Yogasudha Veturi
Summary Statement
Dr. Veturi’s research includes integration of high-dimensional data with electronic health records to dissect shared genetics among complex human traits/diseases and to understand sex and ethnic differences in cognitive decline.
Education
Research Interests
Her research interests include integration of multi-omic, neuroimaging and environmental data with electronic health records to understand the genetic etiology of complex human traits and diseases, pleiotropic relationships among traits and diseases and putative causal trajectories, particularly in the context of neurodegeneration. Her goals are to address important data science and biological challenges by contributing novel methods and algorithms to the biomedical community that will be a step towards improving cognitive health of patients of advancing age, especially women and communities underrepresented in medicine.
Additional Information
Yogasudha Veturi held an undergraduate degree in mathematical statistics from University of Delhi and a master’s degree in statistics from North Carolina State University prior to getting a master’s (thesis) degree in plant quantitative genetics from the University of Delaware under the mentorship of Dr. Randall Wisser. In his lab, she worked with plant breeders and computational/molecular biologists and assisted them in statistical analyses on their projects. As part of her Master’s Thesis, she developed a whole genome simulator to mimic multi-generational plant breeding populations. She also published her work on multivariate models to study longitudinal disease resistance in maize (Phytopathology).
She completed her Ph.D. studies under the mentorship of Dr. Gustavo de los Campos (currently at Michigan State University) at the University of Alabama at Birmingham, where she developed statistical methods to understand genetic architectures of complex human traits. She developed novel Bayesian methods to quantify genetic heterogeneity between ethnically diverse populations and sexes by modeling random effect interactions (Genetics). She also analyzed the genetic architectures of complex human diseases including type-II diabetes, obesity, and breast cancer using whole genome generalized linear regressions.
She completed her postdoctoral research in the Department of Genetics at the University of Pennsylvania, under the mentorship of Dr. Marylyn Ritchie, where she integrated her expertise in statistical model development and quantitative genetics with electronic health records data to address meaningful healthcare related problems. She established diverse collaborations across departments (Department of Biostatistics, Epidemiology and Informatics, Radiology, Neurology, Medicine, Pathology and Laboratory studies) at Penn as well as other research institutions (e.g., Vanderbilt University, TN, University of California San Francisco, CA). She also played important roles in conducting genome-wide association studies for large scale multi-university consortia such as GIANT (Genetic Investigation of Anthropometric Traits), GLGC (Global Lipids Genetics Consortium), and GBMI (Global Biobank Meta-analysis Initiative). She has published 27 papers thus far with first-author papers in journals such as Nature Genetics and Genetics and co-authored papers in Nature, Nature Communications and JAMA Psychiatry among others.