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Detecting climate signals in genomes: A GWA approach to find footprints of climate selection in the Arabidopsis thaliana genome using new measures of climatic variability

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Stegle,  O       
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Wang, G., Stegle, O., Rowan, B., Dillon, M., & Weigel, D. (2011). Detecting climate signals in genomes: A GWA approach to find footprints of climate selection in the Arabidopsis thaliana genome using new measures of climatic variability. Poster presented at 13th Congress of the European Society for Evolutionary Biology (ESEB 2011), Tübingen, Germany.


Cite as: https://hdl.handle.net/21.11116/0000-000C-A40C-E
Abstract
Climate change has had strong and varied effects on natural and domesticated populations of plants and animals and on human health. Although climate has been shown to be associated with specific ecologically and physiologically important traits, the impacts of past climate change on entire genomes are relatively unknown. Here, we investigate the historic selective effects of climate on the Aradidopsis thaliana genome using a Genome Wide Association (GWA) approach. We use new measures of worldwide cyclic and stochastic climate variability directly in a population-structure-controlled GWA model. We are thus able to detect genomic regions that are associated with specific climatic measures. Furthermore, we extend our GWA methods to detect changing influences over time. We find that GWA studies utilizing environmental variables as inputs are a promising methodology for identifying genes that are most sensitive to the environment, and thus most likely to be affected by our changing climate.