Summix2
Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and identifies potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse publicly available summary data resulting in improved and more equitable research.
Hayley Stoneman
PhD Student in Human Medical Genetics and Genomics
Adelle Price
Professional Research Assistant
Katie Marker
Co-mentor Summix Project; PhD Student in Human Medical Genetics and Genomics
Riley Lamont
MS in Statistics (2023)
Souha Tifour
5yr BS Mathematics, MS Statistics Student
Audrey E. Hendricks
Associate Professor of Statistics
I am committed to increasing representation in who completes research, what questions are asked, and for whom the research benefits. My research interests include developing and applying statistical/machine learning methods across genomics and biomedical informatics to better understand and inform health and disease.