Dr. Mohsen Yoosefzadeh Najafabadi is an assistant professor specializing in dry bean breeding and computational biology. He earned his PhD at the University of Guelph, where he focused on soybean breeding and genetics, and has since expanded his research to cover a variety of plant species within the area of plant breeding and computational biology. His lab primarily aims to develop high-yielding dry bean cultivars that are resistant to various abiotic and biotic stresses, particularly important for dry bean production in Ontario, Canada.

“My research primarily focuses on developing dry bean cultivars that provide significant benefits to farmers and growers. I leverage advanced high-throughput technologies to enhance breeding efficiency and employ computational biology approaches to analyze and interpret the results more effectively. By integrating these methodologies, I aim to create resilient and high-yielding dry bean varieties that meet the evolving needs of the agricultural community in different areas.”

Areas of Interest:

Dry Bean Breeding and Genetics: Developing breeding strategies to genetically improve important traits of interest (yield, biotic and abiotic stresses, cooking quality, etc.) in different market classes of dry beans.

Comparative Analysis of Breeding Approaches: Investigating the differences between conventional and modern dry bean breeding techniques, particularly through omics and multi-omics insights to enhance breeding outcomes.

Remote Sensing and Imaging: Utilizing remote, satellite, and aerial imaging technologies to improve the prediction accuracy of complex traits in dry beans, thereby facilitating more informed breeding decisions.

Omics-Based Selection: Implementing genomics, phenomics, metabolomics, and enviromics prediction methods to optimize the selection process for dry beans, while exploring the interplay between various omics fields.

Genetic Studies Across Omics Fields: Conducting research that links genome-phenome, proteome-phenome, envirome-phenome, and other omics relationships to deepen our understanding of dry bean genetics.

Computational Tools Development: Creating and implementing advanced computational tools, including data packages, statistical methods, and data-driven pipelines, designed for processing and analyzing complex crop breeding datasets.