Dissecting quantitative variation in the plant circadian clock

The seasonal timing of growth is critical for plant performance under both ecological and agricultural settings. The circadian clock holds a key place in such seasonal adaptation. This proposed PhD project establishes a unique approach to uncover molecular, quantitative-genetic insights of natural circadian-clock alleles that respond to seasonal changes using the model plant Arabidopsis. The proposed work exploits our successes in QTL identification of clock alleles and cloning of the underlying genetic changes (Anwer et al. 2014; Rubin et al. 2017). Here in this project, allelic variation in the circadian clock will be measured from latitudinal mapping populations of Arabidopsis harbouring promoterluciferase
reporters and used to define novel activities within the circadian clock by means of newly developed mathematical approaches. This work will (1) provide an expansive understanding of Arabidopsis oscillator action using Functional Data Analysis (FDA) to fully explore the quantitative usefulness of genetic variation in the clock following seasonal entrainments; (2) develop novel time-series approaches to explore expression QTL (eQTL) mapping in a dynamic manner that captures all kinetic aspects of the gene-expression rhythms; (3) validate QTL affecting FDA parameters with a focus on phase, and clone a promising circadian QTL associated to a climatic variable, such as warmth; (4) analyse the encoded protein at biochemical and cellular layers of understanding. As Arabidopsis is widely distributed from the equator to the Arctic Circle it serves as a fantastic model for a migratory species. New
approaches to examine clock action will be facilitated by the analysis of naturally occurring genetic variation of rhythmic gene expression in QTL mapping sets. Using our advanced bioluminescence platform and novel mathematics, natural variation holds great promise to uncover new alleleinteractions within the clock in response to changes in seasonal cues, as
understood to the cellular and biochemical context of action. Such genetic factors provide agricultural and ecological insights to the phenotypic benefits natural variation in the clock to serve as a driver of future, predictive breeding.

Anwer et al. eLife (2014)
Rubin et al. Molecular Ecology (2017)

Quantitative-plant genetics, mathematics approaches and imaging technologies in this project will be taught and no previous experiences are required for a highly motivated candidate.

We strongly encourage you to email the project supervisor prior to submitting an application to discuss your suitability for this project. Please email: [Email Address Removed]