Final Report Summary - DECODE (Decoding the complexity of quantitative natural variation in Arabidopsis thaliana)
Following a long history of quantitative genetics in crop plants, it now becomes feasible to use naturally-occuring variation contained in Arabidopsis thaliana accessions (lines isolated from natural populations) as the source of quantitative genomics approaches, designed to map regions of the genome involved in phenotypic trait variation (QTL –quantitative trait loci) and resolve them at the gene level. Apart from being able to exploit –in multiple genetic backgrounds– allelic variation that cannot be easily generated by conventional mutagenesis, the (relatively few) success of the QTL studies has often been because of the use of quantitative phenotyping, as opposed to the qualitative scales used in most typical mutant screens. Among the various genetic mechanisms responsible for natural variation that have just started to be revealed, cis-acting regulation of gene's transcript accumulation is potentially of large impact, despite remaining more difficult to recognize and confirm. The objective of this project was to apply genome-wide quantitative molecular genetics to both, a very integrative and classical quantitative trait (leaf growth in interaction with the environment) and a molecular trait a priori more directly linked to the source of variation (gene expression under cis-regulation). We have been using a combination of our unique high-troughput phenotyping robot, fine-mapping, complementation approaches and association genetics to pinpoint a significant number of QTLs and eQTLs (gene-expression QTLs) to the gene level and identify causative polymorphisms and the molecular variation controlling natural diversity.
We have exploited the unique capacities of our Phenoscope phenotyping robots to limit environmental heterogeneity and precisely apply controlled abiotic stresses (mild drought) to the plant, in a powerful display to reveal loci controlling traits related to biomass accumulation rates. Among 8 large recombinant populations studied, many QTLs were detected in each population, with all combination of interaction with the environment and with the genetic background. Some of these QTLs have been individually confirmed and their impact on phenotype detailed. One major lesson is that, at this scale, we are more likely to be limited by the complexity of the genetic architecture of those traits (i.e. by the genetic resolution of QTL mapping) rather than by our precision in phenotyping, which was an expected answer from such work, not without consequences for the community. Independently, but using the same display to produce ideal samples from plants grown in highly homogeneous conditions, we have performed different approaches to reveal the extent of gene expression variation and understand its control by sequences located either in the vicinity of the gene (acting in cis-) or elsewhere (acting in trans-). Our results reveal the extensive diversity of cis-acting regulation among Arabidopsis accessions and its robustness to environmental perturbation, relative to trans-acting regulations. Combining all these results, we have identified new alleles at already known genes (for example FLM) and genes not-previously known to control growth (for example a putative transcription factor and a putative receptor-like kinase). Even more remains to come and will be worked in the lab in the coming years.
Working at this unprecedented scale is bringing new light as to how and where in the pathways adaptation is shaping natural variation and improve our understanding of the transcriptional cis-regulatory code.
We have exploited the unique capacities of our Phenoscope phenotyping robots to limit environmental heterogeneity and precisely apply controlled abiotic stresses (mild drought) to the plant, in a powerful display to reveal loci controlling traits related to biomass accumulation rates. Among 8 large recombinant populations studied, many QTLs were detected in each population, with all combination of interaction with the environment and with the genetic background. Some of these QTLs have been individually confirmed and their impact on phenotype detailed. One major lesson is that, at this scale, we are more likely to be limited by the complexity of the genetic architecture of those traits (i.e. by the genetic resolution of QTL mapping) rather than by our precision in phenotyping, which was an expected answer from such work, not without consequences for the community. Independently, but using the same display to produce ideal samples from plants grown in highly homogeneous conditions, we have performed different approaches to reveal the extent of gene expression variation and understand its control by sequences located either in the vicinity of the gene (acting in cis-) or elsewhere (acting in trans-). Our results reveal the extensive diversity of cis-acting regulation among Arabidopsis accessions and its robustness to environmental perturbation, relative to trans-acting regulations. Combining all these results, we have identified new alleles at already known genes (for example FLM) and genes not-previously known to control growth (for example a putative transcription factor and a putative receptor-like kinase). Even more remains to come and will be worked in the lab in the coming years.
Working at this unprecedented scale is bringing new light as to how and where in the pathways adaptation is shaping natural variation and improve our understanding of the transcriptional cis-regulatory code.