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Updated: Jun 1, 2025

Pattern-Triggered Oxidative Burst and Seedling Growth Inhibition Assays in Arabidopsis thaliana
Published on: May 21, 2019
Experimental validation of genome-environment associations in Arabidopsis
Yuxin Luo1,2, Claire M Lorts1, Erica Lawrence-Paul1
1Department of Biology, Pennsylvania State University.
Abstract:
Identifying the genetic basis of local adaptation is a key goal in evolutionary biology. Allele frequency clines along environmental gradients, known as genotype-environment associations (GEA), are often used to detect potential loci causing local adaptation but are rarely followed by experimental validation. Here, we tested loci identified in three moisture-related GEA studies on Arabidopsis. We studied 42 GEA-identified genes using t-DNA knockout lines under drought and tested effects on flowering time, an adaptive trait, and genotype-by-environment (GxE) interactions on performance and fitness. In total, 16/42 genes had significant effects on traits involved in local adaptation or performance response to environment. We found that wrky38 mutants had significant GxE effects for fitness; lsd1 plants had a significant GxE effect for flowering time, while 11 genes showed flowering time effects with no drought interaction. However, most GEA candidates did not exhibit GxE. In the follow-up experiments, wrky38 caused decreased stomatal conductance and specific leaf area under drought, indicating potentially adaptive drought avoidance. Additionally, GEA identified natural putative LoF variants of WRKY38 associated with dry environments, as well as alleles associated with variation in LSD1 expression. While only a few GEA-identified genes were validated for GxE interactions for fitness under drought, we likely overlooked some because experiments might not well represent natural environments and t-DNA insertions might not well represent natural alleles. Nevertheless, GEAs apparently identified some genes contributing to local adaptation. GEA and follow-up experiments are straightforward to implement in model systems and demonstrate prospects for GEA discovery of new local adaptations.
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