Combining field phenotyping and next generation genetics to uncover markers, genes and biology underlying drought tolerance in wheat

Lead Research Organisation: Earlham Institute
Department Name: UNLISTED


The overall aim of this project is to combine physiological and modern molecular breeding approaches to provide tools to
accelerate wheat breeding associated with abiotic stress tolerance. This project will use an enrichment platform developed
in Liverpool, to genotype by sequencing a diversity panel of wheat. This will generate tens of thousands of varietal SNPs
for genotyping and provide the raw materials for a SNP-based molecular breeding program in India and an international resource. Information to users will be made publicly available through a web browser interface for immediate impact. The panel of lines will be phenotypically scored for yield, and physiological traits associated with water use efficiency over two seasons under drought regimes at four sites across India. Statistical analysis will be used to associate genomic regions in specific lines, with drought tolerant phenotypes and a series of markers for yield stability identified and tested. Further methods of genotypic selection and bulk segregation will be utilised to further narrow down genomic regions with the aim of potentially identifing candidate genes conferring enhanced drought tolerance. Upon completion, this project will have generated a series of drought tolerant markers matched to drought conditions in India, thus providing important raw materials for breeding programmes aimed at achieving sustainable yield under drought conditions. Drought is also a problem in the UK, with 30 % of wheat grown on drought-prone soils and drought related losses accounting for £224-448M each year. Therefore, this proposal is also likely to have impact on UK breeding as well as in India. Finally, the aim of this project is to generate trained individuals in both India and the UK in the area of computational biology and phenotyping, specifically, how next generation genomic approaches can be applied to crop breeding.


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