Primary Supervisor: Dr Lindsey Compton, School of Biosciences
Secondary supervisor: Dr Estrella Luna-Diez
PhD project title: Understanding genetic mechanisms of complex agricultural traits for improving potato (Solanum tuberosum) breeding
University of Registration: University of Birmingham
As the 3rd most important food crop in the world, cultivated potato (Solanum tuberosum) plays a crucial role in addressing world food security. Traditional potato breeding has focused on phenotypic rather than genotypic selection to improve complex agronomic traits such as yield and disease resistance. Development of new and improved varieties is usually a lengthy process spanning more than a decade, creating an urgent need to develop novel statistical and/or methodological and experimental tools for genetic analysis of complex traits and hence varietal improvement in potato.
Working with national external collaborators, we exploit a diverse germplasm collection of more than 250 tetraploid potato varieties to dissect the genetic basis of those traits most pressing for potato farmers. To do this, we combine statistical genetics with multi-omics approaches and advanced high throughput phenotyping techniques. This project therefore provides diverse opportunities to contribute to world food security in one or more of the following topic areas:
- Unravelling the genetic and physiological mechanisms of tuber greening in potato, by bringing together high throughput phenotyping with genome-wide association and transcriptomic approaches. This will be a key step towards the molecular marker-assisted breeding of new low-greening varieties, thus cutting serious losses in the supply chain and reducing food waste.
- Characterising varietal variation in resistance to potato late blight, the most serious global disease affecting potato production. This work involves a broad spectrum of approaches including Genome-Wide Association Study (GWAS), and infection assays coupled with multi-omics (including transcriptomic and metabolomics) to understand the dynamics and underlying components of resistance responses.
- Development of novel statistical methods and computational tools for mapping Quantitative Trait Loci (QTL) in autotetraploid species such as potato through QTL linkage analysis or Genome-Wide Association Study (GWAS).
- Characterising the response of potato to an organic farming system at phenotypic, genotypic (variety) and transcriptomic levels, which will facilitate omics-informed breeding of robust varieties adapted to lower input agricultural systems.
- Molecular cytogenetic analysis of meiotic recombination in potato, building on methods we have developed to track meiotic chromosome behaviour using fluorescence in situ hybridization and immunolocalisation techniques. This work will study the mechanisms through which the potato chromosomes pair and generate new variation through the formation of genetic crossovers and thereby inform strategies for faster crop improvement.
- Choudhary A, Wright L, Ponce O, Chen J, Prashar A, Sanchez-Moran E, Luo Z & Compton L Varietal variation in meiotic chromosome behaviour in Solanum tuberosum. Heredity https://doi.org/10.1038/s41437-020-0328-6.
- Jing Chen, Lindsey Leach, Jixuan Yang, Fengjun Zhang, Tao Qin, Zhenyu Dang, Yue Chen and Zewei Luo A tetrasomic inheritance model and likelihood-based method for mapping quantitative trait loci in autotetraploid species. New Phytologist https://doi.org/10.1111/nph.16413.
- Prashar A et al. (2013) Infra-red thermography for high throughput field phenotyping in Solanum tuberosum. PLoS ONE 8 (6):e65816.
BBSRC Strategic Research Priority: Sustainable Agriculture and Food: Plant and Crop Science
Techniques that will be undertaken during the project:
Experimental techniques include: plant pathology techniques and protocols, molecular biology and genetic analysis, next generation sequencing including RNA-seq, potato field trials, high throughput phenotyping, fluorescence microscopy, FISH, immunocytology.
Computational techniques include: creating mathematical/statistical models for genetic studies (e.g. using Mathematica), writing programmes and scripts to analyse genetic datasets (e.g. using C++, Perl or language of choice), statistical techniques for data analysis and visualisation (e.g. using R statistical software), development of custom pipelines for omics data analysis (including RNA-seq and metabolomics).
Contact: Dr Lindsey Compton, University of Birmingham