Context Matters
Genetics, such as polygenic scores (PGS), can elucidate underlying biology and inform disease prediction, allowing for targeted screening of men women at risk in midlife. However, differences in PGS by individual characteristics (e.g., age, sex, menopause, genetic ancestry), may result in differential prediction performance and inferior precision medicine. Here, we are evaluating how genetic risk varies across a variety of individual and population contexts.
Jack Staples
Post-doctoral Fellow
Audrey E. Hendricks
Associate Professor of Statistics
I am committed to increasing opportunities for all people to learn about statistics, machine learning, and science. I am motivated to ask novel research questions and ensure the research is robust and accurate. My research interests include developing and applying statistical/machine learning methods across genomics and biomedical informatics to better understand and inform health and disease.