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The least cost path from landscape genetics to landscape genomics: challenges and opportunities to explore NGS data in a spatially explicit context

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Ecosystems are the stage on which the play of evolution is acted, and ecosystems are complex, spatially structured, and temporally varying. The purpose of this Research Topic for Evolutionary and Population Genetics is to explore a number of critical challenges and opportunities for the transition from ...

Ecosystems are the stage on which the play of evolution is acted, and ecosystems are complex, spatially structured, and temporally varying. The purpose of this Research Topic for Evolutionary and Population Genetics is to explore a number of critical challenges and opportunities for the transition from landscape genetics to landscape genomics. To date landscape genetics has focused on spatial analyses of small genetic datasets, typically comprised of less than 20 microsatellite markers, taken from clusters of individuals in putative “populations” or distributed individuals across landscapes. The recent emergence of large scale genomic datasets produced by next generation sequencing (NGS) methods poses tremendous opportunity and challenge to the field. Perhaps the greatest is to produce, process, curate, archive and analyze spatially referenced genomic datasets in a way such that research is led by a priori hypotheses about how environmental heterogeneity and temporal dynamics interact to influence gene flow and selection. Effective progress in this transition to a robust field of landscape genomics will likely depend on integrating vast genomic datasets with powerful modeling, and replicated and controlled experiments to test putative relationships between population processes and evolutionary and population genetic responses. Recent availability of whole genome sequence (WGS) data implies to reconsider sampling strategies in landscape genomics for economic reasons. Molecular resolution in landscape genomics is becoming excellent but it is achieved at the expense of spatial representativeness and statistic robustness. No single person has the expertise or the time to effectively bring these components together. More than ever, success in advancing our field will depend on collaborations across large multi-disciplinary groups. Experts in the development of genomic, epigenomic and transcriptomic data from high throughput technologies are needed to produce the genome wide raw data for subsequent analysis. Bioinformatics specialists are needed to provide programming and computer science expertise to efficiently handle and analyze vast genomic datasets, and to effectively utilize high performance computing resources. Modelers will be needed to work with the bioinformaticians to explore the implications of hypotheses a priori, to refine hypotheses by optimizing fit to observed data, and predict how observed pattern process relationships may propagate across scale through space and time. Experimenters should work closely with modelers to rigorously test hypotheses in controlled and replicated experiments. To be successful this entire integration should be led by theoreticians who have a coherent vision for how each of these parts will synergize to address focused and falsifiable questions of importance in advancing the field. We will recruit leading experts in genomics, landscape genetics modeling and experimental genetics to explore the challenges and opportunities presented by the intersection of NGS data, spatial modeling, and replicated and controlled experimentation, producing a series of 10-15 papers to be published in Frontiers. These papers will include (1) a conceptual overview and “way forward” jointly authored by all the participating editors, (2) several “data and analysis” papers of spatial analysis of empirical NGS data to identify markers under selection and quantify effects of environmental factors and heterogeneity on gene flow and selection, (3) several “simulation and modeling” papers led by the leaders in landscape genetic simulation to explore the application of spatially explicit, individual-based modeling in a NGS context, (4) several “experimental genomics” papers led by leaders in common garden and gene-line selection experiments to discuss the challenges and opportunities of integrating experimental genetics with emerging genomic data, and (5) a final “synthesis” paper jointly authored by all participating ed


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