Oluwaseun Johnson Akinlade

Position: PhD candidate
Place of birth: Nigeria
Language: Yoruba, English
Scholarship: University of Queensland Graduate School Scholarship (UQGSS), Australian Government Research Training Program (RTP)
Advisors: A/Prof Lee Hickey, Dr Roy Costilla Monteagudo, Dr Kai Voss-Fels, Dr Millicent Smith, Prof Rajeev Varshney

 

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Background
Climate change and its projected impacts on rainfall and distribution patterns suggest that many crop producing regions will face drier and hotter conditions in the near future. The need to feed the growing population necessitates accelerating the development of high yield varieties in the face of fluctuating environmental conditions.
New approaches to modify the classic breeding approach may lead to a paradigm shift for faster chickpea varietal development and release. Genomic prediction (GP), the use of genome-wide DNA polymorphisms to estimate breeding values, can reduce the reliance on expensive and time-consuming field evaluation of selection candidates. This approach reduces the length of the breeding cycle and ultimately increase the rate of genetic gain over time. In GP, a predictive model is developed using genome-wide markers and phenotype information, thereby predicting the genetic value of ‘untested’ individuals based on their marker profiles.
This PhD aims to evaluate the potential of new breeding methods that could accelerate genetic gain in chickpea breeding programs. This research includes the development of innovative genomic prediction approaches that harness high-throughput genomic and phenomics datasets. Furthmore, new insights into the genetic architecture of canopy developmental traits related to water-use efficiency will be gained. Finally, the ability to improve yield prediction accuracy by incorporating a range of secondary traits in multi-traits prediction models will be explored.

Objectives
– Develop efficient breeding methods to accelerate genetic gains in chickpea by harnessing high-throughput genomic and phenomics datasets
– Improve prediction accuracy to enhance implementation of genomic selection in chickpea by incorporating multi-traits in genomic prediction models
– Understand the genetic architecture of chickpea canopy development and identify QTL related to drought tolerance
– Compare the accuracy of prediction based on single-trait and multi-trait approaches using GBLUP and BayesR prediction models

Anticipated outcomes 
– New efficient breeding methodology for chickpea and other crops
– Improved understanding of the genetics of drought adaptive traits
– A better understanding of the genetic architecture of chickpea canopy traits
– Increase knowledge of implementing genomic prediction in chickpea.