Dr. Jinying Zhao’s research interests lie in the fields of genetic epidemiology, statistical genetics and bioinformatics for human complex diseases. Her research is well funded by the National Institute of Health. Dr. Zhao has published extensively on topics related to precision health using multiomic approaches such as genomics, epigenomics, and metabolomics for type 2 diabetes, heart disease and their risk factors. She is a deans endowed chair and professor of epidemiology in the College of Public Health and Health Professions and College of Medicine. She is the director for the Center for Genetic Epidemiology and Bioinformatics.
Areas of Expertise (6)
Bioinformatics for human complex diseases
Analysis and Comparison of Mouse and Human Brain Gangliosides via Two-Stage Matching of MS/MS SpectraAmerican Chemical Society
Fanran Huang, et al.
Glycosphingolipids (GSLs), including gangliosides, are essential components of the cell membrane. Because of their vital biological functions, a facile method for the analysis and comparison of GSLs in biological issues is desired. To this end, a new method for GSL analysis was developed based on two-stage matching of the carbohydrate and glycolipid product ions of experimental and reference MS/MS spectra of GSLs.
Associations between Vitamin D, Omega 6:Omega 3 Ratio, and Biomarkers of Aging in Individuals Living with and without Chronic PainNutrients
Angela M. Mickle, et al.
Elevated inflammatory cytokines and chronic pain are associated with shorter leukocyte telomere length (LTL), a measure of cellular aging. Micronutrients, such as 25-hydroxyvitamin D (vitamin D) and omega 3, have anti-inflammatory properties. Little is known regarding the relationships between vitamin D, omega 6:3 ratio, LTL, inflammation, and chronic pain.
An atlas of metallome and metabolome interactions and associations with incident diabetes in the Strong Heart Family StudyEnvironment International
Tiffany R. Sanchez, et al.
Chronic exposure to certain metals plays a role in disease development. Integrating untargeted metabolomics with urinary metallome data may contribute to better understanding the pathophysiology of diseases and complex molecular interactions related to environmental metal exposures.