Primary Supervisor: Professor Zewei Luo, School of Biosciences
Secondary supervisor: Dr Marco Catoni, School of Biosciences
PhD project title: Methods for quantitative genetic analyses in autotetraploids
University of Registration: University of Birmingham
Project outline describing the scientific rationale of the project (max 4,000 characters incl. spaces and returns). There is no need to be too detailed about individual projects – general background, objectives and methods should suffice. One or two key references for potential applicants would be useful.
Methods for quantitative genetic analysis have been well established in almost all important diploid plants and animal including humans, and have served as essential tools for dissecting the genetic architectures underlying agronomically, evolutionarily and medically interesting quantitative and complex traits. In sharp contrast, the corresponding studies in polyploid, particularly autopolyploid, species lags far behind this level of advancement, largely because of complexity in segregation and recombination of genes under polysomic inheritance, leaving quantitative genetic analysis in polyploid species a historical challenge since the era of pioneering quantitative geneticists such as RA Fisher, K Mather, JBS Haldane, etc.
This project will characterize key features of segregation and recombination of genes in both natural and designed populations of autotetraploid species, the most prominent type of autopolyploid. Autotetraploids have great agronomic and economic value, and include cultivated potato (the world’s third most important food crop), alfalfa, domestic trout and salmonidae. There is a wide spectrum of possible quantitative genetic analysis, and we will open the following diverse areas for the PhD project candidate:
- Development of genetic models and methods for modelling and estimating quantitative genetic effects in both natural and crossing populations of autotetraploid species.
- Development of methods for mapping quantitative trait loci (QTLs) in segregating populations of autotetraploid species.
- Development of methods for analysis of population genetic data in autotetraploids.
- Development of methods for linkage disequilibrium based mapping of QTLs (genome-wide association analysis, GWAS) in autotetraploids.
- Modelling and analyzing various quantitative phenotypes, genomics/epigenetic datasets collected from potato projects of our own research and our collaborators in United Kingdom, China and USA.
- Conducting a wide range of computer simulation studies for exploration of the statistical properties and reliability of methods to be developed.
- Development of user-friendly computational tools for quantitative genetic analyses developed and to be developed from the PhD project.
Applications are encouraged from graduates with backgrounds in any of the following disciplines: biology (particularly genetics), statistics, mathematics and computer science. The ideal candidate will have a passion for genetics and an aptitude for statistics/mathematics, modelling and large-scale genomics data analysis.
BBSRC Strategic Research Priority: Sustainable Agriculture and Food: Plant and Crop Science
Techniques that will be undertaken during the project:
The project will involve largely analytical and computer-based work, but the candidate will be encouraged to participate in data generation and collection.
Analytical/computational skills: (i) development of mathematical/statistical models for gene segregation and recombination under tetrasomic inheritance (e.g. using Mathematica), (ii) developing statistical approaches and algorithms for analyzing the quantitative phenotypic and genomic datasets involved in quantitative genetic analysis specified above, (iii) developing computational ability to compile computer programmes and scripts for computer simulation study and analysis of real experimental datasets (e.g. using Fortran 90/95 with IMSL libraries, C++, Perl), and (iv) developing skills to use main stream computer software for mathematical and statistical analyses (e.g. Mathematica, R), for data manipulation (e.g. Perl) on windows or linux platforms.
Experimental skills: crossing experiments in Arabidopsis/potato, phenotyping quantitative traits, lab-based skills for running PCR, RT-PCR, qPCR, sequencing/array based analyses.
Contact: Professor Zwei Duo, University of Birmingham