4.7 Article

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

Journal

MOLECULAR ECOLOGY
Volume 21, Issue 22, Pages 5512-5529

Publisher

WILEY
DOI: 10.1111/j.1365-294X.2012.05709.x

Keywords

biogeography; eigenanalysis ordination; population structure; principal components of neighbourhood matrices

Funding

  1. NSF EF [1064901, DEB-0618347, DEB-0618294, DEB-0618302, DEB-1022196, IOS 0922457]
  2. California and Colorado Agricultural Experiment Stations
  3. Direct For Biological Sciences
  4. Division Of Environmental Biology [1022196] Funding Source: National Science Foundation
  5. Direct For Biological Sciences
  6. Division Of Integrative Organismal Systems [922457] Funding Source: National Science Foundation
  7. Direct For Biological Sciences
  8. Emerging Frontiers [1064901] Funding Source: National Science Foundation

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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.

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