* and adjacent *tapbp* genes, including five nonsynonymous SNPs, shared selective sweeps in aquaculture population pairs in Western Norway and North America. Both genes are members of the major histocompatibility complex (MHC) class IA region in Atlantic salmon (Grimholt, 2018), suggesting crucial roles in the immune system. The *tapbp* gene encodes a transmembrane glycoprotein, which stabilizes the peptide loading complex of MHC class I molecules, aiding in T cell immune recognition (Ortmann *et al.*, 1997; Lehner, Surman and Cresswell, 1998; Garbi *et al.*, 2003). The *daxx* gene has a broad range of functions in vertebrates such as regulation of telomere length and stability (Heaphy *et al.*, 2011) and chromatin remodeling (Drané *et al.*, 2010; Goldberg *et al.*, 2010; Rapkin *et al.*, 2013; Lovejoy *et al.*, 2020). Subsequent research has shown that *daxx* is also involved in apoptosis (Khelifi, D'Alcontres and Salomoni, 2005; Yao *et al.*, 2014; Détrée and Gonçalves, 2019) and as a tumor suppressor (Mahmud and Liao, 2019). However, its interaction with the MHC class I in fish, especially in salmon remains elusive.

Our findings suggest that long-term balancing selection has maintained high allelic diversity in the MHC regions across populations in North America and Western Norway that diverged approximately 1 million years ago (Rougemont and Bernatchez, 2018), as observed in other vertebrates (McConnell *et al.*, 2016; Veríssimo *et al.*, 2023). This diversity allows the shared, ancient SNPs in the MHC region to undergo parallel selection in aquaculture conditions. In contrast, non-MHC regions likely lack shared SNPs already due to genetic drift, which has led to fixation in each lineage and reduced the potential for parallel selection. In addition, genomic structural differences between the two lineages may have reduced the number of shared variants outside the MHC regions.

In natural environments, we can assume that the allelic diversity at the MHC locus of Atlantic salmon has been maintained through balancing selection. However, in aquaculture settings, specific alleles have been selected due to common selective pressures for increased immune response to infectious diseases in high-density net pen populations (Krkosek, 2010). Alternatively, this selection may target other phenotypic effects of these genes, exemplified by the fact that *daxx* also plays a key role in apoptosis (Michaelson *et al.*, 1999; Khelifi, D'Alcontres and Salomoni, 2005). While nonsynonymous SNPs have been identified, pinpointing the causal SNPs needs more targeted approaches, such as MHC-specific sequencing, to fully resolve this rapidly evolving region (Sundaram *et al.*, 2020) and functional analysis, such as gene editing. In the long term, co-evolution between hosts and pathogens could lead to currently advantageous alleles becoming disadvantageous (Spurgin and Richardson, 2010).

In addition, lineage-specific selective sweeps in other MHC genes further support the role of artificial selection in shaping immunity, even when the specific SNPs and genes with adaptive signatures differ between the two domesticated lines.

## Parallel selection on homeologous regions

We observed parallel selection on two paired regions of salmon-specific WGD-derived ohnologs (chr14-27, chr4-13), and one major region is the MHC region with high Tajima's D value in the aquaculture populations. This suggests balancing selection has been contributed in maintaining adaptive immune variation across duplicates in addition to prolonged tetraploid recombination, as observed in the late rediploidization LORe regions (Robertson *et al.*, 2017). However, further in-depth research is needed to fully resolve how WGD-derived variation has contributed to adaptation, particularly in the context of domestication.

In addition, we observed four WGD-derived gene pairs under parallel domestication-related selection across two distinct geographical lineages: *psmb8*, *psmb13a*, *brd2a* on chr14 and chr27 (genes on MHC regions), and *abr* on chr4 and chr13 (gene involved in neurological and brain functions; Chuang *et al.*, 1995; Mulherkar *et al.*, 2014; Guo, Yang and Shi, 2020). Such selection signatures may be associated with ongoing adaptation to aquaculture conditions. In aquaculture environments, fish are raised under higher density conditions than in the wild, with controlled lighting, temperature, and salinity levels. In such artificial environments, advantageous alleles in captivity may differ from beneficial alleles in nature. Disease outbreaks are a major challenge in aquaculture, regardless of species (Cain, 2022). Disease
resistance, a key trait in aquaculture, may be enhanced by such immune genes on homeologous regions under selection. While this suggests that WGD-derived variation may contribute to specific adaptations, such occurrences are rare and its impact appears to be limited, likely due to the ancient nature of the WGD event and subsequent gene diversification or pseudogenization. Nonetheless, these genes may play a supporting role in the adaptation of Atlantic salmon to aquaculture environments. In this context, the contribution of WGD primary contributions seem to be (1) allowing for increased immune variation maintained among duplicated immune gene complexes and (2) reducing pleiotropy through sub-functionalization (Marchant et al., 2019). While genetic changes resulting from WGD may contribute to specific adaptations, domestication has occurred across diverse phylogenetic lineages with varying genetic backgrounds, indicating that multiple genomic pathways can facilitate adaptation to human-mediated environments.

The role of WGD-derived variation in adaptation is not unique to Atlantic salmon. Parallel selection on WGD-originated gene pairs has been reported in other taxa, such as vibrant skin color in common carp and leaf-heading traits in Brassica species (Cheng et al., 2016; Wang et al., 2024), demonstrating that duplicated genes can contribute to adaptive evolution although the underlying genetic mechanisms may differ across taxa. Our findings provide limited direct evidence that WGD derived variation facilitated adaptation to aquaculture conditions, indicating that many other factors, including additional facets of species biology and genetics, selective breeding and environmental pressures, are also crucial in shaping domestication outcomes. Further studies integrating functional validation and broader taxonomic comparisons will be necessary to disentangle these contributions.

## Sema genes under selection in the Western Norwegian aquaculture group

Building on understanding neural-related genetic selection in aquaculture, we underscore the sema genes (Alto and Terman, 2017). Gene ontology (GO) enrichment analysis revealed that genetic SNPs under selection related to semaphorins were over-represented in the Western Norwegian aquaculture group (sema 4ab, sema5ba, sema6d, sema6e). Semaphorin is involved in axon guidance and synapse formation (Luo, Raible and Raper, 1993; Jongbloets and Pasterkamp, 2014). Semaphorins have also been detected under selection in previous studies of aquaculture Atlantic salmon populations (Gutierrez, Yáñez and Davidson, 2016) (Naval-Sanchez et al., 2020), supporting our findings. Such genes under lineage-specific selection should also contribute to the domestication of Atlantic salmon. It has been reported that structural variation showing genetic divergence between farmed and wild Atlantic salmon is linked to synaptic genes responsible for behavioral variation in Atlantic salmon (Bertolotti et al., 2020). Such work suggests that individuals with greater stress tolerance have been selectively favored in aquaculture environments. Meanwhile, it is reported that fast-growing fish tend to be more aggressive (Nicieza and Metcalfe, 1999). So, it is also possible that the fast-growing fish have been selected, potentially leading to the inadvertent selection of genes associated with aggressive behaviors as a byproduct.

## CONCLUSION

Our findings contribute to a better understanding of the genomic mechanisms underlying aquaculture adaptation, across continents. By identifying immune and developmental genes under artificial selection, this study highlights key processes involved in the domestication of Atlantic salmon. These results also emphasize the role of genetic diversity and redundancy in shaping evolutionary trajectories and resilience to challenges in aquaculture-specific environments.

## MATERIALS & METHODS

### *Studied populations*

The present study involved 370 Atlantic salmon individuals from two wild and aquaculture populations across distinct phylogeographical lineages, aiming to compare genomic landscapes of domestic
strains with local wild counterparts. Whole-genome sequencing data was retrieved from publicly available datasets.

We used 80 North American aquaculture samples derived from the Gaspe, St. John, and Penobscot Rivers (NCBI PRJEB34225) and 112 Norwegian MOWI strain samples from the River Bolstad, Årøy, and marine captures near Osterfjord and Sotra (NCBI PRJEB47441, (Glover et al., 2009)). Wild counterparts included 80 North American individuals from 8 Quebec rivers and 98 Norwegian individuals from 17 rivers in Western Norway (NCBI PRJEB38061, Bertolotti et al., 2020), see **Figure 1**, Tables S1 S2).

In North America, aquaculture samples were taken from Gaspe (n=16), St. John (n=53), and Penobscot Rivers (n=11). Wild samples were from 8 rivers: Malbaie Charlevoix, Bonaventure, Petite rivière Cascapedia, Laval, De la Chaloupe, Jupiter, Du Vieux Fort, and Saint-Paul (n=10 each). In Western Norway, aquaculture samples were from the MOWI Norwegian breeding line (n=112). Wild counterparts were from 17 rivers: Vorma, Suldalslågen, Vikedalselva, Etneelva, Eidfjordvassdraget, Oselva i Os, Loneelva i Osterøy, Flåmselva, Lærdalselva, Årøy, Daleelva, Flekkeelva, Nausta, Jølstra, Oselvvassdraget, Ryggelva, and Gloppenelva (ranging from n=2 to n=10, Tables S1 S2).

The North American lineages originate ostensively from single rivers and so should not have an admixed founding population, however, there is documented supplementation from Canadian rivers early in the domestication history into Maine. These lineages also show evidence of European admixture despite use of European fish in Canadian aquaculture never having been approved (Bradbury et al., 2022). The origins of domesticated North American salmon strains are the Penobscot River strain (Gao et al., 2023), the St. John's River strain (Liu et al., 2017) and the Grand Cascapedia River (Nash and Waknitz, 2003). These strains originated from the Gulf of Maine, the Bay of Fundy, and the Gaspé Bay, Quebec, respectively. Sampled populations were primarily from Quebec including the Petite Cascapedia River, which neighbours the source population of the Gaspé strain (**Figure 1 and Table S1 S2**). Historically, the Gaspé and Miramichi Rivers were used for population supplementation in Maine during the 1920s–1960s. This provides a basis for comparing Canadian wild populations with domesticated strains, as the Penobscot strain originated after Canadian stock introductions (FWS Training Center: https://trainingcenter.fws.gov/resources/knowledge-resources/salmon/asalmon4.html). The admixture history of North American domesticated lineages is evident in the PCA plot (**Figure 1**).

The European lineage we analysed, MOWI, is derived from a mixture of west coast lineages, primarily from the Vosso drainage basin and the River Årøy, in the 1960s. The wild sampled rivers include the source population River Årøy in Western Norway. The Vosso population has collapsed since salmon were taken from it for aquaculture, it is highly disturbed by introgression from farmed escapees, which is high in part because of population collapse from multiple other anthropogenic disturbances. The loss of the original Vosso source population due to farm-wild introgression is well-documented. Sægrov et al.(1997) reported that escaped farmed Atlantic salmon replaced the original stock in the River Vosso (Sægrov et al., 1997). Similarly, Glover et al. (2012) provided a spatio-temporal analysis of Atlantic salmon population genetic structure throughout Norway (Glover et al., 2012).Sampling efforts include Årøy, which was used as part of the wild dataset alongside other west coast populations. Vosso, however, was excluded due to its near-total replacement by aquaculture escapees (Våge, 1995). As a result, contemporary samples from Vosso cannot, unfortunately, be used as representative of the original source population. A sample of 16 other west coast population in the surrounding region were used in place of this source population.

The founding population size of aquaculture groups is unknown, though it is assumed that there will have been a population bottleneck at the founding of the domesticated lineages. However, this will have been countered by the admixture during the founding of the European lineage and the supplementation of domesticated lineages in Maine with Canadian and likely also European salmon. Resulting in domesticated lineages that have a history of population bottlenecks, admixture and strong artificial selection. Disentangling these processes from the data will be complex without historical samples. The influence of admixture can be seen in the wider spread of domesticated lineages in PCA space
(European on PC2 and North American across PC1 and PC2, see **Figure 1**) compared to the wild lineages used. The Domesticated lineages cover greater PCA space than the combined spread of the wild populations in both continents, suggesting admixture is more important than population bottlenecks. Despite the Gaspé lineage have the closest source population sampled; this is actually the furthest in PCA space from the cluster of wild populations. The North American wild populations cluster tightly and overlap with the Maine and Bay of Fundy domesticated lineages, probably reflecting the use of Canadian salmon to supplement early aquaculture further south.

## Data preparation

Mapping, SNP calling and quality control were performed independently for each of the four datasets (Aquaculture Norwegian, Wild Norwegian, Aquaculture North American, Wild North American) using the same pipeline and parameters. Illumina reads were mapped to the Atlantic salmon genome (Ssal_v3.1; GCA_905237065.2 ; Stenløkk, 2023) using bwa-mem2 (Li and Durbin, 2009). Duplicate reads were marked with gatk4-spark:4.3.0.0 MarkDuplicates. Genetic variation was identified using gatk4-spark:4.3.0.0 HaplotypeCaller with default parameters and the individual genotypes were merged with gatk4-spark:4.3.0.0 GenotypeGVCFs. To avoid the detection of false positives, SNPs were filtered by the following parameters with gatk4-spark:4.3.0.0 (McKenna et al., 2010) : « QD < 2.0 » which filters out SNPs with a low quality score by depth of coverage; « QUAL < 50.0 » to sort SNPs with a low-quality score; « SOR > 4.0 » that removes SNPs where the number of reads supporting the reference allele is significantly skewed towards one strand (forward or reverse) compared to the other; « FS > 60.0 » to filter out SNPs whose reads supporting the reference allele versus the alternative allele are significantly different between the two strands; and finally « ReadPosRankSum < -8.0 » to remove SNPs with a low score for the difference between the mean position of the reference allele and the alternative allele among the reads supporting the SNP (https://gatk.broadinstitute.org/hc/en-us). From this large pruning, we kept only biallelic markers (SNPs) with depth greater than 4 and less than 30, confident call rate greater than 90% (GQ) and missingness lower than 10% using VCFtools (Danecek et al., 2011). The whole analytical pipeline is summarized in **Figure S1**.

## Population structure analysis

We used two clustering methods to investigate genetic structure among populations and have a broad representation of the genetic differentiation between North American and West Norwegian lineages. Additional filtering steps specific to these analyses were applied to remove loci that deviated from Hardy-Weinberg equilibrium, loci with a minor allele frequency (MAF) below 5%, and linked SNPs using PLINK v1.9 (Purcell et al., 2007). Linkage disequilibrium pruning was performed using a sliding window approach (i.e 50 SNP windows, 10 SNP step size, and an R² threshold of 0.5). We ran ADMIXTURE (Alexander, Novembre and Lange, 2009; Alexander and Lange, 2011) with a number of ancestral populations set from 1 to 20 (K). The optimal K was selected based on the lowest cross-validation error. We then conducted a Principal Component Analysis (PCA) using PLINK v1.9 (Purcell et al., 2007) on the same filtered dataset. The results of both analyses (i.e eigenvectors and genetic variation from PCA; genetic components from ADMIXTURE) were visualized in R 4.2.2 using the *ggplot2* (Wickham, 2011) and *pophelper* (Francis, 2017) R packages.

## Detection of selection sweeps

For the detection of putative genomic regions under artificial selection in domestic strains, we applied a genome scan using three statistical methods based on (1) population differentiation ($F_{ST}$; Weir and Cockerham, 1984); (2) site frequency (Tajima's D; Tajima, 1989) and (3) cross population test of extended haplotype homozygosity (XP-EHH; Sabeti et al., 2002). These tests were first carried out to compare aquaculture and wild populations from the same geographical region independently in order to detect lineage-specific selection signatures. Then, we investigated whether some genomic regions putatively under selection are shared by populations of different origins, indicative of parallel evolution. In the context of domestication, we expect two main scenarios in terms of selective sweeps. A hard selective sweep occurs when a beneficial allele arises and rapidly reaches fixation within a population. There is little genetic variation surrounding the selected allele because the advantageous SNP quickly
"sweeps" through the population, leaving little time for new mutations or recombination to occur. As a result, the genetic diversity around the selected site is reduced, and the favored allele is present on a single haplotype in the population. A soft selective sweep occurs when multiple beneficial alleles, either derived from different mutations or standing genetic variation, rise in frequency and are favored within a population. During a soft sweep, there can be multiple haplotypes carrying different advantageous alleles and the genetic diversity around the selected site is not as reduced as in a hard sweep, and multiple haplotypes may show evidence of positive selection.

First, $F_{ST}$ identifies selection using differences in allele frequencies between populations, here between domesticated and wild populations from each geographic area independently. These statistics were calculated according to the pairwise Weir and Cockerham's (1984) estimator using VCFtools (Danecek et al., 2011) in 20,000 bp non-overlapping sliding windows using wild individuals as the reference population. We extracted the top 1% values as outliers.

Secondly, Tajima's D assumes that neutral genetic variation is maintained by a balance between mutation, genetic drift, and gene flow. The test is computed as the ratio between the number of segregating sites and the average pairwise nucleotide differences. Deviations from this neutral evolution can indicate putative selection or demographic events. Markers under selection can be expressed as positive and negative values, while zero values indicate a putative absence of selection. A significant positive Tajima's D value may indicate an excess of intermediate-frequency alleles, which could be caused by balancing selection, population subdivision, or recent population expansion. A significant negative Tajima's D value may indicate an excess of low-frequency alleles, which could be caused by positive selection, population bottlenecks, or purifying selection. In our study, Tajima's D was conducted on the two aquaculture populations independently by non-overlapping sliding windows of 20,000 bp. As the domestication involved bottlenecks and focus on directional selection, we are more interested in negative values. The lowest values at the 1% level were taken as the significance level. $F_{ST}$ and Tajima's D were calculated from vcf files resulting from the soft filtering (i.e depth, low quality SNPs, and missingness; see Data preparation section).

Finally, we investigated patterns of extended haplotypes between pairs of populations using the *rehh* R package (Gautier, Klassmann and Vitalis, 2017). Haplotype analyses are based on extended haplotype homozygosity (EHH) that represents the model of a selective sweep in which an adaptive de novo mutation appears on a haplotype and rapidly evolves towards fixation, thus reducing diversity around the locus (Sabeti et al., 2002). A hard sweep induces a signal of high haplotype homozygosity to be observed extending from an adaptive locus as strong rapid selection causes strong linkage that is not broken by recurrent mutation or recombination. In other words, under conditions of positive selection, a beneficial new allele can rapidly increase in frequency and create long stretches of homozygosity, or identical copies of the haplotype, around the selected site. This process results in longer and linked haplotypes detectable by EHH methods. In our case, we applied a cross-population (XP-EHH) test that compares the distribution of haplotype homozygosity between aquaculture and wild populations. When there is divergent selection between the two environments (wild and aquaculture), the allele selected in the focal population (aquaculture) will be on a longer haplotype. This signal will not be present when selection is concordant between the two environments. Negative scores of XP-EHH suggest that selection occurred in the reference population, whereas positive scores show selection in the domestic population. Unlike Tajima and $F_{ST}$, these tests cannot directly handle SNP genotypes because they require haplotypes. Added to the soft filtering, SNPs with Minor Allele Frequency below 5% were removed. The remaining SNPs were phased to infer the haplotypes of each population using BEAGLE v4.1 (Browning and Browning, 2007) with a window size of 10,000 bp and 600 bp overlap. One-sided p-values were obtained as -log10(1-Φ(XP-EHH)), where Φ(XP-EHH) represents the Gaussian distribution function for the statistic. False Discovery Rate (FDR) test was performed to estimate the number of false positives and determine the significant threshold. We used -log10(p-value) = 4 (alpha = 0.0001) as a threshold to define significant values of XP-EHH. As XP-EHH searches for the extended haplotype, which is typical of a hard sweep, this method will be more sensitive in detecting hard sweeps than soft selective sweeps.
### SNP annotation

Significant windows or SNPs from the three comparative analyses were intersected using BEDtools (Quinlan, 2014) to examine markers detected by multiple methods and thus increase the repeatability of the results. For each geographical region, three intersections were tested: (i) F<sub>ST</sub>/Tajima's D, (ii) F<sub>ST</sub>/XP-EHH, (iii) Tajima's D/XP-EHH. Candidate SNPs detected by two or more methods were then intersected with annotated genes in the Atlantic salmon reference genome (Ssal_v3.1; GCA_905237065.2 ; Stenløkk, 2023) using BEDtools window to identify SNPs under selection that are contained in the same windows as the genes or that are directly mapped to genes. The overlapping genes were input into ShinyGO 0.77 (Ge, Jung and Yao, 2020); http://bioinformatics.sdstate.edu/go/ to extract biological functions and enrichment. All the intersects were tested independently to observe population-specific signatures. We then tested parallel evolution using only the significant SNPs from XP-EHH, as this was the most suitable method. Categories were extracted where the enrichment False Discovery Rate (FDR) was < 0.05, Fold Enrichment > 2, and the Gene Ontology category contained ≥ 3 genes (Table 1).

### Gene expression analysis

We used available RNA sequencing data online 1) from early development stages (AQUA-FAANG project No. PRJEB51855) and 2) from tissues (NCBI Project No. SRP011583) including brain, eye, gill, gut, head kidney, heart, kidney, liver, muscle, nose, ovary, caecum, skin, spleen and testis. Data were quantified using the latest version of the Ensembl transcriptome (Ssa v3.1). The fastq files were first filtered using fastp v0.23.2 (Chen, 2023) with default settings in order to remove potential sequencing adaptors in the reads. Then, the reads were mapped using the lightweight mapper salmon v1.5.2 (Patro et al., 2017) which tracks, by default, the position and orientation of all mapped fragments. This information is used in conjunction with the abundances from online inference to compute per-fragment conditional probabilities (Patro et al., 2017). The produced TPM (transcripts per million) was visualized using the package ggplot2 in R.

### Test for balancing selection on the MHC region on chromosome 14

Investigation of balancing selection was carried out using Tajima's D (Tajima, 1989). Tajima's D value was computed in the MHC region on chromosome 14 between 64320000 and 64340000 bp. Then, Tajima's D was computed on 500 windows of 20,000 bp with more than 15 SNPs, randomly selected across the genome. We used variant effect predictor (McLaren et al., 2016) to predict SNPs consequences on coding genes.

## AUTHORS CONTRIBUTION

Pauline Buso, Conceptualization, Data curation, Investigation, Writing – Original Draft Preparation
Celian Diblasi, Domniki Manousi, Jun Soung Kwak, Kristina Stenløkk: Investigation, Software
Nicola Barson, Marie Saitou: Conceptualization, Investigation, Writing – Original Draft Preparation
Review & Editing Manuscript was done by all authors.

## ACKNOWLEDGEMENTS

The authors thank Joris Bertrand from the Laboratoire Génome et Développement des Plantes (LGDP) in Perpignan (France) for his advice on genetic structure analysis. The study was supported by EUR TULIP-GSR N°ANR-18-EUR-0019 a grant managed by the French National Research Agency (ANR) in the framework of the program "Investing for the Future". The study was supported by The Research Council of Norway (grant nos. 325874, 275310 and 221734). DM was supported by the Norwegian University of Life Sciences (NMBU), BIOVIT, PhD funding. We acknowledge the use of the Orion computing cluster at the Norwegian University of Life Sciences (NMBU). We thank Louise Chavarie for her comments on the geography of Canada.

## DATA AVAILABILITY

Script used for the analyses in this work are available at
https://github.com/paulinebuso/ATLANTIDES pipelines
TABLES & FIGURES

<caption>**TABLE 1. GO enrichment results on genes under putative selection**</caption>

For genes that overlap with windows or SNPs suggested to be non-neutral by multiple methods, or XPEHH alone, we conducted Gene Ontology (GO) enrichment analysis using ShinyGo v0.77 (Ge, Jung and Yao, 2020). Categories are shown when the enrichment False Discovery Rate (FDR) was < 0.05, Fold Enrichment > 2, and the Gene Ontology category contained ≥ 3 our input genes (nGenes). FDR is adjusted from the hypergeometric test. Fold Enrichment is defined as the percentage of genes in the input list belonging to a pathway, divided by the corresponding percentage in the background genes (Ge, Jung and Yao, 2020).

<table><thead><tr><th>FDR</th><th>nGenes</th><th>Fold Enrichment</th><th>Pathways</th><th>Population</th><th>Tests</th></tr></thead><tbody><tr><td>0.015</td><td>3</td><td>13.8</td><td>Focal adhesion<br>Semaphorin receptor<br>binding</td><td>W. Norway</td><td>Intersect F<sub>ST</sub> /<br>Tajima D</td></tr><tr><td>0.01</td><td>4</td><td>27.8</td><td></td><td>W. Norway</td><td>XPEHH</td></tr><tr><td>0.013</td><td>14</td><td>3.6</td><td>Organelle organization<br>Intracellular protein-<br>containing complex</td><td>W. Norway</td><td>XPEHH</td></tr><tr><td>0.0077</td><td>6</td><td>11.2</td><td></td><td>N. America</td><td>XPEHH</td></tr></tbody></table>
**Table 2. Genes under parallel selection based on the XP-EHH test.**

We described the gene name in this table if the gene or duplicated gene pair is overlapped with one or more outlier SNP(s) in XP-EHH test in the Western Norwegian aquaculture group and North American aquaculture group. Start and End refer to the starting and ending locations of genes on the chromosome, respectively, based on the Ssa v.3.1 annotation on Ensembl. Duplicated genes, gray shade, (See also Figure 2 and Figure 3) are defined according to Gillard *et al.*, 2021.

<table><thead><tr><th>Gene Ensembl ID</th><th>Gene name</th><th>XP-EHH signatures</th><th>Region</th><th>Ch r</th><th>Start</th><th>End</th></tr></thead><tbody><tr><td>ENSSSAG00000040701</td><td>daxx_chr14</td><td>Both_Populations</td><td>MHC</td><td>14</td><td>64,304,883</td><td>64,330,023</td></tr><tr><td>ENSSSAG00000040723</td><td>tapbp</td><td>Both_Populations</td><td>MHC</td><td>14</td><td>64,331,687</td><td>64,338,464</td></tr><tr><td>ENSSSAG00000093386</td><td>tap2a</td><td>NAmerica</td><td>MHC</td><td>14</td><td>64,476,753</td><td>64,493,938</td></tr><tr><td>ENSSSAG00000085704</td><td>psmb13a_chr14</td><td>Parallel_NAmerica</td><td>MHC</td><td>14</td><td>64,463,937</td><td>64,467,854</td></tr><tr><td>ENSSSAG00000041842</td><td>psmb8b_chr14</td><td>Parallel_NAmerica</td><td>MHC</td><td>14</td><td>64,459,783</td><td>64,462,906</td></tr><tr><td>ENSSSAG00000042614</td><td>brd2a_chr14</td><td>Parallel_WNorway</td><td>MHC</td><td>14</td><td>64,524,519</td><td>64,536,467</td></tr><tr><td>ENSSSAG00000077402</td><td>daxx_chr27</td><td>Parallel_WNorway</td><td>MHC</td><td>27</td><td>10,402,918</td><td>10,411,454</td></tr><tr><td>ENSSSAG00000077187</td><td>tcf19</td><td>WNorway</td><td>MHC</td><td>27</td><td>10,323,665</td><td>10,328,897</td></tr><tr><td>ENSSSAG00000077419</td><td>uba</td><td>WNorway</td><td>MHC</td><td>27</td><td>10,426,981</td><td>10,465,906</td></tr><tr><td>ENSSSAG00000078726</td><td>vHSV</td><td>WNorway</td><td>MHC</td><td>27</td><td>10,801,247</td><td>10,804,941</td></tr><tr><td>ENSSSAG00000078638</td><td>vps52</td><td>WNorway</td><td>MHC</td><td>27</td><td>10,789,984</td><td>10,840,875</td></tr><tr><td>ENSSSAG00000077397</td><td>zbtb22b</td><td>WNorway</td><td>MHC</td><td>27</td><td>10,394,411</td><td>10,398,690</td></tr><tr><td>ENSSSAG00000077772</td><td>col11a2</td><td>WNorway</td><td>MHC</td><td>27</td><td>10,645,732</td><td>10,694,689</td></tr><tr><td>ENSSSAG00000077561</td><td>psmb13a_chr27</td><td>Parallel_WNorway</td><td>MHC</td><td>27</td><td>10,521,614</td><td>10,527,869</td></tr><tr><td>ENSSSAG00000085574</td><td>psmb8a_chr27</td><td>Parallel_WNorway</td><td>MHC</td><td>27</td><td>10,518,241</td><td>10,521,084</td></tr><tr><td>ENSSSAG00000077713</td><td>brd2a_chr27</td><td>Parallel_NAmerica</td><td>MHC</td><td>27</td><td>10,621,388</td><td>10,634,539</td></tr><tr><td>ENSSSAG00000048882</td><td>abr_chr13</td><td>Parallel_WNorway</td><td></td><td>13</td><td>77,743,389</td><td>77,966,280</td></tr><tr><td>ENSSSAG00000001855</td><td>abr_chr4</td><td>Parallel_NAmerica</td><td></td><td>4</td><td>56,687,253</td><td>56,919,875</td></tr></tbody></table>
<figure><img src="image_1.png" /><figcaption>**Figure 1.** Sample distribution and genetic structure of Atlantic salmon lines from western Norway and North America. (A) Geographic distribution of Atlantic salmon population in North America. Aquaculture samples were used from Gaspe (Gaspe, n=16), St John River (n=53) and Penobscot River (n=11). Wild samples came from 8 rivers: Malbaie Charlevoix (n=10), Bonaventure (n=10), Petite riviere Cascapedia (n=10), Laval (n=10), De la Chaloupe (n=10), Jupiter (n=10), Du Vieux Fort (n=10) and Saint-Paul (n=10). (B) Geographic distribution of Atlantic salmon population from Western Norway. Aquaculture samples come from MOWI Norwegian breeding line (n=112). Wild counterparts were collected among 17 rivers: Vorma (Vorm, n=2), Suldalslågen (Suld, n=10), Vikedalselva i Vindafjord (Vike, n=10), Etneelva (Etne, n=2), Eidfjordvassdraget (Eidf, n=2), Oselva i Os (Osel, n=2), Loneelva i Osterøy (Lone, n=2), Flåmselva (Flam, n=10), Lærdalselva (Laer, n=2), Årøy (Aroy, n=10), Daleelva (Dale, n=2), Flekkeelva (Flek, n=2), Nausta (Naus, n=10), Jølstra (Jols, n=10), Oselvvassdraget (Osen, n=10), Ryggelva (Rygg,</figcaption></figure>
n=10) and Gloppenelva (Glop, n=2). (C) Scatterplot of principal component analysis (PCA). Principal components 1 and 2 (PC1 and PC2) were calculated for all Atlantic salmon samples. (D) Population genetic structure analysis. All samples were clustered into 6 and 9 genetic components (K=6 and K=9) according to the lowest cross-validation error.
<figure><img src="image_2.png" /><figcaption>**Figure 2. Cross-population test on Extended Haplotype Homozygosity (XP-EHH).**</figcaption></figure>

Each dot is SNPs, while the X-axis displays the position across the genome. The Y-axis represents the -log10(p-value) the XP-EHH (Cross Population Extended Haplotype Homozygosity) values. Blue Diamonds (WNorway) and Red Diamonds (NAmerica) indicates SNPs with significantly high XP-EHH values that overlap genes. We adopted -log10(p-value) = 4 as the cutoff (dashed line) for significant XP-EHH values (Materials and Methods). Gray and black dots are other tested SNPs. Only SNPs with a -log10(p-value) greater than 1 were plotted to ensure the computational efficiency. Labeled genes are genes with exact same SNPs under parallel selection (yellow) or selection on the homeologous regions, with matching colors in both populations showing the WGD-derived gene pairs. The suffix denotes the chromosomal location of the copy. The entire SNP list with significant XP-EHH is available in **Supplementary Table 1**.
<figure><img src="image_3.png" /><figcaption>Figure 3. The MHC region under parallel selective sweeps in both continents</figcaption></figure>

A: Zoomed-in view of XP-EHH results on the MHC loci on chromosome 14 and chromosome 27 exhibiting parallel selective sweeps. X-axis displays the position across the genome. Y-axis represents the -log10(p-value the XP-EHH (Cross Population Extended Haplotype Homozygosity) values. Blue (WNorway) and Red (NAmerica) indicates SNPs with significantly high XP-EHH values that overlap genes. We adopted -log10(p-value) = 4 as the cutoff (dashed line) for significant XP-EHH values (Materials and Methods). Gray and black dots are other tested SNPs. Genes with colored labels are genes under parallel selection (yellow) or parallel selection on the homeologous regions (green), with matching colors in both populations showed the WGD-derived gene pairs. Gray genes are genes with high XP-EHH values in one population. B Haplotype pattern of the region under parallel selection. Each column is a polymorphic site
in the MHC region (126 SNPs in total, chr14: 64326415-64339381), and each row is a phased haplotype (160 North American Aquaculture (red), 224 Western Norwegian aquaculture (dark blue), 160 Wild North American (pink), 196 Wild West Norwegian (light blue)). C Tajima's D plots the four groups. The dashed vertical lines represent the Tajima's D value in the MHC locus on chromosome 14 (chr14: 64326415-64339381). The histogram represents the distribution of the Tajima's D values from the size-matched 500 regions in each population as a neutral expectation.
<figure><img src="image_4.png" /><figcaption>Figure 4. Expression of genes under parallel selection or parallel selection on the homeologous regions in early development stages and mature tissues.</figcaption></figure>

Gene expression is represented as log-transformed Transcripts Per Million (TPM). The sweep panel indicates the gene that have undergone selective sweeps in North America (red) or Western Norway (blue). The left heatmap shows the gene expression levels during early development, from late gastrulation to the eyed egg stage. The right heatmap shows the gene expression levels across different tissues in the mature Atlantic salmon. We used publicly available RNA sequencing data from early development stages (AQUA-FAANG project No. PRJEB51855) and from various tissues of healthy fish (NCBI Project No. SRP011583) including brain, eye, gill, gut, head kidney, heart, kidney, liver, muscle, nose, ovary, caecum, skin, spleen and testis of Atlantic salmon in Europe.

Table S1 Fish individual code and data courses

Table S2 Sample river locations

Table S3 SNPs with high XPEHH values

Table S4. Outlier SNPs by the XP-EHH test, detected in both the Western Norwegian aquaculture group and North American aquaculture group.

Only SNPs overlapped with genic regions in the Ensembl Varian Effect Predictor (McLaren *et al.*, 2016) are shown.
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