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Genetic architecture and adaptation of flowering time among environments

Cite this dataset

Mitchell-Olds, Thomas; Wang, Baosheng; Yan, Wenjie; Chan, Emily (2021). Genetic architecture and adaptation of flowering time among environments [Dataset]. Dryad.


1. The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little is known about how genetic architecture changes among environments.

2. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection.

3. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability.

4. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.


A genome-wide association study (GWAS) examined genetic control of flowering time for 488 genotypes in 10 diverse greenhouse environments. Quantitative genetic and quantitative trait locus (QTL) analyses examined trait correlations among environments, and single nucleotide polymorphisms (SNPs) associated with flowering time and climate characteristics of source environments. Analyses were performed in R using restricted maximum-likelihood, and EMMAX for GWAS. Sequence information is available from the Short Read Archive. Near each QTL peak we tested for evidence of natural selection using molecular population genetic approaches. In addition, we asked how genetic architecture of flowering time changes among environments, and whether genetic constraints may influence adaptive spatial and temporal responses.

Usage notes

Experimental and analytical methods are detailed in the associated New Phytologist publication. Original phenotypic data files are in this data archive, as well as scripts which deal with missing data, statistical analyses, biological conclusions, and figures. A detailed README file summarizes the analytical pipeline, input and output files, and data columns used.


National Institute of General Medical Sciences, Award: R01 GM086496