What is the main focus of your postdoc work?

“Almost one year ago, I began my post-doctoral studies in the laboratory of Prof. Mike Gore at Cornell University. My Postdoc research focuses on combining statistical modeling of multi-omics data to reveal the biological underpinnings of maize leaf cuticular conductance. In recent years genomic models and high-throughput phenotyping have become more accessible, but finding the biological cause for agronomic key traits is still challenging due to their quantitative nature. In this research, we hypothesize that integrating structural equation models and multi-omics data sets will increase the accuracy of genomic inference and prediction for leaf cuticular conductance.

Plant cuticles provide a near-complete barrier from the environment, protecting internal tissues from biotic and abiotic stress. Leaf cuticles can significantly affect water loss during the nighttime by limiting non-stomatal transpiration, which occurs in most plants and accounts for 5-30% of total daily water loss. Since stomata are considered closed at nighttime, most water loss derives from the leaf cuticle. As consequence, a reduction in nighttime water loss can increase water use efficiency without penalty in biomass or yield production.

The growing field of genomic selection in plant breeding is driven by the need to bypass conventional phenotypic evaluations and make selections based on the predicted genetic merit of an individual which is computed from genetic markers. The main advantages of genomic selection over phenotype-based selection in breeding are that it reduces the cost per breeding cycle and the time required for variety development. These predictions can be improved by prioritizing genetic markers that are linked to variants with functional, or putatively functional consequences on the phenotype. Identifying such variants is not trivial for complex traits like leaf cuticular conductance, while integrating multi-omics data with structural equation models may improve our detection ability and accuracy”.

What got you interested in quantitative genetics and statistical modeling research?

“During my Ph.D. research, I was focused on finding new drought adaptive physiological mechanisms in wheat and identifying their underlying genetics. Towards the end of my research, I got interested in plant modeling and applied structural equation modeling to understand how whole plant physiology interacts with the environment to increase transpiration efficiency. I found that the underlying genetics of complex physiological traits can be hard to measure with classical wide association techniques. This gap intrigued me: I then wanted to find a combination of statistical platforms and exploiting multi-omics data sets to improve our understanding of the genetic architecture behind quantitative phenotypes”.

What are your plans once you complete your postdoctoral work?

“My scientific goals following this fellowship are to apply for a position in an academic institute or research organization to work in the interface of plant breeding and advanced research, bringing the benefits of genomics-assisted breeding. In my opinion, teaching and utilizing genomics-assisted breeding will be required to keep producing food for the global growing population under unpredictable climate change”.

What tip would you give someone just beginning a career in agricultural research?

“I think agricultural research is one of the most important fields due to the ongoing climate change. In my opinion, agricultural research requires many aspects of skills, but passion and enthusiasm for science in general and agriculture in particular, is the most vital need across all positions and career stages. In agricultural research, we are fortunate to ask basic scientific questions as well as communicate with the industry and global needs. This is a challenge and a privilege we should all keep in mind when conducting agricultural research”.