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Dissecting quantitative variation in the plant circadian clock

Project Description

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 as crop adaptations to agriclimatic zones is realised. 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. This work exploits our successes in QTL identification of clock alleles and cloning of the underlying genetic changes (Anwer et al. 2014 eLife; Rubin et al. 2017 Mol Ecol.; Rubin et al. 2018 New Phyt; Yarkhunova et al. 2018 Plant Cell Env). Here in this project, allelic variation in the circadian clock will be measured from latitudinal mapping populations of Arabidopsis harbouring vital reporters to measure the clock in individual living plants to define novel circadian activities by means of newly developed informatics and computational approaches. This work will (1) provide an expansive understanding of Arabidopsis oscillator action, using computational models and statistical-genetic innovations based on Functional Data Analysis (FDA) to fully explore the quantitative usefulness of genetic variation in the clock following seasonal entrainments; (2) develop novel approaches to explore expression QTL (eQTL) mapping in a dynamic manner that captures all kinetic aspects of the gene-expression rhythms; (3) validate and clone a circadian QTL associated to a climatic variable; (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 allele-interactions 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. Quantitative-plant genetics, informatics approaches and imaging technologies in this project will be taught and no previous experiences are required for a highly motivated candidate. A strong quantitative background, and a willingness to undertake detailed computational work, is required.

Funding Notes

Funding: This is a 4 year fully-funded studentship part of the BBSRC White Rose Doctoral Training Partnership in Mechanistic Biology. The studentship covers: (i) a tax-free stipend at the standard Research Council rate (around £15,000 per year), (ii) tuition fees at UK/EU rate, (iii) research consumables and training necessary for the project.

Entry requirements: At least an upper second class honours degree, or equivalent in any biological, chemical, and/or physical science. Students with mathematical backgrounds who are interested in using their skills in addressing biological questions are also welcome to apply.


Eligibility: The studentships are available to UK and EU students who meet the UK residency requirements. Students from EU countries who do not meet the residency requirements may still be eligible for a fees-only award. Further information about eligibility for Research Council UK funding

Shortlisting: Applicants will be notified if they have been selected for interview in the week commencing on Monday 28 January 2019.

Interviews: Shortlisted applicants will be invited for an interview to take place in the Department of Biology at the University of York on Wednesday 6 and Thursday 7 February 2019. As part of the interview process candidates will be asked to give a 5 minute presentation on a research project carried out by them.

How good is research at University of York in Biological Sciences?

FTE Category A staff submitted: 44.37

Research output data provided by the Research Excellence Framework (REF)

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