Welcome to the Genetic Epidemiology and Medicine Lab in the Department of Population Health Sciences, Department of Biostatistics and Medical Informatics, and Initiative in Social Genomics at the University of Wisconsin School of Medicine and Public Health.
Our research focuses on developing and implementing rigorous genetic epidemiology and statistical genetics methods to improve the prevention, diagnosis, and treatment of complex diseases, and translating research findings into new medical care approaches and therapies. We strive to conduct research from which all members of society can benefit.
Risk Prediction
We utilize advanced statistical methods to develop genetic risk scores, explore the clinical utility of genetic risk prediction in population-level risk screening, and improve existing risk prediction models.
Biomarker and Target Discovery
We employ causal inference approaches and multiomics data to identify novel biomarkers and potential therapeutic targets, or repurpose existing drugs for diseases with unmet medical needs.
Statistical Genetics Methodology
We develop new statistical and machine learning methods with an emphasis on prediction and causal inference based on large-scale biobanks and genome-wide association studies.
Complex diseases: cardiometabolic and endocrine diseases (esp. coronary artery disease, type 2 diabetes, obesity, hyperlipidemia, hypertension, osteoporosis, and growth disorders), neuropsychiatric diseases (esp. major depressive disorder, bipolar disorder, schizophrenia, and Alzheimer’s disease), autoimmune diseases (esp. type 1 diabetes), hereditary cancers (esp. breast cancer and colorectal cancer)
Statistics: penalized regression, structural equation modelling, causal inference
Multiomics: genetics, epigenetics, transcriptomics, proteomics, metabolomics