GENOMIC AND MICROBIOME CONTRIBUTIONS TO VARIATION IN OMEGA-3 FATTY ACID TRAITS IN ATLANTIC SALMON Salmo salar DURING THE SALTWATER PRODUCTION PHASE
Background
Variation in omega-3 fatty acid traits in Atlantic salmon (Salmo salar) is jointly shaped by host genomic architecture and gut microbiome composition, yet their relative contributions remain largely unquantified under plant-based feeding systems. The replacement of marine ingredients with plant-based alternatives in aquaculture has reduced the levels of long-chain omega-3 fatty acids, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). This poses a major challenge for nutritional quality and sustainability (Skavang et al., 2024). While host genetics and diet are established determinants of fatty acid composition, the gut microbiome is increasingly recognized as a key regulator of nutrient metabolism, including lipid digestion, absorption, and metabolic processing (Bhat et al., 2023; Gajardo et al., 2016). Gut microbiome composition in Atlantic salmon is influenced by environmental and dietary factors, but these alone do not fully explain variation, suggesting a role for host-microbiome interactions (Dehler et al., 2017; Webster et al., 2020). Despite growing interest in these interactions, the extent to which gut microbial taxa contributes to omega-3 phenotypic variation remains poorly quantified. This study aims to quantify the contribution of gut microbial taxa to omega-3 traits and to identify host-microbiome associations underlying variation in fatty acid composition in Atlantic salmon.
Materials and Methods
A total of 425 Atlantic salmon (Salmo salar) from 30 full-sib families (AquaGen, Norway) were reared under standard aquaculture conditions and sampled at a mean body weight of 1,301.5 g. Distal intestinal digesta were collected postmortem for microbiome analysis. Fish were genotyped using a 56K SNP array, and after quality control (call rate ≥ 0.90, MAF ≥ 0.01, HWE P > 1 × 10-7), 47,792 SNPs were used to construct the genomic relationship matrix. Genomic DNA was extracted using the TGuide Smart Envir-DNA Kit and quantified with the Qubit dsDNA HS Assay Kit. V3-V4 region of 16S rRNA gene was amplified using primers 341F/806R in a one-step indexed PCR (10 μl reaction; 21 cycles: 95 °C 30 s, 50 °C 30 s, 72 °C 40 s). Amplicons were purified (AMPure XP beads), pooled equimolarly, and sequenced on Illumina NovaSeq 6000 platform (PE250). Reads were filtered using fastp, trimmed with cutadapt, and processed with DADA2 (truncLen = 240/220; maxEE = 2). Taxonomy was assigned using SILVA v138.1. Gut microbiome data were integrated with host phenotypes, including EPA, DHA, EPA+DHA, ALA, OA, MUFA, SFA, and omega-6 fatty acids. Associations between microbial taxa and omega-3 traits were tested using linear mixed models including fixed effects (batch, tank, sex, weight, fat) and random effects (tank and host genetic structure). Microbiome contributions to phenotypic variance (m2) were estimated across models. GWAS and microbiome GWAS (mGWAS) were conducted to identify host genetic and microbial associations. Microbial diversity metrics (alpha diversity, Faith's phylogenetic diversity, and UniFrac-based beta diversity) were computed using vegan and picante, with PCoA1 used as a microbial trait. Heritability (h2) of prevalent ASVs (n = 243) was estimated using genomic linear mixed models (ASReml v4.2), and significance was assessed using likelihood ratio tests (P < 0.05).
Results
Variance partitioning showed that microbiome, host genomic, and residual effects contributed differentially across fatty acid traits (Fig. 1A). Microbiome contributions to EPA variation ranged from 11-16% (m2 = 0.123-0.157) depending on model specification, with increased explanatory power when fat content was included. Batch effects contributed significantly to variation, indicating strong environmental or technical influences. For DHA, microbiome contributions were lower but increased substantially when fat was included, from 3-5% to 9-10% (m2 up to 0.104), suggesting indirect relationships mediated through lipid metabolism. Across lipid classes, microbiome effects were clearly phenotype-specific, explaining 12.3% of variance in EPA, 10.6% in DHA, and 11.7% in EPA+DHA. Moderate contributions were observed for MUFA (7.5%), OA (8.0%), and ALA (4.4%), whereas negligible contributions were detected for SFA (0.0%), omega-6 (0.0%), and PUFA (0.7%), indicating selective microbial influence on lipid metabolism. Host genomic effects varied across traits and were generally larger than microbiome contributions for several lipid classes (Fig. 1A). Genome-wide association analysis for EPA identified multiple loci exceeding suggestive and genome-wide significance thresholds (Fig. 1B), indicating a genetic basis for EPA variation. Several SNPs showed strong association signals across chromosomes, highlighting candidate genomic regions contributing to variation in omega-3 traits. Several microbial taxa exhibited moderate to high heritability, with top ASVs showing h2 ranging from 0.48 to 0.87 (p < 10-3- 10-8), demonstrating strong host genetic control over specific components of the gut microbiome. Association analyses (GWAS and mGWAS) further identified significant links between microbial taxa and omega-3 traits, including EPA, DHA, EPA+DHA, and ALA, supporting the role of host-microbiome interactions in shaping fatty acid composition.
Discussion
These findings demonstrate that the gut microbiome contributes a modest but significant and phenotype-specific proportion of variation in omega-3 traits in Atlantic salmon, with stronger effects observed for long-chain omega-3 fatty acids (EPA and DHA) than for other lipid classes. The progressive increase in microbiome contribution after accounting for fat indicates that microbial effects are conditional and mediated through host lipid metabolism, consistent with known microbial roles in short-chain fatty acid production, bile acid transformation, and modulation of host lipid absorption and elongation-desaturation pathways (Bhat et al., 2023). The absence of detectable microbiome effects for DHA under baseline models, followed by their emergence after inclusion of fat, further supports a metabolism-dependent mechanism of microbial influence. These results align with previous findings that gut microbiota influences metabolic efficiency and nutrient utilization in Atlantic salmon (Gajardo et al., 2016). The identification of highly heritable microbial taxa up to 0.15 indicates substantial host genomic control over specific microbiome components, supporting previous evidence that host genetics can shape microbial composition (Difford et al., 2018; Wen et al., 2021). These results reveal biologically meaningful host-microbiome interactions influencing omega-3 metabolism and suggest the potential to integrate microbiome information into breeding to improve omega-3 deposition, nutritional quality, and resource efficiency in farmed Atlantic salmon.
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Figure 1. Genomic and microbiome contributions to variation in fatty acid traits and genome-wide association signals for EPA in Atlantic salmon. (A) Proportion of phenotypic variance explained by residual (grey), host genomic (purple), and microbiome (orange) effects across grouped fatty acids and individual lipid traits. (B) Genome-wide association results for EPA showing -log10(p) values across chromosomes. The red horizontal line indicates the genome-wide significance threshold, and the dashed line indicates the suggestive significance level. Highlighted loci represent SNPs with the strongest associations with EPA variation.
Acknowledgements: This work was funded by the European Union under grant No. 101169005. We are grateful to the Research Council of Norway and the Foods of Norway Centre for Research-based Innovation (WP5), as well as AquaGen, for their support.
References
Bhat, R.A., Dhillon, O., Hoque, F., et al. (2023). Insights on fish gut microbiome. Journal of Aquaculture.
Gajardo, K., Rodiles, A., Kortner, T.M., et al. (2016). A high-resolution map of the gut microbiota in Atlantic salmon (Salmo salar) reveals links to diet and gut health. Scientific Reports, 6, 30864.
Dehler, C.E., Secombes, C.J., Martin, S.A.M. (2017). Environmental and physiological factors shape the gut microbiota of Atlantic salmon parr (Salmo salar L.). Aquaculture, 467, 149-157.
Webster, T.M.U., Consuegra, S., Hitchings, M., et al. (2020). Interpopulation variation in the Atlantic salmon microbiome reflects environmental and genetic influences. Frontiers in Microbiology, 11, 605644.
Difford, G.F., Plichta, D.R., Løvendahl, P., et al. (2018). Host genetics and the rumen microbiome jointly associate with methane emissions in dairy cows. PLoS Genetics, 14(10), e1007580.
Wen, C., Yan, W., Sun, C., et al. (2021). The gut microbiota is largely independent of host genetics in chickens. Microbiome, 9, 48.
Skavang, P.K., Strand, A.V. (2024). Conceptualization of the Norwegian feed system of farmed Atlantic salmon. Frontiers in Marine Science, 11, 1378970.