Mandated data archiving greatly improves access to research data

The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available? Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance. We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, STRUCTURE. At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost a thousand-fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost a thousand fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested datasets, albeit with about 8 days delay and some disagreement with authors. Given the long term benefits of data accessibility to the academic community, we believe that journal based mandatory data archiving policies and mandatory data availability statements should be more widely adopted

Local adaptation and the evolution of genome architecture in threespine stickleback.

Theory predicts that local adaptation should favour the evolution of a concentrated genetic architecture, where the alleles driving adaptive divergence are tightly clustered on chromosomes. Adaptation to marine vs. freshwater environments in threespine stickleback has resulted in an architecture that seems consistent with this prediction: divergence among populations is mainly driven by a few genomic regions harbouring multiple quantitative trait loci (QTL) for environmentally adapted traits, as well as candidate genes with well-established phenotypic effects. One theory for the evolution of these "genomic islands" is that rearrangements remodel the genome to bring causal loci into tight proximity, but this has not been studied explicitly. We tested this theory using synteny analysis to identify micro- and macro-rearrangements in the stickleback genome and assess their potential involvement in the evolution of genomic islands. To identify rearrangements, we conducted a de novo assembly of the closely-related tubesnout (Aulorhyncus flavidus) genome and compared this to the genomes of threespine stickleback and two other closely related species. We found that small rearrangements, within-chromosome duplications, and Lineage-Specific Genes (LSGs) were enriched around genomic islands, and that all three chromosomes harbouring large genomic islands have experienced macro-rearrangements. We also found that duplicates and micro-rearrangements are 9.9x and 2.9x more likely to involve genes differentially expressed between marine and freshwater genotypes. While not conclusive, these results are consistent with the explanation that strong divergent selection on candidate genes drove the recruitment of rearrangements to yield clusters of locally adaptive loci

Data from: Establishment and maintenance of adaptive genetic divergence under migration, selection, and drift

There is a long tradition in population genetics of exploring the maintenance of variation under migration-selection balance using deterministic models that assume infinite population size. With finite population size, stochastic dynamics can greatly reduce the potential for the maintenance of polymorphism, but this has yet to be explored in detail. Here, classical two-patch models are extended to predict: i) the probability of a locally beneficial mutation rising in frequency in the patch where it is favored, and ii) the critical threshold migration rate above which the maintenance of polymorphism is much less likely. Individual-based simulations show that these approximations provide accurate predictions across a wide range of parameter space

Evolution of quantitative traits under a migration-selection balance: when does skew matter?

Quantitative-genetic models of differentiation under migration-selection balance often rely on the assumption of normally distributed genotypic and phenotypic values. When a population is subdivided into demes with selection toward different local optima, migration between demes may result in asymmetric, or skewed, local distributions. Using a simplified two-habitat model, we derive formulas without a priori assuming a Gaussian distribution of genotypic values, and we find expressions that naturally incorporate higher moments, such as skew. These formulas yield predictions of the expected divergence under migration-selection balance that are more accurate than models assuming Gaussian distributions, which illustrates the importance of incorporating these higher moments to assess the response to selection in heterogeneous environments. We further show with simulations that traits with loci of large effect display the largest skew in their distribution at migration-selection balance

MacLachlan_etal.(2017)_README_climatemetadata_June9th2017

Metadata explaining the climate variables used in this stud

MacLachlan_etal.(2017)_AdmixtureProjectedIndivs_SNPpedfile_June9th2017

Genotype data in .ped file format (with two columns per SNP) for the 1572 individuals for which hybrid ancestry was estimated using the ADMIXTURE projection analysis functio

MacLachlan_etal.(2017)_SNPmapfile_June9th2017

The details of each SNP analysed in the .ped files for reference and projected ADMIXTURE populations. SNPs (rows) in the .map file match the order of SNPs (columns) in the .ped file. Each locus in the .ped file has two columns per SN