Browsing by Subject "epistasis"

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  • Kemppainen, Petri; Li, Zitong; Rastas, Pasi; Löytynoja, Ari; Fang, Bohao; Yang, Jing; Guo, Baocheng; Shikano, Takahito; Merilä, Juha (2021)
    Repeated and independent adaptation to specific environmental conditions from standing genetic variation is common. However, if genetic variation is limited, the evolution of similar locally adapted traits may be restricted to genetically different and potentially less optimal solutions or prevented from happening altogether. Using a quantitative trait locus (QTL) mapping approach, we identified the genomic regions responsible for the repeated pelvic reduction (PR) in three crosses between nine-spined stickleback populations expressing full and reduced pelvic structures. In one cross, PR mapped to linkage group 7 (LG7) containing the gene Pitx1, known to control pelvic reduction also in the three-spined stickleback. In the two other crosses, PR was polygenic and attributed to 10 novel QTL, of which 90% were unique to specific crosses. When screening the genomes from 27 different populations for deletions in the Pitx1 regulatory element, these were only found in the population in which PR mapped to LG7, even though the morphological data indicated large-effect QTL for PR in several other populations as well. Consistent with the available theory and simulations parameterized on empirical data, we hypothesize that the observed variability in genetic architecture of PR is due to heterogeneity in the spatial distribution of standing genetic variation caused by >2x stronger population structuring among freshwater populations and >10x stronger genetic isolation by distance in the sea in nine-spined sticklebacks as compared to three-spined sticklebacks.
  • Satokangas, Ina; Martin, S. H.; Helanterä, H.; Saramaki, J.; Kulmuni, J. (2020)
    All genes interact with other genes, and their additive effects and epistatic interactions affect an organism's phenotype and fitness. Recent theoretical and empirical work has advanced our understanding of the role of multi-locus interactions in speciation. However, relating different models to one another and to empirical observations is challenging. This review focuses on multi-locus interactions that lead to reproductive isolation (RI) through reduced hybrid fitness. We first review theoretical approaches and show how recent work incorporating a mechanistic understanding of multi-locus interactions recapitulates earlier models, but also makes novel predictions concerning the build-up of RI. These include high variance in the build-up rate of RI among taxa, the emergence of strong incompatibilities producing localized barriers to introgression, and an effect of population size on the build-up of RI. We then review recent experimental approaches to detect multi-locus interactions underlying RI using genomic data. We argue that future studies would benefit from overlapping methods like ancestry disequilibrium scans, genome scans of differentiation and analyses of hybrid gene expression. Finally, we highlight a need for further overlap between theoretical and empirical work, and approaches that predict what kind of patterns multi-locus interactions resulting in incompatibilities will leave in genome-wide polymorphism data. This article is part of the theme issue 'Towards the completion of speciation: the evolution of reproductive isolation beyond the first barriers'.
  • Puranen, Santeri; Pesonen, Maiju; Pensar, Johan; Xu, Ying Ying; Lees, John A.; Bentley, Stephen D.; Croucher, Nicholas J.; Corander, Jukka (2018)
    The potential for genome-wide modelling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has previously been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10(4)-10(5) polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here, we introduce a novel inference method (SuperDCA) that employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 10(5) polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA, thus, holds considerable potential in building understanding about numerous organisms at a systems biological level.