Back

Adaptive growth homeostasis in response to drought in Iberian Arabidopsis accessions

Ferrero-Serrano, A.; Assmann, S. M.

2021-05-09 evolutionary biology
10.1101/2021.05.07.443185 bioRxiv
Show abstract

Natural genetic variation influences plant responses to environmental stressors. However, the extent to which such variation underlies plastic versus homeostatic response phenotypes deserves more attention. We quantified the extent of drought-induced changes in leaf area in a set of Iberian Arabidopsis accessions and then performed association studies correlating plasticity and homeostasis in this phenotype with genomic and transcriptomic variation. Drought-induced plastic reductions in relative leaf area typified accessions originating from productive environments. In contrast, homeostasis in relative leaf area typified accessions originating from unproductive environments. Genome-Wide Association Studies (GWAS), Transcriptome Wide Association Studies (TWAS), and expression GWAS (eGWAS) highlighted the importance of auxin-related processes in conferring leaf area plasticity. Homeostatic responses in relative leaf area were associated with a diverse gene set and positively associated with a higher intrinsic water use efficiency (WUEi), as confirmed in a TWAS metanalysis of previously published {delta}13C measurements. Thus, we have identified not only candidate "plasticity genes" but also candidate "homeostasis genes" controlling leaf area. Our results exemplify the value of a combined GWAS, TWAS, and eGWAS approach to identify mechanisms underlying phenotypic responses to stress. HighlightInformation on phenotype, genotype, and transcript abundance is integrated to identify candidate plasticity and homeostasis genes and processes associated with local adaptation to drought stress in Arabidopsis accessions of the Iberian Peninsula.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.