Genetic diversity insights from population genomics and machine learning tools for Nordic Arctic charr (Salvelinus alpinus) populations

• Published in: Aquaculture Reports
• Main Authors: Palaiokostas, Christos, Kurta, Khrystyna, Pappas, Fotis, Jeuthe, Henrik, Hagen, Ørjan, Beirão, José, Janhunen, Matti, Kause, Antti
• Other Authors: orcid:0000-0002-5651-5287, orcid:0000-0003-0259-6912, 4100111210, 4100210310, Luonnonvarakeskus
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier
• Subjects: Arctic charr; genetic diversity; population structure; DdRAD; SNPs; haplotypes; unsupervised machine learning; Arctic; Norway; Salvelinus alpinus
• Online Access: https://jukuri.luke.fi/handle/10024/555410; https://doi.org/10.1016/j.aqrep.2024.102495

Arctic charr (Salvelinus alpinus) is a salmonid species of high ecological and commercial value in the Holarctic region. Nevertheless, more information is needed about its underlying genetic diversity and population structure in the Nordics, especially regarding farmed populations. High-throughput sequencing was applied in three Arctic charr populations of anadromous or landlocked origin from Finland, Norway and Sweden. More specifically, the animals from the Swedish and Norwegian populations originated from a major egg supplier and producer, respectively. Furthermore, in the case of the Finnish population, the sampled animals originated from the only active conservation program for Arctic charr in the country with a potential interest in farming. Using double-digest restriction site-associated DNA sequencing (ddRAD-seq) on more than 500 fish, over 2000 single nucleotide polymorphisms (SNPs), both in the form of individual SNPs and as read haplotypes, were used to study the genetic diversity and structure of those populations. Genetic diversity metrics were similar between the Norwegian and the Swedish populations. However, substantially lower (40–50 %) genetic diversity was found in the Finnish population. Moreover, considerable genetic differentiation was implied between the studied populations as the mean fixation index (FST) was above 0.1 in all pairwise comparisons. All populations were easily discernible through either principal component analysis (PCA) or discriminant analysis of principal components (DAPC). In addition, unsupervised machine learning models such as K-means, Gaussian and Bayesian Gaussian mixtures were assessed for their ability to detect genetic clusters. A preceding dimensionality reduction step by PCA resulted in all three models, suggesting that the most probable number of clusters was three. Overall, our study affirmed the utility of the developed ddRAD-seq genotyping method and unveiled the genetic structure of the studied populations, both of which could contribute to their more ...