STUDENTSHIP

WHEAT QTL EVALUATION IN THE PANGENOMIC ERA (APPLICATIONS NOW CLOSED)

PROJECT SUMMARY

AREA OF SCIENCE:

Bioinformatics

DURATION:

3 Months

CLOSING DATE/TIME:

Friday, April 8, 2022 - 17:00

DEPARTMENT:

Computational and Analytical Sciences

STUDENTSHIP DETAIL

Crop yields face twin pressures from increasing global population and climate change (Jägermeyr et al., 2021). Rothamsted is part of the Designing Future Wheat (https://designingfuturewheat.org.uk/)  programme (DFW) developing new wheat cultivars.  Quantitative trait loci (QTLs) are genomic regions associated with variation in crop traits, both the desirable (higher yields) and undesirable (vulnerability to disease, drought, or heat).  Understanding the genomic basis of this variation is critical to develop new generations of crop cultivars for the DFW programme.  

Accurate long-read genome sequencing technology and falling costs have revealed substantial variation, both between current crop varieties and their wild landraces (see Toulotte et al., 2022).  This variation is not just in coding sequence (SNPs and INDELs) but in the genes present and chromosome structures which can drive new patterns of gene expression.  This diversity requires genome analysis to move from single references to a ‘pangenome’ that can contain multiple references (see Golicz et al., 2020).    

Thousands of wheat QTLs are known (http://wheatqtldb.net/) but until now they have been evaluated with fragmented genome references or the single chromosome reference for the cultivar Chinese Spring.  Recently the wheat pangenome project Wheat10+ (http://www.10wheatgenomes.com/) has produced annotated chromosome-scale wheat references for wheat cultivars, which enable the re-evaluation of known QTL to isolate candidate genes which may be causing these traits.   

This project will examine the pangenome variation of QTL datasets created at Rothamsted and using methods such as 

PanTools:  https://git.wur.nl/bioinformatics/pantools

VGTools: https://github.com/vgteam/vg)

OMA: https://omabrowser.org/standalone/

The project would suit a bioinformatics or computer science student with an interest to learn about the data structures and analysis of genomes.