My general research interest has been primarily focusing on understanding the dynamics and significance of genetic, environmental and evolutionary forces that shape the complexity of biological diversity. In addition to quantitative genomics aspects regarding genetic architecture underlying traits’ variability and evolvability, my recent research activities have also exploited informatics and data science approaches for predictive analysis using large, heterogeneous biological information.
Dr. Lan Zhu’s research mainly focuses on the development of statistical methods for the analysis of variation in large scale sequencing data and its association with phenotypic traits in a variety of organisms ranging from humans to domesticated plants and animals. Using statistical modeling and biological function network, we seek to understand the role of genetic architecture on the variation of phenotypes with the goal of developing an efficient framework of phenotype prediction from the genome sequence.
Karyn Willyerd received her Ph.D. in Biology from New Mexico State University in 2015. She joined OSU’s Translational Genomics Laboratory at Oklahoma State University in February 2016 where her postdoctoral training has been focused on genomics and data management. Dr. Willyerd’s current research is focused on drought responsive transcriptional variation in winter wheat.
Shuzhen Sun completed her Ph.D. degree in Industrial Engineering and Management from Oklahoma State University in December 2016. Her research interests focus on data analytics and operation research. Shuzhen has been working in the Translational Genomics Lab since August 2015. Her research includes SNP calling, de novo assembly, applying K-domination algorithm to reduce variable dimensionality for SNP data, and missing genotype data imputation.
Bryan Naidenov is a Ph.D student involved in research using non-linear optimization techniques and information theory to supplement the classification and regression of complex, polygenic traits. He is particularly focused on building deep-learning architectures to interpret highly unstructured genomic data, like whole-genome assemblies to aid in phenotypic prediction. His computational background involves programming highly-parallelized bioinformatic software frameworks to process massive quantities of biological data.
Alex joined the translational genomics lab in 2016. His roles in the lab include leading the genome assembly of a high-performing wheat variety, utilization of HPC cluster environments, terabyte-level data processing and differential genomics. He has led the way in creating a high-quality, optimized DNA processing pipeline for maximizing sequencing yield for ultra long-read single molecule real-time sequencing technology.
Xiaowei Hu was a PhD student in Statistics at OSU. Her areas of interest focus on the development and application of statistical prediction models in genetics and complex traits analysis. She is currently working on the project of developing and evaluating methods for analysis and prediction of grain yield of Oklahoma’s ‘Duster x Billings’ wheat population using high-dimensional genomic data. The selected best genomic prediction model will be used to assess the efficiency of genetic gain through two-generation validation. Then the final goal of this project will be selecting an optimal training population for future breeding.
Yuanwen Guo joined Chen Lab since October, 2016. She received her PhD from Oklahoma State University with a major in grass breeding and genetics. Throughout her career Yuanwen has been interested in utilizing genetic, genomic and statistical methods to improve plant breeding. Her current primary research interests are in the field of linkage mapping and genome wide association study.