Linking high dimensional phenotype data with the genome

PhD Research Project

Plants exhibit dramatically different structures, as defined by the interaction of their genetic makeup with environmental factors. It remains a challenge to understand the regulation and dynamics of growth that underpins variation in architecture between closely related species and in response to the environment. Variation in architecture is a key trait in the breeding of high-yield crops or crops that are more resilient to harsh environmental conditions but accurate and objective measurement on a large scale remains a significant challenge. We have recently devised automated dynamic machine vision methods for tracking and quantifying growth variation among 19 natural accessions of Arabidopsis thaliana whose genomes have been defined by re-sequencing. This group have been used as the parents of a Multiparent Advanced Generation Inter-Cross (MAGIC) mapping population (Gan et al. 2010; Nature, 477, 419-423) containing over 700 recombinant inbred lines (RILs) with the genome break points defined for each individual line) providing the potential to undertake very high resolution mapping of traits. Machine-defined shape descriptors have been developed at the NPPC that can be used to quantify relevant architectural features on a very large scale.


Project outline

A 3-year funded PhD studentship is available within the National Plant Phenomics Centre (website www.phenomics.org.uk.) to undertake a comparative and high-throughput phenomics analysis of 19 natural accessions and the derived RILs grown under selected simulated environments to establish the genetic control of growth patterns on a very large scale. Plants grown under selected simulated environments will be automatically imaged using a variety of sensors to capture their pattern of growth and changes in physiology over time, features will be automatically extracted using current and new image processing methods, and this trait data analysed and mapped to the genome. Explanatory and predictive models capturing plant growth patterns will be designed. The methodology developed will be appropriate for use on other breeding populations and in other applications, so post-project employability is likely to be very high.


This project offers an excellent training opportunity, providing the student with skills that span biology, computer science and instrumentation. This studentship is therefore especially suited for excellent graduates in Computational Sciences, Biology, Molecular Biology or Biochemistry aiming for advanced research training in the new research area of Phenomics.

For more details or to discuss this project please contact John Doonan ([Email Address Removed]) or Anyela Camargo-Rodriguez ([Email Address Removed])