Charles Chen

I am a quantitative and computational geneticist by training. As a faculty member, I am currently leading a team that primarily focuses on data-driven statistical and learning approaches for discovery, forecast, and predictive diagnostics. Our general research interests revolve around understand the intricate interplay between biotic and abiotic forces that shape the complexity of our world.

At Chen's Crop Genomics laboratory, we leverage the advancement in genomics and computational technologies to delve into how nature, including biological beings and the ecosystems, natural and managed, adapts to adversity and evolves. More importantly, our pursuits extend beyond mere discovery. We are driven by the conviction that advancing knowledge is integral to fostering promising applications in agriculture and natural resource conservation for a sustainable future.

Research Interests:

The speed, cost and accessibility of DNA sequencing has been transformed in recent years by new technologies, opening up exciting opportunities for disease diagnosis, therapeutic intervention and studying complex trait variations. Chief among these is genome wide association studies, frequently referred as GWAS, where researchers look for SNP genetic polymorphisms that give raise to phenotypic variation or are in linkage disequilibrium with the causative genetic variants. To further annotate the effect of these associations on phenotypes, researchers often take route of searching and collecting relevant information from literatures, public resources and databases, seeking supporting evidence that pillars the peaks of these significant associations.

Working closely with omics research groups here at OSU, we are setting off to connect the dots, by integrating analyses at different omic molecular phenotypes to examine one of the basic biological principle: DNA -> RNA -> proteins/pathways -> phenotypes. As more genomic sequences and functional data becoming available, in Chen's research laboratory we propose that computational, functional predictions would be accelerating discovery by turning wisdom of crowds into testable hypotheses.

In addition, the rich, multidimensional genomic knowledge invites agriculture sectors to use whole-genome approaches to improve efficiency of variety development programs. Oklahoma dual-purpose wheat varieties play a vital role in both yield production and cattle industry in the US. However, wheat variety development is so far centered on creating genetically stable, pure-line genotypes that could be reached by decade-long, resource-dependent endeavor, involving sustained institutional commitment. More over, while successful, the traditional means of variety development are technologically and spatially static, unless extensive territories are spent on expansive phenotype assessment.

To advance technologies used to foster Oklahoma's largest cash crop, we also coordinate advancement in genomics and quantitative genetics with breeding efforts of OSU's Wheat Improvement Team. We are currently investigating means to optimize applicability and efficiency of genomics-enabled selection (genomic prediction), aiming to translate genomic knowledge to genetic gain in wheat field through technological and computational advancement.