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Data from: Range wide parallel climate-associated genomic clines in Atlantic salmon
Jeffery, Nicholas W.; Stanley, Ryan R. E.; Wringe, Brendan F.; Guijarro-Sabaniel, Javier; Bourret, Vincent; Bernatchez, Louis; Bentzen, Paul; Beiko, Robert G.; Gilbey, John; Clement, Marie; Bradbury, Ian R. — 2017-10-13 Clinal variation across replicated environmental gradients can reveal evidence of local adaptation, providing insight into the demographic and evolutionary processes that shape intraspecific diversity. Using 1773 genome-wide single nucleotide polymorphisms we evaluated latitudinal variation in allele frequency for 134 populations of North American and European Atlantic salmon (Salmo salar). We detected 84 (4.74%) and 195 (11%) loci showing clinal patterns in North America and Europe respectively, with 12 clinal loci in common between continents. Clinal SNPs were evenly distributed across the salmon genome and logistic regression revealed significant associations with latitude and seasonal temperatures, particularly average spring temperature in both continents. Loci displaying parallel clines were associated with several metabolic and immune functions, suggesting a potential basis for climate associated adaptive differentiation. These climate-based clines collectively suggest evidence of large scale environmental associated differences on either side of the North Atlantic. Our results support patterns of parallel evolution on both sides of the North Atlantic, with evidence of both similar and divergent underlying genetic architecture. The identification of climate associated genomic clines illuminates the role of selection and demographic processes on intraspecific diversity in this species and provides a context in which to evaluate the impacts of climate change.
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Data from: Supertrees based on the subtree prune-and-regraft distance
Whidden, Chris; Zeh, Norbert; Beiko, Robert G.; Whidden, Christopher — 2014-03-20 Supertree methods reconcile a set of phylogenetic trees into a single structure that is often interpreted as a branching history of species. A key challenge is combining conflicting evolutionary histories that are due to artifacts of phylogenetic reconstruction and phenomena such as lateral gene transfer (LGT). Although they often work well in practice, existing supertree approaches use optimality criteria that do not reflect underlying processes, have known biases and may be unduly influenced by LGT. We present the first method to construct supertrees by using the subtree prune-and-regraft (SPR) distance as an optimality criterion. Although calculating the rooted SPR distance between a pair of trees is NP-hard, our new maximum agreement forest-based methods can reconcile trees with hundreds of taxa and > 50 transfers in fractions of a second, which enables repeated calculations during the course of an iterative search. Our approach can accommodate trees in which uncertain relationships have been collapsed to multifurcating nodes. Using a series of simulated benchmark datasets, we show that SPR supertrees are more similar to correct species histories under plausible rates of LGT than supertrees based on parsimony or Robinson-Foulds distance criteria. We successfully constructed an SPR supertree from a phylogenomic dataset of 40,631 gene trees that covered 244 genomes representing several major bacterial phyla. Our SPR-based approach also allowed direct inference of highways of gene transfer between bacterial classes and genera; a small number of these highways connect genera in different phyla and can highlight specific genes implicated in long-distance LGT.
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Data from: Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America
Sylvester, Emma V.A.; Beiko, Robert G.; Bentzen, Paul; Paterson, Ian; Horne, John B.; Watson, Beth; Lehnert, Sarah; Duffy, Steven; Clément, Marie; Robertson, Martha J.; Bradbury, Ian R.; Sylvester, Emma V. A. — 2018-08-23 Conservation of exploited species requires an understanding of both genetic diversity and the dominant structuring forces, particularly near range limits, where climatic variation can drive rapid expansions or contractions of geographic range. Here, we examine population structure and landscape associations in Atlantic salmon (Salmo salar) across a heterogeneous landscape near the northern range limit in Labrador, Canada. Analysis of two amplicon-based data sets containing 101 microsatellites and 376 single nucleotide polymorphisms (SNPs) from 35 locations revealed clear differentiation between populations spawning in rivers flowing into a large marine embayment (Lake Melville) compared to coastal populations. The mechanisms influencing the differentiation of embayment populations were investigated using both multivariate and machine-learning landscape genetic approaches. We identified temperature as the strongest correlate with genetic structure, particularly warm temperature extremes and wider annual temperature ranges. The genomic basis of this divergence was further explored using a subset of locations (n=17) and a 220K SNP array. SNPs associated with spatial structuring and temperature mapped to a diverse set of genes and molecular pathways, including regulation of gene expression, immune response, and cell development and differentiation. The results spanning molecular marker types and both novel and established methods clearly show climate-associated, fine-scale population structure across an environmental gradient in Atlantic salmon near its range limit in North America, highlighting valuable approaches for predicting population responses to climate change and managing species sustainability.
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Data from: Oceanographic variation influences spatial genomic structure in the sea scallop, Placopecten magellanicus
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Van Wyngaarden, Mallory; Snelgrove, Paul V. R.; DiBacco, Claudio; Hamilton, Lorraine C.; Rodriguez-Ezpeleta, Naiara; Zhan, Luyao; Beiko, Robert; Bradbury, Ian R.; Beiko, Robert G. — 2019-01-02 Environmental factors can influence diversity and population structure in marine species and accurate understanding of this influence can both improve fisheries management and help predict responses to environmental change. We used 7163 SNPs derived from restriction site-associated DNA sequencing genotyped in 245 individuals of the economically important sea scallop, Placopecten magellanicus, to evaluate the correlations between oceanographic variation and a previously identified latitudinal genomic cline. Sea scallops span a broad latitudinal area (>10 degrees), and we hypothesized that climatic variation significantly drives clinal trends in allele frequency. Using a large environmental dataset, including temperature, salinity, chlorophyll a, and nutrient concentrations, we identified a suite of SNPs (285–621, depending on analysis and environmental dataset) potentially under selection through correlations with environmental variation. Principal components analysis of different outlier SNPs and environmental datasets revealed similar northern and southern clusters, with significant associations between the first axes of each (R2adj = .66–.79). Multivariate redundancy analysis of outlier SNPs and the environmental principal components indicated that environmental factors explained more than 32% of the variance. Similarly, multiple linear regressions and random-forest analysis identified winter average and minimum ocean temperatures as significant parameters in the link between genetic and environmental variation. This work indicates that oceanographic variation is associated with the observed genomic cline in this species and that seasonal periods of extreme cold may restrict gene flow along a latitudinal gradient in this marine benthic bivalve. Incorporating this finding into management may improve accuracy of management strategies and future predictions.
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Data from: Applications of random forest feature selection for fine-scale genetic population assignment
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Sylvester, Emma V.A.; Bentzen, Paul; Bradbury, Ian R.; Clément, Marie; Pearce, Jon; Horne, John; Beiko, Robert G.; Sylvester, Emma V. A. — 2017-07-27 Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest, and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP dataset for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each dataset, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic datasets to improve assignment for management and conservation of exploited populations. https://creativecommons.org/publicdomain/zero/1.0/

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(auteur : Beiko, Robert G.)

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