OSU Biochemistry and Molecular Biology

Charles Chen's Laboratory Research


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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.

In Chen's laboratory, we seek the values and knowledge that are highlighted in public databases. The PICARA informatics pipeline is a pioneer work that provides functional inference of a priori candidate genes based on the co-localization of enriched GWAS signals with integrated knowledge that pertains to the same biological phenomena. As more genomic sequences and functional data becoming available, we propose that computational, functional predictions would be accelerating discovery by turning wisdom of crowds into testable hypotheses.




Diversity, associative genomics and systems biology

Over the last few years, my research has been focusing on searching for breed-able, beneficial genetic material from the massive amount of standing variation preserved in international germplasm banks. The thousands of years of using landraces in unfavorable climate, high disease pressure regions, provide natural pre-breeding experiments, resulting locally adapted material that possesses yield potential under drought and heat, as well as desirable traits such as disease resistance. We have analyzed genomic diversity using ~ 1million SNPs, disclosed significant genomic differentiation between Highland-Lowland adaptation and identified new disease resistance alleles from thousands of landraces.

Working closely with transcriptomics and metabolomics research groups here at OSU, we are setting off to connect the dots, by integrating analyses at different omic to examine one of the basic biological principle: DNA -> RNA -> proteins/pathways -> phenotypes.




Genomic prediction and wheat improvement

In addition to gene and allele discovery, the rich, multidimensional genomic knowledge invites breeders to use whole-genome approaches. 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.

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