Characterizing genomic variation of Arabidopsis thaliana: the roles of geography and climate

Abstract

Arabidopsis thaliana inhabits diverse climates and exhibits varied phenology across its range. Although A. thaliana is an extremely well-studied model species, the relationship between geography, growing season climate and its genetic variation is poorly characterized. We used redundancy analysis (RDA) to quantify the association of genomic variation [214 051 single nucleotide polymorphisms (SNPs)] with geography and climate among 1003 accessions collected from 447 locations in Eurasia. We identified climate variables most correlated with genomic variation, which may be important selective gradients related to local adaptation across the species range. Climate variation among sites of origin explained slightly more genomic variation than geographical distance. Large-scale spatial gradients and early spring temperatures explained the most genomic variation, while growing season and summer conditions explained the most after controlling for spatial structure. SNP variation in Scandinavia showed the greatest climate structure among regions, possibly because of relatively consistent phenology and life history of populations in this region. Climate variation explained more variation among nonsynonymous SNPs than expected by chance, suggesting that much of the climatic structure of SNP correlations is due to changes in coding sequence that may underlie local adaptation.

The challenge of separating signatures of local adaptation from those of isolation by distance and colonization history: The case of two white pines

“…Thus, our results show that model differences in the correction for population structure can lead to little overlap between methods. Therefore, more studies comparing GEA methods that account differently for population structure in natural populations (e.g., multivariate RDAs controlling for geography, Lasky et al., 2012; mixed linear models controlling for kinship, Yoder et al., 2014) when adaptive patterns are correlated with demographic history are needed to better understand their relative performance.…”

“…We found in both species that a large amount of the explained among‐population genetic variation was confounded between the effects of climate (IBE), IBD, and IBC, with only a small proportion of the variation attributed exclusively to climate in P. strobus . Such confounding of patterns of local adaptation with neutral population structure is expected to be common in natural landscapes (e.g., Lasky et al., 2012; Lee & Mitchell‐Olds, 2011; Sork et al., 2010). Two main reasons can explain these patterns: (1) selective constraints are often spatially correlated with demographic history (e.g., northward postglacial colonization along climatic gradients); and (2) natural selection and neutral processes can act simultaneously to shape genetic variation and gene flow among populations.…”

Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed

“…Specialized metabolites also are the source of many of our plant‐based medicines and therefore have value to human health and well‐being (Briskin, 2000). Much attention has therefore been placed on environmental and geographic factors influencing specialized metabolite production in plants, particularly in crop species and in model plant systems (Agrawal, Conner, Johnson, & Wallsgrove, 2002; Asai, Matsukawa, & Kajiyamal, 2016; Carrari et al., 2006; Dan et al., 2016; Hirai et al., 2004; Lasky et al., 2012; Tarczynski, Jensen, & Bohnert, 1993; Riedelsheimer et al., 2012). However, the importance of natural variation in the environment in explaining metabolite variation within plant species remains little understood (Maldonado et al., 2017; Moore, Andrew, Külheim, & Foley, 2014).…”

A map of climate change-driven natural selection in Arabidopsis thaliana

“…how much gene c change is required for adapta on to climate change, we thus need to quan fy and model environment-driven natural selec on at the gene c level. Thanks to species-wide genome scans [7][8][9] , as well as genome associa ons with climate of origin [10][11][12][13][14] , we increasingly understand the genomic basis of past selec on and climate adapta on, which has been used to es mate future adapta on debt or "genomic vulnerability" 10,11 .…”