Introduction
Brown trout (Salmo trutta) is known for its life-history variation, including stream and lake ecotypes that differ in growth, pigmentation, maturation and behavior. The genomic basis of this differentiation remains poorly understood, particularly for lake ecotypes (Sch��ffmann, 2021). It is unclear whether similar phenotypes across geographically distant systems reflect shared ancestry, repeated local adaptation, or population-specific evolutionary trajectories (Collins et al., 2022; Moran et al., 2024). Here, we use a cost-effective whole-genome sequencing approach to investigate genomic differentiation between lake and stream ecotypes across four independent European population pairs, aiming to identify regions repeatedly associated with ecotype divergence (Lou et al., 2021).
Materials and Methods
A total of 116 individuals from four lake–stream population pairs were analysed: Lake Bled–Sava Bohinjka and Lake Bohinj–Ribnica (Slovenia), Lake Siljan–Fj��t��lven (Sweden), and Lake Lucerne–Scheidgraben (Switzerland). Using low-coverage whole-genome sequencing, we assessed population structure with PCA and ancestry-based methods. Loci under selection between ecotypes were identified using PCANGSD, while a complementary Bayesian genotype–phenotype association model implemented in BAYPASS was used to detect loci consistently differentiated across population pairs (Foll & Gaggiotti, 2008; Korneliussen et al., 2014). Candidate regions were functionally annotated and evaluated for gene ontology enrichment.
Results
Genome-wide analyses revealed clear differentiation among geographic systems, while lake–stream divergence varied among population pairs. Selection scans identified multiple genomic regions associated with ecotype differentiation, with BAYPASS detecting 49 candidate regions under the strongest evidence of divergent selection. Most signals were narrow and method-specific, suggesting a polygenic basis of divergence rather than control by a single major-effect region.
Analysis of Tajima's D across candidate regions indicated deviations from neutrality between ecotypes, consistent with selection and/or demographic differences. Several candidate regions were associated with genes involved in cell membrane structure, ion transport and intercellular adhesion, suggesting that interactions with the external environment and tissue organization may play a role in ecotype divergence.
Discussion
Our results confirm genomic differentiation between lake and stream ecotypes of brown trout but show that this divergence is not consistent across all systems. The presence of candidate regions across independent population pairs suggests some shared genomic responses, while limited overlap among methods and populations highlights the importance of local demographic history and population-specific adaptation.
Functional annotation indicated enrichment of genes related to cell membrane structure, ion transport and intercellular adhesion, pointing to a potential role of environmental interaction and tissue-level processes in ecotype divergence. These functions are consistent with adaptation to contrasting lake and river habitats, which differ in hydrodynamics, resource availability and physicochemical conditions.
More broadly, this study demonstrates that low-coverage whole-genome sequencing, combined with genotype-likelihood-based approaches, provides a robust framework for detecting adaptive divergence in non-model salmonids. These findings contribute to understanding the genomic basis of life-history variation and may inform conservation and management of locally adapted brown trout populations.
Acknowledgment
This work was conducted as part of doctoral research at the University of Ljubljana, Biotechnical Faculty, with support from collaborating fisheries organizations and international partners who provided samples from Slovenia, Sweden and Switzerland.
References
Collins, E. E., Romero, N., Zendt, J. S., & Narum, S. R. (2022). Whole-Genome Resequencing to Evaluate Life History Variation in Anadromous Migration of Oncorhynchus mykiss. Frontiers in Genetics, 13. https://doi.org/10.3389/fgene.2022.795850
Foll, M., & Gaggiotti, O. (2008). A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective. Genetics, 180(2), 977–993. https://doi.org/10.1534/genetics.108.092221
Korneliussen, T. S., Albrechtsen, A., & Nielsen, R. (2014). ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics, 15(1), 356. https://doi.org/10.1186/s12859-014-0356-4
Lou, R. N., Jacobs, A., Wilder, A. P., & Therkildsen, N. O. (2021). A beginner's guide to low���coverage whole genome sequencing for population genomics. Molecular Ecology, 30(23), 5966–5993. https://doi.org/10.1111/mec.16077
Moran, P. A., Colgan, T. J., Phillips, K. P., Coughlan, J., McGinnity, P., & Reed, T. E. (2024). Whole-Genome Resequencing Reveals Polygenic Signatures of Directional and Balancing Selection on Alternative Migratory Life Histories. Molecular Ecology, 33(23), e17538. https://doi.org/10.1111/mec.17538
Sch��ffmann, J. (2021). Trout and Salmon of the Genus Salmo. In Trout and Salmon of the Genus Salmo. American Fisheries Society. https://doi.org/10.47886/9781934874639