Targeted engineering of quantitative traits in plants

This project is available through the MIBTP programme on a competition basis. The successful applicant will join the MIBTP cohort and will take part in all of the training offered by the programme. For further details please visit the MIBTP website - https://warwick.ac.uk/fac/cross_fac/mibtp/

Our ability to develop novel beneficial crop traits has significantly improved over the last century, although the ability to maintain this trajectory is limited by allelic diversity. While genetic variation at coding sequences has been heavily exploited for crop improvement, most quantitative traits reside in mutations at non-coding regulatory regions. This project aims to identify these regulatory regions in Brassica using genome-wide chromatin conformation analysis and develop methodology to engineer mutations and epimutations at these regions. It is anticipated that the student will develop a platform to enhance phenotypic variation of selected agricultural traits, which will provide the basis for analyzing the link between gene-regulatory elements and quantitative traits.

Key experimental skills involved:

The student will carry out a series of experiments in Brassica designed to engineer mutations and epimutations, using new CRISPR variants, at the regulatory regions of genes known to be implicated in agronomic performance. These plants will be used to select allelic variants or epimutations causing enhanced agricultural performance. He/She will use computational approaches and laboratory experimentation to investigate the relationship between the temporal/spatial regulation of defined regulatory networks and trait performance.

BBSRC Strategic Research Priority: Sustainable Agriculture and Food: Plant and Crop Science

Techniques that will be undertaken during the project:

The student will be working in a multidisciplinary environment: the laboratory of JGM has an international track record in genomic and epigenomic analysis in plants and the group of GB has an international track record in Brassica genomics.

Lab-Techniques: sRNA-seq, RNA-seq, BS-seq, ChIP-seq and ChIA-PET)

Computational-techniques: wide range of tools for Next-Generation-Sequence data analysis.