Primary Supervisor: Professor Zewei Luo, School of Biosciences
Secondary supervisor: Dr Marco Catoni, School of Biosciences
PhD project title: Ploidy driven change in meiotic recombination frequency
University of Registration: University of Birmingham
The world is currently facing what is arguably its most serious challenge yet, to meet the demand for a sustainable food supply for its rapidly expanding population. The “release” of genetic variation, the raw material for both natural and artificial selection, occurs during meiosis, a central process in the life cycle of all sexually reproducing organisms. This project is designed to test if genome duplication, which leads to creation of autopolyploids, will alter meiotic recombination frequency (MRF) in an Arabidopsis experimental model at a genome wide scale. The project will involve both experimental and statistical analyses. Experimentally, it will involve intensive genomic DNA sequencing experiments to collect DNA sequence variant data from a segregating population. It will also involve various statistical modelling and analysing the sequencing data to predict distribution of the genome-wide MRF. Success of the project may represent a breakthrough for artificial manipulation of meiotic recombination and lend modern agriculture a great opportunity for improving the efficiency of crop breeding.
The project is open to undergraduates with a first class degree in biological sciences or medicine, mathematics, physical sciences. Thorough training in wet lab techniques or statistics/programming will be provided as necessary.
BBSRC Strategic Research Priority: Sustainable Agriculture and Food: Plant and Crop Science & Understanding the Rules of Life: Plant Science
Techniques that will be undertaken during the project:
Experimental: Next generation sequencing (NGS), Arabidopsis crossing, plant growth trials, phenotyping, tissue culture, PCR, RT-PCR, microscopy, FISH.
Computational: creating mathematical/statistical models for genetic studies (e.g. using Mathematica), writing programmes and scripts to analyse large-scale sequence datasets (e.g. using Fortran, C++, Perl), statistical techniques for data analysis (e.g. using statistical software R) on windows or linux system.
Contact: Professor Zwei Duo, University of Birmingham