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Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

English version České info

Finding genes involved in adaptation to the environment has long been of interest to evolutionary biologists and ecologists. Most commonly, researchers look for loci whose differences in allelic state correlate with differences in a particular trait or environmental variable such as temperature. The implicit assumption behind such methods is that natural selection by the environment will shape variation in adaptive traits through associated changes in allele frequencies. This means that both environmental and phenotypic variation are relevant for detecting adaptive genes, although we have incomplete knowledge of how the two types of variation relate to adaptation. Here we present a method that aims to identify adaptive genes by combining phenotypic and environmental data. We first predict trait variation from a set of environmental variables as a way to extract the most biologically relevant information from the environment and then look for genes associated with both the predicted and observed trait. Using simulations and published data from the model plant Arabidopsis thaliana, we show that this approach may find adaptive genes more effectively compared to existing methods. We also demonstrate that predicted traits can be used to identify relevant loci in individuals for which no phenotypic data is available.

Vyšlo v časopise: Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits. PLoS Genet 11(10): e32767. doi:10.1371/journal.pgen.1005594
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005594

Souhrn

Finding genes involved in adaptation to the environment has long been of interest to evolutionary biologists and ecologists. Most commonly, researchers look for loci whose differences in allelic state correlate with differences in a particular trait or environmental variable such as temperature. The implicit assumption behind such methods is that natural selection by the environment will shape variation in adaptive traits through associated changes in allele frequencies. This means that both environmental and phenotypic variation are relevant for detecting adaptive genes, although we have incomplete knowledge of how the two types of variation relate to adaptation. Here we present a method that aims to identify adaptive genes by combining phenotypic and environmental data. We first predict trait variation from a set of environmental variables as a way to extract the most biologically relevant information from the environment and then look for genes associated with both the predicted and observed trait. Using simulations and published data from the model plant Arabidopsis thaliana, we show that this approach may find adaptive genes more effectively compared to existing methods. We also demonstrate that predicted traits can be used to identify relevant loci in individuals for which no phenotypic data is available.

Zdroje

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