APGC Seminar | Plant Breeding with Genomic Selection and High-Dimensional Predictors

Date: Thursday, August 15 · 2 – 3:30pm GMT+1

Speaker: Presented by Diego Jarquin (Assistant Professor, The University of Florida)

About the Speaker

Diego Jarquin is an Assistant Professor in the Agronomy Department at the University of Florida. He received his Ph.D. in Statistics from the University of Postgraduate Education in Mexico in 2012 and had postdoctoral training at the University of Alabama – Birmingham and the University of Nebraska – Lincoln. Later, he was promoted to research assistant professor and research associate professor.

Diego Jarquin is a statistician who merges statistical methodology, AI, computer algorithm development, data science, and collaborative work with plant sciences.

He brings an active research agenda that is advancing how prediction models are developed for selection purposes in Plant Breeding.

As part of his work, Diego Jarquin has established an excellent record of peer-reviewed publications on prediction model developments that contributed to producing improved cultivars in Plant Breeding (85 manuscripts and 4 book chapters for about 4,300 citations).

In 2020, Diego Jarquin received the Early Career Scientist Award from the National Association of Plant Breeders, and recently received the 2024 UF/IFAS Plant Breeding Innovation Award.

About this seminar

The objective of plant breeding programs is to create societal value at the minimum cost with appropriate rates of genetic gain to feed a growing population in a world that faces more often and more intense environmental fluctuations. Initially, Genomic Selection (GS) emerged as a disruptive technology that enabled to reduction of costs while increasing the size of breeding programs. This methodology uses information embedded in DNA molecular markers to predict the breeding value of important economic traits of candidate individuals. These genetic profiles are then evaluated in multi-environment trials and often proved to improve selection efficacy over traditional methods based on phenotypic and/or pedigree information. Recently, novel methods were developed to leverage high-dimensional predictors, such as weather, soil, and high-throughput phenotypic covariates with the aim of improving prediction accuracy and dealing with genotype x environment interactions. During my presentation, I will describe some of the models, methods, tools and elaborated techniques that have been used to improve the overall prediction accuracy closing the prediction gaps that stem from the missing heritability.